CN111540407B - Method for screening candidate genes by integrating multiple neurodevelopmental diseases - Google Patents

Abstract

The invention is suitable for the technical field of nerve diseases, provides a method for screening candidate genes by integrating different types of variation data of various nerve developmental diseases, evaluates the number of candidate genes of a certain disease or a certain disease based on a maximum likelihood estimation method, simultaneously identifies the candidate genes of the nerve diseases by combining data such as expected gene variation rate, case variation load and the like based on a Bayesian model method, has a new idea of integrating the various types of variation data of various nerve developmental diseases, overcomes the problem of insufficient statistical effect of single disease and single variation, and provides a new method for identifying new candidate genes and enhancing the credibility of genes. The reliability of the method is verified through known genes, new candidate genes can be searched according to the distribution situation of the mutation spectrum of the disease queue, and the method can be expanded to other important genetic diseases.

Description

A method for screening candidate genes integrating multiple neurodevelopmental diseases

technical field

The invention belongs to the field of neurological diseases, and in particular relates to a method for screening candidate genes integrating multiple neurodevelopmental diseases.

Background technique

Neurodevelopmental diseases bring great suffering and burden to individuals and society due to their high morbidity and disability rates. These diseases are diverse and show a high degree of clinical heterogeneity, which brings great troubles to the diagnosis of diseases and subsequent targeted treatment. At present, the diagnosis of the disease is mainly based on the phenotype described by the patient or family members and the behavior observed by the doctor. These clinical phenotype information are basically based on the results summarized by previous experts, and there is no unified biological index for judgment.

The high heritability of neurodevelopmental diseases provides great opportunities to study the correlation between diseases and understand the pathogenic mechanism of diseases. Previous studies have found high genetic similarities in different neurodevelopmental diseases and tried to find possible candidate genes through genome-wide association methods. However, the number of candidate genes found by this method is very small, and it can explain very few pathogenic causes. Since there is a high probability of overlapping clinical phenotypes and genetic mechanisms among different types of neurological diseases, it is possible to integrate multiple neurological diseases to find possible disease candidate genes. At present, multiple research institutions use whole exome or whole genome sequencing methods to detect de novo mutations (that is, mutations that only appear in patients but not detected in parental genomes) and can find many candidate genes. The highly functionally disruptive nature of this de novo mutation can lead to significant changes in individual phenotypes.

In the past, there were methods to find candidate genes by integrating different disease variant data. However, these methods currently have many limitations, including the following defects:

(1) Only consider whether the mutation site is on the known possible candidate gene. Although this method can make a phenotypic diagnosis, it cannot discover new candidate genes.

(2) For multiple potentially harmful mutations in a gene, it is not necessarily a candidate gene. Parameters such as the mutation rate of a single gene, the number of patients tested, and the number of expected candidate genes also need to be considered.

(3) Previous methods only considered the relationship between DNA level variation and disease, and did not consider tissue-specific expression level data, protein levels and other information. The credibility of the evidence obtained requires further analysis.

(4) Integrated analysis is not really considered. The previous method is to analyze the individual disease and compare multiple disease candidates to study the genetic correlation between different diseases.

Contents of the invention

The invention provides a method for screening candidate genes integrating multiple neurodevelopmental diseases, aiming at integrating new ideas of multiple neurodevelopmental diseases to identify new candidate genes and enhancing gene credibility.

The present invention is achieved in this way, a method for integrating multiple neurodevelopmental diseases to screen candidate genes, comprising the following steps:

S1. Evaluate candidate genes by maximum likelihood estimation method to obtain the number of possible candidate genes for each disease;

S2. At the same time, taking into account the parameters of disease variation load, mutation rate, and the number of candidate genes for evaluation, according to the loss-of-function mutations and harmful missense mutations carried by each gene and the number of patients detected, etc., a single gene is obtained by comprehensive evaluation The Bayesian factor of the level and the probability value of determining the false positive of the candidate gene according to the Bayesian factor;

S3. Further comprehensively evaluate the credibility of the candidate genes according to the data at the gene level.

