WO2011082392A1 - Gene biomarkers of lung function - Google Patents

Abstract

Described herein are a group of 1,013 genes and 1 phenotypic variable are identified as candidate predictors that differentiated smokers (current or former) with or without COPD. The full predictor set can be reduced to a nine-gene classifier (IL6R, CCR2, PPP2CB, RASSF2, WTAP, DNTTIP2, GDAP1, LIPE, and RPL14) with similar performance. Also described herein is the use of the full predictor set and the reduced nine gene set in methods of diagnosing lung disease or an increased risk of developing lung disease, such as COPD, in a subject. Also described herein is the use of the full predictor set and the reduced nine gene set in methods of providing a prognosis for a subject with lung disease, such as COPD.

Description

GENE BIOMARKERS OF LUNG FUNCTION

This application claims the benefit of U.S. Provisional Application Serial No. 61/292,154, filed January 4, 2010, entitled "GENE BIOMARKERS OF LUNG FUNCTION" the entirety of which is hereby incorporated by reference.

BACKGROUND

Lung diseases impair lung function and, according to the American Lung Association, are the third primary cause of death in America;, accounting for one in six deaths. The main categories of lung disease include airway diseases, lung tissue diseases and pulmonary circulation diseases as well as combinations of the above. Examples of diseases affecting lung function include asthma, chronic obstructive pulmonary disease (COPD), lung cancer, alpha- 1 antitrypsin deficiency, respiratory distress syndrome, chronic bronchitis, chronic systemic inflammation, and inflammatory respiratory disease among others.

COPD is the fourth leading cause of morbidity and mortality in the United States and is expected to rank third as the cause of death, worldwide, by 2020 (Mannino and Braman, 2007, Proceedings of the American Thoracic Society 4:502-506). Cigarette smoking is widely recognized as a primary causative factor of COPD and accounts for approximately 80-99% of all cases in the United States. COPD is characterized by chronic airflow limitation, measured spirometrically by the ratio of the forced expiratory volume in one second (FEV)) to the forced vital capacity (FVC), and associated with an abnormal inflammatory response of the lung to noxious particles or gases. The operational diagnosis of lung diseases such as COPD has traditionally been made by spirometry, as a ratio of FEV| to FVC below 70% (Rabe et al., 2007, American Journal of Respiratory and Critical Care Medicine 176:532- 555).

Prior diagnostic methods of COPD and other lung diseases employ diagnostic tests which rely on the presumed correlation of decreased pulmonary function with lung disease such as COPD, asthma, fibrosis, emphysema and others. While lung function tests can provide a general assessment of the functional status of a subject's lungs, the tests do not distinguish between the different types of lung diseases that may be present. For example, certain diseases such as asthma cannot be confirmed based on functional tests alone. In addition, it is only when a measurable change in lung function exists that such tests aid in the diagnosis of a lung disease.

Studies of mechanisms underlying lung diseases are hampered by the procedures required to obtain samples of disease tissue. In particular, studies investigating differential gene expression associated with lung disease have been hindered by the invasiveness of procedures used to obtain sample tissue from diseased and normal subjects. Methods which provide an accurate diagnosis of lung disease prior to development measurable changes in lung function using less invasive tissue sampling techniques would be diserable.

SUMMARY

Novel gene biomarkers of lung function are provided. In one aspect, the gene biomarkers are identified using comparisons of gene expression profiles in subjects with a lung disease and in subjects not having the disease. In another aspect, the profiles are obtained using a method comprising high-throughput analysis. Compositions and devices comprising the novel gene biomarkers are also provided.. The gene biomarkers also are useful as prognostic or diagnostic indicators of lung disease or as an indicator of a subject's risk of developing lung disease. In an additional aspect, the lung disease is COPD.

In one embodiment, gene biomarkers of lung function comprise one, two, three, four, five, six, seven, eight or more genes selected from the group of genes set forth in Supplementary Table II. In another embodiment a gene biomarker of lung function is selected from a nucleic acid molecule (polynucleotide) having a nucleotide sequence of a gene set forth in Supplementary Table II, or a nucleic acid molecule (polynucleotide) having a sequence with 70-99% identity to the nucleic acid sequence of a gene set forth in Supplementary Table II, or a fragment thereof. In another embodiment a gene biomarker of lung function is selected from a nucleic acid molecule comprising a nucleotide sequence of a gene selected from IL6R, CCR2, PPP2CB, RASSF2, WTAP, DNTTIP2, GDAP1 , LIPE, and RPL14, or a a nucleic acid molecule comprising a sequence with 70-99% identity to the nucleic acid sequences of a genes selected from IL6R, CCR2, PPP2CB, RASSF2, WTAP, DNTTIP2, GDAPl, LIPE, and RPL14, or a fragment thereof. It is understood that such nucleic acid molecules and fragments thereof include the sequence of the coding strand or the non-coding strand of the gene, or a fragment thereof unless stated otherwise. It is also understood that such nucleic acid molecules and fragments may comprise the sequences found in either the exons and/or introns of the genes set forth in Supplementary Table II unless stated otherwise.

The present disclosure provides for a composition comprising nucleic acids having the nucleotide sequence of a gene biomarker of lung function. In one embodiment the disclosure provides for compositions comprising two nucleic acid molecules wherein the first nucleic acid molecule comprises a first nucleotide sequence and the second nucleic acid molecule comprises a second nucleotide sequence, wherein the first nucleotide sequence differs from the second nucleotide sequence and the first and second nucleotide sequences are selected independently from the group consisting of the nucleotide sequences of the genes set forth in Supplementary Table II, or a sequence having 70-99% identity to the nucleotide sequences of the genes set forth in Supplementary Table II, or a fragment thereof. In other embodiments the disclosure provides for compositions further comprising a third, forth, fifth, sixth, seventh, eighth and/or ninth nucleic acid molecules.

Also provided is a device comprising a plurality of locations (e.g., a chip or slide bearing an array), wherein 2, 3, 4, 5, 6, 7, 8 or more of said locations each comprise a different nucleic acid molecule comprising a nucleotide sequence of a gene set forth in Supplementary Table II, or a sequence having 70-99% identity to the nucleotide sequences of a gene as set forth in Supplementary Table II, or a fragment thereof (e.g., a fragment of the protein coding exon regions).

In one embodiment, the disclosure provides a method of identifying a gene biomarker associated with lung disease by employing statistical analysis of nucleic acid sequences differentially expressed in subjects having lung disease as compared to control subjects without the disease. In one aspect, the gene biomarkers of lung disease are identified as the group of genes set forth in Supplementary Table II. In another embodiment, the gene biomarkers of lung function are identified as one or more genes (or nucleic acids encoding those genes) selected from: IL6R, CCR2, PPP2CB, RASSF2, WTAP, DNTTIP2, GDAPl , LIPE, and RPL14. Exemplary lung diseases include, for example, asthma, chronic obstructive pulmonary disease, lung cancer, alpha- 1 antitrypsin deficiency, respiratory distress syndrome, chronic bronchitis, chronic systemic inflammation, and inflammatory respiratory disease, among others. In one embodiment, lung diseases or disorders may exclude cancers and/or tumors of the lungs, airways, or of other respiratory tissues. In another embodiment lung diseases may exclude one or more of asthma, chronic bronchitis, chronic systemic inflammation or inflammatory respiratory disease.

In one embodiment, a diagnostic and/or prognostic method of assessing lung disease in a subject is provided, wherein the method includes use of two or more described gene biomarkers. In one aspect, the method includes detecting two or more gene biomarkers in a biological sample obtained from a subject expression. In another embodiment, the method includes measurement of the level of expression of a gene biomarker selected from: IL6R, CCR2, PPP2CB, RASSF2, WTAP, DNTTIP2, GDAPl , LIPE, and RPL14.

In another aspect, the present disclosure provides a method of monitoring an increase in the severity of lung disease in a subject by comparing expression profiles of two or more gene biomarkers in the subject at a first time point versus a second time point, wherein a difference in the expression profiles indicates an increase in severity of the subject's lung disease. In one embodiment, the gene biomarker is selected from: IL6R, CCR2, PPP2CB, RASSF2, WTAP, DNTTIP2, GDAPl, LIPE, and RPL14 (including sequences complementary to those encoding mRNAs ).

In an additional aspect, the gene biomarkers are useful as prognostic indicators of lung disease. Thus, in one embodiment, the present disclosure provides a method of determining the prognosis of a lung disease in a subject by detecting in a subject sample expression of two or more gene biomarkers at a first point in time and then at a second point in time, and comparing the profile of gene biomarkers expressed at the second time point versus the first time point to determine the prognosis of the lung disease in a subject. In one embodiment, the gene biomarker is selected from: IL6R, CCR2, PPP2CB, RASSF2, WTAP, DNTTIP2, GDAPl, LIPE, and RPL14 (and complementary sequences thereof).

Also provided are kits for use in the diagnosis, prognosis and treatment of lung disease comprising one or more of the gene biomarkers or compositions described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1 shows candidate predictors sorted in decreasing order by mean decrease in accuracy (left panel) and mean decrease in Gini impurity (right panel).

Figure 2 shows a set of top Database for Annotation, Visualization and Integrated Discovery (DAVID) annotated biological processes, fifteen in total, including the gene ontology category name, percentage of genes within the category, EASE score, and fold enrichment. Each category has an EASE score (p-value) <0.01 and a fold enrichment >1.5. 'COPD LIST' refers to genes identified by random forest; 'Microarray' refers to all the genes represented on the array.

Figure 3 shows the DAVID annotated biological pathways, including the percentage of genes identified, EASE score and fold enrichment. Pathways have an EASE score (p-value) <0.01 and a fold enrichment >1.5. 'COPD LIST' refers to genes identified by random forest; 'Microarray' refers to all the genes represented on the array. Figure 4 shows some regulatory interactions between proteins and biological outcomes developed with Pathway Studio software. Panel 4A shows protein-protein interactions associated with the MAPK signaling cascade. Panel 4B shows protein-protein interactions associated with the apoptotic cascade. MAP2K4 can phosphorylate and activate MAPKl. Binding of MAP3K1 to TRAF2 can result in their subsequent activation providing two potential links between the two pathways depicted in Panels 4A and 4B (Chadee et al. 2002, Molecular and Cellular Biology 22:737-749; Witowsky & Johnson 2003, The Journal of Biological Chemistry 278: 1403-1406). Random Forest (RF) model-identified genes are shown with the name surrounded by a dashed oval, the other genes are Pathway Studio-identified genes. The abbreviations for human genes and proteins appearing in this figure are from Pathway Studio.

Figure 5 shows an example of gene expression results from an L) penalized logistic regression model. (A) Microarray results for the randomly selected samples from the training set (12 Controls and 12 Cases). Relative mRNA percent difference in expression is calculated using the Control group as the comparator, and p-value for difference between the Case/Control groups mean values obtained by Student's t-test. Asterisks indicate a p-value <0.05 (*), <0.01 (**) or <0.001 (***). (B) Real-time PCR is conducted on the same samples as in A. Relative mRNA expression levels are calculated using a AACt method algorithm. Asterisks indicate a p-value <0.05 (*) or <0.01 (**).

Figure 6 shows a study flow diagram and clear descriptions of the cohort and training and test sets in the described COPD Biomarker Discovery Study.

DETAILED DESCRIPTION

The present disclosure provides compositions and methods of identifying genes as biomarkers of lung disease and compositions and kits comprising materials (e.g., nucleic acids and/or protein affinity reagents such as antibodies) for use in assessing nucleic acid and protein expression from those genes. Also provided are methods of using the novel biomarker for diagnostic, prognostic and predictive measures of a subject's lung disease. In one embodiment, the lung disease is COPD, where by identifying genes differentially expressed in subjects with COPD compared to control subjects, (biomarkers for the diagnostic, prognostic and predictive measures of a subject's lung disease are provided). Other exemplary diseases include, but are not limited to, obstructive pulmonary disease, chronic systemic inflammation, emphysema, asthma, pulmonary fibrosis, cystic fibrosis, obstructive lung disease, pulmonary inflammatory disorder, and lung cancer.

In one embodiment an individual or a population of individuals may be considered as not having lung disease or impaired lung function when they do not have exhibit clinically relevant signs, symptoms, and/or measures of lung disease. Thus, in various aspects, an individual or a population of individuals may be considered as not having chronic obstructive pulmonary disease, chronic systemic inflammation, emphysema, asthma, pulmonary fibrosis, cystic fibrosis, obstructive lung disease, pulmonary inflammatory disorder, or lung cancer when they do not manifest clinically relevant signs, symptoms and/or measures of those disorders. In another embodiment, an individual or a population of individuals may be considered as not having lung disease or impaired lung function, such as COPD, when they have a FEVi FVC ratio greater than or equal to about 0.70 or 0.72 or 0.75. In another embodiment, an individual or population of individuals that may be considered as not having lung disease or impaired lung function are sex- and age-matched with test subjects (e.g., age matched to 5 or 10 year bands) that are current or former cigarette smokers without apparent lung disease who have an FEVl/FVC >0.70 or >0.75. Individuals or populations of individuals without lung disease or impaired lung function may be employed to establish the normal range of proteins, peptides or gene expression. Individuals or populations of individuals without lung disease or impaired lung function may also provide samples against which to compare one or more samples taken from a subject (e.g., samples taken at one or more different first and second times) whose lung disease or lung function status may be unknown. In other embodiments, an individual or a population of individuals may be considered as having lung disease or impaired lung function when they do not meet the criteria of one or more of the above mentioned embodiments.

In one embodiment, control subjects, as that term is used herein are sex- and age-matched current or former cigarette smokers, without apparent lung disease who have FEV1/FVC >0.70. Age matching may be conducted in bands of several years, including 5, 10 or 15 year bands. Control subjects are preferably recruited from the same clinical settings. A control group is more than one, and preferably a statistically significant number of control subjects. In one embodiment control subjects are sex- and age-matched (in 10 year bands) current or former cigarette smokers, without apparent lung disease who had FEV1 FVC >0.70.

In one embodiment, a control sample is a sample from one or more control subjects or which provides a result representative of tests conducted on a control group. In another embodiment, a control sample is a sample from a subject without lung disease (e.g., COPD) or which provides a result representative of tests conducted on a subjects without lung disease. In another embodiment a control sample is a sample containing a known amount (e.g., in mass, number of moles, or concentration) of one or more nucleic acids and/or proteins.

As described herein, a "gene biomarker" is a gene, or a nucleic acid sequence, such as the sequence of a gene, or fragment thereof, which is differentially expressed in a sample obtained from an individual having one phenotypic status (e.g., having a lung disease such as COPD) as compared with individual having another phenotypic status (e.g., control subject without a lung disease). A biomarker is an assayable nucleic acid sequence (or fragment thereof) that is used to identify, predict, or monitor a condition related to lung disease, such as COPD, or a therapy for such a condition, in a subject or sample obtained from a subject. The presence, absence, or relative amount of a gene biomarker can be used to identify a condition or status of a condition in a subject or sample obtained from that subject. Proteins that are encoded by a nucleic acid gene biomarker may be assayed as surrogates for the nucleic acid, and may be understood to be a biomarker or gene biomarker in that circumstance.

A gene biomarker may be characterized using a variety of approaches. Exemplary methodologies include, but are not limited to, the use of the polymerase chain reaction, sequencing, quantitative polymerase chain reaction, quantitative real-time polymerase chain reaction, protein or DNA array, microarray, ligase chain reaction, and oligonucleotide ligation assay, as well as use of high-throughput techniques such as cDNA microarray followed by statistical analysis to identify those nucleic acid sequences which are differentially expressed in subjects having lung disease as compared to control subjects.

A biomarker is differentially expressed between different phenotypic statuses if the expression level of the biomarker in the different groups is calculated to be statistically significantly different. Exemplary statistical analysis includes, among others, Random forest analysis (Breiman, 2001 , Random Forests. Machine Learning 45:5- 32), Li penalized logistic regression (Tibshirani, 1996, Journal of the Royal Statistical Society B 58:267-288) and use of R programming environment (R Development Core Team 2007, R: a language and environment for statistical computing. http://www R-project org).

Gene biomarkers, alone or in combination, are useful as diagnostic markers of: lung disease; determining therapeutic effectiveness of a treatment for lung disease and/or lung disease progression; determining prognosis of lung disease; and/or for determining an individual's relative risk of developing lung disease.

Methods for identifying gene biomarkers are useful as diagnostic or prognostic indicators of different classifications and or severity of lung disease by comparison of gene biomarkers differentially expressed in subjects having lung disease varying in degrees of severity or symptoms. In one embodiment, the gene biomarkers of lung function may be used as prognostic indicators of how likely a subject having lung disease is to experience an increase in disease symptoms or how severe those symptoms may become. In one embodiment, the greater the difference in expression of the gene biomarkers of lung function (e.g., IL6R, CCR2, PPP2CB, RASSF2, WTAP, DNTTIP2, GDAP1, LIPE, and RPL14) in a subject with suspected lung disease from when compared to control subjects, the more likely they will have the disease.

Gene biomarkers may also be identified by analysis of nucleic acid sequences differentially expressed by a subject with a lung disease as compared to nucleic acid sequences expressed by gender-matched control subjects. Identification of nucleic acid sequences that are differentially abundant among subjects with lung disease as compared to control subjects (e.g., COPD subjects having mild to moderate COPD with rapid or slow decline in lung function versus age- and gender-matched smokers without COPD) allows an understanding of the mechanisms underlying a lung disease and its related decline in lung function. Such nucleic acid sequences are useful as gene biomarkers for diagnostic and prognostic determinants of lung disease and or assessing a subject's relative risk of developing a lung disease.

In one embodiment, methods for determining gene expression profiles include determining the amount of RNA that is produced by a gene encoding a polypeptide. Such methods include, but are not limited to, the use of reverse-transcriptase PCR (RT-PCR), competitive RT-PCR, real time RT-PCR, differential display RT-PCR, Northern Blot analysis and other related assays. The methods include the use of individual PCR reactions as well as amplification of complementary DNA (cDNA) and/or complementary RNA (cRNA) produced from mRNA and analysis via microarray.

Gene expression profiling using microarray analysis allows measurement of the steady-state mRNA level of thousands of genes simultaneously. Microarray techniques useful in the methods described herein are known in the art and are described, for example, in U.S. Pat. No. 6,271,002; U.S. Pat. No. 6,218,122; U.S. Pat. No. 6,218, 1 14; and U.S. Pat. No. 6,004,755.

A gene biomarker may be detected in any tissue of interest from a subject suspected of having, at risk of having, or diagnosed as having a lung disease. Biological samples obtained from a subject that are suitable for detection of gene biomarkers include, but are not limited to, serum, plasma, blood, lymphatic fluid, cerebral spinal fluid, saliva, and epithelial cells, such as those available from a buccal swab. It is known that the transcriptome of peripheral blood leukocytes (PBL) reflect a majority of genes actively expressed in a subject. Thus, PBLs are useful as a target tissue "surrogate" for identifying genes differentially expressed in diseased subjects as compared to control subjects. As such, the present disclosure also provides a method of identifying the presence of a gene biomarker in a biological sample of a subject obtained using less invasive sampling techniques. A biological sample includes peripheral blood cells which are readily accessible using traditional blood drawing techniques such as, for example, venipuncture or finger prick.

In one embodiment, a gene biomarker of lung disease is selected from the nucleic acid sequence of a gene set forth in Supplementary Table II. In another embodiment, a gene biomarker of lung disease is a nucleic acid sequence encoding IL6R, CCR2, PPP2CB, RASSF2, WTAP, DNTTIP2, GDAP1, LIPE and RPL14, or a complementary sequence thereof (i.e., IL6R complementary sequence, CCR2 complementary sequence, PPP2CB complementary sequence, RASSF2 complementary sequence, WTAP complementary sequence, DNTTIP2 complementary sequence, GDAP1 complementary sequence, LIPE complementary sequence and RPL14 complementary sequence), or a fragment thereof.

In another embodiment, the present disclosure provides a composition comprising two, three, four, five, six, seven, eight or nine nucleic acid molecules, wherein each nucleic acid molecule differs from the other nucleic acid molecules and each nucleic acid molecule comprises a nucleotide sequence that is selected independently from the nucleic acid sequences of the genes set forth in Supplementary Table II, their complements, or a sequence having 70-99% identity to the nucleic acid sequences of the genes set forth in Supplementary Table II, or a fragment thereof. Moreover, such a composition may contain two, three, four, five, six, seven eight or nine nucleic acid molecules that are directed to different sequences selected independently from the nucleic acid sequences of the genes set forth in Supplementary Table II, or a sequence having 70-99% identity to the nucleic acid sequences of the genes set forth in Supplementary Table II, or a fragment thereof. It is understood that such nucleic acid molecules may have the sequence of the coding strand or the non-coding strand of the gene, or a fragment thereof. In aspects of such an embodiment, the fragments may be selected independently to have lengths greater than about 20, 22, 23, 24, 25, 26, 27, 28, 32, 34, 36, 38, 40, 50, 60, 75, 100, or 150 contiguous nucleotides of those sequences.

In another embodiment, the present disclosure provides a composition comprising two, three, four, five, six, seven, eight or nine different nucleic acid molecules where each comprises a nucleotide sequence that is:

complementary to a fragment greater than about 20, 22, 23, 24, 25, 26, 27, 28, 32, 34, 36, 38, 40, 50, 60, 75, 100, or 150 contiguous nucleotides of the coding or non-coding strand of a gene set forth in Supplementary Table II, an RNA or cDNA transcribed from a gene set forth in Supplementary Table II, or the protein coding (exons) thereof.

Nucleic acid molecules, which may also be referred to herein as polynucleotides, "polynucleotide probes" or simply as "probes" may be immobilized on a substrate. In one embodiment, the present disclosure provides a device comprising one or more nucleic acid molecules immobilized on a substrate wherein each probe includes a gene biomarker. In another embodiment, the device comprises a plurality of nucleic acid molecules, each probe stably associated with (e.g., covalently bound to) and having a unique position on the substrate. In one embodiment, the substrate comprises an array or microarray device. In yet another embodiment the array comprises an array of nucleic acid molecules wherein two, three, four, five, six, seven, eight or nine different nucleic acid molecules are gene biomarkers of lung disease described herein (e.g., IL6R, CCR2, PPP2CB, RASSF2, WTAP, DNTTIP2, GDAP1 , LIPE, and RPL14).

Nucleic acid molecules comprising a nucleotide sequence of a gene biomarker of lung disease may also be immobilized on beads or nanoparticles, such as gold, platinum, or silver nanoparticles. Nucleic acid molecules comprising a nucleotide sequence of a gene biomarker of lung disease may also be detectably labeled. In one embodiment, the label is detectable by fluorescence, or UV Visible spectroscopic means. In other embodiments, the label is a nanoparticle such as a colloidal metal nanoparticle that is detectable by spectroscopic means including plasmon resonance. In still other embodiments, the label is a radioactive label.

