WO2015174825A1 - Procédé de prédiction ou de détermination de phénotypes végétaux dans des palmiers à huile - Google Patents

Procédé de prédiction ou de détermination de phénotypes végétaux dans des palmiers à huile Download PDF

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WO2015174825A1
WO2015174825A1 PCT/MY2015/050034 MY2015050034W WO2015174825A1 WO 2015174825 A1 WO2015174825 A1 WO 2015174825A1 MY 2015050034 W MY2015050034 W MY 2015050034W WO 2015174825 A1 WO2015174825 A1 WO 2015174825A1
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seq
markers
marker
yield
bunch
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Suan Choo Cheah
Ying Wah LEE
Soo Heong Boon
Weng Wah LEE
Kia Ling CHUA
Chong Hee Lee
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ACGT Sdn Bhd
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    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01H—NEW PLANTS OR NON-TRANSGENIC PROCESSES FOR OBTAINING THEM; PLANT REPRODUCTION BY TISSUE CULTURE TECHNIQUES
    • A01H1/00—Processes for modifying genotypes ; Plants characterised by associated natural traits
    • A01H1/04—Processes of selection involving genotypic or phenotypic markers; Methods of using phenotypic markers for selection
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
    • C12Q1/6895—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for plants, fungi or algae
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/156—Polymorphic or mutational markers

Definitions

  • the present invention relates generally to the field of molecular biology.
  • the present invention relates to plant genetics for detecting and using single nucleotide polymorphisms for selection purposes in oil palms.
  • duras having desirable traits are selected and used to generate dura x dura (or DxD) crosses.
  • the seeds generated are planted in the nursery for a year and subsequently field planted.
  • the palms come into bearing between 2 1 ⁇ 2 to 3 years depending on the location at which they are planted.
  • the yields of these palms are then recorded for five years to determine its yield potential for another cycle of selection and improvement.
  • selected pisiferas are crossed with a selected tenera to generate a tenera x pisifera (or TxP) cross.
  • selected teneras can be used to generate pisiferas in a tenera x tenera (or TxT) cross.
  • the seeds are planted in the nursery and subsequently field planted as in the duras, the only difference being that the pisiferas are female sterile or abortive, and hence cannot be yield recorded. Yield recording can only be 5 carried out in the dura or tenera sibs.
  • Oil palm breeding has improved the yield potential of oil palm planting materials over the years. For example, Lee et al. (Lee et al., Proc.
  • heterozygosity of the parent lines used for the production of DxP planting materials result in variability in the yield of individual palm. Typically, a yield improvement of about 15% or more can be expected from good oil palm clones.
  • molecular based methods have been used to accelerate development of superior oil palm planting materials.
  • marker-assisted selection has been used, where genomic data are mined (based on the field data available on the performance of specific progenies) to identify specific genetic sequences which consistently correlate to a specific characteristic, such as high FFB yield. These genetic sequences can then be used as molecular markers to select, for example, for high FFB yield even at the nursery stage, thereby reducing the cycle time in a breeding program from 12 years to 6 years.
  • the present invention refers to a method of predicting or determining a plant phenotype of interest, wherein the method comprises detecting the presence or absence of one or more polymorphic genetic markers selected from the group consisting of SEQ ID NOs. 1 to 1 15.
  • the present invention refers to use of one or more polymorphic genetic markers according to any one of the preceding claims as criteria for the selection of a plant, wherein the selection is based on the presence or absence of one or more polymorphic genetic markers.
  • polymorphism refers to a variation or difference in the sequence of a genetic region that arises in some of the members of a species. Variant sequences can be defined with reference to an arbitrary or non-arbitrary standard sequence for the species. A polymorphism is thus said to be “allelic,” in that, due to the existence of the polymorphism, some members of a species may have the "standard” sequence (i.e. the standard "allele") whereas other members may have a variant sequence (i.e., a variant "allele”).
  • an allele is one of two or more alternative versions of a gene or other genetic region at a particular location on a chromosome. In the simplest case, only one variant sequence may exist, and the polymorphism is thus said to be bi -allelic. In other cases, the species' population may contain multiple alleles, and the polymorphism is termed tri-allelic, etc.
  • a single gene or genetic region may have multiple different unrelated polymorphisms. For example, it may have one bi-allelic polymorphism at one site, another bi -allelic polymorphism at another site and a multi-allelic polymorphism at yet another site.
  • the alleles are said to be "homozygous" at that locus.
  • the sequence of any allele at a particular locus in a plant is different, the population of alleles is said to be "heterozygous" at that locus.
  • Phenotypic traits can vary due to environmental and/or genetic factors. For example, polymorphisms at a particular chromosomal locus can affect the phenotypic trait associated with that locus.
  • phenotypic trait as used herein relates to any particular characteristic or “trait” exhibited by a plant, whether naturally occurring or otherwise, that is capable of being inherited. Moreover, the phenotypic trait of interest may, for example, be transient, permanent or only present when the plant or part thereof is subjected to environmental stimuli or challenge. A phenotypic trait of interest may be a desired or positive trait. In other cases the phenotypic trait of interest may be an undesired or negative trait.
  • phenotypic traits are not limited to visible traits. While the phenotypic trait may be any trait, preferred traits of interest are those that have agricultural significance. Examples of agricultural traits include those that affect a component of yield, those that provide disease or chemical resistance, and those that affect developmental traits such as pollen or ovule production, etc., and those that affect composition of plants or plant parts, including seed proteins or oils, starch or sugar composition, nutrient content and the like.
  • the trait may comprise but is not limited of traits that affect a component of yield such as fresh fruit bunch yield, bunch number, trunk size, or combinations thereof.
  • the yield component may comprise, but is not limited to, fresh fruit bunch weight, bunch size, fruit size, oil extraction rate, bunch numbers or combinations thereof.
  • An example of a method for predicting or determining a plant genotype of interest may include, but is not limited to, the following steps, starting with taking a sample of a plant, seed or sapling to be analysed. This genetic analysis of the plant would show, for example, which single nucleotide polymorphisms (SNPs) are present and where. The result of this type of genetic analysis or mapping for this sample may then be compared with a list of known SNPs, which had previously been correlated with one or more characteristics for that plant, e.g. plant height, fruit yield etc. Thus, this comparison between the genetic analysis of the sample and the list of known SNPs would be able to infer a statistical probability with which the tested plant would display the desired characteristic upon maturity. In order for this to be possible, at least one SNP must be present in the sample.
  • SNPs single nucleotide polymorphisms
  • phenotypic traits are the result of multiple genes or genetic factors, for example, a phenotypic trait that is the result of a quantitative trait allele.
  • An allele of a quantitative trait locus can comprise multiple genes or other genetic factors even within a contiguous genomic region or linkage group.
  • Locus refers to a specific chromosome location in the genome of a species where a specific gene can be found.
  • Quantitative trait locus as used herein relates to a region of DNA containing or linked to the genes that underlie a quantitative trait. Mapping regions of the genome that contain genes involved in specifying a quantitative trait is done using molecular tags such as AFLP or, more commonly SNPs. This is an early step in identifying and sequencing the actual genes underlying trait variation.
  • Quantitative traits refer to phenotypes (characteristics) that vary in degree and can be attributed to polygenic effects, i.e., product of two or more genes, and their environment. It therefore may be defined as a gene affecting the phenotypic variation in continuously varying traits.
  • QTL analysis is a statistical method that links two types of information— phenotypic data (trait measurements) and genotypic data (usually molecular markers) in an attempt to explain the genetic basis of variation in complex traits. Accordingly, QTL analysis allows linking certain complex phenotypes to specific regions of chromosomes. The goal of this process is to identify the action, interaction, number, and precise location of these regions.
  • an "allele of a quantitative trait locus" can therefore encompass more than one gene or other genetic factor where each individual gene or genetic component is also capable of exhibiting allelic variation and where each gene or genetic factor also has a phenotypic effect on the quantitative trait in question.
