TW201606084A - Method of predicting or determining plant phenotypes - Google Patents

Abstract

Methods of identifying polymorphic genetic markers of a plant species linked to a phenotype of interest are described. Polymorphic markers of interest determined by association mapping, and methods of predicting plant phenotypes are further determined.

Description

Method for predicting or determining crop phenotypes

The invention is generally related to the field of molecular biology. In particular, it relates to crop genes for detecting and utilizing single nucleotide polymorphism to screen oil palm.

The oil palm ( Elaeis guineensis ) industry in Malaysia and Indonesia was originally established using Deli duras as a commercial planting crop. Deli duras was bred by four oil palms planted in Bogor Botanic Garden in 1848. At first, as a boulevard, the oil palm planted with palm trees was established as a commercial crop in 1917 at the Tennamaran Estate's first oil palm garden in Selangor. Malaysia's oil palm industry has developed rapidly over the past 50 years, and the oil palm planting area has grown from 54,000 hectares in 1960 to 5 million hectares in 2010.

Since the hereditary properties of shell thickness were revealed in 1941 (Beirnaert and Vanderweyen, Congo Belge, Ser. Sci, 27:1-101, 1941), commercial oil palm planting crops were gradually transferred from duras to dura x pisifera (or DxP hybrid). Or teneras . Compared to dura , DxP has a higher ratio of mesocarp to fruit (60% to over 80%), resulting in an increase in oil production of about 30%.

In order to produce high-yield DxP plants, the oil palm breeders modified dura and pisifera , respectively, and selected dura and pisifera palms to produce seeds. For example, in the example of dura , a dur with a desirable trait is selected and used to generate a dura x dura (or DxD) hybrid. The resulting seeds are incubated in the nursery for one year and then moved to the field for planting. Depending on the location of the plant, these palms will result in two and a half to three years. These palms were then recorded for 5 years to determine their yield potential for another round of selection and improvement.

In pisifera improvement plan, the selected pisifera with tenera hybrid selected to produce tenera x pisifera (or TxP) hybridization. Or the selected tenera can be used to produce pisifera in the tenera x tenera (or TxT) cross. Seeds were bred in nurseries like dura and then moved to the field. The only difference is that pisifera is female infertility or underdeveloped, so production records cannot be made. Production records can only be made in dura or tenera compatriots.

After years of oil palm cultivation, the yield potential of oil palm planting crops has been improved. For example, Lee et al., Proc. Workshop 'Progress of oil palm breeding populations' (1990) pp 81-89. found that the dura ethnic group selected after 4 generations was compared to the unselected species . The dura palm has a significant increase in oil production (5.0 tons of oil per hectare per year vs 3.1 tons of oil per hectare per year, which represents an increase of 61.3% in production). Similarly, the oil palm yield of DxP plants has been significantly improved over the years, as shown in the following table: Table 1: Yield performance of planting crops in Malaysia Source: Jalani et al Paper Presented at 2001 Int. Palm Oil Congr. Malaysia (2001)

The highest commercial yield reported to date is 32.15 hectares with 47.65 tons of FFB/ha/year fresh fruit bunch (FFB) production (Pang Oil Palm: Sabah’s Future, Paper Presented in PIPOC (2005)). Laboratory analysis of fruit bunches harvested from commercial fields of these planted crops was reported to be above 30% oil/fruit bunch, although the highest extraction volume of the oil plant was 25.01%.

Although the production of oil palm has improved since the first oil palm plant was established, its ongoing efforts still face many challenges.

The cultivation of most oil palms in Malaysia is based on the selection of the phenotypes in the field and the selection of the best oil palm for breeding based on the traits, performance and combination of appearance. This method has several limitations: (a) linkage drag due to the combination of desirable genes and undesired phenotypes; (b) the time required to improve oil palms is very long, usually in terms of traditional cultivation. It takes at least 12 years to select and hybridize to the final production record; (c) in the case of complex traits, such as the choice of production, when there may be interactions between different components of production, the net effect of the selection is accurately estimated. Very difficult, for example, an increase in one component may have a negative effect on the other.

In addition, the heterozygous degree of maternal lineage used to produce DxP planting crops can result in changes in individual palm yields. Improved yields of about 15% or more from elite oil palm strains are expected.

In an attempt to address the problems encountered in traditional cultivation and selection methods, and to further improve the yield of superior palm, molecular-based methods have been used to accelerate the development of excellent oil palm planting crops. For example, molecular marker-assisted selection of specific gene sequences that are consistently associated with a particular feature (eg, high FFB production) has been identified using genetic material (based on field data available from a particular progeny). These gene sequences can then be used as molecular markers for, for example, selecting high FFB production even at the nursery stage, thereby reducing the cycle of the incubation process from 12 years to 6 years.

Therefore, there is a genetic analysis based on genetic markers associated with desirable phenotypes, using it to predict crop phenotypes and the need for genetic markers that speed up crop progeny selection procedures, resulting in greater time and expense. The way to promote the crop cultivation process.

In a first aspect, the invention relates to a method of predicting or determining a phenotype of a crop of interest, wherein the method comprises detecting one selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 115 Or the presence or absence of multiple polymorphic gene markers.

In a second aspect, the invention relates to the use of one or more polymorphic gene markers according to any of the foregoing claims as a criterion for crop selection, wherein the selection is carried out by one or more polymorphic gene markers No basis.

definition

The following words and terms used herein have the meanings specified:

The term "polymorphism" as used herein refers to a variation or difference in the sequence of a gene region produced in certain members of a species. Different sequences can be defined by reference to an arbitrary or non-arbitrary standard sequence of the species. Therefore, polymorphism is called "allelic", which means that certain members of a species may have a "standard" sequence (that is, a standard "dual gene") due to the existence of polymorphism. Other members may have a "mutation" sequence (that is, a variant "dual gene"). Thus, as used herein, a dual gene is one of two or more versions of a gene to choose from, or other genetic regions at a particular location on a chromosome. In the simplest case, there can be only one variant sequence, and its polymorphism is therefore called double pair. In other cases, the ethnic group can contain multiple dual genes, and its polymorphism is called three pairs... and so on.

A gene or gene region can have multiple, unrelated polymorphisms. For example, it may have one double dual polymorphism at one location, another dual dual polymorphism at another location, and one polydox polymorphism at yet another location. When the sequence of the dual gene group in the chromosomal locus in the crop is all the same, the dual gene is referred to as "homotype" at the locus. When any of the dual gene sequences in a particular locus in a crop is different, the dual gene group is referred to as a "heterotypic junction" at the locus.

Phenotypic traits may vary depending on environmental and/or genetic factors. For example, polymorphism at a particular chromosomal locus can affect phenotypic traits associated with that locus.

The term "phenotypic trait" as used herein relates to any feature or trait that can be inherited by a crop, whether naturally occurring or otherwise. Further, for example, the trait of interest may be transient, permanent, or only present when the crop or portion thereof is subjected to environmental stimuli or challenges. The phenotypic trait of interest can be a desirable or positive trait. In other situations, the phenotypic trait of interest may be an undesirable or negative trait.

Many traits of interest in agronomy and horticulture can be controlled by a single gene and belong to a few distinct phenotypes. These phenotypic categories are called discontinuous traits and can be used to predict an individual's genotype.

Other traits are not of the separation type. Specifically, when the isolated population is analyzed by these traits, a continuous distribution is found. For example, if a trait does not belong to a small distribution, but rather a bell-shaped curve that exhibits a normal distribution, this trait type is called a continuous trait and cannot be analyzed in the same manner as the discontinuous trait. Because continuous traits are usually given a numerical value, they are often referred to as quantitative traits. In addition, loci controlling these traits are referred to as quantitative trait loci or QTLs.

Furthermore, phenotypic traits are not limited to visible traits. Although the phenotypic trait can be any trait, the preferred trait of interest is of agronomic importance. Examples of agronomic traits include elements that provide effect yields of disease or chemoresistance, as well as developmental traits such as pollen or ovules, as well as components or crop components that affect crops or crops, including seeds, proteins or oils, starch or sugar, and nutrient composition. The composition of similar things. In still further examples, traits include, but are not limited to, traits that affect factors such as yield of FFB production, number of strands, trunk size, or combinations thereof. In another example, the constituent elements of the yield include, but are not limited to, the weight of the fresh fruit bunch, the size of the string, the size of the fruit, the extraction ratio of the oil, the number of strings, or a combination thereof.

Examples of methods for predicting or determining a genotype of a crop of interest include, but are not limited to, the steps of initially obtaining a sample of a crop, seed or sapling to be analyzed. Crop genetic analysis will show where there are single nucleotide polymorphisms (SNPs). Next, the results of this type of genetic analysis or gene mapping of this sample are compared to a list of known SNPs, which have been linked by one or more characteristics of the crop, such as crop height, fruit yield and many more. Therefore, the genetic analysis of the sample compared with the SNPs list can infer the statistical probability that the tested crops will exhibit desirable characteristics. In order for this method to be feasible, at least one SNP must be present in the sample.

As explained above, many phenotypic traits are the result of multiple genes or genetic factors, for example, phenotypic traits caused by quantitative traits of the dual gene. A dual gene of a quantitative trait locus can include multiple genes or other genetic factors even in a continuous gene region or linkage group.

"Locus" means the specific chromosomal location of a particular gene that can be found on the genome of a species. As used herein, "quantitative trait loci (QTL)" refers to a DNA region comprising a gene that constitutes a quantitative trait or a gene that binds to a quantitative trait. Localization of a region comprising a gene involved in a gene of a particular quantitative trait is performed using molecular tags such as AFLP or more common SNPs. This is an initial step in the identification and sequencing of true genes that constitute trait changes. A quantitative trait means a change in the degree and a phenotype (characteristic) attributable to a multi-gene effect, such as the product of two or more genes and their environment. Thus it can be defined as a phenotype that affects gene changes in continuously varying traits.

As used herein, "QTL analysis" is a statistical method that links two types of information, phenotypic data (trait measurement) and genotype data (usually molecular markers), with the intent to clarify the inheritance of mutations in complex traits. basis. Therefore, QTL analysis links certain complex phenotypes to specific regions of the chromosome. The goal of this program is to identify the behavior, interactions, numbers, and precise locations of these areas.

As used herein, "allele of a quantitative trait locus" encompasses more than one gene or genetic factor, each individual gene or genetic component exhibits a dual change, and each gene or Genetic factors have a phenotypic effect on the quantitative traits in question.

The term "chromosome segment" is defined as the linear range adjacent to the genomic DNA present in a crop on a single chromosome. A genetic element or gene located on a single chromosome fragment is physically linked. In the context of the present invention, genetic elements located within a chromosomal fragment are also genetically linked, typically at a distance of less than or equal to 10 points of gene recombination. In other words, at meiosis, two genetic elements in a single chromosome fragment undergo recombination with each other at a frequency of less than or equal to about 10%.

A "marker" as used herein is an indicator of the presence of at least one polymorphism. The label is preferably a nucleic acid molecule. It is understood that the label can be, for example, an oligonucleic acid probe or primer. Molecular markers are preferred for genotyping because they do not affect the trait of interest. In some instances, markers include, but are not limited to, SNPs, simple sequence repeats (SSRs or microsatellites), AFLPs, and random amplification of polymorphic DNAs (RAPDs). Restriction fragment length polymorphisms (RFLPs), index bit positions, and combinations thereof.

The term "single nucleotide polymorphisms (SNPs)" as used herein is defined as a variation of a DNA sequence that occurs in a genomic sequence between members of a biological species or between human pairs of chromosomes (A) , T, C, and G) change or be different. Thus, the SNPs used herein are of any polymorphism whose polymorphism is characterized by a different single nucleotide in a particular physical location of at least one of the dual genes. Each individual in a given group has many SNPs that together create an individual's unique DNA configuration. As used herein, "simple sequence repeats (SSRs)", also referred to as microsatellites or short tandem repeats (STRs), are repeats of base pairs of 2 to 6 DNAs. It is a type of variable number tandem repeat (VNTR). SSRs are usually codominant.

As used herein, the term "amplified fragment length polymorphisms (AFLPs)" differs in the length of restriction fragments caused by SNPs or INDELs that create or eliminate the recognition site for restriction endonucleases. The AFPL technique is based on the selection of polymerase chain reaction (PCR) amplification based on restriction fragments of complete digestion of genomic DNA.

The term "random amplified polymorphic DNA (RAPD)" as used herein is a DNA fragment amplified from a PCR fragment of a random fragment of a single primer gene DNA of any nucleotide sequence. Unlike traditional PCR analysis, RAPD does not require any specific knowledge of the target organism's DNA sequence: the same 10 unit primer will or will not augment the DNA fragment, depending on the complementary position of the primer sequence. For example, if the primers are too open or the 3' ends of the primers do not face each other, no fragments will be generated. Therefore, if the template DNA is mutated at a point complementary to the primer, the PCR product will not be produced, resulting in a different configuration of the amplified DNA fragment on the colloid. RAPD is advantageous in the presence of RAPD bands that do not allow for the differentiation of heterotypic and isomeric states.