Another embodiment is directed to a device comprising two, three, four, five, six, seven or eight different nucleic acid molecules that comprise the sequence of a gene biomarker of lung disease. In one embodiment the nucleic acid molecule(s) comprises a nucleotide sequence having greater than about 20, 22, 23, 24, 25, 26, 27, 28, 32, 34, 36, 38, 40, 50, 60, 75, 100, or 150 contiguous nucleotides of a gene biomarker of lung disease set forth in Supplementary Table II. In such embodiments the device can be an array wherein each nucleic acid molecule is fixed at a spatially addressable location.

The disclosure provided herein employs highly sensitive techniques for identification of gene biomarkers that have low systemic levels in a subject. In one embodiment, a biological sample may be analyzed by use of an array technology and methods employing arrays such as, for example, a nucleic acid microarray or a biochip bearing an array of nucleic acids. An array or biochip generally comprises a solid substrate having a generally planar surface, to which a capture reagent is attached. Frequently, the surface of an array or biochip comprises a plurality of addressable locations, each of which has a capture reagent bound thereon. In one embodiment the arrays will permit the detection and/or quantitation of two, three, four, five, six, seven, or eight or more different biomarkers associated with COPD or its progression. In another embodiment the array will comprise addressable locations for capturing/binding and or measuring two, three, four, five, six, seven, eight or more different gene biomarkers of lung disease. In one embodiment the gene biomarkers of lung disease are selected from nucleic acid sequences of one or more genes selected from IL6R, CCR2, PPP2CB, RASSF2, WTAP, DNTTIP2, GDAP1 , LIPE and RPL14 (including the coding strand, non-coding strand, or exons thereof).

In one particular embodiment, the methods are provided using one or more gene biomarkers for diagnosing the presence of a lung disease or for determining a risk of developing a lung disease in a subject. A gene biomarker may include a nucleic acid sequence or fragment thereof encoding IL6R, CCR2, PPP2CB, RASSF2, WTAP, DNTTIP2, GDAP1, LIPE, RPL14, IL6R complementary sequence, CCR2 complementary sequence, PPP2CB complementary sequence, RASSF2 complementary sequence, WTAP complementary sequence, DNTTIP2 complementary sequence, GDAPl complementary sequence, LIPE complementary sequence or RPL14 complementary sequence. A lung disease may include, but is not limited to, asthma, COPD, lung cancer, alpha- 1 antitrypsin deficiency, respiratory distress syndrome, chronic bronchitis, chronic systemic inflammation, and inflammatory respiratory disease, which may, or may not, include lung cancer in any embodiment described herein. In one aspect the biological sample is a blood sample, a plasma sample, a serum sample, a urine sample, a lymphatic fluid sample, saliva sample or a sputum sample. In one aspect, the present disclosure provides a method for identifying gene biomarkers of a disease that are associated with either a slow decrease or a rapid decrease in lung function. Methods are also provided for discriminating between a rapid and a slow decline in lung function and/or methods for identifying a subject as having an increased risk of developing a rapid decline in lung function or an increased risk of developing a slow decline in lung function by use of a gene biomarker. As used herein, the term "increased risk" refers to a statistically higher frequency of occurrence of the disease or disorder in an individual in comparison to the ?average frequency of occurrence of the disease or disorder in a population. A "decreased risk" refers to a statistically lower frequency of occurrence of the disease or disorder in an individual in comparison to the ?average frequency of occurrence of the disease or disorder in a population.

In another embodiment, the status of a subject's lung disease may be determined by measuring the quantity of one or more particular gene biomarkers present in a biological sample from that subject, and correlating the quantity of each biomarker with a previously determined measure of the severity of the disease based on the presence and/or quantity of one or more particular gene biomarkers present in a test sample from the subject. As used herein, the term "status" refers to the degree of severity of a subject's lung disease such as, for example, the number or degree of severity of symptoms presented or exhibited by the subject with the lung disease. The symptoms associated with different forms of lung diseases may differ between forms of lung diseases or may overlap. For example, exemplary symptoms commonly associated with COPD include, destruction or decreased function of the air sacs in the lungs, cough producing mucus that may be streaked with blood, fatigue, frequent respiratory infections, headaches, dyspnea, swelling of extremities, and wheezing. A subject with COPD may have a few to all of these symptoms. A subject with an early stage of COPD may exhibit one, two, three, or only a few of those symptoms.

In another embodiment, the present disclosure provides a method of determining the status of a subject's lung disease by assessing the level of expression of one or more gene biomarkers during the course of the subject's lung disease. Such assessment includes (1) measuring at a first time point the level of expression of one or more gene biomarkers of lung disease in a subject's sample, (2) measuring the same biomarker(s) at a second time, and (3) comparing the first measurement to the second measurement, wherein a difference between the two

measurements indicates the status of the lung disease, such as an increase or decrease in severity of the disease. In one embodiment a gene biomarker of a lung disease or an impaired lung function measure is selected from the group consisting of: IL6R, CCR2, PPP2CB, RASSF2, WTAP, DNTTIP2, GDAP1 , LIPE, RPL14, or fragments thereof. In other aspects the method further comprises measuring two, three, four, five, six, seven, or eight, or more different gene biomarkers of lung disease.

Techniques for use in a method of measuring an increased or decreased expression of gene biomarkers include the use of quantitative assays for nucleic acids and proteins, including for example, polymerase chain reaction, array detection and measurement of proteins (e.g., using immobilized antibodies), quantitative RT-PCR (reverse transcriptase followed by PCR for measuring mRNA for example), quantative real time PCR, multiplex PCR, quantitative DNA array analysis, autoradiograph analysis, quantitative hybridization, immunoassays (e.g., ELLAS, Western, or sandwich assays), quantitative rRNA-based amplification, fluorescent probe hybridization, fluorescent nucleic acid sequence specific amplification, loop-mediated isothermal amplification and/or ligase chain reaction.

In one embodiment, the present disclosure provides a method of managing a subject's lung disease whereby a therapeutic treatment plan is customized personalized or adjusted based on the status of the disease. Exemplary therapeutic treatments for lung disease include administering to the subject one or more of:

immunosuppressants, corticosteroids (e.g., betamethasone delivered by inhaler), 2-adrenergic receptor agonists (e.g., short acting agonists such as albuterol), anticholinergics (e.g., ipratropium, or a salt thereof delivered by nebulizer), and/or oxygen. In addition, where the lung disease is caused or exacerbated by bacterial or viral infections, one or more antibiotics or antiviral agents may also be administered to the subject.

The materials and reagents required for diagnosing a lung disease, for determining the prognosis of a lung disease, or for use in the treatment or management of lung disease in a subject may be assembled together in a kit. A kit comprises one or more biomarker probes and a control nucleic acid sequence (e.g., present in a known quantity or amount), wherein the control nucleic acid sequence corresponds to a sequence that is not a gene biomarker of lung disease. The kit may be used for diagnosing, identifying prognosis, and/or predicting a lung disease in a subject. The kit generally will comprise components and reagents necessary for determining one or more biomarkers in a biological sample as well as control and/or standard samples. For example, a kit may include, probes, and/or antibodies specific to the one or more proteins, or peptide fragments of proteins, encoded by a gene set forth in Supplementary Table II for use in a quantitative assay such as RT-PCR, in situ hybridization, microarray and/or biochip detection. In another embodiment, the kit may include a compositions with gene expression products in ratios found in individuals having lung disease and and/or compositions with gene expression products in ratios found in individuals not having a lung disease, thus avoiding the use of control gene(s) or control sample(s) from "control" subjects. In some embodiments, the kit includes a pamphlet which includes a description of use of the kit in relation to COPD diagnosis, prognosis, or therapeutic management and instructions for analyzing results obtained using the kit.

EXAMPLES

A cDNA microarray was used to obtain data to identify genes differentially expressed in PBLs between adult cigarette smokers or other subjects with or without COPD. In a training set of Cases and Controls clearly defined by spirometric criteria, random forest statistical modeling was used to generate a list of variables that predicted COPD classification. This list was then subjected to an Li penalized logistic regression model to create a more focused set of variables. Both lists were assessed in a test set of subjects with spirometric parameters that closely bordered the generally acceptable spirometric diagnostic value for COPD. The identified genes were analyzed for their ontology assignment and pathway involvement. The gene expression profiles identified in this study are novel biomarkers for COPD and provide insight into disease mechanisms.

Materials and Methods

Study design and subjects

The COPD Biomarker Discovery Study (CBD) included male and female self-reported cigarette smokers, aged 45 years or older, with at least 10 pack-years smoking history that were recruited from the University of Utah Health Sciences Network of local clinics and hospitals and from community physician offices. COPD was diagnosed in 300 subjects according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) spirometric guidelines as having a ratio of forced expiratory volume in 1 second (FEV)) to forced vital capacity (FVC) <0.70 (Rabe et al. 2007, American Journal of Respiratory and Critical Care Medicine 176:532-555). The Control group included 425 sex- and age-matched, current or former cigarette smokers, without apparent lung disease with FEVi/FVC >0.70. Individuals who had recent exacerbation of COPD, uncontrolled angina, hypertension, or allergy to albuterol, and females who were pregnant or lactating were excluded. Demographic variables, respiratory symptoms, medical history, tobacco use history, and concomitant medications were assessed. Pack-years were calculated as (maximum average cigarettes smoked per day over total smoking history / 20) ^x (total years smoking). Body weight and height were measured. Spirometry was performed with a rolling seal spirometer by certified pulmonary function technicians according to American Thoracic Society guidelines (Miller et al. 2005, European Respiratory Journal 26:319-338). Measurements of FEVi and FVC were made before and at least 20 min after inhaled bronchodilator administration (albuterol 180 μg). The FEV)/FVC ratio was calculated for each subject from the highest post-bronchodilator values of FEV| and FVC. A blood sample was collected for assessment of carboxyhemoglobin (COHb) and complete blood cell counts. In a subgroup of 81 subjects with COPD and 61 unaffected (Control) subjects, a whole blood sample was also obtained for assessment of gene expression in PBLs.

Blood sample collection and processing

Whole blood samples were obtained from each subject by venipuncture using 10 mL EDTA Vacutainer^® tubes (BD, Franklin Lakes, NJ, USA). COHb, hemoglobin, hematocrit and total and differential white blood cell (WBC) counts were measured at ARUP Laboratories™, a national, CLIA (Clinical Laboratory Improvement Amendments of 1988)-certified reference laboratory (Centers for Medicare & Medicaid Services 1992, Federal Register 40:7002-7186). Isolation of PBLs was carried out using the LeukoLOCK™ Total RNA Isolation System (Ambion, Inc., Austin TX, USA) following the manufacturer's protocol. Briefly, after isolation of PBLs, the filter was flushed with 3 mL of phosphate-buffered saline, to remove residual red blood cells, and then with RNA/a/er^®, to stabilize the leukocyte RNA, and frozen at -20 °C until processing for RNA. RNA isolation was then carried out using the mirVana™ miRNA Isolation Kit (Ambion, Inc., Austin TX, USA). The LeukoLOCK™ filter was flushed with 2.5 mL of mirVana miRNA Lysis Solution, and the lysate was collected in a 15-mL conical tube. mirVana miRNA homogenate additive (one-tenth volume) was then added to the cell lysate. A volume of acid- phenolxhloroform, equal to the lysate volume, was used to flush the LeukoLOCK™ filter and was collected into the same 15-mL conical tube as the lysate. The tube was shaken vigorously for 30 seconds and stored for 5 min at room temperature. The samples were centrifuged for 10 min at 10,000 x g (maximum) in a table-top centrifuge. The aqueous phase was transferred into a new tube, and mixed with 1.25 volumes of room-temperature 100% ethanol, and the mixture was filtered through the filter cartridge into the collection tubes supplied with the kit. The isolated RNA was then washed and eluted following the standard steps described in the kit's manual. Quality of the isolated RNA was checked using the Agilent 2100 Bioanalyzer (Agilent Technologies, Inc., Santa Clara, CA, USA) before use and storage at -80°C. Microarray data acquisition

Statistical procedures and analysis involved in pre-processing and identifying differential expression of microarray data were performed using Bead Studio^® v3.0.14 (lllumina Inc., San Diego, CA, USA) and R-2.6.1 software (R Development Core Team 2007). cRNA from each sample following RNA isolation were hybridized to Sentrix^® Human WG-6 BeadChips (lllumina Inc., San Diego, CA, USA). Hybridized BeadArrays™ were examined with respect to number of genes detected, average intensity, 95th percentile of signal intensity, signal-to-noise ratio, and background signal intensity as a means of assessing quality. For each quality control (QC) measure, the BeadArray statistics were plotted and the mean ±3 standard deviations were overlaid on the plot as a method for identifying potentially outlying arrays. All BeadArrays were considered to be within acceptable limits for these QC measures. In addition, the BeadArrays were examined with respect to beadtypes labeled as hybridization, low and high stringency, biotin, housekeeping, and labeling controls (data not shown). All control beadtypes yielded intensities at the expected levels, therefore each of the 142 hybridizations were considered to be of good quality.

Microarray data preprocessing

Prior to analysis, the gene expression data was log₂ transformed. Since negative control bead background correction was demonstrated to negatively impact identifying differentially expressed genes (Dunning et al. 2008, BMC Bioinformatics 9:85), the estimated background from the negative control beads was not subtracted from the mean beadtype signal intensities. The log₂ transformed intensities were subsequently normalized using a global median scaling method. Specifically, the expression for each sample was scaled by an array-specific constant factor so that the median expression values were the same across all arrays. An arbitrarily selected array was set as the baseline against which all other arrays were normalized. For array / and beadtype j, using the log₂ transformed expression values log₂ (Xjj), global normalization was performed as follows: 1) the median expression for the baseline array x_base ⁼ median (log₂ (x_taJ(, , ) was calculated; 2) for the * array, the median expression, x_base/ x, was taken to be the global

scaling factor and was applied to normalize the j expression values for array so that the log₂ transformed and scaled values for beadtype j and array were _χ^^η0Γτη = b, log₂ (x, ).

Random forest analysis

The normalized gene expression data were combined with selected demographic, smoking history and clinical variables (see Supplementary Table I). A random forest consisting of 10,000 trees was derived for predicting COPD-affected (Case) or unaffected (Control) samples/individuals, using a split-sample approach (training and test sets) and the random Forest package in the R programming environment (Breiman 2001, Liaw & Wiener 2002, R News 2: 18-22; R Development Core Team, 2007). An extreme discordant phenotype design (Zhang et al. 2006, Pharmacogenetics and Genomics 16:401-413), based on the FEV|/FVC ratio, was used to select the training set for the analysis. Of 142 subjects, 36 were clearly classified as having COPD (FEVi/FVC <0.60), and 36 were classified as Controls (FEVi FVC >0.75). This set of samples was then used as the training set for the analysis in order to maximally stratify the Case and Control subgroups. The remaining 70 subjects had FEV)/FVC values between 0.60 and 0.75 and were used as the test set.

For each classification tree in the random forest, the observations left out of the bootstrap re-sample (e.g., "out-of-bag") were used as a natural test set for estimating prediction error. The out-of-bag observations were also used to estimate the importance of each variable for the classification task (Archer & Kimes, 2008, Computational Statistics and Data Analysis 52:2249-2260). The bootstrap method was used to estimate the null distribution for the mean decrease in Gini impurity by drawing a random sample with replacement from those variables with a non-zero mean decrease in Gini impurity, estimating the mean decrease of the re-sampled observations and repeating this procedure 2000 times. Candidate predictors with a Gini impurity >99.99795% were considered significant for the classification task.

Li penalized logistic regression

An Li penalized logistic regression model was fit to predict the dichotomous outcome variable

(Case/Control status) using the significant candidate predictors identified by the random forest algorithm. This additional modeling step was used to identify a more focused set of predictor variables that retain a similar error rate as the complete predicted random forest. This model was fit using the same training set used to derive the random forest model. The glmpath library (Park & Hastie, 2007, Journal of the Royal Statistical Society B 69:659-677) in the R programming environment (R Development Core Team, 2007) was used for fitting the Li penalized models. The final model was selected as that model with minimum Akaike's information criterion (AIC) and was subsequently used to obtain fitted probabilities for all testable subjects. Those subjects with probabilities >0.5 were classified as Cases, and all others were classified as Controls.

Gene ontology and pathway analysis

Genes identified statistically as having significant predictive value for the discrete Case/Control outcome were used as the input for subsequent gene ontology and pathway analysis. Gene ontology and functional categories were identified by analyzing isolated gene lists using the Database for Annotation, Visualization and Integrated Discovery (DAVID, on the world wide web at david.abcc.ncifcrf.gov/) (Dennis et al. 2003, Genome Biology 4:3) and Pathway Studio V5.0 (Ariadne Inc., Rockville, MD, USA). EASE scores for gene-enrichment analysis were calculated using a 0.1 threshold. The DAVID annotation tool was also used to probe the Kyoto Encyclopedia of Genes and Genomes (KEGG, www.genome.jp/kegg/kegg2.html), BioCarta (www.biocarta.com/genes/index.asp) and the Biological and Biochemical Image Database (BBID, on the world wide web at bbid.grc.nia.nih.gov/) pathway databases to identify regulated pathways and to complement the gene ontology. "Biological processes" and "Pathways" with a ^-value <0.05 were considered significant. The output analyses were manually filtered to remove overlapping and redundant categories to generate non-redundant lists.

Quantitative real-time PCR (qRT-PCR)

Quantitative real-time polymerase chain reaction (qRT-PCR) was performed on isolated RNA from randomly selected subjects in the training set (12 with and 12 without COPD) to confirm the microarray results in terms of differential expression and statistical significance. First-strand cDNA was synthesized from 1 μg of RNA in a 100 μΐ reaction volume with the TaqMan^® Reverse Transcriptase Reaction Kit (Applied Biosystems, Carlsbad, CA, USA) using random hexamers as primers following the manufacturer's recommended protocol. After the synthesis was complete, the cDNA was diluted 1 :3. Six microliters of diluted cDNA were then used for each qRT- PCR reaction in a final volume of 20 μΐ, using pre-designed Gene Expression Assays (Applied Biosystems, Carlsbad, CA, USA) for the genes of interest. All PCR reactions were carried out in triplicate. Relative expression levels were calculated using the AACt method algorithm provided by Applied Biosystems. The average intensity value obtained for the Control subjects was used as the calibrator. All reactions were run in an Applied Biosystems 7500 Fast Sequence Detection System (Applied Biosystems, Carlsbad, CA, USA). The gene expression assays used were: 18S (Hs99999991_sl), GAPDH (4310884E), DNTTIP2 (Hs00966646_ml), GDAP1 (Hs00184079_ml), IL6R (Hs01075667_ml), LIPE (Hs00943410_ml), WTAP (Hs00374488_ml), CCR2 (Hs00174150_ml), PPP2CB (Hs00602137_ml), RASSF2 (Hs00542460_ml) and RPL14 (Hs00427856_gl).

Results

Subject demographics

Characteristics of the spirometrically defined COPD-affected and unaffected groups (overall and for the training set) are summarized in Table I. The distribution of the COPD group by severity of airflow obstruction (FEVi as percent of predicted) by GOLD spirometric guidelines (Rabe et al. 2007) was GOLD 1 (mild, «=30), GOLD 2 (moderate, «=38), GOLD 3 (severe, «=6), and GOLD 4 (very severe, «=7). It should be noted that 10 subjects with FEV|/FVC >0.70 were categorized as Controls according to the GOLD guideline but had subnormal FEV] (<80%predicted) and could be considered to have spirometrically indeterminate Case/Control status; 3 subjects were in the training set, and 7 were in the test set. In the cohort overall and in the training and test sets, the COPD group was older and had at least 56% greater pack-years of cigarette smoking, on average, than the Control group. However, the proportion of current smokers was similar across all groups, at 58-69%. Although the mean total circulating WBC count did not differ significantly between the groups, those with COPD had significantly higher mean neutrophils and lower mean lymphocytes, as percentages of the total WBC, than the group without COPD.

Table I. Characteristics of the spirometrically defined COPD-affected (Cases) and unaffected (Controls) subjects.

All Subjects Training Subset⁸

Characteristic Cases Controls ?-value^b Cases Controls 7-value

(«=81) («=61) («=36) (n=36)

Male (%) 67 62 0.60 64 61 1.00

Age (y) 61.2 (8.2) 54.8 (9.0) O.0001 63.3 (7.4) 52.6 (7.7) O.000

Current smoker (%) 62 64 0.86 58 69 0.46

Cigarettes per day^c 14.6 (17.0) 12.0 (12.3) 0.30 12.7 (14.1) 13.0 (13.4) 0.92

Pack-years 59.5 (38.0) 38.1 (19.8) <0.0001 64.3 (38.8) 32.8 (19.3) <0.000

FEV, (L) 2.33 (1.01) 3.12 (0.79) O.0001 1.74 (0.94) 3.30 (0.75) <0.000

FEV, (% predicted) 70.6 (24.9) 94.6 (14.3) O.0001 54.2 (23.5) 99.0 (14.1) <0.000

FVC (L) 4.05 (1.32) 4.04 (1.01) 0.94 3.8 (1.47) 4.1 (0.97) 0.32

FEV, FVC (%) 56.3 (12.9) 77.4 (4.9) <0.0001 44.7 (1 1.1) 80.8 (3.1) O.000

WBC, total (10³ μΐ/¹) 7.4 (1.7) 7.6 (2.1) 0.57 7.6 (1.9) 7.3 (1.8) 0.51

Granulocytes (%) 64 (7) 59 (10) 0.004 66 (6) 57 (10) O.000

Lymphocytes (%) 25 (7) 30 (9) 0.002 23 (6) 32 (10) <0.000

Monocytes (%) 6.2 (1.7) 5.9 (1.6) 0.19 6.4 (1.7) 5.7 (1.4) 0.06

COPD, chronic obstructive pulmonary disease; FEV_t, forced expiratory volume in 1 s; FVC, forced vital capacity;

WBC, white blood cells.

Values are mean (±SD) unless otherwise indicated.

" COPD subjects with %FEV,/FVC <60 and control subjects with %FEV,/FVC >75.

b ?-value for difference in mean values between the Case/Control groups was obtained by Welch's /-test for

continuous variables and by Fisher's exact test for categorical variables.

c Average daily cigarette consumption of current smokers during the 3 months prior to study participation

Identification of COPD predictors

Due to the inability of the random forest algorithm to handle missing values among the predictor variables, the medication history of the subjects was not included in the analysis since several subjects had missing values.

For example, 15/81 (18.5%) Cases and 19/61 (31%) Controls failed to indicate whether they were using

glucocorticoids. The final size of the training set was 33 Cases and 34 Controls because 3 Cases and 2 Controls had missing values for other key variables. The out-of-bag estimate of error associated with the random forest analysis in the training set was 6.0% overall, with a misclassification rate of 2.9% for the spirometric Controls and 9.1% for the spirometric Cases (Table II). The random forest algorithm identified 1,014 candidate predictor variables, which included only 1 phenotypic variable, 'years of daily smoking'. The top 30 candidate predictors using the mean decrease in Gini impurity, as well as the mean decrease in accuracy, are displayed in Figure 1. The complete list of predictors can be found in Supplementary Table II.