  • chromosome segment designates a contiguous linear span of genomic DNA that resides in plants on a single chromosome.
  • the genetic elements or genes located on a single chromosome segment are physically linked.
  • the genetic elements located within a chromosome segment are also genetically linked, typically within a genetic recombination distance of less than or equal to 10 centimorgan (CM). That is, two genetic elements within a single chromosome segment undergo recombination during meiosis with each other at a frequency of less than or equal to about 10%.
  • CM centimorgan
  • a "marker” is an indicator for the presence of at least one polymorphism.
  • a marker is preferably a nucleic acid molecule. It is understood that a marker can, for example, be an oligonucleotide probe or primer. Molecular markers are preferred for genotyping, because these markers are unlikely to affect the trait of interest.
  • the marker may comprise but is not limited to single nucleotide polymorphisms (SNPs), simple sequence repeats (SSRs or microsatellites), amplified fragment length polymorphisms (AFLPs), random amplification of polymorphic DNAs (RAPDs), restriction fragment length polymorphisms (RFLPs), and transposable element positions and combinations thereof.
  • single nucleotide polymorphism is a DNA sequence variation that occurs when a single nucleotide (A, T, C, or G) in the genome sequence is altered or differs between members of a biological species or paired chromosomes in a human.
  • a SNP is any polymorphism characterized by a different single nucleotide at a particular physical position in at least one allele. Each individual in a given population has many single nucleotide polymorphisms that together create a unique DNA pattern for that individual.
  • SSRs simple sequence repeats
  • STRs short tandem repeats
  • VNTR variable number tandem repeat
  • SSRs are typically co-dominant.
  • AFLPs Amplitude Length Polymorphisms
  • RAPD Random Amplified Polymorphic DNA
  • RAPD DNA fragments from PCR amplification of random segments of genomic DNA with a single primer of arbitrary nucleotide sequence.
  • RAPD does not require any specific knowledge of the DNA sequence of the target organism: the identical 10- mer primers will or will not amplify a segment of DNA, depending on positions that are complementary to the primers' sequence. For example, no fragment is produced if primers annealed too far apart or 3' ends of the primers are not facing each other. Therefore, if a mutation has occurred in the template DNA at the site that was previously complementary to the primer, a PCR product will not be produced, resulting in a different pattern of amplified DNA segments on the gel.
  • RAPDs are dominant in the sense that the presence of a RAPD band does not allow distinction between heterozygous and homozygous states.
  • the term "Restriction Fragment Length Polymorphism (RFLP)" as used herein refers to differences in restriction fragment lengths caused by SNPs or INDELs that create or abolish restriction endonuclease recognition sites. RFLP assays are performed by hybridizing a chemically labelled DNA probe to a Southern blot of DNA digested with a restriction endonuclease.
  • nucleic acid marker as used herein means a nucleic acid molecule that is capable of being a marker for detecting a polymorphism.
  • linkage phase disequilibrium refers to a non-random segregation of genetic loci. This implies that such loci are in sufficient physical proximity along a length of a chromosome that they tend to segregate together with greater than random frequency.
  • genetic loci including genetic marker loci
  • genetic marker loci that are physically close enough to each other on the same chromosome such that they have a recombination frequency of less than 0.5.
  • "coupling" phase linkage indicates the state where the "favourable” allele at the yield locus is physically associated on the same chromosome strand as the "favourable” allele of the respective linked marker locus.
  • the term "physically linked" is used to indicate that two genetic loci, e.g., two marker loci, a marker locus and a locus contributing to variation in a phenotype, are physically present on the same chromosome.
  • the two loci are located in close proximity, such that recombination between homologous chromosome pairs does not occur between the two loci with high frequency. That is, recombination between two physically linked loci typically occurs with a frequency of less than about 10%, favourably with a frequency of less than 5%, more favourably with a frequency of 2% or less or a frequency of 1% or less.
  • two loci that are localized to the same chromosome, and at such a distance that recombination between the two loci occurs at a frequency of less than 10% are said to be "proximal to" each other.
  • a “genetic map” is a description of the genetic linkage relationships among loci on one or more chromosomes (or linkage groups) within a given species, generally depicted in a diagrammatic or tabular form. "Mapping” is the process of defining the linkage relationships of loci through the use of genetic markers, populations segregating for the markers, and standard genetic principles of recombination frequency.
  • a “map location” is an assigned location on a genetic map relative to linked genetic markers where a specified marker can be found within a given species.
  • a “genotype” is the genetic constitution of an individual (or group of individuals) at one or more genetic loci. Genotype is defined by the allele(s) of one or more known loci that the individual has inherited from its parents.
  • a “haplotype” is the genotype of an individual at a plurality of genetic loci. Typically, the genetic loci described by a haplotype are physically and genetically linked, i.e., on the same chromosome segment.
  • An individual is “homozygous” if the individual has only one type of allele at a given locus (e.g., a diploid individual with two copies of the same allele at a locus).
  • An individual is “heterozygous” if more than one allele type is present at a given locus (e.g., a diploid individual with one copy each of two different alleles).
  • the term “homogeneity” indicates that members of a group have the same genotype at one or more specific loci. In contrast, the term “heterogeneity” is used to indicate that individuals within the group differ in genotype at one or more specific loci.
  • a "line” or “strain” is a group of individuals of identical parentage that are generally inbred to some degree and are generally homozygous and homogeneous at most loci.
  • An “elite line” or “elite strain” is a genetically superior line that has resulted from many cycles of breeding and selection for superior agronomic performance. Numerous elite lines are available and known to those of skill in the art of soybean breeding. An “elite population” is an assortment of elite lines that can be used to represent the state of the art in terms of agronomically superior genotypes of a given crop species, such as oil palm.
  • oligonucleotide refers to short nucleic acid molecules useful, e.g. for hybridizing probes, nucleotide array elements or amplification primers. Oligonucleotide molecules are comprised of two or more nucleotides, i.e. deoxyribonucleotides or ribonucleotides, preferably more than five and up to 30 or more. The exact size will depend on many factors, which in turn depend on the ultimate function or use of the oligonucleotide.
  • Oligonucleotides can comprise ligated natural nucleic acid molecules or synthesized nucleic acid molecules and comprise between 10 to 150 nucleotides or between about 12 and about 100 nucleotides which have a nucleotide sequence which can hybridize to a strand of polymorphic DNA, e.g. to permit detection of a polymorphism.
  • Such oligonucleotides may be nucleic acid elements for use on solid arrays (e.g. synthesized or spotted).
  • such oligonucleotides can comprise as few as 12 hybridizing nucleotides, e.g. for assays where the oligonucleotide also comprises a detectable label.
  • the oligonucleotide can comprise as few as about 15 hybridizing nucleotides, e.g. for single base extension assays.
  • oligonucleotides may also be primers for use in polymerase chain reaction (PGR) or other reactions.
  • PGR polymerase chain reaction
  • the term "primer” as used herein refers to a nucleic acid molecule, preferably an oligonucleotide whether derived from a naturally occurring molecule such as one isolated from a restriction digest or one produced synthetically, which is capable of acting as a point of initiation of synthesis when placed under conditions in which synthesis of a primer extension product which is complementary to a nucleic acid strand is induced, i.e., in the presence of nucleotides and an agent for polymerization such as DNA polymerase and at a suitable temperature and pH.
  • the primer is preferably single stranded for maximum efficiency in amplification, but may alternatively be double stranded. If double stranded, the primer is first treated to separate its strands before being used to prepare extension products.
  • the primer is an oligodeoxyribonucleotide.
  • the primer must be sufficiently long to prime the synthesis of extension products in the presence of the agent for polymerization. The exact lengths of the primers will depend on many factors, including temperature and source of primer. For example, depending on the complexity of the target sequence, the oligonucleotide primer typically contains at least 15, more preferably 18 nucleotides, which are identical or complementary to the template and optionally a tail of variable length which need not match the template.