The candidate genes are evaluated by the maximum likelihood estimation method to obtain the number of possible candidate genes for each disease, specifically:

Define the number of all loss-of-function variants and deleterious missense variants in the disease (K), the number of genes with two or more loss-of-function variants and deleterious missense variants detected in different patients (R), and the number of loss-of-function variants and deleterious missense variants Proportion (E) that the sense variant contributes to the disease;

Do one million permutation tests, randomly select a certain proportion of candidate genes (T) from all coding genes in each permutation test (the value range is 1-2500) and draw a certain proportion according to the binomial distribution (K, E) The variance of the proportion (C);

Assign the number of loss-of-function variants and deleterious missense variants (C) that contribute to the disease to candidate genes (T) and deleterious variants that do not contribute to disease (K-C) to other genes;

Each permutation test counts the number of genes extracted twice or more. If this number is the same as the actual observed number, the candidate gene (T) extracted this time is considered to be the number of possible causative genes of the disease;

After all the permutation tests are completed, a prior probability distribution curve is obtained based on the number of possible disease-causing genes, and the number of candidate genes corresponding to the point with the highest probability is the number of genes that are most likely to be related to the disease.

Preferably, the disease variation load, mutation rate, and the number of predicted candidate genes are taken into consideration, and a comprehensive assessment is performed based on the loss-of-function variation and harmful missense variation carried by each gene and the number of patients detected. Get the Bayesian factor at the single gene level and the probability value of the false positive of the candidate gene according to the Bayesian factor, specifically:

Observe the number of mutations that appear in N families and the expected number 2Nμ, μ represents the mutation rate of a specific gene, and use the Bayesian method to compare the two models H0 and H1. H0 means that the gene is not a disease-causing gene, and H1 means that the gene is Pathogenic gene; when H0 is established, the expected number of mutations is 2Nμ; when H1 is established, the expected number of mutations is 2Nμγ, where γ represents the relative risk, and γ greater than 1 indicates that it may be a pathogenic gene;

Bayes factor is defined as the probability of being H1 divided by the probability of H0; the probability of H1 being positive is determined according to Bayes theorem, a Bayes factor greater than 1 indicates that H1 is likely to be positive, and a Bayes factor greater than 100 indicates a strong The proof of H1 is positive;

By multiplying the Bayesian factors obtained by different mutation types, each variation type will obtain a Bayesian factor separately, and multiplying all Bayesian factors together to obtain the gene-level Bayesian factor.

Using the Bayesian FDR method to control the false positive rate, each gene will get an FDR value, that is, q-value;

Define q-value to be less than a preset value A, and 0≦A≦1, which means that the gene obtained with probability A may be wrong.

Preferably, it also includes: before performing step S1, performing genomic functional annotation on the detected variants, so as to classify the variants into loss-of-function variants, deleterious missense variants, tolerable missense variants, synonymous variants and non-synonymous variants. Coding region variation.

Preferably, the reliability of the candidate gene specifically includes: the level of specific expression of the gene in disease-related tissues, the degree of co-expression and protein interaction with known disease-related genes, and the degree of significant enrichment in known and known disease-related genes. pathways associated with neurodevelopmental disorders.

Compared with prior art, the beneficial effect of the present invention is:

1. The reliability of this method is verified by existing genes, and new candidate genes can be researched based on the harmful variation of unknown genes.

2. Combining the mutation rate, the number of detection samples, the number of candidate genes and other parameters, the reliability of the candidate genes is statistically evaluated. There is a more reliable basis for the discovered disease-causing genes.

3. In order to make up for the shortcomings of insufficient sample size for a single neurodevelopmental disease, identify more candidate genes at the same time. Based on the genetic similarity of different neurodevelopmental diseases, this method integrates the emerging variation data of multiple diseases to enhance the statistical power of genetics.

Description of drawings

Figure 1 is a schematic flow diagram of a method for integrating multiple neurodevelopmental diseases to screen candidate genes provided by an embodiment of the present invention;

Fig. 2 is a schematic diagram of the principle of a method for screening candidate genes integrating multiple neurodevelopmental diseases according to the present invention.

Detailed ways

In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

Please refer to Figure 1, the present invention provides a technical solution: a method for integrating multiple neurodevelopmental diseases to screen candidate genes, comprising the following steps:

S1. Perform genomic functional annotation on the detected variants to classify the variants into loss-of-function variants, harmful missense variants, harmless missense variants, synonymous variants and non-coding region variants. Loss-of-function variants include frame-shift variants, splice site variants, stop variants, and stop codon deletion variants. Considering the mode of inheritance of variants includes de novo variants, transmitted and non-transmitted variants, and case-control variants. Among them, genes carrying loss-of-function variants and deleterious missense variants are more likely candidates.

S2. Evaluate the candidate genes by the maximum likelihood estimation method to obtain the number of possible candidate genes for each disease. And implement it through the following specific steps:

Define the number of all possible loss-of-function variants and deleterious missense variants (K) of the disease, the number of genes with two or more loss-of-function variants and deleterious missense variants detected in different patients (R), and the number of genes with loss-of-function and deleterious missense variants Proportion (E) that the sense variant contributes to the disease.