Table II. Spirometric class versus random forest model-predicted class with associated class-specific discordance rates for the training set (FEVi/FVC <0.60 or >0.7S) and the test set (FEVi FVC 0.60-0.75).

Spirometric class

Training set («=67) Test set (w=65)

Cases Controls Cases Controls

Predicted Class

Cases 30 1 27 2

Controls 3 33 14 22

Discordance rate (%) 9Λ 22 34Λ 8.3

FEVi, forced expiratory volume in 1 s; FVC, forced vital capacity

The random forest model derived using the training set was then applied to the remaining 70 subjects with FEVi/FVC values of 0.60-0.75 (test set). Five subjects were excluded due to missing values for a key variable, leaving 65 subjects as a test set for evaluation of the random forest classifier. The overall misclassification rate for the test set was 24.6% (16/65). Spirometric versus gene expression-predicted classifications for the training and test sets are shown in Table II, along with misclassification rates. Of the discordantly classified subjects in the test group, 14/16 (87.5%) were classified as Cases by spirometry but not by their gene expression profile.

Gene ontology and pathway analyses

In an effort to identify biological processes and pathways that were differentially affected in Cases versus Controls, gene ontology assessment using the DAVID annotation tool (Dennis et al., 2003) was performed. A total of 784 genes (77.4% of the 1 ,013 genes identified by random forest modeling) were represented in the DAVID gene ontology categories. The analysis output list was manually edited to remove redundant and overlapping gene ontologies. Biological processes that were enriched in the set of predictor genes included regulation of apoptosis and cell growth, macromolecule (protein and RNA) transport, post-translational protein modification, cellular defense response, inflammatory response and RNA processing (Figure 2). Major pathways identified by DAVID included apoptosis (mitochondrial apoptotic signaling and caspase cascade), p38 MAPK, WNT and PPAR signaling, focal adhesion and leukocyte transendothelial migration (Figure 3).

The gene ontology analysis revealed a number of up-regulated genes involved in positive regulation of apoptosis (e.g., BAD, CASP4, CASP6, CASP10, DIABLO, FAF1, FASTK and TRADD) as well as a number of genes involved in inhibition of apoptosis (e.g., BCL2L1, BIRC2, CDKN2D, MCL1 , NAIP, SERPINB2, SGMS1 and YWHAZ). A similar situation occurred with cell cycle progression related genes. Several of the genes identified are involved in general regulation of the cell (e.g., CCT7, CDC2L1 , CDK2, CDC42, CDKN2D, MDM4, NEDD9, PCNA, PML, PMS 1 , RASSF2, RASSF4, RASSF5, RB I , TSC1, VEGFB and VHL) with a number of them clearly involved in negative regulation of the cell (e.g., CDKN2D, PML, RASSF2, RASSF4, RB I and TSC1).

A number of genes were identified that were involved in the MAPK signaling pathway (e.g., ATF2, ATF4, DUSP6, DUSP10, IL1R2, MAP2K3, MAP4K3, MAPK 14, MAX, MEF2A, PIK3R5, SOS 1, SOS2 and TGFBR2) and in inflammatory response (e.g., ALOX5, CCL7, CCR2, CCR4, CD97, CD163, NFRKB, NLRP3, PLAA, SPN, TLR4, TLR6, TLR8), consistent with prior reports in the literature and the systemic pro-inflammatory

characteristics associated with COPD (Mossman et al. 2006, American Journal of Respiratory Cell and Molecular Biology 34:666-669; Agusti et al. 2003, European Respiratory Journal 21 :347-360; Rahman et al. 1996, American Journal of Respiratory and Critical Care Medicine 154: 1055-1060; Chung 2001 , European Respiratory Journal Supplement 34:50s-59s ; Chung 2005, Curr Drug Targets lnflamm Allergy 4:619-625; Rahman 2005, Treatments in Respiratory Medicine 4: 175-200; Agusti & Soriano 2008, Journal of Chronic Obstructive Pulmonary Disease

5: 133-138; Fabbri & Rabe 2007, Lancet 370:797-799). A summary of the protein-protein interactions and possible biological outcomes identified by Pathway Studio from the list of candidate predictor genes is shown in Figure 4.

Li penalized logistic regression model

In order to identify a more focused set of variables having a similar predictive capability as the random forest, an Li penalized logistic regression model was fit to predict the dichotomous outcome variable (Case/Control status) using the 1,014 variables identified by the random forest algorithm. Li penalized models are effective in performing automatic variable selection (Tibshirani, 1996). The model was first fit using data from the training set of 33 Cases and 34 Controls used to derive the random forest model. The final model, selected as the L, logistic regression model with minimum AIC (data not shown), comprised 9 predictor genes: IL6R, CCR2, PPP2CB, RASSF2, and WTAP were up-regulated and DNTTIP2, GDAP1, LIPE, and RPL14 were down-regulated in Cases compared with Controls. As shown in Table III, the 9-gene model had an overall error rate of 3.0%, discordantly classifying 1 spirometric Case and 1 spirometric Control. The derived L) penalized logistic regression model was subsequently applied to classify the test set of 70 subjects with FEVi FVC of 0.60-0.75, although one subject was excluded for missing a key variable leaving 69 subjects in the test set. The overall misclassification rate was 21.7% (Table III). The calculated sensitivity, specificity, and positive and negative predictive values in the test set of samples for both models are shown in Table IV.

Table III. Spirometric class versus Li penalized logistic regression model-predicted class with associated class-specific discordance rates for the training set (FEVi/FVC <0.60 or >0.75) and the test set (FEV,/FVC

0.60-0.75).

Spirometric class

Training set (n =67) Test set («= =69)

Cases Controls Cases Controls

Predicted Class

Cases 32 1 31 2

Controls 1 33 13 23

Discordance rate (%) 3.0 2.9 29.5 8.0

FEVi, forced expiratory volume in 1 s; FVC, forced vital capacity

Table IV. Performance characteristics of the model-based classifiers in the test set («=65, FEVi/FVC 0.60- 0.75).

Model Classifier Classifier Performance in Test Set

Number of Discordant Sensitivity Specificity Positive Negative

Variables Classification Predictive Predictive

(%) (%) (%) Value (%) Value (%)

Full random forest 1,014 24.6 65.9 91.7 93.1 61.1

L| -penalized logistic 9 21.7 70.5 92.0 93.9 63.9 regression

FEVi, forced expiratory volume in 1 s; FVC, forced vital capacity Biological validation

Real-time PCR was performed using isolated RNA from 24 randomly selected subjects in the training set (12 Cases and 12 Controls) to confirm the microarray results for the 9 predictor genes. Experimental results are shown in Figure 5. Not all of the predictors from the microarray data were confirmed by qRT-PCR. However, a concordant directional trend in differential expression (Pearson correlation coefficient = 0.795) between the two platforms for 7 of the 9 genes was observed, although in some instances the magnitude of the difference between Cases and Controls by qRT-PCR varied from that detected by microarray. No statistically significant differences were observed for PPP2CB and GDAP 1 by qRT-PCR.

Using microarray analysis of PBL and random forest modeling, 1,013 genes were identified. One phenotypic variable was identified as a candidate predictor capable of differentiating smokers (current or former) with or without COPD. Gene ontology analyses indicate that these genes are involved in various cellular processes including regulation of apoptosis, regulation of cell growth, macromolecule (protein and RNA) transport, post- translational protein modification, cellular defense response, inflammatory response and RNA processing. A 9-gene subset derived from the larger set of candidate predictors that reliably discriminated between COPD and non-COPD subjects was also identified. Differential expression of 7 of the 9 genes identified was confirmed by qRT-PCR, corroborating the microarray results.

The full random forest predictive model discordantly classified, or "misclassified," 6% of the training set and 24.6% of the test set, and the 9-gene model differed from the spirometrically-defined classification for 3% of the training set and 21.7% of the test set. These models performed well in the more phenotypically extreme (by spirometry) training set and less well in the test set whose FEVi FVC values more closely bordered the diagnostic Case/Control cutoff value of 0.70. The great majority of the discordantly classified subjects in the test set were classified as Cases by spirometry but as Controls by their gene expression profile. It is possible for an individual to have a spuriously low airflow measurement that could result in a misdiagnosis of COPD by the GOLD guideline, which uses a fixed, arbitrary cutoff value of FEVi FVC.

Furthermore, although spirometric parameters are the traditional diagnostic and prognostic markers for COPD, it has become clear that they do not adequately represent all of its respiratory and systemic aspects (Marin et al. 2009, Respiratory Medicine 103(3):373-378; Celli 2006, Proceedings of the American Thoracic Society 3:461- 465). FEVi correlates poorly with the degree of dyspnea, and the change in FEV| does not reflect the rate of decline in health status (Celli et al. 2004, Celli 2006, Burge et al. 2000, British Medical Journal 320: 1297-1303). Other factors, such as emphysema and hyperinflation (Casanova et al. 2005, American Journal of Respiratory and Critical Care Medicine 171:591-597), malnutrition (Schols et al. 1998, American Journal of Respiratory and Critical Care Medicine 157: 1791-1797), peripheral muscle dysfunction (Maltais et al. 2000, Clinics in Chest Medicine 21 :665- 677), and dyspnea (Nishimura et al. 2002, Chest 121 : 1434-1440), are independent predictors of outcome. In fact, the multifactorial BODE index that includes body mass index (B), degree of airflow obstruction (O), dyspnea score (D), and exercise endurance (E), is a better predictor of mortality than FEVi alone (Celli et al. 2004, The New England Journal of Medicine 350: 1005-1012). The PBL gene expression profile alone or in combination with clinical markers such as the BODE components and/or lung parenchymal or airway changes on chest CT scans (Omori et al. 2006, Respirology 1 1 :205-210) may be more predictive of the (early) presence, activity, and progression of the multi-component syndrome that is COPD than the clinical parameters alone.

One of the major constraints of COPD biomarker discovery has been the accessibility of suitable samples. In the past, sputum, bronchoalveolar lavage fluid, exhaled breath condensate, and bronchial biopsy tissue have been used (Sin & Man 2008, Chest 133: 1296-1298). However, the sampling methodologies for such specimens are limited by their invasiveness and poor reproducibility. Since COPD is accompanied by systemic changes, as well as increased serum levels of certain proteins [e.g., C-reactive protein (CRP), interleukin 6 (IL-6), IL-8, leukotriene B (LTB4), and TNFa], the use of PBLs as a surrogate biosample is an ideal alternative because they can be easily collected in large quantities at multiple time points using a relatively non-invasive procedure (Celli 2006; Schols et al. 1996, Thorax 51 :819-824; Rahman & Biswas 2004, Redox Report: Communications in Free Radical Research 9: 125-143; Rahman et al. 1996, Vernooy et al. 2002, American Journal of Respiratory and Critical Care Medicine 166: 1218-1224; Agusti et al. 2003, Noguera et al. 1998, American Journal of Respiratory and Critical Care Medicine 158: 1664-1668). As noted earlier, PBL gene expression profiles are successfully used to identify the presence or risk of other diseases having prominent systemic components.

Due to the role of PBLs in inflammation, the gene expression differences between subjects with and without COPD in this population of cells can reflect the degree of systemic inflammation or inflammation in the lungs. Lung inflammation is known to increase with the severity of the disease, as classified by the degree of airflow limitation (Hogg et al. 2004). The gene expression-based classifier is derived from the training set of COPD subjects with the most extreme airflow limitation, who likely also have the greatest degree of inflammation, while the test group with lesser airflow limitation may be predicted to have less inflammation. This may also partially account for the lower predictive ability between spirometric Cases and Controls in the test set compared to the training set.

In the present study, biological processes identified as over-represented in the set of COPD predictor genes include regulation of apoptosis, regulation of cell growth, macromolecule (protein and RNA) transport, post- translational protein modification, cellular defense response, inflammatory response and RNA processing. Major pathways identified include apoptosis, p38/MAP signaling, focal adhesion, and leukocyte transendothelial migration. Changes in these biological processes and pathways may reflect the changes in activation, differentiation and cellular composition of the samples analyzed. The identification of leukocyte transendothelial migration is an important change in this cell population as COPD is characterized by leukocyte infiltration in the lung parenchyma (Panina et al. 2006, Current Drug Targets 7:669-674). Differences in expression of these genes may result in a predisposition of leukocyte subpopulations to infiltrate the lung tissue, and perhaps other tissues. This observation is supported by previously reported changes in chemotaxis and extracellular proteolysis in neutrophils isolated from the blood of subjects with COPD (Burnett et al. 1987, Lancet 2: 1043-1046).

The subset of 9 genes identified using Li penalized logistic regression modeling have similar predictive performance as the full set of candidate predictors identified by the random forest model. It includes 5 up-regulated genes (CCR2, IL6R, PPP2CB, RASSF2, and WTAP) and 4 down-regulated genes (DNTTIP2, GDAP1 , LIPE, RPL14) in COPD Cases compared with Controls. IL6R and CCR2 have been previously reported to have possible roles in COPD development and progression (Owen 2001, Pulmonary Pharmacology and Therapeutics 14: 193-202; Wilk et al. 2007, BMC Medical Genetics 8 Suppl 1 :S8). However, there have been no prior reports of an association with COPD for DNTT1P2, GDAP1, LIPE, PPP2CB, RASSF2, RPL14 and WTAP.

The IL6R gene codes for the IL6 receptor, which is only reported to be expressed in subpopulations of leukocytes (monocytes, neutrophils and T and B lymphocytes) and hepatocytes (Chalaris et al. 2007, Blood 1 10: 1748-1755; Jones et al. 2001, The FASEB Journal 15:43-58; Hamid et al. 2004, Diabetes 53:3342-3345). Many cell types do not express IL6R and are not directly responsive to IL6 (Chalaris et al. 2007, Jones et al. 2001). However, these cell types can be stimulated by IL6 bound to a soluble form of the IL6 receptor in a process called trans-signaling (Chalaris et al. 2007, Jones et al. 2001). IL6R shedding and subsequent release of the soluble form of the receptor results from cleavage of the membrane-bound receptor during apoptosis, a biological process and pathway identified in the gene expression signatures. This process is dependent on the metalloproteinases, ADAM 17 and to a lesser extent ADAM 10 (Chalaris et al. 2007, Matthews et al. 2003, The Journal of Biological Chemistry 278:38829-38839). ADAM17 was also found to be up-regulated in the microarray and was identified as one of the candidate predictor genes. Reported inducers of IL6R shedding include phorbol myristate acetate, cholesterol depletion, CRP, bacterial toxins, Fas stimulation and ultraviolet light (Chalaris et al. 2007, Mullberg et al. 1992, Biochemical and Biophysical Research Communications 189:794-800; Jones et al. 1999, Journal of Experimental Medicine 189:599-604; Matthews et al. 2003). Signaling through IL6R has also been shown to have a role in both inflammation and apoptosis (Finotto et al. 2007, Int Immunol 19:685-693). Furthermore, genome-wide association analyses have identified IL6R as a likely candidate gene for association with lung function (Wilk et al. 2007).

CCR2, which encodes the receptor for monocyte chemoattractant protein 1 and 3 (MCPl and MCP3), is involved in inflammatory processes related to rheumatoid arthritis, alveolitis and tumor infiltration (Owen 2001). Higher levels of MCPl mRNA and protein are detected in the bronchiolar epithelium in subjects with COPD, and increased levels of CCR2 are detected in macrophages, mast cells and epithelial cells of COPD subjects, indicating that MCPl and CCR2 are involved in the recruitment of macrophages into the airway epithelium (Owen 2001, de Boer et al. 2000, Journal of Pathology 199:619-626). This increased expression of CCR2 also correlates with increased levels of mast cells and macrophages in the lungs of COPD subjects (de Boer et al. 2000). In addition, it has been demonstrated that activated neutrophils migrate in response to MCPl (Johnston et al. 1999, The Journal of Clinical Investigation 103: 1269-1276). These findings indicate mechanistic roles of IL6R and CCR2 in systemic and lung inflammation in COPD.

The 7 other genes in the 9-gene profile have varied biological functions. PPP2CB encodes the beta-isoform of the catalytic subunit of protein phosphatase 2 A (PP2A) (Hemmings et al. 1988, Nucleic Acids Research

16:11366; Cohen 1989, Annual Review of Biochemistry 58:453-508). PP2A has been shown to regulate apoptosis in neutrophils by dephosphorylating both p38/MAP and its substrate caspase 3, suggesting that PP2A has a role in the induction of apoptosis and the resolution of inflammation (Alvarado-Kristensson & Andersson 2005, The Journal of Biological Chemistry 280:6238-6244). RASSF2 promotes apoptosis and cell cycle arrest (Vos et al. 2003, The Journal of Biological Chemistry 278:28045-28051). WTAP is involved in the expression of genes related to cell division cycle and the G2/M checkpoint (Horiuchi et al. 2006, PNAS USA 103: 17278-17283). The DNTT- interacting protein 2 (DNTTIP2), also known as estrogen receptor-binding protein, can bind the estrogen receptor- alpha and enhance its transcriptional activity in an estrogen-dependent manner (Bu et al. 2004, Biochemical and Biophysical Research Communications 317:54-59). GDAP1, or ganglioside-induced differentiation-associated protein 1 , is found localized in the mitochondrial outer membrane and regulates the mitochondrial network. Over- expression of GDAP1 induces fragmentation of mitochondria without inducing apoptosis, affecting overall mitochondrial activity, or interfering with mitochondrial fusion (Niemann et al. 2005, The Journal of Cell Biology 170: 1067-1078; Cuesta et al. 2002, Nature Genetics 30:22-25). LIPE, also know as HSL (hormone-sensitive lipase), has a role in the mobilization of free fatty acids from adipose tissue by controlling the rate of lipolysis of the stored triglycerides (Holm et al. 1988, Nucleic Acids Research 16:9879). Finally, RPL14 is a gene coding for a protein of the large ribosomal subunit (Robledo et al. 2008, RNA 14: 1918-1929). The role of these genes in COPD may be linked to the cellular processes and pathways, such as cell cycle regulation and apoptosis, associated with the full list of genes.

Some factors, such as cellular composition of the sample, may influence the gene expression profiles detected by microarray in this study. Although the average total circulating WBC counts were similar between the groups with and without COPD, the mean lymphocyte and granulocyte counts as percentages of the total were significantly different (Table I). These parameters were included in the random forest analysis yet were not retained in the final model, indicating that the gene expression differences were more predictive of COPD status than lymphocyte and granulocyte percentages. Due to the random forest algorithm's inability to handle missing values among the predictor variables, the medication history of the subjects was not included in the analysis as several subjects had missing values. Although it is unclear how corticosteroids might affect gene expression in PBLs, it is known that the small airway inflammation responsible for airflow obstruction in COPD is poorly sensitive to the anti-inflammatory effects of corticosteroids (Hogg et al. 2004, The New England Journal of Medicine 350:2645- 2653; Barnes 2006, Chest 129: 151-155). Recent evidence has attributed this to oxidative and nitrative stress- induced reduction in histone deacetylase expression in inflammatory cells, thus preventing activated corticosteroid receptors from reversing the acetylation of activated inflammatory genes and turning off their transcription (Barnes 2006). Analysis of 10 subjects with possible indeterminate spirometric COPD Case/Control status based on their combination of FEVi/FVC and FEVi % predicted, categorizing them spirometrically as Controls by the GOLD- identified FEV) FVC cutoff value is also included. Only one of these subjects, in the test set, was discordantly classified as a Case by the gene expression profile (both the full and reduced models).

Cigarette smoke exposure can also influence gene expression, and of the 1 ,013 predictor genes identified in this analysis, differential expression of ATF4, MCL1 , MAPK14, SERPINA1 and SOD2 was also identified in a study by van Leeuwen et al. (2007, Carcinogenesis 28:691-697), as strongly correlating with serum cotinine levels, a biomarker of recent exposure to tobacco. Two additional genes in the list, CCR2 and EPB41 , are observed by Lampe et al. (2004, Cancer Epidemiology, Biomarkers & Prevention 13:445-453) as part of a cigarette smoke exposure molecular signature. Both the van Leeuwen and Lampe studies use PBLs isolated from current smokers and non-smokers indicating that the differential gene expression of some of the genes identified in this analysis may be related to tobacco smoke exposure. In a study of bronchial epithelial cells from never, current and former smokers, Beane et al. (2007, Genome Biology 8:R201) found 175 genes differentially expressed between never and current smokers, with irreversible changes in expression for 28 genes, slowly reversible for 6 genes and rapidly reversible for 139 genes. This indicates that duration and possibly intensity of cigarette smoking, and length of time since quitting, may be important confounding variables to gene expression analysis. The 1 phenotypic variable identified as a candidate predictor in this analysis ('years of daily smoking') appears to support this possibility.

This example indicates, among other things, that a training set and test set can be established that permit the identification of differential gene expression (1,013 genes' in this instance) occurring in peripheral WBCs that discriminated between cigarette smokers with or without spirometrically defined COPD. The group of 1,013 genes can be reduced to a 9-gene subset with similar performance in differentiating smokers with or without COPD. Gene ontology and pathway analyses indicate that these genes are involved in regulation of apoptosis, regulation of cell growth, macromolecule (protein and R A) transport, RNA processing, post-translational protein modification, cellular defense response, and inflammatory response. This is the first study to use microarray analysis of PBLs to identify gene expression differences associated with COPD. PBL samples are easy to obtain and their analysis complements current clinical diagnostic procedures for COPD. The gene expression profiles identified are novel biomarkers for COPD.

Supplementary Tablel

Supplementary Table I. Phenotypic and smoking history variables evaluated in random forest analysis.

Phenotypic variables included in random forest model

Gender

Age on spirometry test date

Age when first tried a cigarette

Age when first started smoking daily

Years of daily smoking

Pack-years of smoking

Current smoking status

Average number of cigarettes per day during past 3 months

Whether currently smoking >1 cigarettes on most days

Height (cm)

Weight (kg)

Body mass index [kg (m²)^'1]

Systolic blood pressure (mm Hg)

Diastolic blood pressure (mm Hg)

Blood hemoglobin concentration (g dL^'1)

Blood hematocrit (%)

Total white blood cell count (WBC, 10³ μΙ/')

Blood basophils as % of total WBC

Blood eosinophils as % of total WBC

Blood granulocytes as % of total WBC

Blood lymphocytes as % of total WBC

Blood monocytes as % of total WBC

Carboxyhemoglobin concentration (%saturation)

Supplementary Tablell

Unless otherwise indicated, the nucleic acids listed or set forth in Supplementary Table II include: nucleic acids having the sequences recited in the table and/or their complement; the sequences of nucleic acids transcribed from the genes or loci listed in the table or their complement; and either or both strands (if double stranded) of cDNAs clones of the nucleic acids transcribed from the genes or loci listed in the table. The nucleic acids listed or set forth in Supplementary Table II also include the specific nucleic acid sequences listed under the NCBI accession and/or the NCBI GI number categories and their complementary sequences.