  • the length of the tail should not be so long that it interferes with the recognition of the template. Short primer molecules generally require cooler temperatures to form sufficiently stable hybrid complexes with the template.
  • the primers herein are selected to be “substantially" complementary to the different strands of each specific sequence to be amplified. This means that the primers must be sufficiently complementary to hybridize with their respective strands. Therefore, the primer sequence need not reflect the exact sequence of the template. For example, a non- complementary nucleotide fragment may be attached to the 5' end of the primer, with the remainder of the primer sequence being complementary to the strand.
  • non- complementary bases or longer sequences can be interspersed into the primer, provided that the primer sequence has sufficient complementarity with the sequence of the strand to be amplified to hybridize therewith and thereby form a template for synthesis of the extension product of the other primer.
  • Computer generated searches using programs such as Primer3 www- genome.wi.mit.edu/cgi-bin/primer/primer3.cgi), STSPipeline (www-genome.wi.mit.edu/cgi- bin/www-STS_Pipeline), or GeneUp, for example, can be used to identify potential PCR primers.
  • Exemplary primers include primers that are 18 to 50 bases long, where at least between 18 to 25 bases are identical or complementary to at least 18 to 25 bases of a segment of the template sequence.
  • primer pair means a set of two oligonucleotide primers based on two separated sequence segments of a target nucleic acid sequence.
  • One primer of the pair is a "forward primer” or "5' primer” having a sequence which is identical to the more 5' of the separated sequence segments (+ strand).
  • the other primer of the pair is a "reverse primer” or "3' primer” having a sequence which is the reverse complement of the more 3' of the separated sequence segments (+ strand).
  • a primer pair allows for amplification of the nucleic acid sequence between and including the separated sequence segments.
  • each primer pair can comprise additional sequences, e.g. universal primer sequences or restriction endonuclease sites, at the 5' end of each primer, e.g. to facilitate cloning, DNA sequencing, or re-amplification of the target nucleic acid sequence.
  • additional sequences e.g. universal primer sequences or restriction endonuclease sites
  • cloning used in the context of oil palm refers to a process in which identical copies of a selected palm (ortet), are reproduced by developing plantlets from the leaf tissue of tenera oil palms with desirable characteristics.
  • mapping population is a collection of plants capable of being used with markers to map the genetic position of traits.
  • a "polymorphic marker” is a marker capable of detecting one or more polymorphisms.
  • the present invention provides nucleic acid molecules which are markers, i.e. capable of detecting polymorphisms that are distributed throughout the genome of a mapping population.
  • a "characterized polymorphism” is a polymorphism whose physical position on a genome is known.
  • the physical position of a characterized polymorphism on an isolated nucleic acid molecule, such as a bacterial artificial chromosome comprising oil palm genomic DNA is known.
  • the present invention also provides nucleic acid molecules capable of detecting characterized polymorphisms throughout a genome.
  • a characterized polymorphism is any polymorphism where the nucleic acid sequences of at least two of the polymorphisms present in an oil palm mapping population are known (sequenced characterized polymorphism).
  • the polymorphisms capable of detection by nucleic acid molecules of the present invention are distributed throughout the genome of the mapping population in a manner that allows the efficient identification of a genomic region associated with a phenotypic trait.
  • the polymorphisms are distributed throughout the genome where 60%, preferably 70%, more preferably 80%, even more preferably 90%, 95% or 100% of the genome has a characterized polymorphism at a density of higher than one polymorphism per 100 kb, more preferably higher than one polymorphism per 50 kb, and even more preferably higher than one polymorphism per 25 kb, 10 kb, 7 kb, 5 kb or 3 kb.
  • the polymorphisms are distributed throughout the genome where 60%, preferably 70%, more preferably 80%, even more preferably 90%, 95% or 100% of genome has a characterized polymorphism at a density of higher than one polymorphism per 3.5 cM, more preferably higher than one polymorphism per 3.25 cM, and even more preferably higher than one polymorphism per 3.0 cM, 2.75 cM, 2.5 cM, 2.0 cM, 1.5 cM, 1.0 cM or 0.5 cM.
  • the efficient identification of a genomic region associated with a phenotypic trait is disclosed, where the genomic region is less than 100 kb, more preferably less than 50 kb, and even more preferably less than 25 kb, 10 kb, 7 kb, 5 kb or 3 kb from a characterized polymorphism.
  • the efficient identification of a genomic region associated with a phenotypic trait where the genomic region is less than 3.5 cM, more preferably less than 3.25 cM, and even more preferably less than 3 cM, 2.75 cM, 2.5 cM, 2.0 cM, 1.5 cM, 1.0 cM or 0.5 cM from a characterized polymorphism.
  • polymorphisms need not be uniform in a genome as certain regions will exhibit a higher average density of polymorphisms (e.g. non- centromeric regions) and certain regions will exhibit a lower average density of polymorphisms (e.g. centromeric regions).
  • the efficient identification of a genomic region associated with a phenotypic trait of interest will be obtained by a simultaneous screening for the presence of 25 or more, more preferably 50 or more, even more preferably 75 or more, 100 or more, 150 or more, 200 or more, 250 or more, 300 or more, 400 or more or 500 or more, 1 ,000 or more, 2,000 or more, 3,000 or more, 4,000 or more polymorphisms.
  • high throughput assays e.g. with microarrays, it may be feasible to screen for the presence of 5,000 or more polymorphisms, e.g. at least 10,000 or even 15,000 polymorphisms.
  • the efficient identification of a genomic region associated with a phenotypic trait of interest will be obtained by a simultaneously screening for the presence of 25 or more, more preferably 50 or more, even more preferably (where appropriate) 100 or more, or 250 or more etc. of the polymorphisms.
  • Marker Assisted Selection or “MAS” refers to the practice of selecting for desired phenotypes among members of a breeding population using genetic markers.
  • hybrid plants refers to plants which result from a cross between genetically different individuals.
  • crossing means the fusion of gametes, e.g., via pollination to produce progeny (i.e., cells, seeds, or plants).
  • progeny i.e., cells, seeds, or plants.
  • the term encompasses both sexual crosses (the pollination of one plant by another) and selfing (self- pollination, i.e., when the pollen and ovule are from the same plant).
  • the efficient identification of a genomic region associated with a phenotypic trait of interest will be obtained by screening for the presence of 25 or more, more preferably 50 or more, even more preferably 75 or more, 100 or more, 150 or more, 200 or more, 250 or more, 300 or more, 400 or more or 500 or more, 1,000 or more, 2,000 or more, 3,000 or more, 4,000 or more polymorphisms during a single assay.
  • the efficient identification of a genomic region associated with a phenotypic trait of interest will be obtained by screening for the presence of 25 or more, more preferably 50 or more, even more preferably (where appropriate) 100 or more or 250 or more etc. of the polymorphisms during a single assay.
  • a single assay can comprise many steps. One or more of these steps can occur sequentially.
  • the assay may be carried out using a high throughput system.
  • a high throughput system may involve a solid phase array.
  • the solid phase array may comprise a microarray.
  • a collection of markers for polymorphisms can comprise from a few up to millions of different nucleic acid molecules.
  • membranes with many nucleic acid molecules can be generated for screening.
  • the solid-phase techniques described below and known in the art can be adapted for high-throughput monitoring of polymorphisms. In such methods different immobilized nucleic acid molecule probes can be placed on a solid support at microarray densities of up to millions of nucleic acid molecules per square inch. Similarly, very large sets of nucleic acid molecules can be immobilized for simultaneous screening against one or more probes.
  • the terms “increase” and “decrease” refer to the relative alteration of a chosen characteristic in a subset of a population in comparison to the same characteristic as present in the whole population. An increase thus indicates a change on a positive scale, whereas a decrease indicates a change on a negative scale.