The term "restriction fragment length polymorphism (RFLP)" as used herein relates to a restriction fragment length caused by SNPs or INDELs that create or eliminate the recognition position of a restriction endonuclease. RFLP analysis is performed by hybridizing a chemically tagged DNA probe to a Southern blot method for digesting DNA by restriction endonucleases.

The term "nucleic acid marker" as used herein means a nucleic acid molecule capable of detecting a polymorphic marker.

As used herein, the terms "associated with" or "associated" when used in the context of the present invention for nucleic acids (eg, genetic markers) and phenotypic traits, mean nucleic acids and expressions that are in linkage phase imbalance. Type traits. The term "linkage phase disequilibrium" or "linkage disequilibrium" means a non-random separation of genetic loci. It means that these loci along the chromosome segment are sufficiently physically close that they tend to separate together at a frequency greater than random.

As used herein, the term "genetically linked" means a genetic locus (including a gene marker locus) that is physically close enough to each other on the same chromosome to have a recombination frequency of less than 0.5. When it refers to the relationship between two genetic elements, such as genetic elements affecting yield and proximal markers, "coupling" is linked to indicate that the "favourable" dual gene at the production locus is physically Sexually linked to the same chromosomal strand as the state of the "favorable" dual gene of each of the linked marker loci.

In the coupled phase, both favorable dual genes are inherited by the progeny that inherit the chromosomal strands. In the "repulsion" phase linkage, the "favorable" dual gene at the production locus is physically linked to the "unfavourable" dual gene at the proximal marker locus, and the two "favorable" dualities Genes are not inherited together (in other words, the two loci are "out of phase" with each other).

As used herein, the term "physically linked" is used to mean that two genetic loci (eg, two marker loci, one marker locus, and one locus that affects phenotypic variation) actually exist in the same On the chromosome. The positions of these two loci are usually close together, so recombination between pairs of homologous chromosomes does not occur at high frequency between the two loci. That is, the frequency of recombination occurring between two physically linked loci is less than about 10%, a suitable frequency is less than 5%, and a more suitable frequency is 2% or less, or 1% or less. Thus, two loci located on the same chromosome with a frequency of recombination less than 10% are referred to as proximal to each other.

The term "genetic map" as used herein describes the genetic linkage between loci on one or more chromosomes (or linkage groups) in a given species, usually in the form of a graph or table. "Mapping" is the process of defining locus linkages through the use of genetic markers, marker population segregation, and standard genetic rules for recombination frequencies. A "map location" is a specified position on a genetic map of a given marker in a given species where a corresponding linked gene marker can be found.

The term "genotype" as used herein is a genetic component of an individual (or group of individuals) at one or more genetic loci. A genotype is defined by a dual gene of one or more known loci of an individual that has been inherited from its parent. "Haplotype" is the genotype of an individual at a plurality of genetic loci. The loci, which are generally described as haplotypes, are physically and genetically linked, in other words, on the same chromosomal fragment.

If there is only one type of dual gene at a given locus, the individual is a "homozygous" (eg, a diploid individual with a two-copy copy of the same dual gene at the locus). If there is more than one type of dual gene at a given locus, the individual is "heterozygous" (eg, a diploid individual with one copy of each of the two different dual genes). The term "homogeneity" refers to a member of a group having the same genotype in one or more particular loci. The term "heterogeneity" refers to an individual of a distinct genotype group at one or more specific loci.

A "line" or "strain" is a group of individuals with the same family, which usually have some degree of inbreeding and are homozygous and homogenous at most loci.

"Elite line" or "elite strain" is an excellent gene system produced by a cycle of many cultivation and selection of excellent agronomic performance. Soybean cultivation is known to those of ordinary skill in the art and many elite systems are available. The "elite population" is a classification of elite elites that represents the state of the art in the agronomically superior genotype of a given crop species (eg, oil palm).

The term "oligonucleotide" as used herein means a short nucleic acid molecule, a nucleotide alignment element or an amplification primer for use in, for example, a hybridization probe. Oligonucleotides are composed of two or more nucleotides, namely deoxyribonucleotides or ribonucleotides, preferably more than 5 and up to 30 or more. The actual size depends on a number of factors, which in turn depend on the end function or the use of the oligonucleotide. An oligonucleotide may comprise a bundle of natural nucleic acid molecules or synthetic nucleic acid molecules and comprises between 10 and 150 nucleotides or between 12 and 100 nucleotides having a nucleoside that can hybridize to a strand of polymorphic DNA. The acid sequence, for example, allows for the detection of many types.

Such oligonucleotides can be nucleic acid elements (e.g., synthesized or labeled) for use on solid arrays. In a preferred aspect of the invention, the oligonucleotide comprises as few as 12 hybrid nucleotides, such as an array in which the oligonucleotide also includes a detectable tag. In another preferred aspect of the invention, the oligonucleotide comprises as few as about 15 hybrid nucleotides, for example, arranged in a single base extension.

Such oligonucleotides can also be used as primers in polymerase chain reaction (PCR) or other reactions. As used herein, the term "primer" means a nucleic acid molecule, especially an oligonucleotide, particularly derived from a naturally occurring molecule, such as from a restricted digestion, or a synthetic producer, when Under the synthetic conditions that induce the primer-derived product complementary to the nucleic acid strand, in other words, in the presence of nucleotides and polymerization factors such as DNA polymerase and suitable temperature and pH, it can be used as a starting point for synthesis. To maximize the efficiency of the amplification, the primers are preferably single stranded, but may alternatively be double stranded. In the case of double strands, the double strands are separated prior to being used to prepare the derivative. The primer is preferably an oligodeoxyribonucleotide. The primer must be long enough to initiate synthesis of the derivative product in the presence of a polymerization factor. The actual length of the primer depends on many factors, including the temperature and the source of the primer. For example, depending on the complexity of the sequence of interest, the oligonucleotide primer typically comprises at least 15, preferably 18 nucleotides, which are identical or complementary to the template; and optionally have no need to match the template. Variable length tail. The length of the tail should not be long enough to prevent the identification of the template. Short primer molecules typically require lower temperatures to form a sufficiently stable hybrid complex with the template.

The primers selected herein are "substantially" complementary to the different strands of each particular sequence to be amplified. This means that the primers must be substantially complementary to hybridize to their respective strands. Therefore, the primer sequence does not need to reflect the specific sequence of the template. For example, a non-complementary nucleotide fragment can be affixed to the 5' end of the primer and the remainder of the primer sequence is complementary to the strand. Alternatively, a non-complementary base or a longer sequence may be interspersed in the primer such that the primer sequence is sufficiently complementary to the sequence of the strand to be amplified to hybridize therewith and thus form a synthesis for other primer-derived products. template. Computer applications like Primer3 (www-genome.wi.mit.edu/cgi-bin/primer/primer3.cgi), STSPipeline (www-genome.wi.mit.edu/cgi-bin/www-STS_Pipeline) or GeneUp A search is generated, for example, to identify potential PCR primers. Exemplary primers include primers of 18 to 50 bases in length that are at least 18 to 25 bases identical or complementary to at least 18 to 25 bases of the fragment of the template sequence.

To detect polymorphism, the present invention also contemplates and provides primer pairs for amplification of nucleic acid molecules. As used herein, "primer pair" means a set of two oligonucleotide primers based on two separate sequence fragments of a target nucleic acid sequence. One of the pair of primers is a "forward primer" or a "5' primer" having the same sequence as the 5' end of the fragment of the isolated sequence (+ strand). The other pair of primer pairs is a "reverse primer" or a "3' primer" having a sequence complementary to the 3' end of the excess isolated sequence fragment (+ strand). The primer pair is capable of amplifying the nucleic acid sequence between the fragments of the isolated sequence and the nucleic acid sequence thereof. Alternatively, each primer pair can include additional sequences at the 5' end of each primer (eg, a general primer sequence or a restriction endonuclease site), for example, to facilitate cloning, DNA sequence or re-amplification of the target nucleic acid sequence.

The term "cloning" in the context of oil palm refers to the procedure for the selection of the same copy of the palm (original strain), the copy of which is replicated by the growing seedlings of the leaf tissue of the tenera oil palm with the desired characteristics. Come.

As used herein, "mapping population" refers to a collection of crops that can be used with markers to locate the location of a trait gene.

As used herein, "polymorphic marker" refers to a marker that is capable of detecting one or more polymorphisms.

The invention provides nucleic acid molecules that are labeled, that is, capable of detecting polymorphisms that are interspersed throughout the entire population of the targeted population.

As used herein, "characterized polymorphism" refers to polymorphism in which the actual location of a genome is known. In one example, the actual location of the qualitative polymorphism on an isolated nucleic acid molecule, such as a bacterial artificial chromosome comprising oil palm genomic DNA, is known. The invention therefore also provides nucleic acid molecules capable of detecting qualitative polymorphisms throughout the genome.

In one example, the qualitative polymorphism is that any of the two polymorphic nucleic acid sequences present in the oil palm locating population is any polymorphism of known (sequencing qualitative polymorphism).

The polymorphisms that can be detected by the nucleic acid molecules of the present invention are such that the regions of the genes associated with the phenotypic traits are dispersed to the entire genome of the localized population in a manner that is efficiently recognized to some extent. In one example, polymorphism has more than one polymorphism per 100 kb, more preferably more than one polymorphism per 50 kb, even more than one per 25 kb, 10 kb, 7 kb, 5 kb or 3 kb. The density of polymorphism is spread throughout the genome, with 60%, more preferably 70%, even better 80%, or even 90, 95 or 100% of the genome having qualitative polymorphism. In another case, polymorphism has more than one polymorphism per 3.5 cM, more preferably more than one polymorphism per 3.25 cM, even every 3 cM, 2.75 cM, 2.5 cM, 2.0 cM, 1.5 cM, 1.0 cM or 0.5 cM has a density of more than one polymorphism, spread over the entire genome, with 60%, more preferably 70%, even better 80%, or even 90, 95 or 100% of the genome. Polymorphic.

In one example, efficient identification of gene regions associated with phenotypic traits (eg, QTLs or single genes) is disclosed, wherein the gene region has a self-determining polymorphism of less than 100 kb, more preferably less than 50 kb, or even less than 25 kb, 10 kb, 7 kb, 5 kb or 3 kb. In another case, the efficient identification of the gene region associated with the phenotypic trait, the regional self-determination polymorphism is less than 3.5 cM, more preferably less than 3.25 cM, or even less than 3 cM, 2.75 cM, 2.5 cM, 2.0. cM, 1.5 cM, 1.0 cM or 0.5 cM.

It is to be understood that the distribution of polymorphisms must be uneven in the genome, some regions will exhibit higher average density polymorphism (eg, non-middle region regions), and some regions will exhibit lower average density. Polymorphism (eg mid-section area).

In one example, the efficient identification of a gene region associated with a phenotypic trait of interest is simultaneously screened by 25 or more, more preferably 50 or more, or even 75 or more, 100 or more, 150 One or more, 200 or more, 250 or more, 300 or more, 400 or more, 500 or more, 1,000 or more, 2,000 or more, 3,000 or more, or 4,000 or more polytypes The existence of sex comes to be obtained. When utilizing high throughput assays (e.g., in a microarray), it can process screening for the presence of 5,000 or more (e.g., at least 10,000 or even 15,000 polymorphism) polymorphisms. In another example, the efficient identification of the region of the gene associated with the phenotypic trait of interest will be simultaneously screened for 25 or more, more preferably 50 or more, or even (appropriate) 100 or more or 250 or The above polymorphism exists to obtain.

"Marker Assisted Selection (MAS)" means the use of genetic markers to select desirable phenotypes in an cultivating population.

"Hybrid plants" means crops that are crossed by individuals with different genes.

As used herein, the term "crossed or cross" means the binding of a gamete, for example by pollination to produce progeny (i.e., cells, seeds or crops). This term encompasses sexual crosses (a pollination of one crop by another) and self-pollination (self-pollination, ie when both pollen and ovules are from the same crop).

In another example, efficient identification of gene regions associated with phenotypic traits of interest will be screened for 25 or more, preferably 50 or more, or even 75 or more, 100 or more, during a single assay. , 150 or more, 200 or more, 250 or more, 300 or more, 400 or more, 500 or more, 1,000 or more, 2,000 or more, 3,000 or more, or 4,000 or more The existence of polymorphism is obtained. In yet another example, the efficient identification of the gene region associated with the phenotypic trait of interest is screened by 25 or more, preferably 50 or more, or even (appropriate) 100 or The above or the presence of 250 or more polymorphisms is obtained. A single test involves many steps, and one or more of these steps can occur sequentially.

In some examples, this detection can be performed using a high throughput system. High throughput systems can involve a solid phase array, which in one example can include a microarray.

In the assays described below, the collection of polytype markers includes small to millions of different nucleic acid molecules. For example, a thin film with a plurality of nucleic acid molecules can be produced for screening using a simple dot-and-ink hybridization method. Solid phase techniques, described below and known in the art, can be used as high throughput monitoring for polymorphism. In these methods, different immobilized nucleic acid molecule probes are placed on the solid support of the microarray at a density of up to several million nucleic acid molecules per square inch. Also with one or more probes, a relatively large number of sets of nucleic acid molecules can be immobilized for simultaneous screening.