Supplementary Table II. Complete list of covariates identified as having significant Gini variable importance measures by random forest modeling, with the fold change between cases and controls along with the 95% lower confidence (lcl) and upper confidence limits (ucl). *For the variable included that was not a gene (years daily smoking) the average number of more years daily smoking and its confidence interval are reported rather than

Illumina

Covariate NCBI Accession NCBI GI Illumina Fold

Array Gini lcl uci (Gene Name) and Version* Number * Search Key Change

Address ID

C1ORF108 NM_024595.1 13375790 ILMN_6070 2640243 0.091 1 1.90 1.70 2.09

KIAA0251 M_015027.1 39930344 ILMNJ4287 4290274 0.0907 1.60 1.49 1.72

CECR1 NM_017424.2 29029549 ILMNJ0713 650592 0.0899 3.13 2.56 3.73

LOC653738 XM_929341.1 88961756 ILMN_37845 2370019 0.0892 1.41 1.31 1.50

GLUL NM_001033056.1 74271825 ILMN_26367 670537 0.089 2.67 2.29 3.06

TUBB NM_178014.2 34222261 ILMN_23399 1580484 0.0872 2.50 2.10 2.90

MATR3 NM_018834.4 62750352 ILMN_15182 4810577 0.0862 2.02 1.78 2.27

SON NM_138926.1 21040321 ILMN_ 12440 2940435 0.086 1.40 1.32 1.48

LOC648763 XM 940246.1 88979438 ILMN 30575 3180349 0.0854 2.83 2.33 3.39

ACTG1 NM 001614.2 1 1038618 ILMN_24353 6520497 0.0851 6.23 4.43 8.63

DDX19B NM_001014451.1 62241023 ILMN 17268 7210471 0.085 1.39 1.30 1.47

SRP54 XM_940545.1 89037651 ILMN_138804 7380221 0.0849 1.82 1.63 2.00

GPR97 NM l 70776.3 40538803 ILMN_ 18651 61 10630 0.0848 3.31 2.68 4.01

UTRN NM_007124.1 6005937 ILMN_15375 4570470 0.0845 1.75 1.59 1.92

LOC644330 XM_934365.1 89056804 ILMN_42347 1430079 0.0844 4.26 3.36 5.34

ARFIP1 NM_001025595.1 71040093 ILMN_ 16086 1430364 0.0839 1.85 1.67 2.06

NBR1 NM_005899.2 14110374 ILMN_ 16223 2970324 0.0825 2.01 1.75 2.27

LOC653094 XM_925947.1 89059738 ILMN_35175 1070128 0.0823 1.67 1.53 1.81

LOC644063 XM_931572.1 88965390 ILMN_401 16 6520639 0.0814 6.71 4.87 9.10

C10ORF46 NMJ53810.3 54262140 ILMN_ 14628 2070286 0.0801 1.88 1.67 2.10

LOC653895 XM_936379.1 89033487 ILMN_38756 1440273 0.08 1.26 1.20 1.32

LOC647474 XM_943003.1 89061094 ILMN_42643 6480465 0.0795 1.46 1.37 1.56

LBH NM_030915.1 13569871 ILM _21350 150592 0.0791 1.82 1.61 2.04

CSTF1 NM_001033521.1 75709216 ILMN_28771 3520634 0.0786 1.40 1.32 1.49

LSM12 NM l 52344.1 22748746 ILMNJ510 3990338 0.0784 2.06 1.82 2.32

RASSF1 NM_170712.1 25777679 ILMN_11841 1820470 0.0783 1.25 1.20 1.30

LOC650667 XM_939756.1 8905931 1 ILMN_36687 6071 1 0.078 1.79 1.62 1.95

HS.571253 DA938875 82424570 ILMN 123434 3140414 0.0776 1.73 1.55 1.90

LOC646144 XM_935294.1 89025359 ILMN_45775 3120671 0.0775 1.43 1.32 1.53

MARCH 1 NM O 17923.2 53759068 ILMN_30212 1070326 0.0771 2.60 2.17 3.07

CDC42 NM 044472.1 16357471 ILMN 137677 1030035 0.0766 2.82 2.30 3.41

WAC NM_ 100264.1 18379329 ILMN_28064 6100136 0.076 2.05 1.81 2.28

LOC652388 XM_941821.1 89071419 ILMN_45950 7320259 0.076 1.96 1.73 2.21

CRTAP NM_006371.3 53759127 ILMN_2952 1470044 0.0759 3.64 2.88 4.51

TNPOl NM_153188.1 23510380 ILM _29083 1570397 0.0759 1.68 1.54 1.82

CRK NM_016823.2 4132771 1 ILMN 25875 5810176 0.0758 2.01 1.78 2.25

ALOX5 NM_000698.2 62912458 ILMN 2997 6220097 0.0751 1.36 1.28 1.44

LOC646309 XM 929247.1 89030887 ILMN_44679 4390246 0.0749 1.53 1.42 1.65

FBX07 NM_012179.3 74229026 ILMN 28542 1690070 0.0744 2.03 1.80 2.27

LYPLA1 NM_006330.2 20302148 ILMN_5453 2070673 0.0744 2.56 2.16 3.00

KUA-UEV NMJ99203.1 40806189 ILMN_20084 4540561 0.074 1.61 1.47 1.75

WSB1 NM_134264.2 58331 182 ILMN_674 5260673 0.074 2.12 1.84 2.43

LOC653491 XM_927709.1 890251 1 1 ILMN_3721 1 1 170646 0.0737 1.66 1.51 1.82 Illumina

Covariate NCBI Accession NCBI GI Illumina Fold

Array Gini lcl ucl (Gene Name) and Version* Number * Search Key Change

Address ID

C20ORF14 NM_012469.2 40807484 ILMN_ 18026 4180670 0.0737 2.19 1.93 2.47

LOC389850 XM_372205.4 89059568 IL N_45804 2900619 0.0732 1.50 1.38 1.63 AEA NM_001017405.1 62953130 ILMN_4828 6660470 0.0732 2.83 2.41 3.31

SLIC1 NM_ 182854.1 33504570 ILMN_511 3710767 0.0729 2.03 1.76 2.30

ACSL5 NM_016234.3 42794755 ILMN_6741 4010619 0.0724 1.39 1.30 1.47

GRAP NM_006613.3 50659102 ILMN_5687 1 10703 0.072 1.53 1.39 1.68

NO 02 NM_173614.2 51944972 ILMN_1736 60717 0.072 1.63 1.45 1.80

LOC651 106 XM_940235.1 89061862 ILMN_44908 2570014 0.0718 1.49 1.39 1.61

ZDHHC 13 NM_019028.2 47933345 ILMN_24550 4250592 0.0716 1.26 1.20 1.31

ECD NM_007265.1 6005783 ILMN_25476 2120379 0.0714 2.16 1.88 2.48

MPEG1 XM_ 166227.6 89033974 ILMN_38016 7380008 0.0709 4.19 3.32 5.1 1

WDFY3 NM_014991.3 31317271 ILMN_12455 4260280 0.0708 1.57 1.44 1.71

SPG21 XM 945608.1 89039020 ILMN_137401 4260195 0.0704 2.94 2.41 3.59

RASSF2 NM_170773.1 25777674 ILMN_137091 4570333 0.0704 1.31 1.24 1.37

CDV3 NMJH7548.3 52856418 ILMN_11989 4860386 0.0703 1.37 1.28 1.46

SLC3A2 NM_001013251.1 61744482 ILMN_12826 4280458 0.07 2.42 2.1 1 2.74

NIPA2 NM_030922.5 57013273 ILMN_3795 5270682 0.0698 1.48 1.38 1.58

TFG NM 006070.4 56090655 ILMN_7895 6520180 0.0698 1.48 1.37 1.58

LOC654189 XM_942687.1 88968995 ILMN_30702 7100386 0.0698 1.61 1.48 1.76

EL Ol NM_014800.8 18765699 ILMN_137709 4880133 0.0696 1.54 1.41 1.67

FLJ25037 XM_941208.1 89067009 ILMN_137053 1570376 0.0694 1.44 1.34 1.55

MAP2 3 X _944206.1 89042496 ILMN_137034 4640131 0.0692 2.65 2.28 3.05

TPM3 NM_153649.2 39725631 IL N_ 17262 6590730 0.069 3.73 2.97 4.67

PDLIM5 NM 006457.2 58533152 ILMN_12134 4480484 0.0688 1.51 1.40 1.62

ST3GAL1 NM — 003033.2 27765097 ILMN_2099 3370292 0.0686 1.91 1.71 2.12

ARHGAP25 NM 001007231.1 55770897 ILMN_1674 4850079 0.0685 1.80 1.63 1.97

LOC653133 X _926881.1 89024662 1LMN_138087 2900288 0.0682 1.70 1.54 1.88 UA-UEV NM_199203.1 40806189 ILMN_20084 6280270 0.0677 1.75 1.58 1.92

MDM4 NM_002393.1 4505138 IL N_137381 4490671 0.0676 2.46 2.12 2.85

HS.105636 BX417162 46930487 ILMNJ74929 5220014 0.0675 1.99 1.76 2.22

VASP NM_003370.3 57165437 ILMN_28263 5260161 0.0674 2.08 1.84 2.31

NUP98 NM_016320.3 56550110 ILMN_21954 7650669 0.0668 1.64 1.49 1.80

PICALM NM_007166.2 56788365 ILMN_23418 1580364 0.0665 2.63 2.20 3.09

GGT2 NM_002058.1 62079286 IL N_3296 4590523 0.0665 1.58 1.46 1.72

LOC648189 XM 937239.1 89039190 ILMN 40837 1980059 0.0663 1.48 1.37 1.58

GPR141 3MM_181791.1 32401434 ILMN_20517 2260672 0.066 1.57 1.42 1.71

BTN2A 1 NM 078476.1 17975771 ILMN_28434 7210379 0.0656 1.73 1.56 1.90

NEK7 NM_133494.1 19424131 ILMN_23490 4880553 0.0653 2.42 2.04 2.80

LBR NM_002296.2 37595749 IL N_7414 2360731 0.0649 5.61 4.31 7.10

RPL14 NM 003973.2 16753224 1LMN_138835 3800280 0.0641 -2.73 -3.24 -2.26

UNC93B1 NM_030930.2 45580708 ILMN_8587 4560370 0.0641 2.46 2.10 2.84

TM2D3 NM_078474.1 17865799 ILMN_28191 940273 0.0639 1.57 1.42 1.71

GRINL1A NM_001018102.1 70166831 ILMN_20762 2850343 0.0637 1.79 1.60 1.99 Illumina