Do one million permutation tests, randomly select a certain proportion of candidate genes (T) from all coding genes in each permutation test (the value range is 1-2500) and draw a certain proportion according to the binomial distribution (K, E) Variation in proportion (C). Assume that there are no more than 2,500 genes that may cause each disease.

Loss-of-function and deleterious missense variants (C) that contribute to disease are assigned to candidate genes (T) and deleterious variants that do not contribute to disease (K-C) are assigned to other genes.

Each permutation test counts the number of genes extracted twice or more. If the number is the same as the actual observed number, the candidate gene (T) extracted this time is considered to be the number of possible causative genes of the disease.

After all the permutation tests are completed, a prior probability distribution curve is obtained based on the number of possible disease-causing genes, and the number of candidate genes corresponding to the point with the highest probability is the number of genes that are most likely to be related to the disease.

S3. At the same time, taking into account the parameters of disease variation load, mutation rate, and the number of predicted candidate genes, a single gene is obtained through comprehensive evaluation based on the loss-of-function variation and harmful missense variation carried by each gene and the number of patients detected. The Bayesian factor of the level and the probability value of the false positive of the candidate gene according to the Bayesian factor. And implement it through the following specific steps:

Observe the number of mutations in N families and the expected number 2Nμ, μ represents the mutation rate of a specific gene, first use the Bayesian method to compare the two models H0 and H1, H0 is a gene that is not a disease-causing gene, and H1 is a base because of the disease-causing gene. When H0 holds, the expected number of mutations is 2Nμ. When H1 is established, the number of expected mutations is 2Nμγ, where γ indicates the relative risk, and γ greater than 1 indicates that it may be a pathogenic gene.

Define the Bayes factor as the probability of being H1 divided by the probability of H0. According to the Bayesian theorem, the probability of H1 being positive is determined. The Bayesian factor greater than 1 indicates that H1 may be positive, and the Bayesian factor greater than 100 indicates a strong proof that H1 is positive.

By multiplying the Bayesian factors obtained by different mutation types, each variation type will obtain a Bayesian factor separately, and multiplying all Bayesian factors together to obtain the gene-level Bayesian factor.

Using the Bayesian FDR method to control the false positive rate, each gene will get an FDR value, that is, q-value.

Define q-value to be less than a preset value A, and 0≦A≦1, which means that the gene obtained with probability A may be wrong.

S4. Further comprehensively evaluate the credibility of the candidate gene based on the data at the gene level. The credibility of the candidate gene is specifically: the level of specific expression of the gene in disease-related tissues, and the presence of co-expression and protein with known disease-related genes. The degree of interaction was significantly enriched on pathways known to be associated with neurodevelopmental disorders.

Among them, it is generally believed that a disease-causing gene will have a higher mutation load, that is, more mutations are detected in the same patient sample than in random cases. This can be obtained simply by analyzing the Poisson test. We can compare the observed number of mutations in N families with the expected number 2Nμ, where μ is the mutation rate of a particular gene. But the problem with this approach is that it does not consider the efficacy of each variant type, for example, loss-of-function variants and missense variants may have different effects on the disease. When this method detects the variation load of each gene, it takes into account the contribution of different variation types to the disease phenotype. For example, if the loss-of-function mutation is a high-risk mutation type, a relatively high weight will be given.

For different types of neurodevelopmental diseases, the method first identifies candidate genes by individual disease analysis. At the same time, in view of the genetic similarity between different neurodevelopmental diseases, we adopt the method of integrating all neurodevelopmental new variants. This allows the identification of novel causative genes that are common among different diseases but cannot be identified in a single disease due to insufficient sample size.

The above-identified genes are further enhanced by methods such as gene expression, protein interaction, and enrichment analysis. Whether the gene is expressed in specific disease-related tissues, and whether it co-expresses or interacts with known genes. Whether the candidate gene set is enriched in known disease-related gene sets or signaling pathways.

A method for screening candidate genes integrating multiple neurodevelopmental diseases of the present invention has the ability to integrate new ideas for multiple neurodevelopmental diseases to identify new candidate genes and enhance gene credibility. The reliability of this method can be verified by existing genes, and new candidate genes can be searched based on the consistent variation of unknown genes. Combined with the mutation rate, the number of detection samples, the number of candidate genes and other parameters, the reliability of the candidate genes is statistically evaluated. There is a more reliable basis for the discovered disease-causing genes. In order to make up for the shortcoming of insufficient sample size for a single neurodevelopmental disease, more candidate genes were identified simultaneously. Based on the genetic similarity of different neurodevelopmental diseases, this method integrates the emerging variation data of multiple diseases to enhance the statistical power of genetics.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present invention should be included in the protection of the present invention. within range.