As used herein, the terms "increase" and "decrease" mean the relative change in a selected feature in a subset of a population compared to the same feature present in the overall population. Therefore, the positive change in scale is increased, and the negative change in scale is reduced. The term "change" as used herein also means that the selected features of the independent ethnic sub-set differ from the same features of the overall ethnic group. However, this term is not used to calculate the difference.

"Substantially" does not exclude "completely". For example, a composition "substantially free" Y may be completely free of Y. "Substantially" may be omitted from the definition of the present invention as necessary.

Unless specifically stated otherwise, the terms "comprising/comprise" and variations of the grammar are intended to mean "open" or "contained" grammar so that they contain not only the elements but also additional, unmentioned And components.

As used herein, the term "about", when used in the context of the concentration of a formulation ingredient, generally means +/- 5% of the data, more commonly +/- 4%, more commonly +/- 3%, more commonly +/- 2%, more common even +/- 1% and more commonly even +/- 0.5%.

Throughout this disclosure, certain embodiments may be disclosed in a range format. It is to be understood that the description of the range format is for convenience and conciseness and should not be construed as an unrestricted limitation of the scope of the disclosure. The recitations in the range are therefore to be considered as all the sub-ranges that are explicitly disclosed and the singular values in the range. For example, statements such as 1 through 6 should be considered to have a clearly disclosed sub-range such as 1 to 3, 1 to 4, 1 to 5, 2 to 4, 2 to 6, 3 to 6, etc. and Individual values for ranges, such as 1, 2, 3, 4, 5, and 6. It can be applied regardless of its scope.

Certain embodiments are also broadly and broadly described herein. Each of the species and sub-groups that fall within the general disclosure limits form part of the disclosure. This includes general disclosures of books or negative limitations that remove any item from the species, whether or not the substance removed therefrom is specifically mentioned herein.

Detailed description of the invention

Molecular methods are associative positioning, which does not require the use of progeny to detect the properties of the connected markers. Compared to considering only the linkage of meiosis that occurs when the parent is passed on to the offspring, the relevance of the association is better considering the associations that have been established in the distant past, including meiosis of several generations. This allows associative positioning to provide higher resolution or detection capabilities to identify links between markers and phenotypes.

Relevance positioning mark and have been used to find a number of human diseases associated with crops such as maize (Zea mays, L.), Soybean (Glycine max (L.) Merr. ), Barley (Hordeum vulgare L.), wheat ( Triticum aestivum L. ), tomato ( Lycopersicon esculentum Mill. ), sorghum bicolor (L.) Moench and potato ( Solanum tuberosum L. ), and some species like Populus tremula L. and Pinus taeda ( Pinus taeda L. ).

In this disclosure, the population of oil palms can include oil palm populations with different genetic backgrounds. For example, oil palm populations may include, but are not limited to, breeding populations of restricted origins (BPRO), such as the Algemene Vereniging van Rubberplanters ter Oostkust van Sumatra, AVROS ), Delhi (Deli), Yangambi, Cameroon, Ekona, Calabar, and combinations thereof. This group can be selected based on its structure, because of geographic factors, natural choices, or choices. For associative positioning, a given sample can fall into one of the five categories defined by the ethnic structure, which is related to regional adaptation, diversity of natural selection, and family relevance from the closest common ancestor. Ideally, samples with minimal ethnic structure or family association produce the greatest statistical power and provide well-distributed traits of interest.

The period during which phenotypic data collection is provided is exemplified below, and other time periods in which data are collected may be used for this procedure. For example, the collection period of phenotypic data may be three months or more, more preferably six months or more, or even nine months or more, twelve months or more, eighteen months or more, twenty The phenotypic data were measured, collected and analyzed for four months or more, thirty-six months or more, forty-five months or more, sixty months or more. In some cases, phenotypic data was collected for forty-five months. In a further example, phenotypic data was collected for fifty-four months.

In some instances, the phenotype is used to analyze and link one or more polymorphic genetic markers in a crop (such as oil palm), which may include, but are not limited to, fresh fruit bunch yield, number of strands, trunk height, Disease resistance, seed trees, height increments, oil components, oil content, carotene content, vitamin E/double bond retort, crop measurements and the like. Any measurable quantity or quality trait can be used as phenotypic data.

In certain instances, a method as described herein is described, wherein the method further comprises a genotype of genetic material of the crop that has been recorded for the phenotypic and phenotypic data of interest, from a crop group record of at least one crop species Steps for phenotypic data.

For quantitative traits, the Quantitative Transmission Disequlibrium Test (QTDT) is used in association locating systems to be familiar to those of ordinary skill in the art. Samples that are larger in species and more diverse in their duality usually contain a population structure or a family relationship with a constructed population.

This method can be applied to any phenotype that can be observed or measured. In cases where it is desirable to reduce the trait of interest rather than increase, the marker used to predict the effect will be replaced by a negative number.

The present invention further describes the development of a panel of genetic markers that can be used individually or in combination to predict or determine phenotypic traits of a crop, such as that applied to oil palm. This early prediction or determination of phenotypes that are not yet apparent is to aid in the selection of phenotypic crops that have been determined to be beneficial, thereby making it possible to reduce the time required for cultivation and reduce the cost of the cultivation process.

In some examples, in the method recited, wherein the crop material used to identify the polymorphic marker is obtained from at least two, at least three, or at least four or more different crop populations of the same species having different genetic backgrounds. . Advantageously, the present invention finds the ability to enhance the ability of its distinguishing markers to be linked to a trait by utilizing a different genetic background.

In one example, a method of identifying a polymorphic genetic marker for a phenotype-linked crop species of interest is described, wherein the method includes identifying the polymorphism identified for the crop and the phenotype of interest by relevance mapping. A statistically significant link between the markers; the marker found in the following is selected a) to be linked to the phenotype of interest, which has the most significant effect on the phenotype of interest, whether negative or positive, By determining the effect size of each of the identified markers, a) and selecting the most positive or negative effect on the phenotype of interest.

In another example, a method for predicting or determining a phenotype of a plant of interest is disclosed, the method comprising detecting the presence or absence of one or more polymorphic gene markers, including but not limited to SEQ ID NOs. 1 to 115. In another example, the method includes detecting two or more, three or more, four or more, five or more, six or more, seven or more, eight or more The presence or absence of one, nine or more, eleven or more or all of the polytype gene markers including, but not limited to, SEQ ID NOs. 1 to 115. In another example, the gene signature includes, but is not limited to, marker number 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), 7 (SEQ ID NO. 90), 8 (SEQ ID NO. 105), 9 (SEQ ID NO. 106), 10 (SEQ ID NO 99) and 11 (SEQ ID NO. 80).

The term "predicting" or "prediction" as used herein is defined to pre-state or make something known in advance, particularly using inferences or expertise. It is hereby meant that this knowledge is gathered to enable a person of ordinary skill in the art to be able to predict whether a particular crop can exhibit a deduced phenotype when the location and number of polymorphic gene markers for a particular crop are predicted.

The term "determining" or "determination" as used herein is defined as the precise identification or verification of something by research or calculation. In this case, the determination of the crop phenotype of interest is a direct result of the genetic analysis of the crop sample.

In a further example, a method as described above is disclosed, the crop of which is oil palm. Further in another example, as previously disclosed, the oil palm is of the genus Elaeis . In another example, the genus as disclosed above consists of dura and tenera . In another example, the genus as disclosed above consists of a variant pisifera . The biological term "form" as used herein is defined as the second classification level, the second classification level is below the variety, and the breed order is below the species. When used in this field of technology, types are often used to identify groups with obvious but less important differences. For example, a white flower type in a species that usually has colored flowers can be named "f. alba." The biological term "variety" as used herein is defined as the classification level below the species, but above the type. Varieties differ from other varieties, but when brought into contact with other species, they can cross without restriction and produce offspring. In general, species are geographically separated from one another.

The ultimate goal of any breeding program is to combine as much of the beneficial dual genes as possible into a species of elite elite that is superior to its ancestral genes (in terms of one or more agronomic traits). The markers described herein identify chromosome fragments, that is, gene regions and dual genes (dual forms) that are favored for yield through long-term selection. Therefore, these markers can be used for molecular marker-assisted selection of oil palm crops with excellent agronomic achievements. For example, hybridization between parental counterparts of a favorable dual gene at a locus of interest may select progeny that contain more favorable dual genes than the parent. We predict that these offspring are phenotypically superior to the parent. For example, in practice, after incubation, the methods disclosed herein can be used to sample or analyze the presence or absence of a polymorphic genetic marker from the crop and based on such markers and previously identified markers and their known expressions. Types are compared to test whether they exhibit a selected phenotype and predict whether these particular crops will exhibit a desirable phenotype during growth or maturity.

For restricted progeny palms, the resolution of the markers linked to the traits is reduced and the marker distance expressed in units of centimorgans (cM) is increased, thereby reducing the predictive power of the markers. Including more markers increases the chance that the marker falls within the resolution limit without reducing its resolution. This is due to the limitations of the linkage localization method, which detects gene exchange through meiosis and comparison between progeny and parental palm.

To overcome this problem, advanced intercross lines, such as the sixth or higher generation, are crossed to apply to crop improvement programs for other crops. However, these crop improvement programs are not applicable to oil palm, as it will take hundreds of years to produce these hybrid lines.

The use of linkage targeting in oil palm results in the development and identification of gene markers that are statistically linked to the trait of interest and used for screening. This method facilitates nurturing and increases the accuracy of predictive traits by enabling early stage screening, thereby reducing the cycle time of the incubation process and reducing its cost.

The use of the labeled molecular marker-assisted selection (MAS) of the present invention and its identified chromosomal fragments, in the context of, for example, an oil palm cultivating program, contributes to increased yield improvement efficiency. For traits of interest for large numbers of samples, such as phenotypes of yield, screening can be expensive and time consuming. In addition, phenotypic screening alone is often unreliable due to epistatic effects and non-genetic (eg, environmental) effects on phenotypes. MAS provides advantages in field assessment that can be performed at any time of the year regardless of the growing season or developmental stage. In addition, MAS contributes to the assessment of biological growth in isolated areas or under different conditions.

Breeders with the usual knowledge of cultivating oil palms with increased yield can be identified using the MAS methods described herein, using, for example, the exemplary markers described herein or positioned by the markers listed in Tables 1 and 2 below. The chromosomal fragment is linked to the oil palm line with superior agronomic performance.

Gene-tagged dual genes, linked markers, QTLs, recognition chromosomal fragments containing genetic elements important for yield are used to identify crops that contain a desired genotype at one or more loci and are expected to deliver this desired genotype , along with the desired phenotype, passed to its offspring. A marker dual gene (or QTL dual gene) can be used to identify a crop that contains a desired genotype at a locus or at several unlinked or linked loci (eg, haplotypes) and is expected to deliver this consensus. The genotype is passed along with the desired phenotype to its offspring. Likewise, by identifying a crop lacking a desirable dual gene, crops with undesired phenotypes (e.g., low yield crops) can be identified and removed, for example, from subsequent hybridization. It will be appreciated that for the use of MAS, the term "marker" encompasses both the marker and the QTL locus, all of which can be used to identify crops with desirable traits.

After the desired phenotype and polymorphic chromosomal loci (eg, marker loci or QTL) are determined to be isolated together (ie, determined to be a linkage phase imbalance), a dual gene corresponding to the desired phenotype can be selected. Briefly, the nucleic acid corresponding to the labeled nucleic acid is detected on the biological sample of the selected crop. This detection behavior may take the form of hybridization of the probe nucleic acid to the label, for example, using allele-specific hybridization, Southern analysis, Northern analysis, in situ hybridization. ( in situ hybridization), amplification of the primers by PCR containing the labeled product or similar. After confirmation of the presence or absence of a particular marker in a biological sample, the crop can be selected and selectively hybridized to produce a progeny crop.

When a population is separated by multiple loci that affect one or more traits (eg, multiple loci associated with a single disease resistance or multiple loci associated with different disease resistance), the efficiency of MAS is comparable Phenotypic screening is even better because all loci from a single DNA sample can be processed together in the lab. Therefore, the marking information of each personality in the cultivation process is convenient.

It will be appreciated that crops (marked positive for the present invention) can be selected and hybridized according to any incubation protocol associated with a particular breeding program. Thus progeny can be produced from selected crops by crossing selected crops with one or more additional crops selected according to the same or different markers, such as different markers associated with superior agronomic performance or different interests of interest. Phenotype (eg resistance to a particular disease). Or the selected crop can go back to hybridize with one or two parents.

Backcrossing is typically used to infiltrate one or more locus genes from the donor parent into other desirable genetic backgrounds from the recurrent parent (usually elite). The more repeated the reverse hybridization cycle, the greater the genetic effect of the recurrent parent on the resulting variety. The selected crop can also be outcrossed with, for example, crops or lines that are not present in its pedigree. Such crops can be selected from the population that was analyzed in the previous round or reintroduced into the breeding program. Crops that are desirably labeled as positive can also self-produce a standard breeding line with the same genotype.