Covariate NCBI Accession NCBI GI Illumina Fold

Array Gini lcl ucl (Gene Name) and Version* Number * Search Key Change

Address ID

MLKL XM_936963.1 89041041 ILMN_139138 6350274 0.0637 2.60 2.18 3.07

SETD3 NMJ99123.1 40068482 ILMN_27724 2570035 0.0636 1.74 1.56 1.92

SS18 NM_001007559.1 561 17845 ILMN_22307 3890047 0.0635 1.25 1.18 1.32

HFE NM_139007.1 21040348 ILMN_21360 2000487 0.0631 1.24 1.18 1.28

LOC653383 XM 927177.1 89030160 ILMN_35816 2120521 0.0628 2.03 1.77 2.31

MAPK14 NM_139013.1 20986513 ILMNJ7267 6860717 0.0628 3.31 2.68 4.00

FASTK NM_006712.3 39995105 ILMN_ 1 1299 650753 0.0626 1.81 1.63 2.00

MRRF NM_199176.1 40317621 ILMN 4576 7380736 0.0625 1.33 1.24 1.41

MAP2K3 NM 1451 10.1 21618350 ILMN_10112 4290524 0.0624 2.17 1.92 2.44

MCRS1 NM_006337.3 34222264 ILMN_9875 5570445 0.0623 2.10 1.82 2.39

NCOA2 NM 006540.2 76253684 ILMN_1913 4780039 0.0622 1.62 1.47 1.77

EGFL5 XM_929502.1 89029942 ILMN 37703 7560615 0.0622 1.73 1.54 1.94

WBSCR1 NM_022170.1 1 155992¾ ILMN 6141 4540047 0.0614 1.57 1.41 1.73

GTF2I XM_939506.1 8902611 1 ILMN_138994 3830348 0.0611 2.22 1.87 2.57

NSF XM 938198.1 89042742 ILMNJ 36981 160735 0.0606 1.99 1.76 2.21

NSF NM_006178.1 1 1079227 ILMN_23282 3830040 0.0605 2.04 1.79 2.30

TSC22D3 NM_198057.2 62865623 ILMN_23548 1740327 0.0602 1.49 1.38 1.59

MCFP NM_018843.2 46094064 ILMN_15963 6510326 0.0602 1.67 1.50 1.83

CREB5 NM_00101 1666.1 59938775 ILMNJ 9827 4220026 0.06 2.70 2.20 3.27

C10RF183 NM_019099.3 39545578 ILMN_9599 6280431 0.0599 1.66 1.51 1.82

PSEN 1 NM_000021.2 21536454 ILMN_28849 6220754 0.0598 1.91 1.72 2.1 1

RASSF5 NM_182664.1 32996732 ILMN_690 7560563 0.0596 3.55 2.92 4.30

LOC648394 XM_942936.1 89066728 ILMN_33220 2630601 0.0593 2.01 1.75 2.32

WDR1 NM_017491.3 53729350 ILMN_14280 3610767 0.059 5.09 4.01 6.39

TCF20 NM_181492.1 31652241 ILMN_25080 3450093 0.0587 1.30 1.24 1.37

MGC 15875 NM_153373.1 24119276 ILMN_28180 7320288 0.0587 1.85 1.64 2.05

DPP7 NM_013379.2 62420887 ILMN_6361 110274 0.0585 2.07 1.82 2.34

ABCC1 NM_019900.1 9955955 ILMN_12532 2480543 0.0585 1.41 1.32 1.50

CEPT1 NM_006090.3 56119170 ILMN 14637 7380441 0.0583 1.75 1.58 1.91

USP4 NM_003363.2 40795664 1LMN_5953 3060709 0.0582 2.66 2.26 3.09

SON NM 032195.1 21040313 ILMN_8462 6450128 0.058 2.33 2.02 2.66

ADAM9 NM_003816.2 54292119 ILMN_922 7550082 0.0578 1.24 1.18 1.29

OAS2 NM_016817.2 74229018 ILMN_5994 150056 0.0573 1.82 1.61 2.02

ATF4 NM_001675.2 33469975 ILMN_10757 2900170 0.0572 1.68 1.51 1.87

USP22 XM 942262.1 89042515 ILMN_38059 6560438 0.0569 1.90 1.69 2.12

PBEF1 NM l 82790.1 33386694 ILMN_13867 2690068 0.0567 6.05 4.36 8.24

STK24 NM_001032296.1 73808091 ILMN_10104 4850373 0.0567 1.60 1.46 1.75

C 190RF6 NM_001033026.1 74229024 ILMN_ 12941 3930064 0.0564 2.04 1.82 2.27

TXNDC5 NM 030810.2 42794770 ILMN_24968 2900458 0.0553 1.55 1.39 1.72

MAX NM_197957.2 59814750 ILMN_1660 6860682 0.055 2.08 1.80 2.40

ERGIC1 NM_00103171 1.1 7253471 1 ILMN_7272 6060333 0.0549 2.31 1.97 2.68

CLSTN1 XM 937951.1 88945307 ILMN_136995 270372 0.0544 1.29 1.22 1.36

DPH2 1 NM_001384.3 41352701 ILMN l 37484 670450 0.0539 1.37 1.28 1.45 lllumina

Covariate NCBI Accession NCBI GI lllumina Fold

Array Gini lcl ucl (Gene Name) and Version* Number * Search Key Change

Address ID

CTBP1 NM_001012614.1 61743966 ILMN_21952 67701 13 0.0539 2.17 1.90 2.43

CDK5RAP3 NM_176095.1 28872789 ILMN_ 1 1403 2940722 0.0538 3.14 2.59 3.65

CDK2 NM_001798.2 16936527 ILMN 12332 450315 0.0537 1.45 1.35 1.54

LOC344620 XM 937279.1 88970732 ILMN 35635 6100168 0.0536 2.33 2.03 2.64

ELM02 NM_022086.6 33469944 ILMN_19 1 1 160403 0.0535 1.73 1.56 1.90

DPAGT1 NM_001382.2 42794008 ILMN 10306 1990347 0.0534 1.75 1.56 1.93

C90RF72 NM_145005.3 37039614 ILMN_9580 840242 0.0534 2.31 1.96 2.70

PHF12 NM_020889.2 75677337 ILMN_8914 1470025 0.0533 1.28 1.22 1.35

RNF187 XM 047499.9 88943868 ILMN_37839 1440504 0.0531 1.28 1.21 1.33

MAT2B NM_013283.3 33519456 ILMN_18923 5080494 0.0531 3.60 2.80 4.57

LOC654174 XM_940438.1 88999456 ILMN_44671 12601 12 0.0529 2.23 1.94 2.52

VPS13C NM_018080.2 66348090 ILMN_2446 5890136 0.0529 1.48 1.39 1.58

LOC652626 XM_942172.1 89073794 ILMN_44442 10274 0.0527 1.62 1.48 1.79

TOP1MT NM_052963.1 16418460 ILMN_15321 1940594 0.0521 1.59 1.40 1.77

DGKA NM_001345.4 41393585 ILMN_4980 4670021 0.052 1.42 1.31 1.54

CTN B 1 NM_001904.2 40254459 ILMN_21386 6040201 0.0516 2.18 1.90 2.50

HSPD1 NM_002156.4 41399283 ILMN_7269 940767 0.0513 1.34 1.26 1.41

R F135 NM_032322.3 37655166 ILMN_26639 3370041 0.0507 1.97 1.75 2.20

TRUB1 NM_139169.3 34303921 ILMN_26216 4570215 0.0507 1.33 1.24 1.42

HM13 NM_030789.2 30581114 ILMN_2780 3370326 0.0505 2.93 2.42 3.48

MGAT4B NM_014275.2 16915933 ILMN_139177 1090328 0.0495 1.59 1.45 1.73

RAE1 NM_003610.3 62739174 ILMN_24358 4010519 0.0492 1.69 1.53 1.84

RAB37 NM_001006638.1 54859684 ILMN_8592 6940551 0.0492 3.16 2.61 3.78

TAP2 NM_018833.2 73747916 ILMN_437 1780528 0.0491 1.81 1.58 2.10

ACTB NM_001101.2 5016088 1LM _2565 2650079 0.0478 3.10 2.49 3.80

CPNE1 NM_003915.2 23397694 ILMN_22052 6520577 0.0478 1.80 1.62 1.99

TPST2 NM_003595.3 56699462 ILMN_13359 620014 0.0477 1.77 1.59 1.96

MRE1 1A NM_005590.3 56550106 ILMN_6718 2030762 0.0472 1.32 1.25 1.40

CTGLF1 N _133446.1 19263342 ILMN_22934 7000437 0.0472 1.88 1.68 2.07

NFX1 NM_147133.1 22212924 ILMN_17577 5900338 0.0469 1.61 1.45 1.77

LOC652878 XM_942594.1 89065158 ILMN_41407 6040634 0.0469 3.35 2.63 4.19

LOC653518 XM_934555.1 88961609 ILMN_35512 270242 0.0467 3.02 2.46 3.65

LOC441511 XM_497141.2 89059964 ILMN_41472 1980367 0.0466 1.66 1.50 1.82

PTGS1 NM_000962.2 18104966 ILMN_24170 4060438 0.0463 1.59 1.46 1.73

VNN2 NM_078488.1 17865815 ILMN_24337 2690079 0.0461 1.84 1.62 2.07

GPR97 XM_936582.1 89065470 ILMN_138901 2690338 0.0461 3.61 2.86 4.42

B3GNT1 NM_006577.3 15451893 ILMN_138549 4610082 0.0461 1.82 1.62 2.05

DDB1 XM_943551.1 89034785 ILMN_139085 2690300 0.046 1.50 1.37 1.61

FBX09 NM_033480.1 15812200 ILMN_26635 2190129 0.0459 1.59 1.46 1.74

GIMAP6 NM_02471 1.3 561 19213 ILMN_1753 730327 0.0459 1.25 1.19 1.31

FAM21C N _015262.1 59814410 ILMN_17686 4890519 0.0457 1.92 1.71 2.15

TES NM_015641.2 23238186 ILMN_ 17251 780524 0.0457 1.65 1.51 1.78

TC1RG1 NM_006019.2 19924144 ILMN_2161 3850128 0.0455 2.47 2.06 2.93 Illumina

Covariate NCBI Accession NCBI GI Illumina Fold

Array Gini lcl ucl (Gene Name) and Version* Number * Search Key Change

Address ID

STOM NM_004099.4 38016910 ILMN_ 17469 4640484 0.0455 3.16 2.52 3.90

ARHGAP30 NM_001025598.1 71040097 ILMN_15952 830189 0.0454 3.05 2.51 3.65

LOC647481 XM_936545.1 88952403 ILMN_40110 540154 0.0453 1.63 1.47 1.78

DHX9 NM_001357.2 13514819 ILMN_7196 130328 0.0452 2.60 2.18 3.06

UBE2Z NM_023079.2 20149671 ILM _17384 1030504 0.0451 1.38 1.29 1.46

CIAS1 NM_004895.3 34878692 ILMN J 1278 3190520 0.0449 1.49 1.39 1.60

LOC645367 XM_932672.1 89058763 ILMN_34862 2680379 0.0446 1.25 1.19 1.31

LOC652506 XM_941975.1 89062938 ILMN_39645 4920593 0.0445 2.16 1.89 2.41

ITGAX N _000887.3 34452172 ILM _7741 270373 0.0443 1.44 1.35 1.54

WBSCR20B NM 145645.1 21717802 ILMN_ 137520 6060452 0.0441 1.46 1.35 1.58

LOC654135 X _945932.1 88999049 ILM _31315 620360 0.0439 1.51 1.40 1.62

AGPAT2 NM 006412.3 68835055 ILMN_6967 6860039 0.0439 2.18 1.90 2.48

LOC652184 XM_941546.1 89062473 ILMN_46021 7320553 0.0439 2.07 1.81 2.36

TOPI NM_003286.2 19913404 ILMNJ3071 5890326 0.0438 1.97 1.74 2.21

LOC645600 XM_928616.1 89031346 ILM _31564 2750594 0.0437 1.22 1.17 1.27

SPTBN1 NM_178313.1 30315657 ILM _17508 4480091 0.0431 1.35 1.27 1.44

GPR27 NM_018971.1 9506746 ILMN_ 16834 2600670 0.0428 2.14 1.88 2.44

SMYD2 NM_020197.1 9910273 ILMN_4244 6900050 0.0428 1.61 1.45 1.76

MAT2B NM_182796.1 33519454 ILMN_ 19777 4610133 0.0426 1.60 1.45 1.75

LOC644615 XM_927730.1 89035568 ILMN_44684 4560414 0.042 1.41 1.32 1.49

DNAJB 12 XM_944538.1 89031976 ILMN_137399 3360204 0.0419 1.97 1.74 2.23

LOC650230 |XM_941946.1 88970975 ILMN_39890 5390315 0.0419 2.40 1.99 2.88

PSMC4 |NM_153001.1 24430154 ILMN_27399 6180192 0.0415 2.73 2.20 3.32

USF2 NM_003367.2 46877103 ILMN_7790 5220079 0.0414 1.31 1.24 1.39

PHF17 NM_199320.1 40556392 ILMN 26400 7200709 0.0412 1.54 1.42 1.66

PIK3R5 NM_014308.1 7657432 ILMN 21503 6650564 0.0407 1.70 1.55 1.84

LOC375133 XM_942088.1 89071779 ILMN_32434 3400632 0.0405 2.14 1.84 2.47

C7ORF20 INM_015949.2 38570061 ILMN_23467 6660377 0.0405 1.44 1.34 1.53

CASC4 |NM_138423.2 29826288 ILMN_15514 3930458 0.0403 1.93 1.69 2.18

CUGBP1 NM_198700.1 38570080 ILM _ 10496 450243 0.0403 1.41 1.32 1.50

HIATL2 XM_939817.1 89030482 ILMN_137017 1170750 0.0399 2.23 1.90 2.58

CASP8 NM_033358.2 73623022 ILMN 29186 1300750 0.0397 3.46 2.66 4.37

LIMK2 NM_005569.3 73390104 ILMN_5825 3840475 0.0396 1.35 1.27 1.43

HCAP-H2 NM_014551.3 34303963 ILMN_14918 1850685 0.0394 1.28 1.21 1.35

CASP8 NM_033356.2 73623020 ILMN_2110 2120719 0.0393 2.60 2.14 3.1 1

NFATC2IP NM_032815.3 46447822 ILMN_ 17542 7160671 0.0393 1.34 1.27 1.40

MAWBP NM_001033083.1 74316008 ILMN_1251 1 2120184 0.0391 1.19 1.14 1.24

SIGIRR NM_021805.1 11141876 ILMN_18194 7380328 0.0391 2.60 2.20 3.05

HS.569340 DA483022 80904863 ILMN_121521 4280181 0.039 1.21 1.16 1.26

BTBD1 NM_025238.3 59814019 ILMN 19868 5860717 0.039 2.17 1.86 2.51

ERBB2IP NM_018695.2 56237019 ILMN_26248 450646 0.0388 1.75 1.57 1.95

AMY2B NM_020978.3 56550100 ILMN_5982 2970192 0.0386 1.45 1.35 1.55

ATP1B3 NM 001679.2 49574492 ILMN_3785 5490403 0.0386 1.89 1.65 2.15 Illumina

Covariate NCBI Accession NCBI GI Illumina Fold

Array Gini lcl ucl

(Gene Name) and Version* Number * Search Key Change

Address ID

AFF1 NM_005935.1 5174572 ILMN_8254 3130070 0.0385 1.25 1.19 1.31

PML XM_945882.1 89039091 ILMN_137695 3400017 0.0384 1.24 1.18 1.30

LOC643025 XM_926168.1 89060501 ILMN_38834 380243 0.0383 2.79 2.30 3.36

UGP2 NM_001001521.1 48255967 ILMN_24547 7400035 0.0379 1.41 1.30 1.51

STARD7 NM_020151.2 21450854 ILMN_9703 130707 0.0378 1.49 1.37 1.60

SLC25A24 NM_013386.2 33598953 ILMN_15753 6400017 0.0377 1.19 1.14 1.23

DMXL2 NM_015263.1 19745147 IL N_24373 3060360 0.0376 2.29 1.98 2.63

APOL6 NM_030641.2 22035660 ILMNJ38012 6380338 0.0376 1.47 1.36 1.58

AZIN1 NM_015878.4 62526034 IL N_4825 5810504 0.0375 2.19 1.88 2.49

PARP8 NM_024615.2 24432008 ILMN 26673 630671 0.0373 1.37 1.27 1.46

LOC653504 XM_930804.1 89059736 IL N_351 14 6220255 0.0372 1.35 1.27 1.43

KAT3 NM 001008661.1 56713253 ILMN_1 120 6220474 0.0372 1.47 1.37 1.58

POLDIP3 NM 03231 1.3 30089917 ILMN_1 1068 630743 0.0372 1.51 1.39 1.62

IHPK2 NM_00100591 1.1 55769523 ILMN 4437 3130521 0.0369 2.12 1.83 2.45

CXCR4 NM_003467.2 56790928 ILMN_26085 6650142 0.0369 4.26 3.19 5.69

VHL NM_000551.2 38045904 ILMN_21046 5670746 0.0367 2.19 1.90 2.50

TGIF NM_003244.2 28178841 ILMN_9308 4230014 0.0366 1.18 1.13 1.22

DUSP6 NM_001946.2 42764682 ILMN_5440 4780754 0.0366 3.72 2.90 4.62

AMD1 NM_001634.4 74275345 IL N_21529 1430021 0.0365 3.43 2.63 4.42

TSC 1 NM 001008567.1 56699467 ILMN_24230 5080452 0.0365 1.20 1.16 1.24

YWHAE NM_006761.3 34304385 ILMN_18524 160372 0.0364 1.37 1.29 1.45

GPIAP1 NM_203364.2 61676202 ILMN_9771 5810438 0.0363 2.50 2.09 2.97

SDHA N _004168.1 4759079 ILMN 22058 1660341 0.0362 2.75 2.27 3.27

HS.580138 DA783170 82134687 ILMN_132319 6580634 0.0362 1.47 1.37 1.58

SLC25A3 NM 21361 1.1 47132594 ILMN_ 18748 1230196 0.0361 1.47 1.36 1.58

MCL1 NM_021960.3 33519459 ILMN 18397 6020280 0.0361 5.20 3.84 6.81

GALNACT-2 NM_018590.3 24429591 ILMN_1 1419 6060730 0.0356 2.37 2.00 2.75

DNAJB 12 NM_017626.3 50593535 ILMN_22702 2340750 0.0355 1.77 1.58 1.99

SLC25A24 NM_013386.2 33598953 ILMN_15753 380376 0.0355 2.04 1.80 2.32

LOC646358 XM 929287.1 89038440 IL N_30990 1940520 0.0354 1.22 1.17 1.26

LOC440836 NM_001014440.1 62198217 ILMN_26695 5960025 0.0354 1.25 1.19 1.30

MGC5139 XM 934229.1 89035770 ILMN_39523 6040053 0.0352 1.12 1.09 1.15

CTN B 1 XM_945650.1 88968748 ILMN_137682 6400066 0.0352 2.27 1.96 2.62

TOP1MT XM_944877.1 89028998 ILMNJ37050 60343 0.035 1.31 1.19 1.40

BCL2L 1 NM 138578.1 20336334 ILMN_12148 60162 0.0349 1.64 1.47 1.82

PRIM2A XM 942683.1 88999106 ILMN_139106 3290273 0.0348 1.51 1.38 1.64

DHX40 NM_024612.3 31542728 ILMN 1864 6280639 0.0348 1.66 1.49 1.82

SLC25A3 NM_213612.1 47132596 ILMN J9383 7000167 0.0348 1.25 1.19 1.31

RASSF5 NM_ 182665.1 32996734 ILMN_2837 7560215 0.0348 2.69 2.22 3.18

RFFL NM_001017368.1 62865648 ILMN 18313 7000059 0.0347 1.17 1.12 1.22

HIST2H2BF NM 001024599.1 66912161 ILMN_138755 3850021 0.0346 1.46 1.34 1.59

SNX14 NM 153816.2 39777616 ILMN_590 4900040 0.0345 1.19 1.14 1.23 1AA0319L NM_024874.3 33359220 ILMN_21669 3370470 0.0344 2.39 2.04 2.77