  • the term “change”, as used herein, also refers to the difference between a chosen characteristic of an isolated population subset in comparison to the same characteristic in the population as a whole. However, this term is without valuation of the difference seen.
  • the term "about”, in the context of concentrations of components of the formulations, typically means +/- 5% of the stated value, more typically +/- 4% of the stated value, more typically +/- 3% of the stated value, more typically, +/- 2% of the stated value, even more typically +/- 1 % of the stated value, and even more typically +/- 0.5% of the stated value.
  • range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the disclosed ranges. Accordingly, the description of a range should be considered to have specifically disclosed all the possible sub-ranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed sub-ranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1 , 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.
  • association mapping allows for the detection of marker-linked traits without requiring the use of progeny plants.
  • association mapping advantageously takes into account events that have already created association in the distant past, including many generations of meiosis. This allows association mapping to provide a higher resolution or detection power for identifying the link between marker and phenotype.
  • Association mapping has been used in humans to discover disease related markers, in some plants such as maize (Zea mays, L.), soybean ⁇ Glycine max (L.) Mem), barley (Hordeum vulgare L.), wheat (Triticum aestivum L.), tomato (Lycopersicon esculentum Mill.), sorghum (Sorghum bicolor (L.) Moench), and potato (Solatium tuberosum L.), as well as in some tree species, such as aspen (Populus tremula L.) and loblolly pine (Pinus taeda L.).
  • a population of oil palm may comprise a population of oil palm having different genetic background.
  • a population of oil palm may comprise, but is not limited to, breeding populations of restricted origins (BPRO) such as AVROS, Deli, Yangambi, Cameroon, Ekona, Calabar and combinations thereof.
  • BPRO breeding populations of restricted origins
  • the population may be selected according to its structure, owing to geography, natural selection or artificial selection.
  • a given sample may fall in one of five categories defined by population structure associated with local adaptation or diversifying selection and familial relatedness from recent co-ancestry. Ideally, samples with minimal population structure or familial relatedness result in the greatest statistical power, provided that the trait of interest is well distributed.
  • the duration of phenotype data collection is given as an example below and it is conceivable that other time periods of data collection can be used for this process.
  • the phenotype data collection period may be 3 months or more, more preferably 6 months or more, even more preferably 9 months or more, 12 months or more, 18 months or more, 24 months or more, 36 months or more, 45 months or more, 60 months or more for measuring, collecting and analysing phenotype data.
  • the phenotype data collection is 45 months. In further examples the phenotype data collection is 54 months.
  • the phenotype used for analysis and linkage to one or more polymorphic genetic markers in plants such as oil palm may comprise, but is not limited to, fresh fruit bunch yield, bunch number, trunk height, disease resistance, seed number, height increment, oil composition, oil content, carotene content, tocopherol/ tocotrienol, vegetative measurements and such. Any measureable quantitative or qualitative trait can be used as phenotype data.
  • the method further comprises the step of recording phenotype data from at least one group of plants of the plant species for the phenotype of interest and genotyping the genetic material of the plants for which phenotype data have been recorded.
  • QTDT Quantitative Transmission Disequilibrium Test
  • This method can be applied for any type of phenotype that can be observed or measured.
  • markers with negative values for the effect estimate will be used instead.
  • the present invention further describes the development of a set of genetic markers that can be used individually or in combinations to predict or determine phenotypic traits in plants, such as in oil palm. This early prediction, or determination, of not yet obvious phenotypes is performed in order to aid the selection plants of the determined, advantageous phenotypic type, thus making it possible to reduce the breeding time required and also to save costs of the breeding program.
  • a method is described, wherein plant material used for identifying polymorphic markers is obtained from at least two, at least three, or at least four or more different plant populations of the same species with different genetic backgrounds.
  • plant material used for identifying polymorphic markers is obtained from at least two, at least three, or at least four or more different plant populations of the same species with different genetic backgrounds.
  • a method of identifying polymorphic genetic markers of a plant species linked to a phenotype of interest comprises identifying a statistically significant link between polymorphic markers identified for the plant and the phenotype of interest by association mapping; selecting markers which were found under a) to be linked to the phenotype of interest which have either the most significant negative or positive effect on the phenotype of interest by determining effect size for each of the markers identified under a) and selecting those with the most positive or negative effect on the phenotype of interest.
  • a method for predicting or determining a plant phenotype of interest comprising detecting the presence or absence of a one or more polymorphic genetic marker including, but not limited to SEQ ID NOs. 1 to 115.
  • the method comprises detecting the presence or absence of two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, the or more, or eleven or more or all polymorphic genetic marker(s) including, but not limited to SEQ ID NOs. 1 to 115.
  • the genetic markers include, but are not limited to Markers nos. 1 (SEQ ID NO. 10), 2 (SEQ ID NO. 20), 3 (SEQ ID NO.
  • predicting or “prediction” is defined as to state, or make something known in advance, especially using inference or special knowledge.
  • the term "determining” or “determination” in defined as ascertaining or establishing something exactly by research or calculation. In this case, the determination of a plant phenotype of interest is a direct result of the genetic analysis performed on a plant sample.
  • the method as previously described wherein the plant is an oil palm.
  • the oil palm as previously disclosed, is of from the genus Elaeis.
  • the genus as previously disclosed, may consist of the forms dura and tenera.
  • the genus as previously disclosed, may consist of the variation pisifera.
  • the biological term "form" is defined as a "secondary" taxonomic rank, which is below that of variety, which in turn is below that of species. When used in the art, a form usually designates a group with a noticeable but minor deviation. For instance, white-flowered forms of species that usually have coloured flowers can be named a "f. alba".
  • variety is defined as a taxonomic rank below that of species, but above that of a form. A variety will appear distinct from other varieties, but, when brought into contact with other varieties will freely hybridize and provide offspring. In general, varieties are geographically separated from each other.
  • the ultimate goal of any breeding program is to combine as many favourable alleles as possible into elite varieties of germplasm that are genetically superior (with respect to one or more agronomic traits) to their ancestors.
  • the markers described herein identify chromosome segments, i.e., genomic regions, and alleles (allelic forms) that have been favoured by long- term selection for yield. Accordingly, these markers can be used for marker assisted selection of oil palm plants with superior agronomic performance. For example, in a cross between parents that complement favourable alleles at the target loci, progeny can be selected that include more favourable alleles than either parent. Such progeny are predicted to be phenotypically superior to either parent.
  • the method disclosed herein can be used for testing progeny plants to see if they would exhibit the selected phenotype by taking a sample from said plant, analysing for the presence or absence of polymorphic genetic markers and based a comparison of those markers with previously identified markers and their known phenotypes, make a prediction if this particular plant will exhibit the desired phenotype during growth or at maturity.
  • the resolution of detection of markers linked to traits is reduced and the marker distance which is given in centimorgans (cM) becomes larger, thereby- making the predictive power of the markers weaker. Inclusion of more markers will increase the chance that a marker will fall within the resolution limit but will not reduce the resolving power itself. This is due to the intrinsic limitation of the linkage mapping method, which detects genetic crossover through meiosis events and compares it between the progenies and the parent palms.
  • Marker assisted selection employing the markers of the present invention, and the chromosome segments they identify are useful in the context of, e.g. an oil palm breeding program to increase efficiency in yield improvements.
  • Phenotypic screening for a trait of interest, such as yield, for large numbers of samples can be expensive, as well as time consuming.
  • phenotypic screening alone is often unreliable due to the effects of epistasis and non-genetic (e.g., environmental) contributions to the phenotype.
  • MAS offers the advantage over field evaluation that it can be performed at any time of year regardless of the growing season or developmental stage.
  • MAS facilitates evaluation of organisms grown in disparate regions or under different conditions.
  • a breeder of ordinary skill, desiring to breed oil palm plants with increased yield, can apply the methods for MAS described herein, using, e.g., the exemplary markers described herein or linked markers localized to the chromosome segments identified by markers listed in Tables 1 and 2 below, to derive oil palm lines with superior agronomic performance.