In certain instances, such as the methods described herein, wherein the polytype marker is a polymorphic marker from oil palm. The markers used in the present invention are single nucleotide polymorphism (SNP) markers, although this method can be applied to any gene marker well known to those of ordinary skill in the art.

This article describes some examples of gene markers. In some instances, there are methods as described herein in which polymorphic genes are labeled as SNPs (single nucleotide polytype), SSRs (simple sequence repeats), AFLPs (amplified fragment length polymorphism), RAPDs (random amplified polymorphic DNA) and combinations thereof.

In one example, there is a method as described herein in which the polymorphic gene is labeled as SNPs (single nucleotide polytype).

In one example, the presence or absence of one or more polymorphic gene markers is determined in a crop sample comprising genetic material selected from the group consisting of leaves, tender stems, stems, saplings, roots, shoots a group of flowers, seeds, and fruits.

Alternative methods may also perform the use of different markers in conjunction with appropriate analysis steps to identify markers that display cluster patterns that can be used for further analysis. These examples are SSR markers, AFLP markers, RAPD markers, and other known gene markers.

Once the ethnic group and the marker are selected, describe the method of performing the QTL analysis. In certain instances, hybridization mother plant can produce individual shaped joining (F _1), and these individuals then hybridized in the same manner as described above. Finally, the phenotype and genotype of the derived (F ₂ ) population are scored. Markers that are genetically linked to QTLs that affect the trait of interest will be more frequently separated from trait values, whereas unligated markers will not show a significant association with phenotype.

For traits controlled by tens or hundreds of genes, the maternal lines do not need to be really different from the phenotypes in question, but instead they must simply contain different dual genes, which are then recombined by the derivative group. Produces a range of phenotypic values. For example, assume a trait controlled by four genes, in which a capitalized dual gene increases the trait value and a lowercase dual gene reduces the trait value. Here, if the dual genes of the four genes have similar influences, individuals with the AABBccdd and aabbCCDD genotypes should have approximately the same phenotype. Members of the F ₁ generation ( AaBbCcDd ) are unchanged and have intermediate phenotypes. However, F ₂ generations or progeny from a backcross F ₁ individuals of any one of the parental, it will change. F ₂ progeny in any one will have zero to eight uppercase allele, backcross progeny in any one will have four to eight uppercase allele.

Thus, the term "effect size" as used herein relates to an index name given to one or two or one family, which is used to measure the magnitude of the treatment effect. These indices are independent of the sample size. The measure of effect size is the general trend of pooled analytical research that integrates findings from specific areas of research. For example, the effect size can be measured as a normalized difference between two means, such as the normalized mean difference between two populations or the correlation between the classification as an independent variable and the individual scores of the corresponding variables. . This relationship is called "effect size corrlation." In other words, the effect size is a measure of the intensity of the phenomenon.

The magnitude of the effect calculated from the data is a narrative statistic that conveys the estimated size of the relationship without any explanation as to whether the data surface relationship reflects the true relationship of the ethnic group. In that case, the magnitude of the effect and the inference statistics are complementary to the P -value. In genetic studies, the magnitude of the effect can be defined as the coefficient of the SNP (β), and when its association with the result is modeled by a regression module (such as a linear regression of quantitative traits or a logistic regression of qualitative traits), A linear trend in the copy of each pair of genes is assumed. The regression coefficient of the quantitative trait can be expressed as the standard deviation (SD) unit of the trait, so it is a comparable cross-trait.

The primary goal of QTL analysis has been to answer whether the difference in phenotype is mainly due to a small number of loci with considerable effects, or multiple loci with small effects. This shows that a significant proportion of phenotypic variants in many quantitative traits can be interpreted as a small number of loci with large effects, while the rest are many loci with minor effects. Once the QTL is identified, molecular techniques can be used to narrow the QTL to candidate genes (the process is described later in Examples 1 and 2 below). It may be possible to use the methods described herein to use assays to predict the role of regulatory genes or genes encoding transcription factors and other signaling proteins.

As with all statistical analyses, sample size is a key factor. The small sample size can cause the detection of small effect QTLs to fail and lead to overestimation of the effect size of the identified QTL. Methods have been proposed for comparing the detected QTL to the expected value distribution to estimate how many loci may be missed.

In some instances, as with the association positioning method described herein, the relevance of the location includes the application of a general statistical linear model (GLM) combined with a mixed statistical linear model (MLM). .

When considering the QTL between traits and taxa, it can be noted that phenotypes are often influenced by various interactions (eg, environmentally affected genotypes and epistatic interactions between QTLs), although not all QTL studies are designed. To detect these interactions. In addition, significant dominant, epistatic, and environmentally affected genotype effects were recorded throughout the life cycle.

Similarly, QTL studies examining crop structural differences between crops have repeatedly demonstrated significant superior interactions. It is also possible to perform QTL analysis of uncontrollable natural populations using hybrid, sibling relationships (semi-sib or full sibling) and/or pedigree messages.

Therefore, there are many statistical methods for genetic mapping in association mapping. In some instances, the association positioning described herein may include, but is not limited to, the most approximate-likelihood of simple regression of samples, and the analysis of variance (ANOVA) at the marker locus. ), methods of least square, Wright's F statistics, structured association, genomic control, quantitative transmission disequilibrium test (QTDT), These methods of population structure and relative kinship are analyzed. In genomic control, random markers are used to estimate and adjust the expansion of experimental statistics generated by the population structure. Genome control and structural integration are common methods for controlling false positives (type 1 errors) caused by ethnic structures.

In some examples, the described methods, where the relevance of the location includes the application of a general statistical linear model (GLM) combined with a mixed statistical linear model (MLM). The general statistical linear model is a mixture of several different statistical models such as ANOVA, ANCOVA, MANOVA, MANCOVA, ordinary linear regression, t test and F test. A general statistical linear model is a generalization of a multiple linear regression model for an example of more than one strain number.

A mixed statistical linear model can be used to identify multi-level relationships detected by random genetic markers. Advantageously, the inventors of the inventors applying GLM and MLM combinations can more accurately predict binding.

In some of the examples below, the inventor used STRUCTURE to perform the analysis. STRUCTURE uses genotype data to infer the software of ethnic structure. The detection of the number of individual plexes is based on the posterior probability of a given population, using the Bayesian approach (Evanno, G., Regnaut, S., Goudet, J.; Molecular Ecology; 2005; 14: 2611 -2620). The relative family matrix, which defines the degree of gene degeneration between individual pairs, can be calculated using the SPAGeDi software. Structural Binding (Pritchard, JK, Stephens, M., Rosenberg, NA, Donnelly, Am. J. Hum. Genet .; 2000; 67: 170-181) and the Unified Mixed Model Method by considering the ethnic group structure between ethnic groups and Family correlation to reduce false positives (Yu, et. al., Nature Genetics , 2006, vol. 38, no. 2, pp. 203). These pseudo-bindings can occur when the relevant marker is not linked to the causal locus, but rather to the sub-population, when any of the labeled dual genes are present in the subpopulation at a high frequency.

The use of cofactor data can also be added to increase the ability to detect links. For example, when performing FFB association positioning, we can include the fruit size as a cofactor to help increase the association location to pick the ability to link to the trait tag.

It is to be understood that different cutoff values can be used depending on the urgency of the analysis, and that the high cutoff values are determined by the type of population, organism, sample size, and other considerations. For example, a polymorphic gene signature with a p-value is empirically inferred to be less than about 5%, or less than about 4%, or less than about 3%, or less than about 2%, or about less than one. %, or approximately less than 0.5%, or approximately less than 0.1% is considered to be statistically significantly correlated with the phenotype of interest.

In some examples, methods are described in which a method of localizing a polymorphic gene marker with a p-value of less than 5% empirical inference for association is considered to be statistically significantly correlated with a desirable phenotype. In a further example, a method as described herein is a method of locating a polymorphic gene marker with a p-value of less than 1% empirical inference for association, which is considered to be statistically significant with a desirable phenotype Related.

In some instances, methods have been described herein, along with methods for locating QTLs based on phenotypic regression of marker effects. When further details are explained under the heading "Validation", the coefficient of the decision (R square or R ² ) is used to evaluate the proportion of the phenotypic variation using the marker. That is to say, the effect of R ² can be used to determine or predict a marker or a dual gene, and how many traits of interest are expected to be affected by the presence of a particular dual gene. In some examples, the R-squared value can be applied to a set of markers having a p-value of less than 0.01. Thus, in some examples, the R square cutoff value can be 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. It is 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. In some examples, for a set of markers having a p-value of less than 0.01, which is described as having an R-squared cutoff of more than about 0.1.

When compared to the total variance of traits in the sample set, the R-squared value shows how many pairs of dual or dual genes can interpret the variants in the trait. For example, if a set of markers has an R-squared value of 0.8, then the set of markers can predict 80% of the trait of interest. It also means that there are additional unknown or undiscovered markers affecting 20% traits. In other words, the R-squared value can be viewed as one, two, three, four or more markers that accurately predict phenotypic traits. However, the magnitude of the effect is about the magnitude of the effect of the dual gene on the phenotype. For example, if the effect on the weight of the fruit is 50, then the dual gene will affect 50 kg of the trait. In other words, the magnitude of the effect can be seen as an indicator of the change in the number of dual gene controls.

In some examples, the description further provides a method wherein the polytype marker comprises a decision coefficient having a magnitude of less than 0.1, and when the posterior test, the polytype marker with the most important negative effect is those having an evaluation effect size For <-10, <-15, <-20 or <-30.

In some instances, methods for predicting crop phenotypes of interest for a phenotypic crop of interest are described using the methods described herein to screen and select those having a phenotype of interest. Crops are planted or cultivated with other plants of the same species. In other words, this method does not wait until the trait can be measured to anticipate the phenotype of interest. Advantageously, the inventor can save time and money by being able to predict phenotype by determining phenotype.

In a further example, a method is described in which the predicted phenotype is yield. Other phenotypes of interest can be predicted. Some examples are described in this article. In another example, the crop wherein the crop is oil palm and the predicted phenotype is fresh fruit bunch yield, number of strands, or a combination thereof. In a further example, the method wherein the crop phenotype of interest is selected from the group consisting of fresh fruit bunch weight, string size, fruit size, oil extraction ratio, number of strings, and combinations thereof. The components of production.

In another example, the phenotype of interest is related to the number of fresh fruit bunches or strings.

The disclosure also provides other yield components including, but not limited to, increasing the quality (weight) of one or more parts of the crop, particularly the (harvestable) portion above the surface, increasing the root mass or adding any other harvest. Part of the raw mass, increasing total seed yield, including increasing seed quality (seed weight) and increasing the seed weight per unit crop or individual seed, increasing the flowers per unit of flowering (florets) The number, the number of (full) seeds, and the increase in seed size can also affect seed composition, increase seed volume, and can also affect seed composition (including total oil and protein and carbohydrate content and composition), and increase individual seeds. Area, increase individual seed length and / or width, increase harvest index, which represents the yield of the harvestable part, such as seed, overall raw mass and increase of thousand weight (TKW), which is calculated by counting the number of full seeds and The total weight of the full seed is inferred. An increase in seed size and/or seed weight results in an increase in TKW.

In one example, the presence of the one or more polymorphic markers is associated with a fresh fruit bunch (FFB) phenotype, wherein the markers include, but are not limited to, SEQ ID NOs: 1 to 79. In another example, the method includes detecting two or more, three or more, four or more, five or more, six or more, seven or more, eight or more , nine or more, ten or more, eleven or more or all of the polymorphic gene markers are present or not, and the polymorphic gene signature includes, but is not limited to, SEQ ID NOs: 1 to 79 . The presence of one or more polymorphic markers in another example is related to a bunch number yield (BNO) phenotype, wherein the polymorphic marker includes, but is 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: SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 101, SEQ ID NO: 102, SEQ ID NO: 103, SEQ ID NO: 104, SEQ ID NO: 105, SEQ ID NO: 106, SEQ ID NO: 107, SEQ ID NO: 108, SEQ ID NO: 109, SEQ ID NO: 110, SEQ ID NO: 111, SEQ ID NO: 112, SEQ ID NO: 113, SEQ ID NO: 114 and SEQ ID NO: 115. This polymorphic marker is selected according to the methods described herein. Exemplary combinations of markers and their use in determining crop phenotypes can be found in this specification, for example in Table 6, Table 7, and Table 12.

In one example, a method as described herein provides SNPs associated with increasing/decreasing the yield of fresh fruit bunches, the SNPs of which are one or two or more gene markers including, but not limited to, SEQ ID NO: 1, SEQ ID NO: 3. SEQ ID NO: 6, 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, SEQ ID NO: 51, SEQ ID NO: 52, SEQ ID NO: 53, SEQ ID NO 54. SEQ ID NO: 56, SEQ ID NO: 57, SEQ ID NO: 58, SEQ ID NO: 60, SEQ ID NO: 62, SEQ ID NO: 63, SEQ ID NO: 64, SEQ ID NO: 65 SEQ ID NO: 66, SEQ ID NO: 67, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ ID NO: 73, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO : 77 and SEQ ID NO: 79.