Illumina

Covariate NCBI Accession NCBI GI Illumina Fold

Array Gini lcl ucl (Gene Name) and Version* Number * Search Key Change

Address ID

RPSA NM_001012321.1 59859884 ILMN_20469 70307 0.0264 -1.22 -1.28 -1.16

TRPM7 NM O 17672.2 29893551 ILMN 1 1670 610187 0.0262 1.28 1.21 1.35

CASP9 NM_001229.2 14790123 ILMN_2760 770754 0.0262 1.52 1.41 1.63

SLIC1 NM_182854.1 33504570 ILMN_51 1 1980338 0.026 2.21 1.87 2.58

BTN2A2 NM_006995.3 31881700 1LMN_ 15223 4220494 0.026 1.29 1.23 1.36

ENTPD6 NM_001247.1 4557422 ILMN_ 17684 6480669 0.026 1.20 1.15 1.25

CR1 NM_000651.3 21536275 ILMN_137353 770075 0.026 1.61 1.46 1.78

ZNF655 NM 001009956.1 58331255 ILMN 2214 1780370 0.0259 1.13 1.09 1.17

APOL2 N _145637.1 22035652 ILMN_ 19232 5870376 0.0259 1.63 1.49 1.77

CHMP6 N _024591.3 52851447 ILMN_26654 510142 0.0256 1.57 1.44 1.71

SERTAD3 NM_203344.1 42741651 ILMN_1527 1 190634 0.0254 1.78 1.59 1.97

IFIT3 NM_001031683.1 72534657 ILMN_22925 3830041 0.0254 2.69 2.16 3.31

GFM2 NM_170681.1 25306282 ILMN_ 16025 4070735 0.0254 1.41 1.30 1.52

TAGAP NM_138810.2 23199968 ILMN_ 1 1224 4250369 0.0254 3.30 2.58 4.13

UBE2L6 N _004223.3 38157980 ILMN_7531 201 10 0.0252 2.91 2.35 3.56

BCL6 NM_138931.1 21040335 ILMN_ 18289 4640044 0.025 1.23 1.16 1.30

AP1S1 NM 001283.2 16950626 ILMN_21653 6270301 0.025 1.51 1.38 1.63

NOD27 NM_032206.2 28951070 ILMN_23914 6650445 0.025 2.13 1.83 2.41

STX16 NM_001001433.1 47778942 ILMN_12925 3290307 0.0249 1.39 1.30 1.48

HIATL2 NM 032318.1 14150087 ILMN_ 138936 6480390 0.0249 1.93 1.69 2.19

C 10RF58 NM_144695.1 21389600 ILMN_11942 2710612 0.0248 1.52 1.37 1.67

OASL NM_003733.2 38016933 ILMN_4735 7150196 0.0248 1.85 1.65 2.07

LOC255809 XM_930239.1 89052292 ILMN_31703 49001 14 0.0247 2.16 1.86 2.51

PTPN22 NM O 1241 1.2 15619017 ILMN_25877 6100338 0.0247 1.99 1.72 2.27

LOC440349 XM_496129.2 89040448 ILMN_40146 6380239 0.0246 1.33 1.24 1.42

PDE7A NM 002603.1 24429565 ILMN_13515 2350646 0.0245 1.64 1.49 1.81

LOC554223 XR_001 1 15.1 88998673 ILMN_42290 1510341 0.0244 3.62 2.82 4.60

RAB27A NM 183235.1 34485708 ILMN_13878 1580730 0.0244 1.37 1.28 1.46

NPEPPS NM_006310.2 15451906 ILMN 8237 2190519 0.0244 1.31 1.24 1.37

SLC39A3 NM_144564.4 47080101 ILMN_27676 3520605 0.0243 1.66 1.51 1.83

IL1RN NM_173842.1 27894318 ILMN_3867 2190653 0.0241 3.72 2.92 4.71

THAP4 NM_015963.4 47059038 ILMN_8784 540452 0.0241 1.23 1.17 1.29

BMX NM_203281.1 42544181 ILMN_1 1912 1 110341 0.024 1.15 1.1 1 1.20

LOC652615 XM_942150.1 89072185 ILMN_40224 7160039 0.024 1.43 1.32 1.53

ARHGAP25 NM_014882.2 55770896 ILMN_14823 7570280 0.024 1.35 1.28 1.42

TSC22D3 NM_001015881.1 62865624 ILMN_20126 3800707 0.0239 1.40 1.29 1.52

LBH NM_030915.1 13569871 ILMN_21350 4120086 0.0239 1.58 1.43 1.74

SNAP23 NM_003825.2 18765728 ILMN_2921 1 4490053 0.0237 3.78 2.87 4.99

DNTTIP2 NM_014597.3 54633314 ILMN_26105 2260411 0.0236 -1.17 -1.22 -1.13

MLL3 NM_170606.1 24586652 ILMN 14020 6330332 0.0236 2.20 1.91 2.52

MAGED2 M 177433.1 29171704 ILMN_16101 2630132 0.0235 1.29 1.22 1.36

PPP2R2D NM_018461.2 51093850 ILMN_22358 3460026 0.0234 2.06 1.77 2.37

TRI 5 NM_033092.1 1501 1943 ILMN_29177 4040095 0.0233 1.25 1.19 1.32 Illumina

Covariate NCBI Accession NCBI GI Illumina Fold

Array Gini lcl ucl (Gene Name) and Version* Number * Search Key Change

Address ID

LIMK2 NM_001031801.1 73390139 ILMN_6284 5390349 0.0233 2.41 2.02 2.82

ATF2 NM_001880.2 22538421 ILMN_12901 1450546 0.0232 1.67 1.49 1.87

ATP2A2 NM_001681.2 27886536 ILMN_4412 4250093 0.0232 1.67 1.49 1.87

PPP1R9B NM_032595.1 1421 1926 ILMN_138367 5820717 0.0231 1.25 1.18 1.32

MEF2A NM_005587.1 5031906 ILMN_ 17271 1450291 0.023 1.38 1.29 1.47

HP1BP3 NM_016287.2 56676329 ILMN_29502 150291 0.023 1.85 1.62 2.09

CRLF3 NM_015986.2 27764872 ILMN_22668 4390397 0.023 2.89 2.37 3.53

C90RF77 NM 016014.2 71051599 ILMN_12685 4850008 0.023 1.63 1.47 1.78

ADAM 17 NM_003183.4 73747888 ILMN_5977 2900468 0.0229 1.30 1.23 1.38

METRNL XM_941466.1 89043124 ILMN_42199 1 170288 0.0228 1.97 1.72 2.27

HIATL2 NM_032318.1 14150087 ILMN_138936 3460424 0.0228 2.24 1.89 2.59

DHRS9 NM_ 199204.1 40548396 ILMN_25196 1300746 0.0227 1.69 1.50 1.90

SP3 NM 0031 1 1.3 67078401 ILMN 15345 2060768 0.0227 1.41 1.31 1.51

FYN NM_153047.1 23510361 ILMN_25662 1090372 0.0226 2.75 2.21 3.36

CDC42EP3 NM_006449.3 30089964 ILMN 1066 1780072 0.0226 2.02 1.75 2.32

HS.559151 AW292488 6699124 ILMN_ 113570 5130402 0.0224 1.53 1.40 1.68

CASP6 NM_032992.2 73622127 ILMN_1 1438 58601 13 0.0224 -1.34 -1.43 -1.25

VPS 16 NM_022575.2 17978478 ILMN_11344 4210524 0.0223 1.62 1.47 1.77

LOC653650 XM_935348.1 89039623 ILMN 45794 2320066 0.0222 1.98 1.73 2.27

PRKCD NM 006254.3 47157323 ILMN_17715 1770554 0.0221 1.22 1.17 1.27

Sep 7, 2010 NM_00101 1553.1 58535460 ILMN_24703 2680754 0.0221 -1.74 -1.99 -1.49

FLJ38973 NM_153689.3 31581540 ILMN_23846 7160577 0.0221 -1.17 -1.22 -1.13

USP21 NM_001014443.2 74027268 ILMN 18137 7330504 0.0221 1.17 1.13 1.21

MANEA NM_024641.2 41393555 ILMN_6991 620474 0.022 1.46 1.35 1.57

LOC648022 XM_943614.1 88952757 ILMN 36041 1030243 0.0219 1.84 1.64 2.03

LOC644614 XM 927729.1 88943047 ILMN_42864 4670373 0.0219 1.12 1.09 1.16

CD200R1 NM_138940.2 68215643 1LMN_3165 1570687 0.0218 1.47 1.35 1.59

GMPR2 NM 016576.3 50541955 ILMN_1280 870551 0.0218 1.15 1.1 1 1.18

LOC642323 XM_925863.1 88943701 ILMN_32302 6590066 0.0217 1.50 1.36 1.64

NFS1 NM_181679.1 32307129 ILMN_3492 6980053 0.0217 1.27 1.19 1.34

LOC650654 XM 939739.1 89039101 ILMN 37996 5490121 0.0216 -1.22 -1.28 -1.16

VNN3 NM_078625.2 66932886 ILMN 25942 2060600 0.0215 1.55 1.41 1.69

CXORF40B NM_001013845.1 62241037 ILMN_ 12545 4640068 0.0215 1.20 1.14 1.26

LOC653942 XM 9381 16.1 89033520 ILMN_39856 360382 0.0214 2.67 2.15 3.31

CCR4 NM_005508.4 48762930 ILMN_10745 6270246 0.0214 1.33 1.22 1.44

LOC643025 XM_926168.1 89060501 ILMN_38834 7550139 0.0214 2.39 2.00 2.82

ROCK1 NM_005406.1 4885582 ILMN_23091 6250497 0.0213 1.77 1.58 1.98

REPS2 NM_004726.1 4758943 ILMN_21036 6450220 0.0213 2.01 1.74 2.33

MCTP2 NM_018349.2 50657351 ILMN_3204 2570338 0.0212 1.80 1.60 2.02

XKR8 NM_018053.2 24431976 ILMN_1 1071 2350338 0.021 1 1.40 1.30 1.50

LOC645625 XM_935208.1 89041729 ILMN_35948 130070 0.021 1.65 1.48 1.81

RAB37 NM 175738.3 54859694 ILMN_520 4570279 0.021 1.21 1.16 1.26

FABP5 NM_001444.1 4557580 ILMN_27564 2350040 0.0209 -1.13 -1.16 -1.09 IUumina

Covariate NCBI Accession NCBI GI IUumina Fold

Array Gini lcl ucl (Gene Name) and Version* Number * Search Key Change

Address ID

MCCC2 NM 022132.3 14251210 ILMN_19445 290400 0.0209 1.32 1.23 1.40

UBXD7 XM_931517.1 88967344 ILMN_36533 4490564 0.0207 1.36 1.27 1.44

OPN3 NM_014322.2 71999130 ILMN_26561 5220170 0.0205 1.44 1.33 1.56

TMLHE NM_018196.1 8922624 ILMN_29460 840053 0.0199 1.28 1.21 1.36

LOC553158 NM_181334.3 66346696 ILMN_2156 2750253 0.0198 1.36 1.27 1.45

LSP1 NM_001013253.1 61742788 ILMN_12132 5720192 0.0198 2.51 2.04 3.03

MICAL2 NM_014632.2 41281417 ILMN_27460 4010753 0.0197 1.50 1.38 1.63

DERPC NM_017804.3 5081 1884 ILMN_251 10 4290673 0.0197 1.65 1.49 1.82

UBL7 NM_032907.3 41 152105 ILMN_17890 4900348 0.0197 1.76 1.57 1.95

GTDC1 NM_001006636.1 54859762 ILMN_13831 6660154 0.0197 1.26 1.19 1.33

HS.570636 AK023371 10435278 ILMN_122817 2750068 0.0194 1.35 1.26 1.44

ATRX NM_138270.1 20336204 ILMN_16109 6020156 0.0194 1.39 1.29 1.49

PPM1G NM_ 177983.1 29826281 IL N_878 1300470 0.0193 1.45 1.34 1.57

DPP7 XM_939309.1 89030620 ILMN_137782 1440102 0.0193 1.90 1.64 2.20

IFIT3 NM_001549.2 31542979 ILMN 1944 430021 0.0193 4.44 3.29 5.85

HERC3 NM_014606.1 7657151 ILMN_21657 4860138 0.0192 1.88 1.67 2.13

CCNDBP1 NM_037370.1 16554567 ILMN_23609 4760520 0.0191 1.20 1.15 1.26

UNC45A N _017979.1 8922201 ILMN_27819 670255 0.0191 1.32 1.24 1.38

HNRPA3 NM_194247.1 34740328 ILMN_5256 1070138 0.019 1.89 1.65 2.16

C150RF44 XM_940546.1 89039133 ILMN_138325 1340477 0.019 1.18 1.14 1.23

PPP2R5D NM_006245.2 31083266 ILMN_5210 1410411 0.019 1.53 1.39 1.67

CUGBP2 NM_001025076.1 68303644 ILMN_21 178 5700392 0.0189 1.19 1.13 1.25

PPM1A NM_177952.1 29557938 ILMNJ0552 580520 0.0189 1.47 1.36 1.58

TGFBR2 NM_001024847.1 67782325 ILMN_22189 7100403 0.0189 1.49 1.35 1.63

TRADD NM_003789.2 24234723 ILMN_27933 3610020 0.0188 2.39 2.01 2.80

GRIP API NM_020137.3 46592990 ILMN_2781 1 4730215 0.0188 1.53 1.42 1.63

MATK NM_139355.1 21450845 ILMN_ 13609 7650424 0.0188 1.82 1.62 2.05

TBL2 NM_032988.1 14670378 IL N_ 136934 5090703 0.0187 1.52 1.39 1.65

PHC2 NM_004427.2 37595529 ILMN 28897 6650739 0.0185 1.55 1.42 1.68

RPLP1 NM_001003.2 1690551 1 ILMN_23181 65601 14 0.0184 -2.29 -2.78 -1.86

PPP1R3B NM_024607.1 13375814 ILMN_9571 1070626 0.0182 1.54 1.40 1.70

API GBP 1 NM_007247.3 38569408 ILMN_ 13930 2350474 0.0182 1.68 1.51 1.87

LOC651621 XM_940809.1 89031867 ILMN_45641 3840215 0.0182 1.43 1.31 1.54

TSC22D1 NM_006022.2 31543826 ILMN 26720 4200719 0.0182 2.58 2.12 3.17

CUTL1 NM_001913.2 31652235 ILMN_8630 4850189 0.018 1.39 1.29 1.48

LOC647100 XM_9301 15.1 89040267 ILMN_34067 4900577 0.0179 -2.26 -2.73 -1.82

RPL27A NM_000990.2 14141 189 ILMN 139166 3420367 0.0178 -2.18 -2.60 -1.82

DUSP10 NM_007207.3 21536334 ILMN_17179 5420242 0.0178 1.22 1.16 1.28

BIRC2 NM_001166.3 41349435 ILMN_23760 2570064 0.0177 2.07 1.79 2.38

MGC3123 NM_177441.1 28973798 ILMN_9166 3520386 0.0177 2.05 1.82 2.28

PCK2 NM_001018073.1 66346722 ILMN_18787 60671 0.0177 1.33 1.25 1.42

PSEN 1 NM_007319.1 7549814 ILMN 762 5690561 0.0176 2.14 1.84 2.45

LAIR1 NM_021708.1 1 1231 178 IL N_26463 2340646 0.0175 1.70 1.52 1.90

Illumina

Covariate NCBI Accession NCBI GI Ulumina Fold

Array Gini lcl ucl

(Gene Name) and Version* Number * Search Key Change

Address ID

INPP4A NM_001566.1 4504704 ILMN J9517 1740048 0.0145 1.31 1.23 1.39

LOC51 136 NM_016125.2 21361528 ILMN_27239 2750403 0.0145 2.09 1.77 2.45

PLD3 NM_001031696.1 72534683 ILMN_6460 4780037 0.0144 1.26 1.20 1.32

DIABLO NM_138930.2 42544194 ILMNJ9433 2480041 0.0142 1.66 1.47 1.86

LOC651575 XM_940750.1 89066735 ILMN_33487 4210041 0.0142 1.23 1.15 1.30

LOCI 24216 XR_001518.1 89039499 ILMN_37388 6270768 0.0142 1.73 1.56 1.92

SFI1 NM_014775.2 55956783 ILMN_23938 630181 0.0142 -1.32 -1.42 -1.22

C10RF9 NM_014283.2 29837653 ILMN_9240 6520424 0.0142 -1.16 -1.21 -1.12

SCAMPI NM_004866.3 33598919 ILMN_28654 2690709 0.0141 1.45 1.33 1.60

ARPC4 NM_005718.3 68161505 ILMN_24602 1070196 0.014 1.16 1.12 1.20

FAM73A NM_ 198549.1 38348383 ILMN_4385 3940747 0.014 -1.22 -1.28 -1.15

PKNOX1 NM_004571.3 37595549 ILMN_29895 6250379 0.014 1.21 1.16 1.26

SERPINA1 NM_001002236.1 50363218 ILMN_30268 2060592 0.0139 2.50 2.04 3.01

FCGR2A XM_938849.1 88952546 ILMN_138445 2100100 0.0139 3.82 2.92 5.00

FBXL17 NM_022824.1 45238579 ILMN 8400 610164 0.0139 1.15 1.10 1.19

PIK3C2A NM_002645.1 4505798 ILMN 2470 6270181 0.0139 1.45 1.33 1.56

LOC650020 XM_93911 1.1 88952884 ILM _40049 2480152 0.0138 1.43 1.32 1.55

LOC642998 XM_931228.1 88995794 ILMN_38499 2140170 0.0137 1.13 1.10 1.17

SULT1A1 NM_177536.1 29540542 ILMN_29763 5270477 0.0137 1.26 1.20 1.32

C 17ORP60 XM_945975.1 89042847 ILMN_33002 130010 0.0136 1.91 1.60 2.26

WDR43 XM_944889.1 88954702 ILMN 43073 4210164 0.0136 1.42 1.31 1.54

LOC652826 XM_942509.1 89064749 ILMN_34282 4250373 0.0136 2.14 1.83 2.49

SFRS 1 1 NM_004768.2 23111060 ILMN_4847 1400626 0.0134 -1.45 -1.59 -1.31

COG5 NM_006348.2 32481215 ILMN 10374 4920463 0.0134 1.42 1.31 1.55

CASP8 NM_001228.3 73623018 ILMN_29639 650241 0.0134 1.36 1.27 1.44

GOLGA7 NM_016099.2 50541949 ILMN_30279 1 170619 0.0133 1.36 1.28 1.45

HSPBP1 NM_ 012267.2 21361406 ILMN_19625 160543 0.0133 1.21 1.15 1.27

MOCS2 NM_176806.2 35493763 ILMN_27055 3450484 0.0133 1.53 1.39 1.68

RAB33B NM_031296.1 13786128 ILMN_21878 3930138 0.0133 -1.60 -1.85 -1.38

CLSTN1 NM_001009566.1 57242756 ILM _29098 6760600 0.0133 1.25 1.17 1.31

TALDOl XM_938697.1 89034447 ILMN 138767 1010491 0.0132 1.59 1.41 1.77

JAK1 NM_002227.1 4504802 ILMN_554 2510246 0.0132 3.31 2.58 4.17

LOC652613 XM_942146.1 89063256 ILMN 42004 4890576 0.0131 2.07 1.81 2.36

SPN NM_003123.3 71892475 ILMN_19780 7210192 0.0131 1.22 1.16 1.29

FAM18B NM_016078.3 71061433 ILMN_18985 1 190706 0.013 1.91 1.63 2.22

DOK2 NM_003974.2 41406049 ILMN_21820 3890605 0.013 1.63 1.47 1.81

LOC647392 XM_942791.1 88987475 ILMN_41904 6270685 0.0129 1.16 1.12 1.20

CGI-09 M_015939.3 29244922 ILMN_29842 1440100 0.0128 -1.16 -1.22 -1.10

LOC651319 XM_944594.1 88957160 ILMN_45901 2190424 0.0128 1.24 1.17 1.30

CTDSP 1 NM_182642.1 32813442 ILMN_13739 3610072 0.0128 1.83 1.61 2.06

RPS6KA3 XM_9441 12.1 89060584 ILMN 137549 7330136 0.0128 2.37 2.02 2.78

LOC388122 XM_370865.3 89038392 ILMN 46143 5290064 0.0127 -1.16 -1.20 -1.1 1

HNRPULl NM_144732.1 21536319 ILMN_4191 6180091 0.0127 1.46 1.31 1.62 Illumina

Covariate NCBI Accession NCBI GI Illumina Fold

Array Gini lcl ucl (Gene Name) and Version* Number * Search Key Change

Address ID

ATP1A1 NM 000701.6 4876268C ILMN 677 6370121 0.0127 1.95 1.67 2.27

HDLBP NM_005336.2 42716278 ILMN 5820 696041 1 0.0127 1.19 1.14 1.25

IL1R2 NM_004633.3 27894332 ILMN_25995 5960754 0.0126 1.95 1.66 2.27

LOC648081 XM_937132.1 88951496 ILMN_33238 7560598 0.0126 1.89 1.66 2.14

OGT NM_003605.3 32307145 ILMN_4667 1050168 0.0124 1.13 1.09 1.17

CD151 NM 004357.3 34328913 ILMNJ 39293 4830504 0.0124 1.98 1.72 2.26

AD NM 001 123.2 32484972 ILMN_4107 6840209 0.0124 1.34 1.24 1.45

DBR1 N _016216.2 565491 12 ILMN_19003 6130494 0.0122 1.63 1.46 1.82

BLR1 NM_001716.2 14589867 IL N_27589 1440291 0.0121 1.34 1.21 1.47

EZH2 NM_004456.3 23510382 ILMN .25740 580296 0.0121 1.23 1.17 1.29

LOC653276 XM_931495.1 89035740 ILMN_34785 2570717 0.012 1.38 1.28 1.49

GLT8D1 NM_001010983.1 58331224 ILMN_8696 3930754 0.012 1.19 1.14 1.24

RQCD1 NM_005444.1 4885578 ILMN_29301 3990243 0.01 19 1.36 1.27 1.45

RAB39B NM 171998.2 64762487 ILMN 22924 2690142 0.01 17 -1.15 -1.19 -1.1 1

NR3C 1 NM_001018076.1 66528585 ILMN_6719 6550079 0.01 17 1.22 1.17 1.27

HS.574855 DN917404 77945616 ILMN_127036 4070192 0.01 15 1.1 1 1.07 1.15

NEDD9 NM l 82966.1 33667052 ILMN_ 137978 4570091 0.01 15 1.24 1.18 1.30

ILF3 NM_153464.1 24234755 ILMN_24364 4810139 0.01 15 1.41 1.31 1.51

LOCI 96264 NM_198275.1 38093644 ILMN 23862 2340131 0.01 14 1.89 1.66 2.15

AP1S1 NM 001283.2 16950626 ILMN_21653 2650075 0.01 14 1.48 1.36 1.61

LOC653382 XM_934354.1 89042081 ILMN_44569 5220243 0.01 14 1.26 1.19 1.33

SMARCD3 NM_003078.3 51477705 ILMN_8015 1400605 0.01 13 1.58 1.40 1.78

ILF3 NM_004516.2 24234752 ILMN_12252 5090168 0.01 13 1.87 1.61 2.15

SNX15 NM 013306.3 46370087 ILMN_1967 2750605 0.01 12 1.20 1.15 1.26

LOC652773 XM 942415.1 89077406 ILMN_38539 7610167 0.01 12 1.49 1.36 1.63

LOC651023 XM 940136.1 89030314 ILMN 40631 2120048 0.01 1 1 1.23 1.17 1.30

CDKAL1 NM_017774.1 8923317 ILMN_26274 4120445 0.01 1 1 1.56 1.41 1.72

CDKN2D NM 001800.3 39995074 ILMN 28866 1500364 0.01 1 1.91 1.69 2.14

SORD NM_003104.3 34147623 ILMN 27787 4260075 0.01 1 1.38 1.28 1.49

HS.514843 BX094382 27841938 ILMN_98745 2680400 0.0109 1.50 1.37 1.65

CEP57 NM_014679.3 597101 14 ILMN 27141 6370445 0.0109 -1.34 -1.45 -1.24 IAA0564 N _015058.1 57863270 ILMN_16560 7380594 0.0109 -1.27 -1.36 -1.18