  • Genetic marker alleles, linked markers, QTL, identifying the chromosome segments encompassing genetic elements that are important for yield, are used to identify plants that contain a desired genotype at one or more loci, and that are expected to transfer the desired genotype, along with a desired phenotype to their progeny.
  • Marker alleles or QTL alleles can be used to identify plants that contain a desired genotype at one locus, or at several unlinked or linked loci (e.g., a haplotype), and that would be expected to transfer the desired genotype, along with a desired phenotype to their progeny.
  • markers can encompass both marker and QTL loci as both can be used to identify plants with a desired trait.
  • a nucleic acid corresponding to the marker nucleic acid is detected in a biological sample from a plant to be selected. This detection can take the form of hybridization of a probe nucleic acid to a marker, e.g., using allele-specific hybridization, Southern analysis, Northern analysis, in situ hybridization, hybridization of primers followed by PGR amplification of a product including the marker, or the like. After the presence (or absence) of a particular marker in the biological sample is verified, the plant is selected and, optionally, crossed to produce progeny plants.
  • plants positive for a marker of the invention, can be selected and crossed according to any breeding protocol relevant to the particular breeding program. Accordingly, progeny can be generated from a selected plant by crossing the selected plant to one or more additional plants selected on the basis of the same marker or a different marker, e.g., a different marker correlating with superior agronomic performance, or a different phenotype of interest, e.g., resistance to a particular disease. Alternatively, a selected plant can be back crossed to one or both parents.
  • Backcrossing is usually done for the purpose of introgressing one or a few loci from a donor parent, into an otherwise desirable genetic background from the recurrent (typically, an elite) parent. The more cycles of backcrossing that are performed, the greater is the genetic contribution of the recurrent parent to the resulting variety.
  • a selected plant can also be outcrossed, e.g., to a plant or line not present in its genealogy. Such a plant can be selected from among a population subject to a prior round of analysis, or may be introduced into the breeding program de novo.
  • a plant positive for a desired marker can also be self-crossed ("selfed") to create a true breeding line with the same genotype.
  • the method is as described herein, wherein the polymorphic markers are polymorphic markers from oil palms.
  • the markers used for this invention are single nucleotide polymorphism (SNP) markers but the methods can be applied to any genetic markers familiar to the person skilled in the art.
  • polymorphic genetic markers are SNPs (single nucleotide polymorphisms), SSRs (simple sequence repeats), AFLPs (amplified fragment length polymorphisms), RAPDs (random amplification of polymorphic DNAs) and combinations thereof.
  • polymorphic genetic markers are SNPs (single nucleotide polymorphisms).
  • the presence or absence of one or more polymorphic genetic markers is determined in a plant sample containing genetic material, wherein the sample material is selected from a group consisting of leaves, spear leaves, stem, saplings, roots, buds, flowers, seed and fruit.
  • the parental strains may be crossed, resulting in heterozygous (Fi) individuals, and these individuals are then crossed as described above.
  • phenotypes and genotypes of the derived (F 2 ) population are scored. Markers that are genetically linked to a QTL influencing the trait of interest will segregate more frequently with trait values, whereas unlinked markers will not show significant association with phenotype.
  • the parental lines need not actually be different for the phenotype in question; rather, they must simply contain different alleles, which are then resorted by recombination in the derived population to produce a range of phenotypic values.
  • a trait that is controlled by four genes wherein the upper-case alleles increase the value of the trait and the lower-case alleles decrease the value of the trait.
  • the effects of the alleles of the four genes are similar, individuals with the AABBccdd and aabbCCDD genotypes might have roughly the same phenotype.
  • effect size relates to the name given to one or two or a family of indices that measure the magnitude of a treatment effect. Unlike significance tests, these indices are independent of sample size. Effect size measures are the common currency of meta-analysis studies that summarize the findings from a specific area of research.
  • Effect size can be measured for example as the standardized difference between two means, such as the standardised mean difference between the two groups, or as the correlation between the independent variable classification and the individual scores on the dependent variable. This latter correlation is called the "effect size correlation".
  • an effect size is a measure of the strength of a phenomenon.
  • effect size calculated from data is a descriptive statistic that conveys the estimated magnitude of a relationship without making any statement about whether the apparent relationship in the data reflects a true relationship in the population. In that way, effect sizes complement inferential statistics such as -values.
  • the effect size may be defined as a coefficient ( ⁇ ) for a SNP when its association with the outcome is modelled through a regression model, such as linear regression for a quantitative trait or logistic regression for a qualitative trait, assuming a linear trend per copy of an allele.
  • the regression coefficients for quantitative traits may be presented in units of standard deviation (SD) of the trait so that they are comparable across traits.
  • sample size is a critical factor. Small sample sizes may fail to detect QTL of small effect and result in an overestimation of effect size of those QTL that are identified. There has been suggested a method for comparing detected QTL to a distribution of expected values in order to estimate how many loci might have been missed.
  • association mapping comprises applying a general statistical linear model (GLM) combined with mixed statistical linear model (MLM).
  • GLM general statistical linear model
  • MLM mixed statistical linear model
  • association mapping as described herein may be analysed using methods comprising, but not limited to, simple regression based for example on maximum- likelihood, analysis of variance (ANOVA) at the marker loci, methods of least square, Wright's F statistics, structured association, genomic control, quantitative transmission disequilibrium test (QTDT), population structure and relative kinship.
  • ANOVA analysis of variance
  • genomic control random markers are used to estimate and adjust the inflation of test statistics generated by population structure.
  • Genomic control and structured association are common methods to control for false positives (type I eiTor) caused by population structure.
  • association mapping comprises applying a general statistical linear model (GLM) combined with mixed statistical linear model (MLM).
  • the general statistical linear model incorporates a number of different statistical models such as ANOVA, ANCOVA, MANOVA, MANCOVA, ordinary linear regression, t- test and F-test.
  • the general linear model is a generalization of multiple linear regression models to the case of more than one dependent variable.
  • the mixed statistical linear model may be used to account for multiple levels of relatedness simultaneously as detected by random genetic markers.
  • the inventors by applying the combination of GLM and MLM may predict association with more accuracy.
  • the inventors carried out analysis using STRUCTURE.
  • STRUCTURE is software used for inferring population structure using genotype data.
  • Detection of the number of clusters of individuals is obtained based on the posterior probability of the data for a given population using a Bayesian approach (Evanno, G., Regnaut, S., Goudet,
  • the relative kinship matrix, defining the degree of genetic covariance between pairs of individuals may be calculated using SPAGeDi software.
  • Donnelly, Am. J. Hum. Genet.; 2000; 67: 170-181) and unified mixed model method are used to reduce the false positives by taking account of population structure and family relatedness within populations (Yu, et. al., Nature Genetics, 2006, vol.38, no. 2, pp.203). These spurious associations can occur when the associated markers are not linked to the causative loci but biased towards particular subpopulations when any marker allele is present with a high frequency in said subpopulations.
  • co-factor data can also be added in to increase the ability to detect associations.
  • perfonning FFB association mapping we may include the fruit size as a cofactor to help increase the ability of the association mapping to pick out markers linked to traits.
  • cut-off values can be used depending on the stringency of the analysis and efficient cut-off values will have to be determined based on the population type, organism, sample size and other consideration.
  • a polymorphic genetic marker with a p-value, empirically derived, of about below 5%, or about below 4%, or about below 3%, or about below 2%, or about below 1%, or about below 0.5%, or about below 0.1% is considered to have a statistically significant association to the phenotype of interest.
  • the method is described, wherein for association mapping a polymorphic genetic marker with a p-value below a 5% empirically derived value is considered to have a statistically significant association to the phenotype of interest.
  • the method as described herein is a method for association mapping a polymorphic genetic marker with a p-value below a 1% empirically derived value is considered to have a statistically significant association to the phenotype of interest.