In one example, the SNPs provided by the methods described herein, wherein the presence of SNPs is associated with an increase or decrease in the yield of fresh fruit bunches, and wherein the SNPs include, but are not limited to, marker number 1 (SEQ ID NO: 10), Marker 2 (SEQ ID NO: 20), marker number 3 (SEQ ID NO: 13), marker number 4 (SEQ ID NO: 8), marker number 5 (SEQ ID NO: 19), marker number 6 (SEQ ID) NO: 34) and combinations thereof. In further examples, including but not limited to marker number 1 (SEQ ID NO: 10), 2 (SEQ ID NO: 20), 3 (SEQ ID NO: 13), 4 (SEQ ID NO: 8), 5 The presence of at least one, at least two, at least three, at least four, at least five or all of the markers (SEQ ID NO: 19), 6 (SEQ ID NO: 34) is associated with an increase in the weight yield of fresh fruit bunches. In another example, including but not limited to at least one of marker number 1 (SEQ ID NO: 10), 3 (SEQ ID NO: 13), 5 (SEQ ID NO: 19), 6 (SEQ ID NO: 34) The presence of two, three or all of the markers is associated with a decrease in the weight yield of the fresh fruit bunch. Examples of marking applications can be found in Tables 4, 6, and 7 of this specification.

In one example, the SNPs are described as described above, and the SNPs associated with the increase or decrease in the number of strands (BNO) are labeled 7 (SEQ ID NO: 90), 8 (SEQ ID NO: 105), 9 ( SEQ ID NOs: 106), 10 (SEQ ID NO: 99), 11 (SEQ ID NO: 80), and combinations thereof. In a further example, the SNPs are one or two or more gene 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. In a further example, the SNPs thereof are, but are not limited to, one or two of SEQ ID NO: 80, SEQ ID NO: 90, SEQ ID NO: 99, SEQ ID NO: 105, and SEQ ID NO: 106 or The above gene markers.

In one example, the presence of a combination of marker number 8 (SEQ ID NO: 105) and marker number 11 (SEQ ID NO: 80) as described herein is related to a change in the number of strands produced. In another example, the presence of a combination of marker number 7 (SEQ ID NO: 90), marker number 9 (SEQ ID NO: 106), and marker number 10 (SEQ ID NO: 99) is associated with a change in the number of strands produced. In another example, the presence of marker number 11 (SEQ ID NO: 80) is related to a change in the number of strands produced. Examples of marking applications can be found in Tables 10 and 12 of this specification.

Also described herein is the use of one or more polymorphic gene markers as a selection criterion for crops, wherein the selection is based on the presence or absence of one or more polymorphic gene markers.

example

The non-limiting examples of the present invention, including the best mode and the comparative examples, are further described in detail with reference to the specific examples, which are not to be construed as limiting the scope of the invention.

Materials and methods

The crop material used in the identification method of "fresh fruit bunch yield" and "number of bunches" is obtained from three oil palm groups, each of which contains a hybrid of Deli, Nigerian and AVROS backgrounds, and thus has different genetic backgrounds. Mixture. The dual gene combinations found in these three ethnic groups are the expression of these dual genes in commercial oil palm genetic material. For example, commercial materials sold and grown in Malaysia are different hybrid combinations of restricted origin breeding groups (BPROs), and all parental BPROs used to generate association-targeted populations cover all common genetic backgrounds, The breeder's genes are moved in to produce commercial palm oil grown in Malaysia and Indonesia. These oil palms are individually recorded for production over a specific period of time.

DNA extract

The tender stems and leaves, the first unopened young leaves from the highest leaves, are derived from oil palm trees. The leaves were cleaned and frozen in liquid nitrogen prior to storage in a freezer (-80 °C). When DNA samples are required, approximately 3 to 5 grams of leaf samples are removed and removed from the reservoir, treated with liquid nitrogen and ground into a powder using a mortar and pestle, and the ground leaves are then moved to 15 ml of modified bromination. Cetyl trimethyl ammonium bromide (CTAB) buffer in a test tube. CTAB buffer contains 2% (w/v) CTAB, 20 nM EDTA (pH 8.0), 1.4 M sodium chloride, 100 mM Tris-HCL (pH 8.0), 5 mM ascorbic acid, 4 mM diethyl Diethyldithiocarbamic acid sodium salt, 2% polyvinylpyrolidone-40 (PVP40) and 100 μl of 2-mercaptoethanol. The mixture was incubated at 60 ° C for 30 minutes, followed by an equal volume of chloroform:isoamyl alcohol before complete mixing. The mixture was centrifuged at 10,000 rpm for 15 minutes and the upper liquid phase was transferred to a clean tube. Ice cold isopropanol was then added to precipitate the DNA. The solution was then stored at -20 ° C for several hours and then centrifuged at 12,000 rpm for 15 minutes to rinse the DNA pellets seen at the bottom of the tube with 5 ml of wash buffer containing 70% ethanol and 10 mM ammonium acetate. The sample was then centrifuged to precipitate DNA, which was dried and dissolved in Tris-EDTA (TE) buffer containing 10 mM Tris-HCL (pH 8.0) and 1 mM EDTA (pH 8.0). This DNA solution is then treated with RNAse to remove RNA.

Tag recognition

The extracted DNA was then sequenced using an Illumina Genome Analyser according to the manufacturer's instructions. Biometric information tools such as, but not limited to, Burrows-Wheeler Aligener and SAMTOOLS analysis data are used to detect possible SNPs. The list of SNPs was transmitted to Illumina and 1208 SNP markers were arranged in the GoldenGateTM analysis format. Illumina's method was used to perform GoldenGateTM analysis using 269 palm DNA from the three populations defined above, 96 palms from the first population, 96 palms from the second population, and 77 palms from the first Three ethnic groups.

The results of the analysis were then interpreted using Illumina's Genome StudioTM Genotyping Module. The results of the cluster of dual genes are displayed and transferred to the expansion table and further analyzed as described below.

Relevance targeting

The previously identified SNP markers are then statistically tested to confirm whether they are linked to phenotypic data, and by extension, phenotypic performance can be predicted and which differentiated genotypes will affect traits and which will not. A statistical linear model was used to estimate whether there was a significant link between the genotype and the phenotype being tested. Genotype data was obtained from the above GoldenGateTM analysis, and phenotypic data was collected over a 54-month period to increase data accuracy. The phenotype used for this analysis was the number of fresh fruit bunches and the number of strings.

This analysis was performed using a general statistical linear model (GLM) to identify SNP gene markers linked to two phenotypes (fresh fruit bunch yield and number of strands). To improve accuracy, a mixed statistical linear model (MLM) is used along with the GLM model. SNP markers that are significantly correlated with yield traits use general statistical linear model (GLM) and mixed model correlation (MLM) analysis with TASSEL (Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES. Bioinformatics; 2007; 23:2633-2635.) and selected phenotype data combining genotypes, including fresh fruit bunch yield data and string numbers, and two paired relative coefficients derived from SPAGeDi (Hardy OJ, Vekemans X; 2002) (K - Matrix) program. SPAGeDi: A versatile computer program for analyzing spatial genetic structures at the individual or ethnic level (Molecular Ecology Notes 2; 618-620) and from STRUCTURE software (Pritchard, JK, Stephens, M., Rosenberg, NA, Donnelly, Am. J. Hum. Genet.; 2000; 67: 170-181) Structural information (Q-value) to reduce the possibility of false combinations due to group stratification. The parameters used to perform STRUCTURE are the same as 10,000 repetitions for BURN-IN and MCMC. The K value was determined using an analysis proposed by Evanno et al. (Evanno, G., Regnaut, S., Goudet, J.; Molecular Ecology; 2005; 14: 2611-2620). The P-value of the syllabus was obtained and studied. The lower the p-value, the greater the chance that the marker will be linked to the trait of interest. A polymorphic position with a P-value below 5% or 1% of the empirical inference value is considered to have a statistically significant binding 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.). Therefore, the cutoff point is judged to be 0.01 and the best few markers with acceptable P values are selected for further analysis.

For the selected marker, the genotype of the crop is compared to the trait of interest. The effect assessment is calculated to determine the dual gene effect, whether positive or negative, associated with the trait of interest.

verification

The inclusion marker was performed on the oil palm of a commercially available plant material from the Oil Palm Farm by performing an associated positioning step. The sample is a genotype analyzed using the OpenArray platform and using the correlation mapping described above. The results of the analysis provide a series of markers, different pairs of genes that can be detected, and how accurately the markers can be used to predict the trait of interest (p-value), and the traits to which they are linked How strong is the influence (dual gene effect) and how well the labeling is to explain the mathematical pattern of the variation of all tested traits caused by the marker (R square).

The markers were selected based on a cut-off p-value of <0.01, an R-squared (R ² ) cutoff value of <0.1, and a predicted dual gene effect that predicts how much oil palm is affected by the presence of a particular dual gene. The R-squared value can be derived using standard statistical methods. The closer the value is to 1, the stronger the effect of the marker on all of the effects of the overall trait of the test trait. The dual gene effect predicts a specific dual gene combination that will give a positive or negative change to the tested crop compared to the average oil palm. The greater the positive and negative values, the better the screening and selection of oil palms with desirable dual genes and lack of undesired dual genes.

The ideal oil palm selected should contain only the dual genes that are predicted to give positive effects. Care should be taken to avoid oil palms with dual genes whose predicted traits are predicted to be negatively affected, but this is practically difficult, so an ideal genotype combination like this is very rare. In order to select a dual gene, a dual combination that gives the best predictive value for the effect of the dual gene is selected, and the dual combination is predicted to give the strongest negative impact on the removed test trait. The data from the genotype identification and the predictive data of the association with the genotype are analyzed to determine whether the prediction of the selected marker is accurate and whether the phenotypic performance of the oil palm can be predicted based on the genotype.

According to the performance verification test, the analysis confirmed that the palms were superior for the tested traits, and compared their trait data with the average of the traits to show that the selection of these markers would accurately predict the oil palm, and the oil palm had an increase to be evaluated. The potential for trait performance. The above method is used to identify gene markers linked to two phenotypic traits (fresh fruit bunch yield and number of strands), the results of which are described in the following first and second examples.

result

First 1 example: FFB Yield related mark

Marking identification of crop performance predicting FFB production is performed to select high yield crops for planting and cultivating.

Genotype identification was performed as described above for 269 palms of the aforementioned three ethnic groups. Phenotypic identification of FFB production was performed by measuring the weight of fruit bunches produced by individual oil palms. Perform correlation positioning using GLM and MLM.

In order to determine the mark that is positively correlated with the FFB yield, a cutoff value is required such that the number of select marks is manageable. If the cutoff is too high, it will be difficult to find enough crops to meet this criterion, but the phenotypic effect will be higher. Conversely, if the cutoff is too low, crops that meet this criteria can be easily found, but there will be too many markers, some of which have only minor improvements (increased) for the tested trait. The p-value used in this example with a cut-off value of <0.01 is intended to produce 79 inclusion markers that are presumed to be associated with fresh fruit bunch (FFB) yield traits. These 79 inclusion markers are predicted to be associated with FFB and are listed in Table 2 below along with the p-valued GLM and MLM predictions.

Table 2: General Statistical Linear Model (GLM) and Mixed Statistical Linear Model (MLM) values of the tested markers associated with fresh fruit bunch (FFB) yield traits

The 79 markers were validated with a population of 225 palms planted in 75 separate plots on each of the three experimental plots. When the crops in these three blocks are from different sources, these three blocks should not be considered as replicas, but rather representative of the diversity of different oil palm groups, which are in commercial materials in the oil palm industry. Find the symbol of diversity.

Genotype identification was performed to identify the dual content of individual oil palms. Collect phenotypic data for these palms. Genotyping was performed using the 79 identified markers listed in Table 2 above, and the different pairs of genes linked to the markers were identified based on individual trees. Multiple dummy genes may be feasible as for one of the 79 markers, applying a cutoff based on the R square (R ² ) value. To reduce the complexity of the data and simplify the analysis, delete all dual genes that provide an R ² value of <0.1. The R ² value is a description of how many specific dual gene interpretation data points are consistent with the statistical model used to predict the trait or phenotypic variation being determined. Thus a dual gene with a small R ² value will not be particularly accurate and therefore not useful for the purposes described herein. For this example, we use 0.1 as the cutoff point, and all of the dual genes below 0.1 are considered useless for further selection. However, it is known to accept different values of R ² values, and it depends how much selective trait markers can be used, or how much the prediction accuracy required.

The R ² values are determined and applied to individual ethnic groups, where different R ² values are induced when tested in different ethnic groups. This is because some markers are better trait predictions in certain ethnic groups, while some markers work well across ethnic groups. By determining the R ² value of the marker based on the population marker for the specific trait of the ethnic group, the marker of the universal trait can be identified such that the combination of markers used to predict the trait is determined to have greater degrees of freedom in a given population.