HNRPAl NM_031157.1 14043069 ILMN_138150 610400 0.0108 1.38 1.26 1.50

ARF4 NM_001660.2 6995998 ILMN 5548 2490243 0.0107 1.96 1.66 2.32

LOC649095 XM_945154.1 89059185 ILMN 32679 580709 0.0107 1.64 1.45 1.87

SUM02 NM_001005849.1 54792070 ILMN_16713 1070181 0.0106 -2.02 -2.39 -1.65

LOC653743 XM_929369.1 88953184 ILMN 34703 1770068 0.0106 1.79 1.54 2.04

MC 7 NM_005916.3 33469967 ILMN_1986 2360278 0.0106 1.23 1.16 1.30

HS.562444 AI961 125 5753763 ILMN_1 15550 4120300 0.0106 1.28 1.21 1.36

LOC388621 XM_371243.4 88942623 ILMN_43918 4180564 0.0106 -2.05 -2.46 -1.66

CCT7 NM_006429.2 58331183 ILMN_22959 7150017 0.0106 2.81 2.26 3.45

SF3B1 NM_001005526.1 541 12118 ILMN_13059 7150072 0.0106 1.96 1.68 2.25

SIAH1 NM_003031.3 63148617 ILMN_18192 7200398 0.0106 1.18 1.14 1.22 Illumina

Covariate NCBI Accession NCBI GI Illumina Fold

Array Gini lcl ucl (Gene Name) and Version* Number * Search Key Change

Address ID

CPT1A NM_001876.2 73623029 ILMN_14446 6130450 0.0105 1.14 1.10 1.18

RBMS1 NM_002897.3 46249390 ILMN_ 18726 1500411 0.0104 1.28 1.18 1.37

UTP11L NM_016037.2 52856412 ILMN_2243 2190554 0.0104 -1.25 -1.32 -1.18

ING3 NM_198267.1 38201658 ILMN_23155 58201 13 0.0104 1.83 1.62 2.06

STAM2 NM_005843.3 21265030 ILMN_9193 4480608 0.0103 1.65 1.48 1.85

PTPRA NM_002836.2 18450367 ILMN_8330 6060603 0.0103 1.36 1.27 1.44

C30RF23 NM_001029839.1 71067097 ILMN_10936 1 170128 0.0102 1.54 1.39 1.68

SERPINA1 NM_000295.3 50363216 ILMN J034 1470719 0.0102 1.23 1.16 1.30

OPA3 NM_025136.1 13376716 ILMN_ 1 1296 4150189 0.0102 1.1 1 1.07 1.15

ERCC8 NM_001007234.1 55956772 ILMN_5204 4120292 0.0101 1.25 1.18 1.33

H BS NM_000190.3 66933007 ILMN_16358 4560315 0.0101 1.47 1.35 1.60

LOC649707 XM_938775.1 89059247 ILMN 34741 1050142 0.01 1.16 1.12 1.20

LOC644295 XM 927468.1 89037300 ILMN_38707 380390 0.01 1.22 1.15 1.28

CCT6A NM_001762.3 58331169 ILMN_21650 70347 0.01 2.22 1.85 2.63

ML L XM 936963.1 89041041 ILMN_139138 780148 0.01 1.25 1.19 1.32

HS.570385 DA674107 80937528 ILMN_122566 6450408 0.0099 2.18 1.87 2.53

HS.385555 BC035378 23273407 ILMN 89057 2710148 0.0098 1.30 1.21 1.39

LOC646144 XM_935294.1 89025359 ILMN_45775 2750152 0.0098 1.14 1.09 1.19

ZBTB41 NM_194314.2 61743929 ILMN_7261 2100471 0.0097 -1.21 -1.28 -1.15

DAP3 NM 033657.1 16905525 ILMN_13395 270528 0.0097 1.65 1.48 1.84

LOC644037 XM_933604.1 88983852 ILMN_37144 5390719 0.0097 2.91 2.29 3.71

LOC648294 XM_939952.1 89030185 ILMN_36674 6330133 0.0097 -2.14 -2.61 -1.73

SIGLEC7 NM_014385.1 7657569 IL N_29432 5860538 0.0096 1.29 1.21 1.37

PDLIM2 NM_021630.4 40288188 ILMN_11298 3930564 0.0095 1.15 1.1 1 1.20

DNAJC1 1 NM_018198.1 8922628 ILMN_14957 3290136 0.0093 1.26 1.19 1.32

LIG4 NM 002312.3 46255050 ILMN_25322 5670129 0.0093 1.22 1.17 1.28

SFRS12 NMJ39168.1 21040254 ILMN 8967 6420356 0.0093 -1.24 -1.35 -1.15

TMEM23 NM 147156.3 41350331 ILMN_26608 4540102 0.0092 1.26 1.19 1.33

RIOK1 NM_031480.2 23510355 ILMN 8030 4780593 0.0092 1.27 1.19 1.36

QKI XM_942223.1 88999422 ILMN_45956 1660746 0.0091 1.72 1.52 1.94

KIAA1432 NM_020829.1 75832028 ILMN_26728 1820026 0.0091 1.12 1.08 1.16

CSNK1A1 NM_001892.4 68303571 ILMN_24977 4850092 0.0091 1.99 1.68 2.36

BRD4 NM 014299.1 7657217 ILMN_ 19745 5360523 0.0091 1.20 1.15 1.25

KLHL7 NM_001031710.1 72534709 ILMN_8698 2760411 0.009 -1.32 -1.41 -1.22

CCM2 NM_031443.3 71067339 ILMN 4086 4040681 0.009 1.63 1.47 1.80

CES 1 NM_001025194.1 68508964 ILMN_4194 4670402 0.009 1.35 1.25 1.46

TRPV4 NM_147204.1 22547179 ILMN_649 7320291 0.0089 1.35 1.25 1.45

IHPK1 NM_153273.3 58530860 ILMN_1661 2120433 0.0088 1.33 1.25 1.42

APP NM 000484.2 41406053 ILMN_30235 7210167 0.0087 1.23 1.16 1.28

C40RF13 NM_001030316.1 71896704 ILMN_1 1 185 4120025 0.0086 1.18 1.13 1.23

PPP1CA NM_002708.3 45827796 ILMN_26836 5570035 0.0086 2.26 1.88 2.73

LOC643035 XM_931996.1 88943744 ILMN_33896 2100022 0.0085 -1.69 -1.90 -1.47

LOC642684 XM_926137.1 89025519 IL N_34902 5290661 0.0085 1.83 1.61 2.08 Illumina

Covariate NCB1 Accession NCBI GI Illumina Fold

Array Gini lcl ucl (Gene Name) and Version* Number * Search Key Change

Address ID

QKI NM_206854.1 45827709 ILMN_4669 6660097 0.0085 1.91 1.67 2.16

HS.570444 AJ003554 2792050 ILMN_122625 670477 0.0084 1.23 1.15 1.32

PTMA NM 002823.2 21359859 ILMN_7102 730129 0.0084 -2.42 -3.00 -1.90

LOC651726 XM_940945.1 89062121 ILMN_42644 1090050 0.0083 1.39 1.29 1.49

RPL23 NM_000978.2 14591907 ILMN_137528 4120707 0.0083 -1.92 -2.29 -1.58

LOC651816 XM_941060.1 89062188 ILMN_46354 61 10053 0.0083 -1.16 -1.22 -1.1 1

CASP10 NM_032974.2 47078268 ILMN_13756 6770253 0.0083 1.20 1.15 1.25

LOC6421 12 XM_936252.1 89026476 ILMN_33587 4220148 0.0082 1.91 1.68 2.18

TIA1 NM_022173.1 1 1863162 ILMN_29910 1030358 0.0081 1.84 1.58 2.12

RBM3 NM_001017430.1 63054839 ILMNJ 5994 2970356 0.0081 1.47 1.35 1.60

CCS NM_005125.1 4826664 ILMN_23509 4200286 0.0081 1.84 1.58 2.13

LOC650155 XM 939236.1 89032028 ILMN_35338 4610753 0.0081 1.47 1.35 1.59

C 140RF124 NM_020195.1 9910257 ILMN_4144 6590270 0.0081 1.40 1.28 1.53

CRSP8 XM_933599.1 88983845 ILMN_45168 7400739 0.0081 1.64 1.45 1.84

NCF1 NM_000265.1 4557784 ILMN_ 136961 1230538 0.008 3.66 2.71 4.88

LOC652537 XM_942027.1 88971364 ILMN_31258 3290600 0.0079 1.16 1.1 1 1.20

CLEC7A NM_197953.1 37675384 ILMN_3417 1090170 0.0078 1.95 1.59 2.41

RNU108 NR_002324.1 68342028 ILMN_19266 160326 0.0078 1.20 1.15 1.26

CPEB4 NM_030627.1 32698754 ILMN_5007 1690360 0.0078 2.01 1.72 2.35

HPCAL1 NM_134421.1 19913442 ILMN_12582 240019 0.0078 1.23 1.15 1.30

CSNK2A1 NMJ77559.2 47419901 ILMN_30267 2750767 0.0078 1.15 1.10 1.20

BCR NM_004327.2 1 1038638 ILMN_ 136932 4250463 0.0078 1.16 1.12 1.21

LOC641949 XM_935713.1 89026832 ILMN_45778 6660162 0.0078 1.24 1.19 1.30

C6ORF106 NM_024294.2 46094084 ILMN_24069 6770070 0.0078 1.18 1.13 1.23

LOC642817 XM_926703.1 88990450 ILMN 46700 1 190079 0.0077 2.82 2.17 3.64

ALDH3B1 NM_000694.2 71773289 ILMN_27131 1780202 0.0077 1.20 1.14 1.26

SNX 1 1 NM_152244.1 2311 1027 ILMN_9237 5690280 0.0077 1.21 1.16 1.27

LOC653328 XM_926913.1 8894261 1 ILMN_43519 7320709 0.0077 -1.51 -1.70 -1.34

N T NM_012343.2 33695083 ILMN_20204 10674 0.0076 -1.12 -1.16 -1.09

CXORF53 NM_024332.2 64762482 ILMN_18443 1820541 0.0076 1.14 1.10 1.18

IQWD 1 NM_018442.2 63252907 ILMN_ 16460 5490068 0.0076 1.20 1.13 1.26

TMCC1 NM_001017395.1 62859976 ILMN_29162 6290131 0.0076 1.36 1.26 1.47

HS.550193 U43604 1 171236 ILMN_1 10215 1450088 0.0075 1.34 1.22 1.48

LOC641913 XM_935667.1 89026774 ILMN_43603 2810605 0.0075 1.17 1.12 1.23

FLJ1 1712 NM_024570.1 13375741 ILMN_20578 4010600 0.0075 -1.61 -1.83 -1.40

LOC647743 XM_936805.1 89065527 ILMN_46476 510753 0.0075 1.56 1.40 1.72

LOC644482 XM_927612.1 88943848 ILMN_37198 5560097 0.0075 -1.17 -1.22 -1.12

ZMATl NM_032441.1 58533171 ILMN_9028 6280603 0.0075 -1.17 -1.22 -1.12

LOC650696 XM_944334.1 89031821 ILMN_44076 940241 0.0075 1.31 1.21 1.40

RNU64 NR_002326.1 68510027 ILMN_ 19397 10736 0.0074 1.30 1.21 1.39

PLB 1 NM_153021.3 76096365 ILMN_27755 3120600 0.0074 1.34 1.24 1.43

C30RF17 NM_015412.3 75812961 ILMN_26202 3420044 0.0074 1.19 1.15 1.25

ABR NM_001092.3 38679953 ILMN_23502 3780131 0.0074 1.30 1.22 1.38 Illumina

Covariate NCBI Accession NCBI GI Illumina Fold

Array Gini lcl ucl (Gene Name) and Version* Number * Search Key Change

Address ID

TOP1MT NM_052963.1 16418460 ILM _ 15321 4480465 0.0074 1.41 1.26 1.56

SNCB NM_001001502.1 48255902 ILMN_8144 5960309 0.0074 1.30 1.21 1.40

SBDSP NR_001588.1 38348442 ILMN 12233 1450102 0.0073 1.96 1.67 2.25

HS.543405 AA668142 2629641 ILMN_ 107001 2350500 0.0073 1.04 1.01 1.06

C 17ORF80 NM_017941.3 34222156 ILMN_21070 4290609 0.0073 1.58 1.43 1.72

SLC25A30 NM_001010875.1 58197561 ILM 27751 610717 0.0073 1.14 1.10 1.19

ADAM 18 |NM_014237.1 7656860 ILMN 5673 7150338 0.0073 1.28 1.19 1.36

MTMR3 NM_021090.2 23510385 ILM _27578 7330435 0.0073 1.49 1.36 1.62

RAB27A NM l 83234.1 34485705 ILMN_10265 1580619 0.0072 1.15 1.1 1 1.21

AMACR NM 203382.1 42822892 ILMN_2954 2260039 0.0072 1.22 1.15 1.29

LOC653141 XM_926169.1 89040568 ILM _44352 48501 12 0.0072 1.77 1.58 1.99

FBX07 NM_001033024.1 74229028 ILM _28646 4920435 0.0072 1.36 1.23 1.49

ITGB1 NMJ33376.1 19743822 ILM _11529 5890707 0.0072 2.94 2.29 3.70

TFEC NM_012252.2 64762384 ILMN_15030 1240082 0.0071 1.1 1 1.09 1.14

ZNF655 NM_001009957.1 58331259 ILMN_3621 1940138 0.0071 1.34 1.24 1.45

LOC652481 XM_941942.1 89062863 ILMN_35551 3120056 0.0071 1.69 1.49 1.91

ASB3 NM_0161 15.3 22208952 ILMN_25973 4640020 0.0071 1.49 1.36 1.63

HNRPAB NM 031266.2 55956918 ILM _757 540437 0.0071 1.31 1.19 1.43

CPT1B NM l 52247.1 23238257 ILM _13033 2850468 0.007 1.25 1.18 1.32

PvIFl NM_018151.3 56676334 ILMN 4664 3460307 0.007 1.19 1.14 1.24

RBI NM 000321.1 4506434 ILMN_4636 42601 13 0.007 1.86 1.62 2.13

LOC6531 17 XM_931656.1 88986976 ILMN_37789 4570487 0.007 2.21 1.80 2.68

MAPKAP1 NM_001006618.1 56788400 ILMN 13996 50053 0.007 1.55 1.42 1.69

CCL7 NM_006273.2 13435401 ILMN_24123 6590500 0.007 1.28 1.20 1.38

PTPN6 NM_080548.2 34328901 ILMN 25213 6900291 0.007 1.29 1.21 1.38

ZA20D3 NM_019006.2 21359917 ILMN_16822 7380577 0.007 2.25 1.82 2.74

NUP50 NM_153645.1 24497446 ILMN_138009 2370463 0.0069 1.18 1.12 1.24

CD74 NM_001025159.1 68448543 ILM _21963 3420154 0.0069 2.05 1.72 2.45

HS.579654 AW887586 8049599 ILMN_131835 160025 0.0068 1.31 1.21 1.42

C150RF23 NM_033286.1 57528365 ILMN_28790 6770176 0.0068 1.26 1.17 1.37

B3GALT2 NM_003783.2 15451871 ILMN_14361 1010187 0.0067 -1.20 -1.27 -1.14

PHC2 NM_198040.1 37595527 ILMN_7686 3180735 0.0067 1.94 1.67 2.25

EEF1B2 NM_021121.2 16519563 ILMN_138368 5690162 0.0067 1.54 1.38 1.73

FAM19A2 NM_178539.3 52486623 ILMN 2438 5900731 0.0067 1.28 1.18 1.37

C50RF4 NM_032385.1 14150216 ILMN_ 16262 1710747 0.0066 1.22 1.16 1.27

ASPSCR1 NM_024083.2 17572803 ILM _9446 1820014 0.0066 1.15 1.10 1.20

WBSCR16 NM_030798.2 22538491 ILM _26391 5570408 0.0066 1.21 1.16 1.26

LOC649986 XM_939071.1 89066123 ILMN_31648 2260082 0.0065 2.71 2.18 3.35

MCM7 NM l 82776.1 33469921 ILMN_1133 1690475 0.0064 1.15 1.09 1.20

HS.560098 BQ214365 20395765 ILMN_1 14052 2630730 0.0064 1.25 1.19 1.32

SH3BP2 HM_003023.2 19923154 ILM _1 151 6250201 0.0063 1.30 1.22 1.39

WNT1 NM_005430.2 16936523 ILMN_22389 6380215 0.0063 -1.08 - 1.12 -1.05

CDC2L1 NM_033493.1 16332371 ILM _4002 1260041 0.0062 2.15 1.81 2.52 Illumina

Covariate NCBI Accession NCBI GI Illumina Fold

Array Gini Id ucl

(Gene Name) and Version* Number * Search Key Change

Address ID

ZNF278 NM 032051.1 14670363 ILMN_2933 2970520 0.0062 1.15 1.10 1.20

ETFB NM_001985.2 62420878 ILMNJ7194 1340044 0.0061 1.90 1.63 2.23

KIAA1967 NM_ 199205.1 40548407 ILMN_ 15274 2690477 0.0061 1.10 1.05 1.14

NCF4 NM O 13416.2 47519769 ILMN_7892 3610102 0.0061 1.89 1.64 2.17

LIPE NM_005357.2 21328445 ILMN_896 70047 0.0061 -1.09 -1.12 -1.06

CSDE1 NM_001007553.1 56117851 ILMN_3664 4780347 0.006 1.44 1.32 1.57

ESM1 NM_007036.2 13259505 ILMN_138415 1090743 0.0059 -1.13 -1.17 -1.09

TRIM5 NM_033034.1 14719417 ILMN_760 2360598 0.0059 2.23 1.87 2.65

HS.571222 AB032973 71891696 ILMN_ 123403 4640544 0.0059 1.15 1.1 1 1.20

SOS2 NM 006939.1 39930603 ILMN_12037 71601 14 0.0059 1.81 1.62 2.03

RTN1 NM 021 136.2 45827774 ILMN 2601 7210520 0.0059 1.20 1.15 1.26

NOM03 NM_001004067.1 51944968 ILMN_5042 4490035 0.0058 1.67 1.47 1.86

SERPINB2 NM_002575.1 4505594 ILMN_14466 5090327 0.0058 1.39 1.26 1.52

FBXW7 NM 033632.2 61743923 1LMN_7221 5270152 0.0058 1.42 1.29 1.56

GPR109B NM 006018.1 5174460 ILMN 22584 5960360 0.0058 2.35 1.90 2.90

LOC652253 XM 941661.1 889551 19 ILMN 34827 6220220 0.0058 -1.1 1 -1.16 -1.07

NFKBIZ NM_031419.2 53832022 ILMN_18526 6380039 0.0058 1.19 1.13 1.25

FLT3LG NM 001459.2 38455415 ILMN 4754 780544 0.0058 - 1.39 -1.53 -1.25

MAP4 3 NM_003618.2 15451901 ILMN 5588 870095 0.0058 1.23 1.16 1.30

TNPOl NM 002270.2 23510378 ILM _18758 460368 0.0057 1.1 1 1.07 1.15

BIRCl XM_936944.1 88987995 ILMN_137577 60541 0.0057 1.95 1.64 2.28

C90RF77 NM_001025780.1 71051601 ILMN_ 12321 870070 0.0057 -1.46 -1.63 -1.30

PMS2CL XR 001272.1 89025732 ILMN 39709 3520521 0.0056 1.22 1.17 1.28

HS.445121 BM545878 18778358 ILMN 92941 4570136 0.0056 1.26 1.19 1.33

EPIM NM 194356.1 37577161 ILMN 17438 4590241 0.0056 1.21 1.15 1.27

GPR89A NM_016334.2 56181388 ILMN_ 10695 4780709 0.0056 1.48 1.33 1.63

DDX17 NM_030881.2 3820171 1 ILMN 28024 7400475 0.0056 1.82 1.60 2.04

C 190RF12 NM_001031726.1 72534737 ILMN_10211 1470605 0.0055 1.23 1.17 1.30

SBDS NM O 16038.2 28416939 ILMN_ 15766 20181 0.0055 -1.57 -1.77 -1.36

AKAP10 NM_007202.2 21493032 ILMN_5307 2120349 0.0055 1.82 1.59 2.09

HS.170828 AI498339 4390321 ILMN_80247 5810706 0.0055 -1.04 -1.07 -1.01

SYPL1 NM_182715.1 33239442 ILMN_20394 5890202 0.0055 1.37 1.25 1.51

DCUN1D4 NM_0151 15.1 32698693 ILMN 9395 610010 0.0055 -1.18 -1.23 -1.13

HCRTR2 NM 001526.2 6006037 ILMN_4206 6200736 0.0055 1.04 1.02 1.07

STX5A NM_003164.2 31543665 ILMN_22175 6860288 0.0055 1.77 1.51 2.05

CLEC4E NM_014358.1 7657332 ILM _136933 940754 0.0055 1.77 1.51 2.05

PSMA1 NM_002786.2 23110933 ILMN_2036 3130040 0.0054 1.82 1.59 2.10

EV12A NM 001003927.1 5151 1748 ILMN_29280 3990538 0.0054 1.34 1.24 1.46

LOC651076 XM 940198.1 89057421 ILMN_46930 4220138 0.0054 1.34 1.24 1.45

CDC42SE2 NM_020240.1 9910377 ILMN_138762 4250682 0.0054 2.59 2.10 3.14

CLASP2 NM O 15097.1 57863300 ILMN_25670 4780070 0.0054 1.50 1.34 1.68

MAGED1 NM 001005332.1 52632378 ILMN_27182 61 10086 0.0054 1.21 1.16 1.27

RASGRP4 NM_ 170604.1 26051257 ILMN_17558 6200021 0.0054 2.00 1.71 2.32 Illumina

Covariate NCBI Accession NCBI GI Illumina Fold

Array Gini lcl ucl (Gene Name) and Version* Number * Search Key Change

Address ID

AGT NM_000029.2 73622269 ILMN_1261 6380273 0.0054 1.01 -1.01 1.04

HS.291319 CR627122 50949744 ILMN_85013 6980537 0.0054 -1.50 -1.73 -1.31

PDCD8 NM_004208.2 22202627 ILMN_20381 7320433 0.0054 1.32 1.25 1.40

LOC649679 XM_945045.1 88981262 ILMN_34833 840433 0.0054 -1.13 -1.18 -1.08

CHKB NM_005198.3 23238259 ILMN_1067 2470592 0.0053 1.67 1.42 1.95

STK16 NM 001008910.1 57165435 ILMN J23507 2640066 0.0053 1.27 1.20 1.34

C190RF6 NM_001033026.1 74229024 ILMN_ 12941 2900128 0.0053 1.46 1.31 1.61

C60RF25 NMJ 38275.1 19913380 ILMN_20734 3440161 0.0053 1.27 1.21 1.35

LOC400197 XM_928858.1 89037276 ILMN_37870 39301 12 0.0053 1.80 1.57 2.05

ABLIM1 NM_006720.3 51 173716 ILMN_21737 4570445 0.0053 1.60 1.42 1.78

PDP 1 NM_002613.3 60498971 IL N_27765 4850471 0.0053 2.15 1.81 2.56

TMPO NM_003276.1 4507554 ILMN_ 12700 6590221 0.0053 1.47 1.32 1.63

HS.579980 CR984787 68223121 ILMN_132161 7210358 0.0053 1.33 1.22 1.44

THAP1 NM_018105.2 40068498 1LMN_15754 2600167 0.0052 1.91 1.64 2.21

AMACR NM_014324.4 42794624 ILMN_3438 270603 0.0052 1.55 1.41 1.72

C140RF1 18 NM_017926.2 40018645 ILMN_23576 2900167 0.0052 1.33 1.23 1.43

BRMS1 NM_001024958.1 68348703 IL N_18543 3800730 0.0052 2.15 1.82 2.51

TM9SF1 NM_001014842.1 62460634 ILMN_1371 3840491 0.0052 1.89 1.64 2.16

C 170RF55 NM l 78519.2 31341837 ILMN_17830 4880367 0.0052 1.17 1.12 1.24

DYRK2 NM_006482.1 5922003 ILMN_18934 5270446 0.0052 1.08 1.05 1.10

MR1 NM_001531.1 4504416 ILMN_10108 5310274 0.0052 1.91 1.62 2.22

CD 163 NM_203416.1 44889962 ILMN_17347 5570414 0.0052 1.77 1.50 2.08

LOC642269 XM_930699.1 89028396 ILMN_30585 6060372 0.0052 1.28 1.20 1.37

COP1 NM_052889.2 629531 1 1 ILMN_21555 6100010 0.0052 -1.34 -1.46 -1.22

DDX17 NM_006386.3 38201709 ILMNJ28983 6220035 0.0052 1.58 1.42 1.77

HS.578712 BG427758 13334264 ILMN_ 130893 7200619 0.0052 1.30 1.21 1.39

LOC90379 XM_944706.1 89057238 ILMN_34089 840452 0.0052 1.27 1.20 1.34

PHF6 NM 032335.2 63478059 ILMN_21948 840520 0.0052 1.19 1.13 1.26

FA 18B2 X _936923.1 89065553 ILMN_137075 1230386 0.0051 1.77 1.54 2.03

KCNH1 NM_002238.2 27436999 ILMN_6368 2030181 0.0051 -1.06 -1.09 -1.02

HS.56141 1 CN364852 47364786 IL N_114851 3140241 0.0051 -1.36 -1.47 -1.25

CXORF15 NM_018360.1 8922939 ILMN_26850 3360592 0.0051 1.21 1.14 1.27

SPCS3 NM_021928.1 1 1345461 ILMN_14718 5130255 0.0051 1.79 1.55 2.04

LOC641848 XM_935588.1 89027387 ILMN_45490 5290070 0.0051 -1.88 -2.21 -1.59

PLAA NM_001031689.1 72534669 ILMN_14096 5310070 0.0051 1.19 1.15 1.24

PHF17 NM_199320.1 40556392 ILMN_26400 5310152 0.0051 1.51 1.35 1.68

ZNF124 NM_003431.2 42733607 ILMN_19934 7050474 0.0051 1.21 1.15 1.28

NFRKB NM_006165.2 23346419 ILMN_ 18461 7560372 0.0051 1.30 1.21 1.40

HS.432352 BX1 13158 27838052 ILMN_90908 1240204 0.005 1.09 1.05 1.12

LOC651633 XM_940830.1 89062068 ILMN_3981 1 1510176 0.005 -1.30 -1.39 -1.20

HMGB 1 NM_002128.3 31982879 ILMN_23421 2230367 0.005 -1.23 -1.30 -1.16

LOC653972 XM_938779.1 89038888 IL N_31 1 1 1 2510554 0.005 1.40 1.27 1.55

ANAPC7 NM_016238.1 7705283 ILMN_4717 3440278 0.005 1.39 1.28 1.51 Illumina

Covariate NCBI Accession NCBI GI Illumina Fold

Array Gini lcl ucl (Gene Name) and Version* Number * Search Key Change

Address ID

CKAP5 NM_001008938.1 57164941 ILMN_ 12487 4280246 0.005 1.14 1.10 1.19

HS.580128 DA861647 82131639 ILMNJ 32309 430181 0.005 1.27 1.19 1.36

LOC650224 XM 939316.1 89036235 ILMN_34466 4480181 0.005 1.18 1.12 1.24

ZNF200 NM_003454.2 37675272 ILMN_25695 4560672 0.005 1.16 1.12 1.20

GCET2 NM_001008756.1 57165368 ILMN 21048 4730328 0.005 1.18 1.12 1.25

LOC648998 XM_938078.1 89065846 ILMN_32035 4810543 0.005 1.42 1.30 1.55

LOC647596 XM .936646.1 89060867 ILMN_35176 4900731 0.005 1.15 1.12 1.19

DDX47 N _016355.3 41327774 ILMN_8096 5310431 0.005 1.96 1.66 2.31

CTNS NM_004937.1 4826681 ILMNJ 1769 5390273 0.005 1.33 1.25 1.41

LOCI 29607 NM_207315.1 46409273 ILMN_3648 5720438 0.005 1.42 1.29 1.58

HIST2H4 NM 003548.2 29553982 ILMN_22069 610300 0.005 1.88 1.62 2.20

ZNF658 NM_033160.4 55769536 ILMNJ4759 6770543 0.005 -1.16 -1.22 -1.1 1

C 120RF23 NM_ 152261.1 22748614 ILMNJ 1 109 7040753 0.005 -1.25 -1.32 -1.17

BAD NM 032989.1 14670387 1LMN_27816 770739 0.005 1.22 1.16 1.29

BTN3A3 NM_006994.3 37574626 ILMN_20620 160446 0.0049 2.54 2.01 3.16

CSN 1G1 NM_00101 1664.2 71773653 ILMNJ9857 2190056 0.0049 1.13 1.10 1.17

VEGFB NM_003377.3 39725673 ILMNJ 5862 2350739 0.0049 -1.11 -1.21 -1.02

WDSOF1 NM_015420.4 31542525 ILMN_7024 3800170 0.0049 -1.25 -1.34 -1.16

LOC649419 XM_941569.1 89036024 ILMN_43489 3850100 0.0049 2.57 2.06 3.20

C150RF44 XM_940546.1 89039133 ILMN l 38325 4610050 0.0049 1.71 1.51 1.92

RUSC1 NM_014328.2 42476122 ILMN_ 13485 478041 1 0.0049 1.50 1.32 1.69

HIST1H2AC NM_003512.3 21396481 ILMN_26493 4890192 0.0049 2.45 1.90 3.14

DPY 19L3 NM_207325.1 46409291 ILMN_171 1 1 50278 0.0049 1.22 1.15 1.28

ACBD5 NMJ45698.1 21735486 ILMNJ 2634 53601 12 0.0049 1.41 1.29 1.55

JAG 1 NM_032492.2 31982910 ILMN_2462 5360348 0.0049 1.35 1.24 1.47

SPTLC1 NM_006415.2 30474867 ILMN JO 107 5490768 0.0049 1.99 1.70 2.31

LOC645472 XM_928498.1 8905081 1 ILMN 0737 5810154 0.0049 -1.19 -1.26 -1.13

DPP3 NM_005700.2 18491023 ILMNJ 38296 6370541 0.0049 1.35 1.26 1.44

LOC440732 XM_496441.2 88943885 ILMN 38370 6550709 0.0049 -1.76 -2.08 -1.45

LOC644096 XM_927323.1 89056790 ILMNJ 1860 6660474 0.0049 1.28 1.19 1.37

MBD2 NM_015832.3 48255922 ILMNJ3743 7560255 0.0049 1.61 1.42 1.81

HS.557625 AW132136 6133743 ILMNJ 12916 1 110068 0.0048 1.08 1.05 1.13

HS.557745 AW 138070 6142388 ILMNJ 12965 1710204 0.0048 1.22 1.14 1.29

HS.553605 DN831967 62640651 ILMNJ 1 1520 2070544 0.0048 1.27 1.19 1.37

SEC24B NM_006323.1 5454045 ILMN 13898 2490520 0.0048 1.69 1.50 1.90

PHTF2 NM_020432.2 40254932 ILMNJ3666 2900438 0.0048 -1.46 -1.64 -1.28

LOC643995 X _930156.1 89042169 ILMN 31229 430066 0.0048 1.29 1.18 1.42

TLR4 NM_138557.1 19924152 ILMNJ390 4390615 0.0048 2.08 1.68 2.56

FLJ 1 1016 NM_018301.2 38454187 ILMNJ 6421 4860735 0.0048 1.29 1.20 1.38

LOC644134 XM_932013.1 89025548 ILMN 35992 4880369 0.0048 1.31 1.22 1.39

LOC402573 NM_001004323.1 51972225 ILMN_9570 4880709 0.0048 1.22 1.13 1.31

NFATC2IP XM_944125.1 89040767 ILMNJ 37710 5960356 0.0048 1.28 1.18 1.39

SRM NM_003132.2 63253297 ILMN_2445 6510725 0.0048 -1.25 -1.36 -1.15 Illumina

Covariate NCBI Accession NCBI GI Ulumina Fold

Array Gini lcl ucl (Gene Name) and Version* Number * Search Key Change

Address ID

DLEU1 XR_001515.1 89036588 ILMN_34088 6520274 0.0048 -1.15 -1.21 -1.10

SACM1L NM_014016.2 41281578 ILMN_19838 7040768 0.0048 -1.48 -1.72 -1.28

SEPT10 NMJ44710.2 30795194 ILMN_5056 7400392 0.0048 1.09 1.06 1.12

HSPBP1 XM 938008.1 89057639 ILMNJ38021 7550736 0.0048 1.18 1.13 1.24

HS.545128 R07429 759352 ILMN_108408 7560161 0.0048 1.18 1.12 1.25

DEDD NM_004216.2 14670395 ILMN_ 13705 780709 0.0048 1.36 1.24 1.48

CBWD3 NM_201453.1 42558280 ILMN_ 18690 130180 0.0047 -1.05 -1.08 -1.02

ASPSCR1 XM_941362.1 890431 16 ILMN_138126 26901 12 0.0047 1.40 1.29 1.52

SBNOl NM_018183.2 33620762 ILMNJ2636 3610041 0.0047 1.47 1.35 1.60

HS.545727 AA668234 2629733 ILMN_ 108864 4860500 0.0047 1.05 1.02 1.08

HIPK3 NM_005734.2 29469068 ILMN_20690 6650301 0.0047 1.62 1.46 1.78

ANKRD13D XM_945567.1 89034918 ILMN_ 138354 2470189 0.0046 1.17 1.12 1.23

LOC390414 XM_940915.1 89031778 ILMN_42621 3420458 0.0046 -1.10 -1.14 - 1.07

RFXDC1 NM_173560.1 27734870 ILMNJ0052 3440053 0.0046 -1.05 -1.07 -1.03

HDAC9 NM_058177.1 17158040 IL N_20565 5050634 0.0046 1.24 1.18 1.30

MBTPS1 NM_003791.2 41350325 ILMN 12720 5310037 0.0046 1.67 1.48 1.88

HS.379327 CX165253 56795333 ILMN_88679 540142 0.0046 1.52 1.36 1.68

HS.578208 AA431 122 21 14830 ILMN_130389 6480138 0.0046 1.22 1.15 1.29

SLC26A2 NM_0001 12.2 45935386 IL N_21352 6480692 0.0046 1.28 1.17 1.38

HS.581533 AA431235 21 14943 ILMN_133714 6550373 0.0046 1.07 1.04 1.1 1

TUBG1 NM_001070.3 34222287 ILMN_1608 6770553 0.0046 1.15 1.10 1.19

P2RX5 NM_175081.1 28416936 ILMN_10544 730040 0.0046 1.38 1.24 1.52

LOC641808 XM_935566.1 89027344 ILMN_43943 830541 0.0046 1.26 1.18 1.34

AFTIPHILIN NM_203437.2 50409938 ILMN_16341 2230392 0.0045 -1.62 -1.83 -1.43

EXOSC1 NM_016046.2 22035626 ILM _138117 2360433 0.0045 1.33 1.23 1.43

MDS1 NM 004991.1 4826827 ILMN_9694 240332 0.0045 1.21 1.15 1.28

HS.247659 BI752029 15743607 ILMN 83183 3930450 0.0045 1.10 1.07 1.13

HS.201 1 13 BX 108670 27835318 ILMN_81638 4560328 0.0045 1.33 1.22 1.45

GPR177 NM_02491 1.4 50541968 ILMN 3521 4760309 0.0045 1.51 1.34 1.70

CBX3 NM_016587.2 20544150 ILMN_ 1 1642 4880020 0.0045 1.61 1.42 1.83

ANKRD13D XM_945565.1 89034916 ILMNJ38345 5670091 0.0045 1.13 1.08 1.18

FLJ45187 NM_207371.2 50726976 ILMN_5232 6350528 0.0045 1.08 1.05 1.1 1

HS.291377 CN430296 47417890 ILMN_85018 6420288 0.0045 -1.12 -1.17 -1.09

HNRPA1 NM_002136.1 4504444 ILMN_137048 6620292 0.0045 1.39 1.29 1.50

SEPT11 NM_018243.2 38605734 ILMN_27161 7380670 0.0045 -1.34 -1.47 -1.21

HS.577425 DB337747 83130755 ILMN_ 129606 830368 0.0045 1.42 1.28 1.57

LOC644122 XM 934731.1 89040142 ILMN_46404 1070707 0.0044 1.89 1.63 2.17

LOC644380 XM_929628.1 89058831 ILMN 36938 2060358 0.0044 -1.21 -1.28 -1.15

BCR M_021574.1 1 1038640 ILMN_136985 4050427 0.0044 -1.17 -1.26 -1.09

SNRPN NM_003097.3 29540556 ILMN_15998 4060195 0.0044 2.17 1.77 2.63

LOC646426 XM_929353.1 89030901 ILMN_45139 4540296 0.0044 1.19 1.14 1.25

PP 1A NM_177951.1 29557854 ILMN 13918 5340066 0.0044 1.37 1.26 1.48

GCH1 NM_000161.2 66932966 ILMN_2599 5550767 0.0044 1.41 1.31 1.52

Ulumina

Covariate NCBI Accession NCBI GI Ulumina Fold

Array Gini lcl ucl (Gene Name) and Version* Number * Search Key Change

Address ID