  • the method as disclosed herein relates on a method of mapping
  • the coefficient of determination (R-squared or R 2 ) estimates the proportion of the phenotypic variation using markers. That is to say, R 2 may be used to determine or predict the marker or allele effect, which anticipates how much a trait of interest, is affected by the presence of specific alleles.
  • the R-square value may be applied to a set of markers having a p-value of less than 0.01.
  • the R-square cut-off value may be about more than about 0.01, or more than about 0.02, or more than about 0.03, or more than about 0.04, or more than about 0.05, or more than about 0.06, or more than about 0.07, or more than about 0.08, or more than about 0.09, or more than about 0.1 , or more than about 0.15, or more than about 0.2.
  • an R-square cut-off value for a set of markers having a p-value of less than 0.01, there is described an R-square cut-off value of more than about 0.1.
  • the R-square shows how much an allele or combination of alleles can explain the variances in a trait as compared to the total variance of that trait in the sample set. For example, if the R-square is 0.8 for a set of markers, that set of markers can predict 80% of the trait of interest. It also means that there additional unknown or undiscovered markers that effect the traits by 20%. In other words, the R-square value may be seen as an index of the ability of one, two, three, four or more markers to predict a phenotypic trait accurately.
  • the effect size however relates to the magnitude at which the allele impacts the value of the phenotype. For example, if the effect size for fruit weight is 50, then that allele influences the trait by 50kg. In other words, the effect size may be seen as an index of the quantitative changes that the allele controls.
  • the description further provides a method, wherein the polymorphic markers comprising a coefficient of determination having a value of less than 0.1 and wherein the polymorphic markers with the most significant negative effect are those with an estimate effect size of ⁇ -10 or ⁇ -15 or ⁇ -20 or ⁇ -30, when tested a posteriori.
  • the method allows anticipating the phenotype of interest without having to wait until the traits is measurable.
  • the inventors are able to save time and costs.
  • a method wherein the phenotype predicted is yield. Other phenotype of interest may be predicted. Some examples have been described herein.
  • a method wherein the plant is oil palm and the phenotype predicted is fresh fruit bunch yield, bunch number or a combination thereof.
  • the plant phenotype of interest is a yield component selected from the group consisting of fresh fruit bunch weight, bunch size, fruit size, oil extraction rate, bunch numbers and combinations thereof.
  • the phenotype of interest is associated with fresh fruit bunch weight or bunch number yield.
  • the present disclosure may also provide other yield component comprising, but not limited to, increased biomass (weight) of one or more parts of a plant, particularly above- ground (harvestable) parts, increased root biomass or increased biomass of any other harvestable part; increased total seed yield, which includes an increase in seed biomass (seed weight) and which may be an increase in the seed weight per plant or on an individual seed basis; increased number of flowers ("florets") per panicle; increased number of (filled) seeds; increased seed size, which may also influence the composition of seeds; increased seed volume, which may also influence the composition of seeds (including oil, protein and carbohydrate total content and composition); increased individual seed area; increased individual seed length and/or width; increased harvest index, which is expressed as a ratio of the yield of harvestable parts, such as seeds, over the total biomass; and increased thousand kernel weight (TKW), which is extrapolated from the number of filled seeds counted and their total weight.
  • An increased TKW may result from an increased seed size and/ or seed weight.
  • the presence of one or more polymorphic markers is associated with a fresh fruit bunch yield (FFB) phenotype, wherein the markers include, but are not limited to, SEQ ID NOs: 1 to 79.
  • the method comprises detecting the presence or absence of two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, the or more, or eleven or more or all polymorphic genetic marker(s) including, but not limited to SEQ ID NOs. 1 to 79.
  • the presence of one or more polymorphic markers is associated with a bunch number yield (BNO) phenotype
  • the markers include, but are not limited to, SEQ ID NO:8, SEQ ID NO: 27, SEQ ID NO: 48, SEQ ID NO: 53, SEQ ID NO: 56, SEQ ID NO: 57, SEQ ID NO: 58, SEQ ID NO: 62, SEQ ID NO: 80, SEQ ID NO: 81, SEQ ID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89, SEQ ID NO: 90, SEQ ID NO: 91 , SEQ ID NO: 92, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 8
  • the method as described herein provides SNPs associated with an increase/decrease in fresh fruit bunch yield, whereby the SNPs are one, or two, or more genetic markers including, but not limited to, SEQ ID NO: 1, SEQ ID NO: 3, SEQ ID NO: 6, SEQ ID NO:
  • SEQ ID NO: 7 SEQ ID NO: 8, SEQ ID NO: 10, SEQ ID NO: 12, SEQ ID NO: 13, SEQ ID NO: 14, SEQ ID NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21 , SEQ ID NO: 23, SEQ ID NO: 26, SEQ ID NO: 27, SEQ ID NO: 30, SEQ ID NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO: 41, SEQ ID NO: 43, SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ ID NO: 48,
  • the method as described herein provides SNPs, wherein the presence of said SNPs is associated with an increase or decrease in fresh fruit bunch yield and wherein the SNPs include, but are not limited to, Marker Nos. 1 (SEQ ID NO: 10), 2 (SEQ ID NO: 20), 3 (SEQ ID NO: 13), 4 (SEQ ID NO: 8), 5 (SEQ ID NO: 19), 6 (SEQ ID NO: 34), and combinations thereof.
  • the presence of at least one, at least two, at least three, at least four, at least five, or all markers including, but not limited to, Marker no. 1 (SEQ ID NO: 10), Marker no. 2 (SEQ ID NO: 20), Marker no.
  • the SNPs as previously mentioned are described, wherein the SNPs associated with an increase or decrease in bunch number (BNO) yield are Marker Nos. 7 (SEQ ID NO: 90), 8 (SEQ ID NO: 105), 9 (SEQ ID NO: 106), 10 (SEQ ID NO: 99), 1 1 (SEQ ID NO: 80) and combinations thereof.
  • the SNPs are one or two or more genetic markers including, but not limited to, SEQ ID NO: 48, SEQ ID NO: 53, SEQ ID NO: 58, SEQ ID NO: 80, SEQ ID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 86, SEQ ID NO: 90, SEQ ID NO: 92, SEQ ID NO: 94, SEQ ID NO: 96, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 105, SEQ ID NO: 106 and SEQ ID NO: 113.
  • the SNPs are one, or two ,or more genetic markers including, but not limited to SEQ ID NO: 80, SEQ ID NO: 90, SEQ ID NO:99, SEQ ID NO: 105 and SEQ ID NO: 106.
  • the method as described herein provides that the presence of a combination of Markers no. 8 (SEQ ID NO: 105) and 11 (SEQ ID NO: 80) is associated with a change in bunch number yield.
  • the presence of a combination of Markers no. 7 (SEQ ID NO: 90), 9 (SEQ ID NO: 106) and 10 (SEQ ID NO: 99) is associated with a change in bunch number yield.
  • the presence of Marker no. 11 (SEQ ID NO: 80) is associated with a change in bunch number yield. Examples of marker usage can be seen in Tables 10 and 12 of the present description.
  • Non-limiting examples of the invention including the best mode, and a comparative example will be further described in greater detail by reference to specific Examples, which should not be construed as in any way limiting the scope of the invention.
  • the plant materials for use in the 'fresh fruit bunch yield' and 'bunch number' marker identification methods were derived from three populations of oil palms, each population containing crosses of Deli, Nigerian and AVROS backgrounds and therefore having a mixture of different genetic backgrounds.
  • the combination of alleles found in these three populations is a representation of the alleles present in commercial oil palm genetic materials.
  • the commercial materials sold and planted in Malaysia are a combination of different crosses of breeding populations of restricted origins (BPROs).
  • BPROs breeding populations of restricted origins
  • the total parental BPROs used to generate the populations for the association mapping covers all the commonly found genetic backgrounds that are introgressed by plant breeders to create the commercial oil palm that is planted in Malaysia and Indonesia. The palms were individually yield recorded over a specified period of time.