After the p-value cutoff of less than 0.01 was used, 53 of the 79 markers remained a good predictor of the FFB trait.

Table 3: Marker values with less than 0.01p-value in three tested populations

The genotype of the crop from the associated localized sample set can be a positive homozygous junction, a negative homozygous junction, or a heterozygous duplex, obtained from the analysis of different dual genes present in the population. This data is used to predict FFB production performance.

Unlike quality traits, phenotypic traits are affected by a single gene, yield is a quantitative trait, influenced by the interaction and integration of many constitutive traits (multiple genes), which can vary with environmental conditions like rainfall, area, Soil type, temperature and sunshine affect each other. Therefore, in order to increase the accuracy of selecting high yield oil palms to increase the overall average yield of the planted area, multiple markers were utilized. Markers with dual gene effects that are considered secondary are also included as a combination of "minor" variants that can be used to predict significant changes to the expected value.

The identified markers are then used to screen oil palm populations that are predicted to be high yield crops. At least one, at least two, at least three or more markers are selected to symbolize the positive/negative dual gene effect. This effect increases with more markers and the number of markers used depends on the total sum of the R-squared values. The cut-off point is selected by taking into account the cost of connecting to a plurality of tags in comparison to the accuracy of utilizing a large number of tags. In other words, the more the number of tags, the more accurate the prediction will be, but the higher the cost of executing the experiment.

The sum of the R-squared values indicates how many variations can be explained by the combination of dual genes in the regression model. For example, if the combination of markers gives a sum of R squared values of 1, then the combination of markers will accurately predict the change in phenotype. For example, if the sum of R squares is 0.3, it means that the prediction of the regression model marker according to the trait can have an accuracy of 30%.

An ideal mark with the highest R-squared value, such as an R-squared value of 1, or an R-squared value of 0.9, or an R-squared value of 0.8, or an R-squared value of 0.7, or an R-squared value of 0.6, or an R-squared value. A value of 0.5, or an R-squared value of 0.4, or an R-squared value of 0.3, or an R-squared value of 0.2, or an R-squared value of 0.1 will be selected with the highest mark effect, and optional additional marks for recursion up to R The sum of squares achieves reasonable and reproducible prediction accuracy. However, this is as subjective as it depends on the actual measurement.

If the range of effect sizes is very small, such as a difference of 100 kg to 110 kg (in other words, 10 kg is the effect size effect), then when the accuracy cannot be selected, it is as significant as the trait of interest. With the increased/decreased palm, many markers cannot be used desirably. If the range of variation is very large (eg, a difference between 50 kg and 150 kg), then more markers can be desirably selected to select the crop with the most significant increase or decrease in traits.

At least one, at least two, at least three, at least four or more markers indicating the effect of the forward dual gene are selected. The mark is added recursively to balance the highest possible R-squared value with the least likely mark. The tested population is screened to obtain a dual gene crop with an effect linked to a good dual gene, and the crop with the dual gene is selected. In this example, six markers were selected as shown in Table 4, which also shows the estimated GLM values for the different dual genes associated with the markers and for each of the observed dual genes.

Table 4: Selected Marks Selected to Screen Palm Population

To validate the markers, these six markers were selected as the best example and used for genotyping to identify all 225 different background oil palms, the background of which included a mixture of Deli, AVROS and Nigerian lineages. The individual yields of these crops were recorded for 54 months and the average FFB yield for each crop was calculated. The dual genes in each of the six markers discussed are listed in Table 5.

Table 5: Genotyping results for six markers for the validation population of 225 palms

Crops were selected according to the dual gene criteria labeled in Table 4 above, and the FFB average of the selected crops was recalculated to determine if there was an increase in the average FFB for the overall unselected population. The unselected 225 palms had an average fresh fruit bunch of 233.8 kg/palm/year.

If palm is selected according to marker 1, it is to discard the palm of the dual gene combination C:C containing the positive effect, and discard the palm combination of the dual gene combination T:T with negative effect and the dual combination of effectless C: The palm of T, then 45 palms will be picked from 225 palms with an average yield of 255.77 kg/palm/year and an increase in FFB of 21.97 kg/palm/year.

If the palm is selected according to marker 2, by picking the palm of the dual gene combination A:T or A:A containing the positive effect, then 161 palms will be picked from 225 palms and the average yield is 239.66. In kg/palm/year, the increase in FFB is 5.86 kg/palm/year.

In order to select palms, markers of a combination of dual genes with only negative effects are used, and palms containing these dual gene combinations will be excluded from selection. The example micro-use marker 3, which contains the dual-genome T:T and A:T of the negative effect, has 208 palms discharged from 225 palms, and will pick 17 palms with an average yield of 271.58 kg/ Palm/year, its promotion is 37.7 kg / palm / year.

If palms are selected according to marker 4, which combines A:G with a dual gene comprising a positive effect and discards the palm of the dual gene combination G:G containing a negative effect, then 67 out of 225 palms will be picked. The palm has an average yield of 249.71 kg/palm/year, which is an increase of 15.91 kg/palm/year.

If the palm is selected according to the marker 5, it combines A:A with the dual gene comprising the positive effect, and discards the palm of the dual gene combination G:G containing the negative effect and the dual combination of the neutral effect A:G, Then 45 palms will be picked from 225 palms and the average yield is 252.91 kg/palm/year, which is an increase of 19.11 kg/palm/year.

If palm is selected according to marker 6, it combines C:T with a dual gene comprising a positive effect, and discards the palm of the dual gene combination C:C containing a negative effect and the dual effect of the neutral effect T:T, Then 161 palms will be picked from 225 palms and the average yield is 238.97 kg/palm/year, which is an increase of 5.1 kg/palm/year.

The selection of palms can be carried out using a single marker or a plurality of markers. Table 6 shows that if a plurality of markers are used to select the FFB of the 255 palm verification population, yield is obtained.

Table 6: Verification with multiple markers

To further determine whether the obtained marker predicted a dual gene that must be avoided in the selection of oil palm, the same experiment was performed on the same validation population, with the negative dual gene effect being chosen instead of the positive dual gene effect. This is similar to the dual gene selection performed with marker 3 in the above example. The results are shown in Table 7, from which it can be observed that the selection of the negative dual gene effect has an adverse effect on the tested trait.

Table 7: Verification with multiple markers

The above examples clearly show that by looking for crops with dual genes predicted to be beneficial for the tested trait (positive dual gene effect), and avoiding adverse effects on the tested traits ( The dual gene of the negative dual gene effect) is predicted to achieve oil palm traits.

The second example: the number of strings related to the mark

The marker recognition used to predict the number of oil palm strings is performed using the same method as described in the first example.

Using the above method, the selected 44 markers (P value less than 0.01) were presumed to have the number of strings of oil palm, the oil palm of which was identical to the crop material in the first example. It is listed in Table 8 below.

Table 8: GLM and MLM values of the tested markers associated with the number of strands (BNO) yield traits

The label was then tested to confirm that it had a R ² >0.1 value as shown in Table 8. Except for the number of strings to be tested (BNO), the verification was performed using the above procedure, and the results are shown in Table 9 below.

Table 9: R ² values for labels with <0.01 p-values in the three tested populations

The identified tags are tested in the manner described in the first example above. Displaying these tags can predict the traits of the string.

Table 10: Selecting the inclusion mark with it to screen the palm population

As with the five markers in Table 10, the dual gene combinations having an effect on the number of traits of the string are listed, and the crops predicted to be higher or lower than the number of average strings can be selected by themselves or in combination. These markers are named as markers 7, 8, 9, 10 and 11. The genotype identification results for 225 palms for markers 7, 8, 9, 10 and 11 are shown in Table 11, and the validation of their selection ability is shown in Table 12.

Table 11: Genotyping results for five markers for the 225 palm verification populations

Table 12: Verification using multiple markers for selection

Table 12 shows that the markers identified to be linked to the number of strings can be used to select the best dual combination that includes statistically linked to the average compared to the unselected population, whether the number of higher strings or the number of lower strings. crop. This example serves as a demonstration that the method disclosed herein can be used to link different phenotypes to genotypes and thereby detect useful or desirable dual combinations.

Table 13: Sequence ID No. and SNP of fresh fruit bunches

Table 14: Sequence ID No. and SNP of BNO

no.