Claims (4)

1. A method for integrating a plurality of candidate genes for screening for neurodevelopmental diseases, comprising the steps of:

the method comprises the following steps:

s1, evaluating candidate genes by a maximum likelihood estimation method to obtain the number of candidate genes of each disease;

the candidate genes are evaluated by a maximum likelihood estimation method, and the number of the candidate genes for each disease is obtained, specifically:

defining the number of all loss-of-function variations and detrimental missense variations of the disease, the number of genes and the proportion of loss-of-function variations and detrimental missense variations contributing to the disease for which two or more loss-of-function variations and detrimental missense variations are detected in different patients by the same gene;

performing one million replacement tests, randomly extracting a certain proportion of candidate genes T from all coding genes in each replacement test, wherein the value range is 1-2500, and extracting a certain proportion of harmful missense variation numbers according to binomial distribution (K, E);

assigning a loss-of-function variation and a detrimental missense variation number contributing to the disease to the candidate gene T and assigning a detrimental missense variation not contributing to the disease to the other genes;

counting the number of extracted genes twice or more per substitution test, and if the number is the same as the number actually observed, considering the candidate gene T extracted at the time as the number of the disease pathogenic genes;

after all the replacement tests are finished, a priori probability distribution curve is obtained according to all the obtained pathogenic gene numbers, and the candidate gene number corresponding to the point with the highest probability is the most probable gene number related to the disease;

s2, simultaneously taking the disease variation load, the expected mutation rate of genes and the number parameters of candidate genes into consideration, and comprehensively evaluating the number of detected patients according to the function deletion variation and harmful missense variation carried on each gene to obtain a Bayesian factor of a single gene level and judging the probability value of false positive of the candidate genes according to the Bayesian factor;

s3, further comprehensively evaluating the credibility of the candidate genes according to the gene level data.

2. A method of integrating a plurality of candidate genes for screening for a neurodegenerative disease according to claim 1, wherein:

meanwhile, taking the disease variation load, the expected variation rate of genes and the number parameters of estimated candidate genes into consideration, and comprehensively estimating to obtain a Bayesian factor of a single gene level and a probability value for judging false positives of the candidate genes according to the Bayesian factor according to the function deficiency variation and harmful missense variation carried on each gene and the number of detected patients, wherein the probability value comprises the following specific steps of:

observing the number of variations and the expected number 2Nμ in N families, wherein μ represents the mutation rate of genes, comparing two models of H0 and H1 by using a Bayesian method, wherein H0 is not a pathogenic gene, and H1 is a pathogenic gene; when H0 holds, the number of desired variations is 2N μ; when H1 is established, the number of desired variations is 2N [ mu ] gamma, where gamma represents the relative risk, and gamma greater than 1 represents the causative gene;

defining a bayesian factor as the probability of being H1 divided by the probability of H0; according to the Bayes theorem, determining the probability that H1 is positive, wherein a Bayes factor greater than 1 indicates that H1 is possibly positive, and a Bayes factor greater than 100 indicates that H1 is strongly proved to be positive;

multiplying the Bayesian factors obtained by different mutation types, wherein each mutation type can independently obtain one Bayesian factor, and multiplying all the Bayesian factors to obtain a gene level Bayesian factor;

controlling false positive rate by using Bayes FDR method, wherein each gene can obtain an FDR value, namely q-value;

definition q-value is smaller than a preset value A, and 0.ltoreq.A.ltoreq.1, indicates that the gene obtained with A probability is erroneous.

3. A method of integrating a plurality of candidate genes for screening for a neurodegenerative disease according to claim 1, wherein:

further comprises:

the detected variants are genomically annotated to classify the variants into loss-of-function variants, deleterious missense variants, tolerable missense variants, synonymous variants and non-coding region variants prior to performing step S1.

4. A method of integrating a plurality of candidate genes for screening for a neurodegenerative disease according to claim 1, wherein:

the credibility of the candidate genes is specifically as follows: the genes are expressed in a specific way in the tissues related to the diseases, the genes are expressed together with the genes related to the known diseases, the interaction degree of the genes and the proteins related to the known diseases is high, and the genes are enriched on the paths related to the nerve development diseases obviously through analysis.

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