  • spear leaves which are the first unopened young leaves from the highest frond, were obtained from the oil palm trees.
  • the leaves were cleaned and frozen in liquid nitrogen before being placed in a freezer (-80°C) for storage.
  • a leaf sample weighing approximately 3-5 grams was removed from storage, treated with liquid nitrogen and ground to a powder in a mortar and pestle. The ground leaves were then transferred into a tube, into which 15 ml of modified cetyl trimethyl ammonium bromide
  • CTAB computed tomamic acid sodium salt
  • the CTAB buffer contained 2% CTAB (weight/volume), 20 nM EDTA (pH 8.0), 1.4 M NaCl, 100 mM Tris-HCL (pH 8.0), 5 mM ascorbic acid, 4 mM diethyldithiocarbamic acid sodium salt and 2% polyvinylpyrolidone-40 and 100 ⁇ of ⁇ - mercaptoethanol.
  • the mixture was incubated at 60°C for 30 minutes, after which an equal volume of chloroform: isoamyl alcohol was added before mixing thoroughly.
  • the mixture was centrifuged at 10,000 rpm for 15 minutes and the upper aqueous phase was transferred into a fresh tube.
  • DNA was then precipitated with the addition of 0.6x volume of ice-cold isopropanol.
  • the solution was then stored in -20°C for a few hours and then centrifuged at 12,000 rpm for 15 minutes.
  • the DNA pellet seen at the bottom of the tube, was washed in 5 ml wash buffer containing 70% ethanol and 10 mM ammonium acetate.
  • the sample was then centrifuged to precipitate the DNA, dried and dissolved in Tris-EDTA (TE) buffer containing 10 niM Tris-HCl (pH 8.0) and 1 mM EDTA (pH 8.0).
  • TE Tris-EDTA
  • pH 8.0 10 niM Tris-HCl
  • 1 mM EDTA pH 8.0
  • the extracted DNA was then sequenced using the Illumina Genome Analyser as per manufacturer's instruction.
  • the data was analysed using bioinformatics tools such as but not limited to Burrows -Wheeler Aligener and SAMTOOLS to detect possible SNPs.
  • a list of SNPs was sent to Illumina, Inc., and 1,208 SNP markers were arrayed onto a GoldenGateTM assay format.
  • Illumina' s method was used to perform the GoldenGateTM analysis using the DNA of the 269 palms, from three populations as defined above with 96 palms from a first population, 96 from a second population and 77 from a third population.
  • the SNP markers identified above were then statistically tested to identify if they are linked to phenotype data and by extension, able to predict phenotype performance and differentiate genotypes that influence traits and those that do not. Statistical linear models were used to assess if there is a significant link between the genotype and the phenotype tested.
  • the genotype data were obtained from the GoldenGateTM analysis described above and the phenotype data were collected over a period of 54 months to increase the accuracy of the data.
  • the phenotypes used in this analysis were fresh fruit bunch yield and bunch number.
  • the analysis was conducted using the general linear model (GLM) to identify SNP genetic markers that were linked to two phenotypes, fresh fruit bunch yield data and bunch number.
  • the mixed linear model (MLM) was used together with the GLM model.
  • SNP markers significantly associated with yield traits were shortlisted using general linear model (GLM) and mixed model association (MLM) analysis with TASSEL (Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES.
  • the parameter used to ran STRUCTURE was 100,000 repetitions for both the BURN-IN and MCMC parameters.
  • the K value was determined using the analysis proposed by Evanno et al. (Evanno, G., Regnaut, S., Goudet, J.; Molecular Ecology; 2005; 14: 2611-2620). P-values for the markers were obtained and studied. A lower the p-value represents a higher chance that the marker is linked to the traits of interest.
  • a polymorphic site with a p-value below the 5% or 1% empirically derived value is considered to have a statistically significant association to the trait (Greenhalgh T; BMJ 315; 1997; 540-543.; Tommasini L., Schnurbusch T., Fossati D., Mascher F., Keller B. , Theor. Appl. Genet.;2007; 115: 697-708.).
  • the cut-off point was determined to be 0.01 and the best few markers with the acceptable p-value were selected for further analysis.
  • the genotype of the plant was compared with the trait of interest. Effect estimate was calculated to determine the effect of that allele either positively or negatively associated to the trait of interest.
  • the shortlisted markers from performing the association mapping step were validated on oil palms from commercially available planting materials available at oil palm plantations.
  • the samples were genotyped using the "OpenArray” platform and analysed using the association mapping methods described above.
  • the results of the analysis provided a list of markers, the different alleles that the marker can detect, and a measure of how accurately the marker can be used to predict a trait of interest (p-value), how strongly they affect the trait that they are linked to (allele effect), and how strongly the marker fits into a mathematical model to explain the variation that the marker causes to the overall trait that is tested (r-squared).
  • Markers were shortlisted based on a cut-off p-value of ⁇ 0.01, R-squared (R 2 ) cut-off value of ⁇ 0.1 and their predicted allele effect which predicts how much the oil palm is affected by the presence of specific alleles.
  • the r-squared value can be derived using standard statistical methods and the closer the value is to 1 , the stronger influence the marker has on the overall total effect of the trait tested.
  • the allele effect predicts whether that particular combination of allele will give a positive or negative change to the tested plant as compared to an average oil palm. Larger positive and larger negative values will be more advantageous for screening and selecting oil palms that have the desired allele and lack the unwanted alleles.
  • the selected oil palms should only contain alleles predicted to give positive effects. Care should be taken to avoid oil palms carrying alleles predicted to have a negative impact on the trait tested, but in practice, this may be difficult as such an ideal genotype combination will be very rare.
  • allelic combinations that gave the best predicted value for allele effect were selected, and those that were predicted to give the strongest negative influence on the trait tested were removed. The data from the genotyping, combined with the prediction of the association mapping together with the phenotype data were analysed to check if the predictions of the shortlisted markers were accurate and if oil palm phenotype performance can be predicted based on the genotype.
  • the cut-off value used in this example was a p-value ⁇ 0.01, in order to produce a list containing 79 shortlisted markers that are putatively related to fresh fruit bunch (FFB) yield trait.
  • the 79 shortlisted markers predicted to be associated to the FFB trait together with the GLM and MLM predicted value for the p-value are listed in Table 2 below.
  • the 79 markers were validated on a population of 225 palms which were planted as three separate experimental blocks consisting of 75 oil palms per block. As these three blocks of planting materials were from different origins, these three blocks would not be considered as a replication, but as a representative of the diversity of different oil palm populations that are indicative of the diversity found in commercial materials in the oil palm industry.
  • Genotyping was conducted so that the allelic content of the individual oil palms were identified. Phenotype data for these palms were collected. The genotyping was carried out using the 79 markers that have been identified as listed in Table 2 above, and the various alleles that are linked to the markers were identified on an individual tree basis. As multiple alleles may be possible for each of the 79 markers, a cut-off was applied based on the R-squared (R 2 ) value. All alleles that gave an R value of O. l were removed in order to reduce the complexity of the data and simplify the analysis. The R 2 value is a description of how much a particular allele explains how well the data points fits the statistical model used to predict the variations in the trait or phenotype being determined.
  • R 2 value an allele which has a low R 2 value will not be particularly accurate and thus, not useful for the purpose stated herein.
  • R 2 value it is known that different values of the R 2 value can be accepted, depending on the how many markers is feasible for the selection of the trait or how much accuracy is required for the prediction.
  • the R 2 value was determined and applied on an individual population basis, where the markers will result in different R 2 values when tested in the different populations. This is because some markers are better predictors for traits in certain populations and some markers functions well across populations. By determining the R 2 value of markers based on population markers for population-specific traits, markers for universal traits can be identified, allowing for greater freedom in determining which combination of markers should be used to predict traits in a given population.