<110> Genting Plantations Berhad <120> Gene markers for selection of phenotypic traits <130> 28896SG2 <160> 115 <170> PatentIn version 3. 5     <210>   1   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   1   Ctagcctaga   Gcttgaatat   Tttctttgaa   Ttggctctag   Ggtgcataga   Aacctgaccc   60     Rgagcctaat   Tctagcatta   Tttgtgccta   Ctgtttactt   Taaaaaataa   Aataatagta   120     a   121       <210>   2   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   2   Atgtatagag   Gatagttcct   Cttccagtgg   Ccctgtttct   Tgcaaaaaaa   Gcactctgcc   60     Wagctctggt   Cgggcttgtg   Cttctcggac   Tcacgcttct   Tggtctgacc   Atgtgcagac   120     a   121       <210>   3   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   3   Tcatgtcagt   Gtctacaata   Ttgtttttgt   Gtttcttgtt   Tcctatatta   Taattttcaa   60     Ygaaacttgt   Ggtgtgacat   Agttactcca   Ccttggaatt   Ttaaattgct   Ttagctgaaa   120     a   121       <210>   4   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   4   Ttcgcaactc   Attgtcagac   Aatactgagg   Agaatgtgca   Gcctgagatc   Ctgttctctc   60     Yagatactta   Caaagcttaa   Gtctctacaa   Tcgtattttg   Attattttaa   Aatttctcac   120     a   121       <210>   5   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   5   Tatatataac   Ttcttggcct   Gagactcaat   Taaaaaccaa   Attgagacat   Ggcttgaatg   60     Yattagttaa   Attgacttgt   Gaagtaatgc   Cggatcaaca   Tgcacatgga   Caagaaaatt   120     a   121       <210>   6   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   6   Catatatacc   Aactattatt   Ccagcatgca   Tcttctaagc   Tggtcatgct   Ggtccatctg   60     Waaaggatca   Tggttcgcac   Atggccttgt   Tttctccaat   Gaaaaacaaa   Aaagaaaaaa   120     g   121       <210>   7   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   7   Gtccatttaa   Agatgatggg   Ggctgatgac   Cacatcgagg   Ctgatggaaa   Agagggactt   60     Ragaattggg   Taggggagga   Gataaaaaga   Gaaggaaaga   Cttgttgcaa   Cgaagtttga   120     g   121       <210>   8   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   8   Atccaggtta   Tggactttga   Tgatgatgaa   Accattgggc   Caatctctga   Tgagattaaa   60     Rtacttgctc   Aagggccaaa   Ccatattgca   Agaaggttta   Aagctttgc   Tatggataat   120     g   121       <210>   9   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   9   Gaaatttttt   Gccttcaagg   Ttttcttggt   Ttttatcatg   Tattttgcca   Tttctattgg   60     Yaggctgatc   Aatgcaaaca   Tttaggtcac   Gtaattgtct   Ttgccgaacc   Ccttccacaa   120     a   121       <210>   10   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   10   Ttgctttctc   Taccccttta   Tataagttcc   Ttccagcaga   Tctctatctc   Tctctcccat   60     Yttgttagac   Cctactctgt   Ggcccgtctt   Tctatacctc   Tctctcccct   Ccccaccaca   120     c   121       <210>   11   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   11   Gcatgattca   Gattaaaagt   Attttaatct   Tatattctgc   Tttgatattt   Aaaaaataaa   60     Rttttgaaat   Acacatgcat   Gccccaaacc   Tctaattcca   Acagtggtat   Cagagccacg   120     a   121       <210>   12   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   12   Ctttatgagg   Aatcatttcg   Agccgagctt   Cgattacctg   Tacacccttt   Tgtcatagag   60     Ktctttcatt   Ttttcaatat   Tttttcttat   Tctttagttt   Ccaactcttt   Tcatttcatt   120     a   121       <210>   13   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   13   Tcattgtgct   Tgccagattt   Tagcctaaac   Atctctgaaa   Atagaggtaa   Gatcctctag   60     Wcatcaagat   Aatttagttt   Gctatcttcc   Tcagccttta   Aaacctcaat   Ttcagtcgac   120     a   121       <210>   14   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   14   Gaatatgggt   Ataaatgtcc   Cttcgatctg   Agatcaccat   Agtgacttgc   Aagcaactca   60     Ytatgcttta   Ggttggact   Atttaaattt   Ttaattcagt   Gatgaaaaat   Ttttaggcac   120     a   121       <210>   15   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   15   Gtacttggat   Taagttgatc   Accaggccgt   Ccggtcatag   Gagcaaaaag   Atttaaaagc   60     Ygtctcttcc   Tattcgatag   Ggtgctctta   Tggtgtgtag   Gggtgccatg   Tgatttgatc   120     c   121       <210>   16   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   16   Agctcgattg   Gacctataatc   Aattagattg   Aaatctaatc   Aaatttgatc   Aacttatatt   60     Yggacctact   Gccagttaga   Tatggaaccc   Aatgagacac   Acacatatag   Aaattggcca   120     a   121       <210>   17   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   17   Gctcgattgg   Acctaaatcg   Attagattga   Aatctaatca   Aatttgatca   Acttatatct   60     Rgacctattg   Ccaactagat   Gtggaaccca   Atgggtcaca   Cacatataga   Aattggccaa   120     g   121       <210>   18   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   18   Taaggggcta   Tttgattttg   Aataagattc   Aaaccttttg   Cctatttaaa   Gggatcttct   60     Ytaaggctct   Aagcatcaca   Ttttcaacag   Ccccaccccct   Ccctctcttc   Tctcctttgg   120     t   121       <210>   19   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   19   Tagcttatat   Aggaagagac   Tatttttttc   Atgtgaggtg   Tctttgaaga   Gcaaaactgt   60     Raggctctaa   Atgacaaagg   Cccactgaag   Tccaaagatg   Gtcatgtgct   Ttttaaagag   120     c   121       <210>   20   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   20   Tgatccatca   Agttgtgttt   Atggaagggt   Aacgtaactt   Tggcgatggc   Tttaaaacca   60     Wtgaatatat   Atattttttt   Catttctctt   Cctctctttc   Tccctctctg   Tttcttcctt   120     c   121       <210>   twenty one   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   twenty one   Ctaaaactga   Gccatcaacc   Tctttctgat   Accagttgaa   Agaaaaaaaa   Tttatttttt   60     Ycttgtagga   Tctttcagat   Ttcatcaaat   Ctgaggtgaa   Ataattttaa   Aaaaaattta   120     t   121       <210>   twenty two   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   twenty two   Tatatagttg   Ttagcattgt   Tgtaagtaat   Ttagtattac   Catagtagct   Aatcacattc   60     Yatctttgtc   Aggagcttta   Aagtttgggc   Gggaagtttg   Tctcacttgg   Tgtcccaatg   120     g   121       <210>   twenty three   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   twenty three   Cttgagctac   Ccaagagttg   Gtttcttacc   Atgtcataac   Tcatatagtg   Tggtaaaaaa   60     Wgatttaaaa   Aaaatctgat   Tcaaaaaatg   Aatcacaata   Aataatatat   Atccctagag   120     a   121       <210>   twenty four   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   twenty four   Gatgccgata   Ttaatattct   Cagtcattat   Taaaattcac   Tgaatagata   Ttagtaatga   60     Yagtatcaat   Gcactcgacc   Atcaataaag   Tcattattta   Aaatgcacca   Aaaatttggc   120     t   121       <210>   25   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   25   Ccaaacttcc   Tattttaaaa   Aacagcgtca   Tgcctggatg   Atggtatcat   Gtcccttctt   60     Ygttttcgac   Gaacataggt   Ttccatgcag   Ccggaaatta   Ttcagaaatt   Tgatcattat   120     t   121       <210>   26   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   26   Ggggagagag   Aacagtctcc   Cctttaaata   Gggtcagagg   Ggaggagttt   Gactcctcct   60     Yagggtgttt   Tttgactccg   Attaggagtc   Ggtcgggaag   Aagaagaaga   Ctctcaggag   120     t   121       <210>   27   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   27   Aaattaaggc   Catacaggag   Atgactccct   Caagaacgat   Caaagaagtg   Caacatcata   60     Ygggaagact   Tgttgctttg   Aaccgatttg   Tcttgagatc   Gacagaatgg   Tgcttaccct   120     t   121       <210>   28   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   28   Ctcaaccatt   Tcatgatgtt   Cagtcaattt   Taaagacaac   Ccatgcagtg   Atagtaaaga   60     Kgtaaatgtg   Tgtataagaa   Tataaaagga   Ctactcatgt   Tgatatgcac   Aagttagacg   120     a   121       <210>   29   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   29   Gaatgtatgg   Atatcgagca   Gatggtctgc   Cgatggctgg   Aggtgtggca   Ggcccgtgca   60     Ytgtctatga   Tggatcggat   Caccttgatg   Aggtctgttc   Tcagctttat   Ttcagtgtac   120     c   121       <210>   30   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   30   Tcttcaattt   Atgaccactc   Tccttttcta   Ccatagtatt   Aaatgcttta   Aaaatagaaa   60     Stgtagcagg   Cttttcttga   Aggaaataaa   Ctcatagttt   Tatagtgtaa   Tttacaatga   120     a   121       <210>   31   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   31   Caatgaatgg   Acaggtgctg   Ttacatatct   Aaacactgaa   Aatttgatcc   Attatacttc   60     Yttatcagct   Gaagttttaa   Aattatatgc   Aatactctct   Agattgtaga   Gtgaaagaaa   120     t   121       <210>   32   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   32   Cattcttatt   Ttaaaaaaga   Aaaaaccttt   Atgagactct   Ttgatacaac   Actgacctat   60     Kgtaagctat   Gaaggcattg   Cagtggttta   Ggtgcagtct   Agatagcttt   Atttcttgtt   120     t   121       <210>   33   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   33   Atagtagtga   Gtaaaattaa   Ttttgatgta   Ctacgatatg   Tatcaaattt   Tggtaccatt   60     Rtcggagatc   Ttagcacgat   Tgttttaaaa   Aaaatcaaaa   Aaaaatttat   Aaaataataa   120     a   121       <210>   34   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   34   Cctgtgcag   Aaggctatca   Gtttcaactc   Ctagtcgaca   Tacaacactc   Tctgattttt   60     Yctcagaagc   Atatcgaaaa   Gctcaagttc   Aaagtgcagg   Caccaactgg   Ccacagctct   120     c   121       <210>   35   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   35   Actgcatggt   Taacaaggaa   Cttttctatt   Gctcaacttt   Gaactaatct   Tatgtaggtt   60     Rttagcctac   Cgagagagac   Actatcatgg   Tctttttgca   Tgaacctttt   Aaagacatgt   120     t   121       <210>   36   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   36   Tattgttttt   Aaaatacagt   Tactaatttg   Tcactagtct   Ctctccaata   Ttttgtttga   60     Raatacatat   Ccattgataa   Tcgatcgcta   Ttcagtcgct   Aaaaaaaaac   Tattcgggac   120     a   121       <210>   37   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   37   Aaaacagacg   Gatgaaacaa   Tttattagaa   Ggctataatt   Cgaggaattc   Atggtgttat   60     Mtaaaggcct   Ttgtttgcag   Ttttcacatt   Ctattttctt   Gccatttttc   Ttcttgttat   120     a   121       <210>   38   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   38   Tttaaacatc   Actataagca   Taaacatcat   Atcttatcct   Aaccagtcac   Cgagctactc   60     Rtgtgaatat   Ttggattgga   Ccaattgagc   Ttcttctcat   Attttgaata   Cttcatagaa   120     a   121       <210>   39   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   39   Catgtcatat   Aacaaatacc   Cacgactaac   Aacttagtga   Gtgggtggat   Ttcctatcct   60     Wgctacctat   Agattcagac   Ataacaaact   Ctggtacttc   Tctatataaa   Ggtaacaaga   120     g   121       <210>   40   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   40   Tttttttttt   Tgggtaaacg   Tgtactagtc   Ttgtttgatt   Tgtcttgcag   Aagaggcatc   60     Wattgtgga   Tcttttaaat   Aaaagtttga   Tgaaagagta   Gaccattata   Atgggtcaaa   120     t   121       <210>   41   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   41   Ctcagctttt   Aaaaattcaa   Ccttctatct   Tttgctgccc   Tttgagacat   Agtcggctaa   60     Ytttctgact   Ttagcgacta   Agtttgcgtg   Ccaagcgtac   Gaagggttct   Ttgaaatgtt   120     t   121       <210>   42   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   42   Atccgtttaa   Cattggccaa   Aaacatcaca   Ttcaacatcc   Tggaggaaaa   Gacgatagct   60     Ratttgatga   Aggctctatc   Taacatgtat   Aaaaaacctt   Cgacttccaa   Caagatatac   120     c   121       <210>   43   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   43   Gatgtatgct   Gtggtgtgtt   Gtagattaca   Tcttgcccat   Gcagtgagtc   Agatcagcag   60     Rtttatggta   Tggcctagca   Agaagcattg   Acgagcactg   Aaggagatct   Tcagatatat   120     g   121       <210>   44   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   44   Tttagcttga   Aaggatatag   Aagatgttga   Agaagattat   Ttttgaagac   Ttgctgaaat   60     Ytgtgcataa   Ctttcctctc   Tctttatttt   Ttggatattc   Aatgtttttc   Acaaaaataa   120     a   121       <210>   45   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   45   Aacgagaagt   Tcgggcccca   Agctcgaaca   Ccgagagaaa   Gcacaaattt   Gaagggtctc   60     Raaaggacct   Tcggtcatga   Acaaaaagga   Tgaatcaatg   Tggcgatgac   Cagaaacctt   120     c   121       <210>   46   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   46   Ttggagcctt   Tgcatctact   Ttttcatgtg   Tcacatagag   Ctagagggat   Gccccatgct   60     Rgtcccacat   Gtcctcttta   Aaatgggcat   Gtccaacttg   Atcaaatcaa   Gtgacgcccc   120     t   121       <210>   47   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   47   Tttaaaaaaa   Gctgtgcatt   Acagatgact   Gcatctaaaa   Ccatcatttt   Ggtaacaaaa   60     Magtcacatt   Atttccgaac   Catgaacagc   Aaatggtgca   Ggataacttt   Cgttagattg   120     c   121       <210>   48   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   48   Aatatttatt   Atttaaaatt   Aattttgcag   Catatttaca   Gaaagctcta   Tgcctcgtgt   60     Kattaccgag   Atcatctaac   Gattgatgtc   Gggcccagtg   Aacgagttct   Ctacttgacc   120     a   121       <210>   49   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   49   Ttatagataa   Ttctcatgat   Ctaataagta   Taggagttct   Aacccaaatc   Taatttaaat   60     Yagtccaata   Cttataaaaa   Aaaatttcta   Catgagatag   Aattgccatc   Acatatgaca   120     a   121       <210>   50   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   50   Atattttcat   Caaattttaa   Atctttcgga   Tagtcatgac   Ttattgctaa   Acatcaatct   60     Kgacttatgg   Attcatcaaa   Attaaaaaa   Tttaattcaa   Gaattaatta   Ggaagaatct   120     t   121       <210>   51   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   51   Agtaaaatgt   Aaaaatgcag   Tttggaaagt   Tacataattt   Tttaaacaca   Aaagaaaact   60     