HS.580148 DB338928 83154923 ILMN_ 132329 5390672 0.0038 1.37 1.26 1.49

SMAP1L NM_022733.1 23943871 ILMN_1697 1010168 0.0037 1.76 1.47 2.10

LOC653125 XM_931236.1 89038164 ILMN_38938 380544 0.0037 1.18 1.12 1.24

LDHB NM_002300.3 22726178 ILMN_ 16800 4040609 0.0037 -1.71 -2.03 -1.43

HS.570348 All 99741 3752347 ILMN_ 122529 4250176 0.0037 -1.02 -1.05 1.01

SOS1 NM_005633.2 15529995 ILMN_ 1 1376 5720719 0.0037 1.39 1.24 1.53

AGPAT7 NM_153613.1 23957707 ILMN_137968 6350427 0.0037 -1.13 -1.19 -1.07

HS.125087 BQ437417 21 176493 ILMN_76085 6900603 0.0037 1.30 1.20 1.41

LOC643007 XM_927198.1 89038191 ILMN_39863 1110524 0.0036 -1.64 -1.90 -1.41

HS.560740 BQ775960 21984436 ILMN_1 14429 1400164 0.0036 1.04 1.02 1.06

HS.574780 BG198379 13720066 ILMN_ 126961 4150228 0.0036 1.35 1.25 1.46

EIF1AX NM_001412.3 77404356 ILMN 22164 4610546 0.0036 -1.41 -1.55 -1.27

SACS NM_014363.3 38230497 ILMN_3633 4780400 0.0036 -1.12 -1.16 -1.09

LOC400807 XM_933808.1 88986393 ILM _32838 5670551 0.0036 1.18 1.12 1.23

LOC284361 NMJ75063.3 45580693 ILMN_139159 670369 0.0036 -1.14 -1.19 -1.09

APOBEC3A NM_145699.2 22907036 ILMN_ 12846 2810040 0.0035 -1.72 -1.99 -1.48

NEDD1 NM_ 152905.2 34303960 ILMN_4251 4610132 0.0035 1.39 1.28 1.52

HS.547807 BQ888875 22280889 ILMN l 09650 4640575 0.0035 1.39 1.28 1.53

HS.163416 CR745073 51667560 ILMN_79907 7570722 0.0035 -1.01 -1.04 1.01

AAAS NM_015665.3 34222322 ILMN_22994 870088 0.0035 -1.13 -1.18 -1.07

APPBP1 NM_001018159.1 66363685 ILM _19510 4060465 0.0034 1.15 1.08 1.22

LOC643550 XM_926853.1 89035757 ILMN 35341 4120458 0.0034 -1.14 -1.19 - 1.09

CEP72 NM_018140.2 62899064 ILMN_ 10995 430279 0.0034 -1.06 -1.11 -1.02

SNAP23 NM_130798.1 18765730 ILMN_679 4880390 0.0034 -1.68 -1.97 -1.42

HS.570330 AW962683 8152519 ILM _12251 1 7000300 0.0034 1.12 1.08 1.16

PNLIPRP2 NM_005396.3 37059783 ILMN_9007 7210465 0.0034 1.06 1.03 1.10

FAM1 1A NM_032508.1 22296883 ILMN 24307 1450750 0.0033 1.43 1.30 1.56

HS.574453 AK024399 10436778 ILMN_ 126634 1710398 0.0033 -1.03 -1.06 -1.01

ASAH1 NM_004315.2 30089929 ILMN_27657 2030010 0.0033 1.08 1.04 1.11

ASB 15 NM_080928.2 38261966 ILMN_8926 2060132 0.0033 -1.06 -1.08 -1.04

HS.582091 DA326910 7874101 1 ILMN_ 134272 3130176 0.0033 -1.09 -1.1 1 -1.06

SCRIB NM_ 182706.2 45827730 ILMN_21867 3800470 0.0033 -1.04 -1.07 -1.01

HS.552431 AA579194 2357378 ILMN l 10992 4060142 0.0033 1.10 1.03 1.16

PPHLN1 NM_016488.5 48255928 ILMN 6863 4120259 0.0033 -1.12 -1.16 - 1.08

MGC40499 XM_941945.1 89026172 ILMN_139128 4780487 0.0033 1.23 1.16 1.30 IAA0423 NM_015091.1 44888819 ILMN_ 18327 6100692 0.0033 -1.30 -1.41 - 1.20

HRIHFB2122 NM 138632.1 20336762 ILMN 138238 630669 0.0033 1.31 1.24 1.39

UBE2D3 NMJ 81889.1 33149315 ILMN_28535 7200097 0.0033 1.15 1.1 1 1.19

SLC27A6 INM O 14031.3 62865629 ILMN_10102 7550131 0.0033 1.42 1.30 1.55

GSR pJM_000637.2 50301237 ILMN_14467 7560093 0.0033 1.79 1.55 2.05

FBXQ43 |NM_001029860.1 71 143129 ILMN_7498 2900669 0.0032 1.08 1.05 1.1 1

*For each covariate entry the United States National Center for Biotechnology Information (NCBI, U.S. National Library of Medicine, 800 Rockville Pike, Bethesda, MD, 20894 USA) identifiers (accession number/version and NCBI GI Number) are provided. Those NCBI identifiers uniquely identify nucleic acid and/or protein sequences present in the NCBI databases and are publicly available, for example, on the word wide web at www.ncbi.nlm.nih.gov. Where an NCBI accession number or GI number is provided for a nucleic acid sequence encoding a protein produced by a gene indicated herein (e.g., a cDNA sequence) the corresponding gene sequence is also available in the NCBI database and considered part of this disclosure. Where any accession number does not recite a specific version, the version is taken to be the most recent version of the sequence associated with that accession number at the time the earliest priority document for the present application was filed.

NA = Not Applicable

Supplementary Table III. Posterior probabilities from the spirometric class, and random forest model- predicted class for the 16 misclassified subjects in the test set (w=65, FEVi/FVC 0.60-0.75).

P (Control|RF) P (Case|RF) Spirometric class Random forest model-predicted class

0.517 0.483 Case Control

0.684 0.316 Case Control

0.886 0.1 14 Case Control

0.912 0.088 Case Control

0.925 0.075 Case Control

0.912 0.088 Case Control

0.936 0.064 Case Control

0.641 0.359 Case Control

0.821 0.180 Case Control

0.860 0.140 Case Control

0.894 0.106 Case Control

0.572 0.428 Case Control

0.606 0.394 Case Control

0.955 0.046 Case Control

0.042 0.958 Control Case

0.477 0.528 Control Case

FEVi, forced expiratory volume in 1 s; FVC, forced vital capacity

Substitutions, modifications, changes and omissions may be made in the design, operating conditions and arrangement of the aspects and embodiments described herein without departing from the spirit of the subject matter as expressed, inter alia, in the appended claims. Additional advantages, features and modifications will readily occur to those skilled in the art. Therefore, the subject matter of this disclosure, in its broader aspects, is not limited to the specific details, examples, or representative devices, shown and described herein. Accordingly, various modifications may be made without departing from the spirit or scope of the general concepts as defined, inter alia, by the appended claims and their equivalents.

All of the references cited herein, including patents, patent applications, and publications, are hereby incorporated in their entireties by reference.

The scope of the claims below is not restricted to the particular embodiments described herein. The following examples describe for illustrative purposes and are not intended to limit the methods and compositions of the present disclosure in any manner. Those of skill in the art will recognize a variety of parameters that can be changed or modified to yield the same results.

Claims

1. A composition comprising two nucleic acid molecules, wherein the first nucleic acid molecule comprises a first nucleotide sequence and the second nucleic acid molecule comprises a second nucleotide sequence, wherein the first nucleotide sequence differs from the second nucleotide sequence and the first and second nucleotide sequences are selected independently from the group consisting of the sequences of the nucleic acids set forth in Supplementary Table II or a fragment of any thereof, and nucleotide sequences having 70-99% identity to the nucleic acid set forth in Supplementary Table II or a fragment of any thereof.

2. The composition of claim 1 , further comprising a third nucleic acid molecule comprising a third nucleotide sequence, wherein the third nucleotide sequence differs from the first and the second nucleotide sequences and the third nucleotide sequence is selected independently from the group consisting of the sequences of the nucleic acids set forth in Supplementary Table II or a fragment of any thereof, and nucleotide sequences having 70-99% identity to the nucleic acid set forth in Supplementary Table II or a fragment of any thereof.

3. The composition of claim 1, further comprising a fourth nucleic acid molecule comprising a fourth nucleotide sequence, wherein the fourth nucleotide sequence differs from the first through third nucleotide sequences and the fourth nucleotide sequence is selected independently from the group consisting of the sequences of the nucleic acids set forth in Supplementary Table II or a fragment of any thereof, and nucleotide sequences having 70-99% identity to the nucleic acid set forth in Supplementary Table II or a fragment of any thereof.

4. The composition of claim 1, further comprising a fifth nucleic acid molecule comprising a fifth nucleotide sequence, wherein the fifth nucleotide sequence differs from the first through fourth nucleotide sequences and the fifth nucleotide sequence is selected independently from the group consisting of the sequences of the nucleic acids set forth in Supplementary Table II or a fragment of any thereof, and nucleotide sequences having 70-99% identity to the nucleic acid set forth in Supplementary Table II or a fragment of any thereof.

5. The composition of claim 1, further comprising a sixth nucleic acid molecule comprising a sixth nucleotide sequence, wherein the sixth nucleotide sequence differs from the first through fifth nucleotide sequences and the sixth nucleotide sequence is selected independently from the group consisting of the sequences of the nucleic acids set forth in Supplementary Table II or a fragment of any thereof, and nucleotide sequences having 70-99% identity to the nucleic acid set forth in Supplementary Table II or a fragment of any thereof.

6. The composition of claim 1, further comprising a seventh nucleic acid molecule comprising a seventh nucleotide sequence, wherein the seventh nucleotide sequence differs from the first through sixth nucleotide sequences and the seventh nucleotide sequence is selected independently from the group consisting of the sequences of the nucleic acids set forth in Supplementary Table II or a fragment of any thereof, and nucleotide sequences having 70-99% identity to the nucleic acid set forth in Supplementary Table II or a fragment of any thereof.

7. The composition of claim 1 , further comprising a eighth nucleic acid molecule comprising a eighth nucleotide sequence, wherein the eighth nucleotide sequence differs from the first through seventh nucleotide sequences and the eighth nucleotide sequence is selected independently from the group consisting of the sequences of the nucleic acids set forth in Supplementary Table II or a fragment of any thereof, and nucleotide sequences having 70-99% identity to the nucleic acid set forth in Supplementary Table II or a fragment of any thereof.

8. The composition of claim 1 , further comprising a ninth nucleic acid molecule comprising a ninth nucleotide sequence, wherein the ninth nucleotide sequence differs from the first through eighth nucleotide sequences and the ninth nucleotide sequence is selected independently from the group consisting of the sequences of the nucleic acids set forth in Supplementary Table II or a fragment of any thereof, and nucleotide sequences having 70-99% identity to the nucleic acid set forth in Supplementary Table II or a fragment of any thereof.

9. The compositions of any of claims 1-8, wherein said first, second, third, forth, fifth, sixth, seventh, eight, and ninth nucleic acid molecules are each selected from different nucleic acid set forth in Supplementary Table II or a nucleotide sequences having 70-99% identity to different nucleic acid set forth in Supplementary Table II, or a fragment of any thereof.

10. The composition of claim 9, wherein the first through the ninth nucleotide sequences each comprise a nucleotide sequence of a nucleic acid expressed by a gene selected from the group consisting of IL6R, CCR2, PPP2CB, RASSF2, WTAP, DNTTIP2, GDAP1 , LIPE, and RPL14, or a sequence having 70%-99% identity to a nucleic acid expressed by a gene selected from the group consisting of IL6R, CCR2, PPP2CB, RASSF2, WTAP, DNTTIP2, GDAP1 , LIPE, and RPL14, or a sequence complementary to any thereof , or a fragment of any of the foregoing.

1 1. A composition comprising greater than 10, 20, 30, 40, 50, 75, 100, 200, 300, 400 or 500 different nucleic acid molecules each comprising a nucleotide sequence selected independently from the group consisting of the sequences of the nucleic acids set forth in Supplementary Table II or a fragment of any thereof, and sequences having 70-99% identity to the sequences of nucleic acids set forth in Supplementary Table II or a fragment of any thereof.

12. The composition of any of claims 1 - 1 1, wherein said 70-99% identity is at least 80%, 85%, 90%, 95%, 97%, 98% or 99% identity.

13. The composition of any of claims 1 - 12, wherein each fragment has a length greater than about 20, 22, 24, 26, 28, 30, 32, 35, 40, 50, 75, 100, or 200 contiguous nucleotides.

14. The composition of any of claims 1 - 13, wherein each fragment has a length less than about 225, 250, 300, 350, 400, 450 or 500 nucleotides.

15. The composition of any of claims 1-14, wherein said nucleic acid molecules are each independently selected from DNA or RNA molecules.

16. The composition of any of claims 1-15, wherein each nucleic acid molecule is immobilized on a substrate.

17. The composition of claim 16, wherein each nucleic acid molecule has a unique position on an array or microarray and is stably associated with the substrate.

18. The composition of claim 16, wherein each nucleic acid molecule is covalently bound to the substrate.

19. The composition of any of claims 16, wherein the substrate is a nanoparticle or bead.

20. The composition of any of claims 1-18, wherein one or more nucleic acid molecules are labeled with a radioisotope, fluorescent label, UV/Visible label, or a metal nanoparticle.

21. A method of identifying a gene marker associated with a lung disease comprising:

a. isolating one or more expressed nucleic acid sequences from a biological sample obtained from a subject having a lung disease and one or more expressed nucleic acid sequences from a biological sample obtained from a control subject without lung disease;

b. hybridizing the expressed nucleic acid from the subject diagnosed with the lung disease and the

expressed nucleic acid from the control subject without lung disease to a microarray having a plurality of polynucleotide members, each member having a unique position on the microarray and stably associated to the microarray; and

c. performing a statistical analysis of the nucleic acid sequence differentially expressed in the subject diagnosed with the lung disease as compared to the control subject without lung disease wherein the analysis identifies the nucleic acid sequence as a gene marker of the lung disease.

22. The method of claim 21, wherein the analysis includes use of random forest modeling and a split-sample analysis.

23. The method of claim 21 or 22, wherein the gene marker is validated as a marker of the lung disease using a real-time polymerase chain reaction (PCR) assay.

24. The method of any one of claims 21 - 23, wherein the lung disease is selected from the group consisting of: asthma, chronic obstructive pulmonary disease (COPD), lung cancer, alpha- 1 antitrypsin deficiency, respiratory distress syndrome, chronic bronchitis, chronic systemic inflammation, and inflammatory respiratory disease.

25. The method of any of claims 21 -24, wherein the biological sample is selected from the group consisting of blood, white blood cells, peripheral blood mononuclear cells, plasma, serum, lymph, urine, saliva, and sputum.

26. A method of diagnosing lung disease or an increased risk of developing lung disease in a subject comprising measuring the expression of 2, 3, 4, 6, 8, 10, 12, 25, 20, 30, 40, 50, 75, 100, 200, 300, 400, 500, or more nucleic acids expressed from the nucleic acids set forth in Supplementary Table II or fragments there of.

27 A method of diagnosing lung disease or an increased risk of developing lung disease in a subject comprising measuring the expression of 2, 3, 4, 6, 8, or 9 nucleic acid molecules expressed one or more genes selected from the group consisting of: IL6R, CCR2, PPP2CB, RASSF2, WTAP, DNTTIP2, GDAPl, LIPE, and RPL14.

28. The method of any of claims 26-27, wherein the expression is measured in a biological sample selected from the group consisting of blood, white blood cells, peripheral blood mononuclear cells, plasma, serum, lymph, urine, saliva, and sputum.

29. The method of any one of claims 26-28, wherein the lung disease is selected from the group consisting of asthma, chronic obstructive pulmonary disease (COPD), lung cancer, alpha- 1 antitrypsin deficiency, respiratory distress syndrome, chronic bronchitis, chronic systemic inflammation, and inflammatory respiratory disease.

30. The method of any one of claims 26-29, wherein the lung disease is COPD.

31. The method of any of claim 26-30, wherein increased expression correlates with a diagnosis of lung disease or an increased risk of developing lung disease.

32. The method of any of claims 26-31 , wherein measuring the expression is conducted by a method comprising reverse transcription of said nucleic acids expressed from the nucleic acids set forth in Supplementary Table II.

33. The method of any of claims 26-31 , wherein measuring the expression is conducted by measuring the concentration, level or amounts of proteins encoded by said one or more nucleic aids set forth in Supplementary Table II or peptide fragments of said proteins.

34. A method of diagnosing lung disease or an increased risk of developing lung disease in a subject comprising use of a kit comprising a nucleic acid composition according to any of claims 1-20 or antibodies to two or more different proteins encoded by two or more different nucleic aids set forth in Supplementary Table II or peptide fragments thereof.

35. A method of diagnosing lung disease or an increased risk of developing lung disease, in a subject comprising:

a. obtaining a measurement of the level of expression of one or more nucleic acids set forth in Supplementary Table II in a sample from a subject; and

b. comparing the measurement of the levels of expression in the sample from the subject to the level of expression of said one or more nucleic acids set forth in Supplementary Table II in a control sample; wherein said control sample is obtained from an individual or population of individuals not having lung disease; and

wherein a difference in levels of expression in the sample from the subject as compared to the levels of expression in the control sample indicates that the subject has or is at risk of developing lung disease.

36. A method screening a subject who smokes tobacco products for the risk of developing lung disease or a decline in lung function comprising:

(a) obtaining a measurement of the level of expression of one or more nucleic acids set forth in Supplementary Table II in a sample from the subject; and

(b) comparing the measurement of the levels of expression in the sample from the subject to the level of expression of said one or more nucleic acids set forth in Supplementary Table II in a control sample; wherein said control sample is obtained from an individual or population of individuals not having lung disease; and

wherein a difference in levels of expression in the sample from the subject as compared to the levels of expression in the control sample indicates that the subject has or is at risk of developing lung disease or a decline in lung function.

37. The method of claim 35 or 36, comprising obtaining a measurement of the expression of 2, 3, 4, 6, 8, 10, 12, 25, 20, 30, 40, 50, 75, 100, 200, 300, 400, 500, or more nucleic acids expressed from the nucleic acids set forth in Supplementary Table II or fragments there of

38. The method of any of claims 35 - 37, wherein the said one or more nucleic acids set forth in Supplementary Table II comprise any one, two, three, four, five, six, seven, eight, or all nine of: CCR2, IL6R, PP2CB, RASSF2 WTAP DNTTIP2, GDAPl, LIPE, RPL14,

39. The method of any of claims 35-38, wherein the difference is an increased expression of any one, two, three, four or five of CCR2, IL6R, PP2CB, RASSF2 and WTAP and/or a decreased expression of any one, two, three, or four of DNTTIP2, GDAPl, LIPE, RPL14.

40. The method of any of claims 35-39, wherein obtaining a measurement is conducted with: polymerase chain reaction, an array, quantitative RT-PCR, multiplex PCR, a quantitative DNA array, autoradiograph, quantitative hybridization, immunohistochemistry, one or more antibodies, immunoelectrophoresis, SDS-PAGE,

chromatography, quantitative ligand-binding, quantitative rRNA-based amplification, fluorescent probe hybridization, fluorescent nucleic acid sequence specific amplification, loop-mediated isothermal amplification and ligase amplification (ligase chain reaction).

41. A method of treating a subject having or suspected of having a lung disease or of following the course of lung disease in a subject having or suspected of having a lung disease comprising:

(a) obtaining a measurement of the level of expression of one or more nucleic acids set forth in Supplementary Table II in a sample from the subject at a first time; and

(b) obtaining a second measurement of the level of expression of at least the same one or more nucleic acids set forth in Supplementary Table II in a second sample obtained from the subject at a second time; and comparing the first measurement to the second measurement to determine the progression or regression or stability of the lung disease.

42. The method of claim 41 , wherein at least one measurement is conducted by measuring or observing the quantity or concentration of one or more proteins encoded by a nucleic acids set forth in Supplementary Table II.

43. The method of any of claims 41-42, wherein at least one therapeutic agent is administered to said subject.

44. The method of claim 43, wherein said first sample was obtained from said subject before said second sample, and wherein said therapeutic agent is administered after said first sample was obtained from said subject, and before said second sample was obtained from said subject.

45. The method of any of claims 41-42, wherein said therapeutic agent is selected from the group consisting of, but not limited to: immunosuppressants, corticosteroids, P2(beta 2)-adrenergic receptor agonists, anticholinergics, and oxygen.

46. The method of any of claims 41 - 45, further comprising changing the treatment of a subject based upon said progression or regression or stability of said lung disease.

47. A device comprising a plurality of locations, wherein 2, 3, 4, 5, 6, 7, 8 or more of said locations each comprise a different nucleic acid molecule having a nucleotide sequences of a nucleic acid set forth in

Supplementary Table II, or a sequence having 70-99% identity to a nucleic acid sequences a nucleic acid set forth in Supplementary Table II, or a fragment of any of the foregoing.

48. The device of claim 47, wherein said 2, 3, 4, 5, 6, 7, 8 or more of said locations comprise a nucleic acid molecule encoding a proteins expressed from a different gene selected from CCR2, IL6R, PP2CB, RASSF2, WTAP, DNTTIP2, GDAPl , LIPE, and RPL14, or a sequence having 70-99% identity to a nucleic acid molecule encoding a proteins expressed from a different gene selected from CCR2, IL6R, PP2CB, RASSF2, WTAP, DNTTIP2, GDAPl , LIPE, and RPL14, or a complement or fragment of any of the foregoing.

49. The device of claim 47 or claim 48, wherein said locations comprise a nucleic acid molecule having a length greater than about 20, 22, 24, 26, 28, 30, 32, 35, 40, 50, 75, 100, or 200 contiguous nucleotides.

50. The device of any of claims 47 - 49, wherein said fragment has a length less than about 225, 250, 300, 350, 400, 450 or 500 nucleotides.