  • the genotype of the plants from the association mapping sample set can be either homozygous positive, homozygous negative or heterozygous alleles. These data were obtained from analysing the different alleles present in the population. The data were used to predict FFB yield performance.
  • yield is a quantitative trait that is affected by the interaction and integration of many component traits (multiple genes) that can interact with variations in environmental conditions such as rainfall, terrain, soil type, temperature and solar radiation.
  • component traits multiple genes
  • multiple markers were used. Markers with allele effects that are considered minor were also included as a combination of these 'minor' variants can be used to predict a significant change to the expected value.
  • the identified markers were then used to screen oil palm populations for plants that are predicted to be high yielding. At least one, at least two, at least three or more markers were selected that indicated a positive/negative allele effect. The effects are more enhanced with more markers and the number of markers used will depend on the sum total of the R-squared value.
  • the cut-off point may be selected by taking into consideration the cost linked to the number of markers used as opposed to the accuracy of using a larger number of markers. In other words, in case a larger number of markers, the more accurate the prediction, but the higher the cost of running the experiment.
  • the sum value of the R-square indicate how much of the variation can be explained by the allele combination in a regression model. For example, if the combination of markers gives a sum R-square of 1 then the combination of markers will accurately predict the phenotype change. If the sum R-square is 0.3 for example, that means the marker can predict with 30% accuracy based on the regression model for that trait.
  • the markers with the highest R-square value such a R-square value of 1 , or a R- square value of 0.9, or a R-square value of 0.8, or a R-square value of 0.7, or a R-square value of 0.6, or a R-square value of 0.5, or a R-square value of 0.4, or a R-square value of 0.3, or a R- square value of 0.2, or a R-square value of 0.1 may be selected with the highest marker effects and recursively additional markers may be selected until the sum R-squared reaches an accuracy of prediction that is reasonable and reproducible. This however is subjective as it depends on the practicality of the measurement.
  • the range of effect size is very small, such as for example, the difference between 100kg and 110kg (in other words 10kg is an effect size of effect size), then it would not be desirable to use many markers as accuracy would not enable the selection of plants, such as palms that have significant increase/decrease to the trait of interest.
  • the range of variation is very large (e.g. the difference between 50kg - 150kg), then it would be desirable to select more markers so that the plants with the most significant trait increase or decrease will be selected. At least one, at least two, at least three, at least four or more markers that indicated a positive allele effect were selected. Markers were recursively added in to balance the highest possible R-squared value with the lowest possible markers.
  • the population tested was screened for plants that carry the allele linked to good allele effects, and plants that carry that allele were selected.
  • six markers selected as shown in Table 4, which also shows the different alleles associated with the marker and the estimated GLM value for each of the allele observed.
  • markers were selected as best examples and were used to genotype a total of 225 oil palms of various backgrounds comprising of mixes of Deli, AVROS and Nigerian pedigree. These plants were individually yield recorded for 54 months and the average FFB yield of each plant was calculated. The allele in question for each of the six markers is listed in Table 5. Table 5: Genotyping results for the six markers for the validation population of 225 palms.
  • T:T - k A. T:T G:G G:G C:T 232.92
  • Plants were selected based on the allele criteria of the markers in Table 4 above, and the FFB average of the selected plants were recalculated to determine if there is an improvement over the average FFB for the total unselected population.
  • the average FFB of the 225 palms that were unselected was 233.8 kg/palm/year.
  • palms were selected based on Marker 1 by choosing palms that contain the positive effect allele combination C:C and discarding palms that have negative allelic effect T:T and zero allelic effect combination C:T then 45 palms will be selected from the 225 palms and the average yield would be 255.77 kg/palm/year which is an improvement of FFB by 21.97 kg/palm/year.
  • palms were selected based on Marker 2 by choosing palms that contain the positive effect allele combination A:T or A: A then 161 palms will be selected from the 225 palms and the average yield will be 239.66 kg/palm/year which is an improvement by 5.86 kg/palm/year.
  • palms containing these allele combinations will be rejected for selection.
  • An example is the use of Marker 3 which contains negative effect allele combination T:T and A:T where 208 palms will be rejected and 17 palms will be selected from the 225 palms and the average yield will be 271.58 kg/palm/year which is an improvement by 37.7 kg/palm/year.
  • palms were selected based on Marker 4 which contains the positive effect allele combination A:G and discarding palms that contains the negative effect allele combination G:G then 67 palms will be selected from the 225 palms and the average yield will be 249.71 kg/palm/year which is an improvement by 15.91 kg/palm/year.
  • palms were selected based on Marker 5 which contains the positive effect allele combination A:A and discarding palms that contains the negative effect allele combination G:G and the neutral effect allele combination A:G then 45 palms will be selected from the 225 palms and the average yield will be 252.91 kg/palm/year which is an improvement by 19.1 1 kg/palm/year.
  • the selection of palms can be done using either a single marker or a plurality of markers.
  • Table 6 shows the yield gain for FFB if the validation population of 225 palms were selected using a plurality of markers.
  • oil palm trait prediction can be made by searching for plants that carry alleles that are predicted to be beneficial to the trait (positive allele effect) being tested, and avoiding alleles with detrimental effect on the traits tested (negative allele effect).
  • the markers were then tested to identify markers that have an R 2 >0.1 value as shown in Table 8.
  • the validation was performed using the steps described above, with the exception that the phenotype tested for was bunch number (BNO), and the results are set out in Table 9 below.
  • the five markers lists allele combinations that have an effect for the bunch number trait and can be used by themselves or in combination for selection of plants with predicted bunch number that is higher than average or lower than average.
  • the markers are named Marker 7, 8, 9, 10 and 1 1.
  • the genotypmg results of the 225 palms for Markers 7, 8, 9, 10 and 11 are as in Table 11 and validation of the ability to select are as in Table 12.
  • Table 11 Genotyping results for the six markers for the validation population of 225 palms.
  • Table 12 shows that the markers identified to be linked to bunch number can be used to select plants containing the best allelic combination statistically linked to either higher bunch number or lower bunch number, as compared to the mean of the unselected population. This example serves to demonstrate that the method disclosed here can be used to associate different phenotypes to genotypes and therefore detect beneficial or desired allelic combinations.

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Abstract

L'invention concerne des méthodes d'identification de marqueurs génétiques polymorphes d'une espèce végétale associée à un phénotype d'intérêt. Des marqueurs polymorphes d'intérêt déterminés par mappage associatif, et des méthodes de prédiction de phénotypes végétaux sont en outre déterminés.
PCT/MY2015/050034 2014-05-14 2015-05-14 Procédé de prédiction ou de détermination de phénotypes végétaux dans des palmiers à huile Ceased WO2015174825A1 (fr)

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WO2017069607A1 (fr) * 2015-10-23 2017-04-27 Sime Darby Plantation Sdn. Bhd. Procédés de prédiction du rendement en huile de palme d'un plant de palmier à huile d'essai
WO2017116224A1 (fr) * 2015-12-30 2017-07-06 Sime Darby Plantation Sdn. Bhd. Procédés de prédiction du rendement en huile de palme d'un plant de palmier à huile d'essai

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Publication number Priority date Publication date Assignee Title
WO2016133380A1 (fr) * 2015-02-18 2016-08-25 Sime Darby Malaysia Berhad Procédés et nécessaires de détection de polymorphisme mononucléotidique (snp) pour prédire le rendement d'huile de palme d'un plant de palmiers à huile d'essai
WO2017069607A1 (fr) * 2015-10-23 2017-04-27 Sime Darby Plantation Sdn. Bhd. Procédés de prédiction du rendement en huile de palme d'un plant de palmier à huile d'essai
WO2017116224A1 (fr) * 2015-12-30 2017-07-06 Sime Darby Plantation Sdn. Bhd. Procédés de prédiction du rendement en huile de palme d'un plant de palmier à huile d'essai

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