Ygatatggtc   Tttcttggtt   Tgtgacataa   Acacgaaata   Tgtacccaaa   Gttttattag   120     t   121       <210>   52   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   52   Ttttctgact   Ttcccaatgc   Tgctagtttt   Gcctggtgct   Gctcagcttc   Tcttgctgcc   60     Rtcgctgaat   Ttttttcaag   Ttcttcttgg   Tttcacactt   Ccgcttggcc   Tcccccggtg   120     c   121       <210>   53   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   53   Aagggtctag   Ccactgtagc   Ggaccctcat   Caccctactc   Cacaaacagt   Ccgattggat   60     Ygagaatcta   Actgcatgcc   Taatgatcaa   Aacctccctc   Ctcatcaaca   Aggagtggat   120     c   121       <210>   54   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   54   Atatgtctaa   Cgatcctcct   Ggatcaaagg   Atgataaata   Ctcatacact   Agtgttttc   60     Yagtgacccc   Tcatgctcca   Tccaataatt   Ttaattatca   Tgtaaattta   Aatatttttt   120     t   121       <210>   55   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   55   Tcagaaaaat   Agcaaaatta   Aagcttaata   Gtaaacattt   Tatatggaaa   Tcaaaccctc   60     Raccaaacct   Gaaggcattt   Aaatataggg   Gacaccaatg   Ggtaagaaat   Ttaagcccat   120     g   121       <210>   56   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   56   Tctttctcct   Caactgactg   Tcctgcacta   Tcaggaatat   Ctttgtcctt   Gtagaggctt   60     Ytttcattga   Agatcatggc   Cttgcttctg   Atgaatttcc   Tatcctgctc   Attccagaac   120     t   121       <210>   57   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   57   Catggctggg   Tttggaggtc   Gaggacgcct   Gcggccaaga   Aagcgagaga   Gaaacaagga   60     Rcttctccga   Gatggggcgg   Ggggatagcg   Gatcgatcga   Gatttttctg   Gaatggaatt   120     g   121       <210>   58   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   58   Taatcttaat   Tagtttacga   Ttgtcatata   Tcctaatttt   Ttaaaactta   Ttttaagaac   60     Raaagggatt   Ctaatccctc   Cactactct   Atacccactc   Tcctaacata   Gattgaagtt   120     t   121       <210>   59   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   59   Aagggctcac   Ttggttgagg   Ctctacattg   Gccgacctcg   Atggcctttg   Gcttcctcaa   60     Wgaaccttgg   Gcagtcgacg   Ctaccctctt   Cttagggata   Tgttgcgcca   Agatccaata   120     c   121       <210>   60   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   60   Aaagctatga   Tgattgttca   Gcaattgtct   Atatagtgaa   Ataatatcag   Tcaccgaata   60     Wgagagtgca   Actattgtct   Ggcgtgatat   Attgaagctg   Gcctaacaac   Catgaatgtg   120     a   121       <210>   61   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   61   Ttttcattat   Tcatcatcat   Ggaaaagatc   Gtgcagcacc   Cctatacc   Ataggagatc   60     Ycatcgaatt   Ggtaaataag   Attattagaat   Cttgatttct   Tctaggatcg   Gatatctacg   120     a   121       <210>   62   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   62   Ccttatgttg   Ggtaatgtac   Ttggggtggc   Arggtgtagg   Cttgaatctg   Acttaagcct   60     Rtgataggag   Ggcctattag   Gattagtttt   Aaatggctag   Atcccctctc   Caatttctat   120     a   121       <210>   63   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   63   Accaaccaaa   Atcttacaag   Tccaattcta   Ggtttggatg   Ggcctaatcc   Aaatttcttt   60     Yatccccaag   Aaacttgaaa   Agaaaacaag   Aatataagat   Gcagaatttc   Agaataaatt   120     a   121       <210>   64   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   64   Ttgttccaat   Catgtatgga   Tttatctcaa   Tgattttttt   Gacagtttga   Gaagctgcaa   60     Raaaaatagg   Tatccaatgg   Taagactaat   Gttttaaacc   Agatcattga   Caaaactgga   120     a   121       <210>   65   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   65   Aagagtacc   Cagcttactg   Aaccatggcc   Gatcagcaca   Agatccaatt   Ccgatacggc   60     Raaccttact   Tctagtttca   Tcaaagtaat   Aagtaacaag   Caaaggggta   Gagcactgat   120     g   121       <210>   66   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   66   Tatccctttt   Aaagtactat   Gatatctctc   Actaactaat   Agacttaaa   Ggctatttat   60     Rtcatcaaaa   Actacatagc   Atatgacttg   Gtatgcttca   Aataggtgtg   Ttgatggttt   120     t   121       <210>   67   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   67   Ttaaacttat   Caaagacttt   Ggatttatgt   Ttcattaaat   Ccatatatcc   Aaaccttgat   60     Raatcattta   Taaaagtgat   Gaaataggag   Catcctcttc   Tgatttgagt   Tgtcattgac   120     c   121       <210>   68   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   68   Tgtcattaaa   Gtaacactaa   Ctatagggtt   Accaaccact   Atcttatcta   Atttaaagtc   60     Yttttcatca   Caattaccaa   Tgtgccgtaa   Cataaaacg   Tgcaaaatga   Tcttttaatg   120     c   121       <210>   69   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   69   Ataacgactg   Gatcatcatt   Tgagtcacaa   Aagggtgctc   Ttcgattcat   Tgattaataa   60     Wttttgtaaa   Gccaccatat   Agtctttgta   Tttttaaaag   Tttcctcccc   Tattttctca   120     a   121       <210>   70   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   70   Tcaaggtttc   Aagctatcaa   Gctagcttga   Caagttggtg   Gaagatgttg   Atggatagag   60     Rgacttaatc   Tcgggcaaaa   Taacaactag   Gatttgggcc   Ggtccacttt   Aaaaatactt   120     t   121       <210>   71   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   71   Agtatcatat   Tagataagtg   Tcattattgt   Ccttctttga   Tgatgaatgt   Catttctaca   60     Rgttttttgg   Ccaaatttaa   Ttttaaattt   Tattaaaaag   Ataatttttg   Tgatatcatt   120     a   121       <210>   72   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   72   Tcttcaaaat   Atctctctca   Atattcaaaa   Aatttaaaga   Taaaacgtca   Tgattatgat   60     Wtttaaatcc   Ctaaaattag   Tttcaaggag   Tgaacattga   Acacctaccg   Agcacatcgc   120     c   121       <210>   73   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   73   Atacatggcc   Attagggaca   Cactaaagta   Gtaaacacta   Aaataaaatt   Ttaaaaaatg   60     Rtcaaaagtc   Ctgaagggta   Acaataaagg   Tttatgaacc   Tcaaaggtct   Cacatagtgg   120     a   121       <210>   74   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   74   Tacatttcca   Gtcagtgtag   Gcttcataac   Aatgattcag   Attgtgtgag   Gcacggcatc   60     Mtggttttcc   Catgaaacaa   Gacacgatgc   Catctcggcc   Catcctgaca   Taatgccttt   120     g   121       <210>   75   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   75   Ccaaaagctc   Ttgcttccta   Tttattgcat   Gcaactgatt   Aacccatatg   Agttctattt   60     Rgactcttct   Tctctctttt   Tttctttttt   Atttaaaaaa   Aatgtagtcc   Cacatgagga   120     g   121       <210>   76   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   76   Accccctttt   Aaggccagtt   Gttgatagca   Atttttggat   Ggtttaaaaa   Gaaaatgatt   60     Yttagtgaaa   Gaaggctgta   Taccctcttt   Ccttttctta   Tgctccctct   Tcttttcctt   120     t   121       <210>   77   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   77   Gcaaaagagt   Aggtgtacag   Actgtgaatt   Aaaccatatt   Caagttattt   Aaagatgtca   60     Ygtcttcttc   Tatgaactaa   Agcgaagatg   Ttcccttgtt   Gtacaagtat   Tattttttca   120     t   121       <210>   78   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   78   Agtaaaaacc   Tctgcctcca   Gtttgctgac   Gatactattg   Ttttttgaga   Agcgaaaaag   60     Raagttgctg   Gccatctcaa   Gttcatatta   Tactcatatg   Aattggtatc   Tgatctcaaa   120     a   121       <210>   79   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   Fresh fruit bunch (FFB) SNP     <400>   79   Tattttctac   Tatctcataa   Gctgatgcta   Tggcacatcc   Tattggttga   Tcactcctat   60     Rgatagcatg   Ctgaaaaata   Tgcttgataa   Attctcaaaa   Gctgacaagt   Tggtttaaaa   120     t   121       <210>   80   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   80   Gtccatgaag   Cttagaagtc   Agtatcccaa   Agttgcatca   Actagttggt   Cgatctttga   60     Yaaagaaaaa   Ctgtccttcg   Gatgagcttc   Atttaaattt   Gtatagtcga   Tgtaaattct   120     c   121       <210>   81   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   81   Ctaagtgtgg   Gggcgtgcgg   Gatgcccaaa   Gaagagggag   Aaggaagagg   Aagagagggc   60     Rtgggaagag   Cttttgggca   Taaggggctg   Ctaggtttta   Tgccctaagg   Atcttaaggg   120     a   121       <210>   82   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   82   Ctgctgaact   Gtatggtttt   Aattctccaa   Atcattgaat   Atgggggctc   Acaacctaag   60     Kgtcatgtat   Gttatatcct   Ttcgaaaagt   Cttttagagg   Tttcatgtag   Ggccaaattt   120     g   121       <210>   83   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   83   Tatcccaata   Aatgcggtcc   Aggtaagttt   Ttatcagagg   Caagagtcac   Caaagaaaag   60     Rtagagagaa   Caactagccc   Aaggaaattt   Tgcagcactt   Tttcatttag   Aagagaagga   120     t   121       <210>   84   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   84   Tatcaacatg   Tcagagtcaa   Cttgaaatct   Cttgaagttg   Actttaaggc   Tttttcactt   60     Rattttttat   Gaaaaatctt   Tatctaatca   Tcctttttta   Ctttcaaaat   Actttaaata   120     c   121       <210>   85   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   85   Ctgtggccag   Tatggccagt   Ccattaattt   Gaaagctgtt   Ggaatccata   Cacctcataa   60     Kttaaatttt   Tgagaaatag   Aaatcacact   Tttagatgaa   Ttcatgtgct   Tcaagaaagt   120     c   121       <210>   86   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   86   Acctcctctc   Ctggacctcc   Gaggaggtac   Ttgatgttag   Tcttgttcgc   Cgtctcagct   60     Rtacattgc   Tgggcctcgt   Ctctccgttt   Taccatggag   Gagaattggc   Gtccgaccgg   120     g   121       <210>   87   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   87   Tagatttgat   Tattttattg   Gccatccgaa   Tccgaatcta   Actcatttaa   Aatagaattg   60     Ractatattt   Ttcgatccaa   Tggatggggt   Cgagcctaaa   Attcaaatcc   Cattggtgaa   120     g   121       <210>   88   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   88   Agatgaaatt   Tactagtagt   Caaagtcggg   Gacgaagtct   Gactcccgta   Ggagtccaga   60     Yggagtttcc   Ccatggccag   Agtcagagat   Gatgtccggc   Tcccgtagga   Gtccggacgg   120     a   121       <210>   89   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   89   Tgggttattt   Ggttggccat   Gacttgtttt   Taaacaggtt   Aaacagttgg   Gaaaggatca   60     Sctcacctgg   Gcaataaaca   Ggttagttag   Ggtttgactg   Tctaacctac   Ttagggtccg   120     t   121       <210>   90   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   90   Atgaaaggaa   Acattcctta   Tgcagttcac   Ataaagatga   Gtaataagac   Ctgaggcctg   60     Rgcaacagtg   Atgctcacat   Taagaaggtc   Aaattgttgc   Tcatagttac   Caaggtacta   120     t   121       <210>   91   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   91   Catatctgta   Cttttttgcc   Tgaatagcga   Agacatgaat   Ttgatgttac   Tattccatat   60     Wtgtttaaaa   Tgaagatgaa   Tatgagctgg   Atataaatac   Ttctttttta   Attgaatgtg   120     a   121       <210>   92   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   92   Acggctccac   Cctcttctcc   Agattgcctg   Ctcctgggct   Ggggttggga   Cttggataca   60     Raaacatgtc   Ttatcaatgc   Tatagcagtc   Tgtgaactta   Aaggttcatg   Cgggacggta   120     c   121       <210>   93   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   93   Caactcgtgg   Gctaggcatg   Gcctgacatg   Gtccaggcct   Agtccaagcc   Ctgcttgatc   60     Yttttttctg   Aatttaaaaa   Atatattttt   Ttataaatta   Taaaagtgtt   Aaggagtaaa   120     a   121       <210>   94   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   94   Tacataatag   Tgagtaggtt   Taattttggt   Atgctacgac   Acatataaaa   Ttttgacatc   60     Rttatcggag   Atcttggtat   Gattgtttta   Aaaaaaatca   Aaaaatatat   Atttactact   120     t   121       <210>   95   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   95   Aaaagcggga   Tgaattgaat   Tctttaaaaa   Tttttactaa   Atccaaatct   Gaacaattat   60     Rtgcttcaat   Ttaagctaaa   Ttgagtgttt   Gtgaagtatt   Tgatatgtgt   Agcagcaaaa   120     t   121       <210>   96   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   96   Gtaaatgcaa   Aatttaataa   Ctaaaattta   Aatccatggt   Tcaaaaaaaa   Gtcaaagata   60     Wtttttgatt   Tttaagaccg   Atgctagaaa   Tcgatgcaac   Aatttggcaa   Aatatgaatt   120     a   121       <210>   97   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   97   Catctccatg   Ctatctttct   Aattcaaatt   Gaacctatga   Ttctctagca   Aaacagtaat   60     Mgcatttgct   Tacaccattg   Atgaattatg   Tttgttttta   Aaactcaaag   Agcattctcg   120     t   121       <210>   98   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   98   Gtggtgcagc   Tccaaggggc   Taataaatga   Gaatgctgga   Tctcctgatg   Tttgtccgta   60     Ygaaagttat   Gaaccgcaac   Agggggtta   Aagtcattta   Acatgctggg   Tagggtaaag   120     g   121       <210>   99   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   99   Agtgtcctgc   Acatcaggca   Cgcctttaaa   Tgccccttta   Acacacatgat   Tccagcaaca   60     Ragagaagca   Gaaaatcacg   Aaaaccatgt   Aaattactcc   Aattcttgat   Caacctccca   120     t   121       <210>   100   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   100   Cctctctctt   Ggactatctc   Aagaaaattc   Aattctctca   Ccaagaatgt   Tttctctttt   60     Mttccctctt   Ggatcacttc   Cctaattgat   Tttatatctt   Aacttagttg   Aagtatactg   120     g   121       <210>   101   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   101   Tatatcggcc   Acatacaaca   Cgtaccagat   Taggaggatt   Gaaaactgta   Atggctgaga   60     Yaactttata   Tgaaaatgaa   Gccgctgtta   Gttatgatcc   Taatcctact   Gatgatgaag   120     a   121       <210>   102   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   102   Tttaaatcgc   Gatttcagga   Cacatagtac   Tgttgtgtgg   Ccatcttttt   Tgactttgtt   60     Satgcaatta   Tacagggtaa   Taattaaaaa   Tagggtaata   Attaaccaga   Gtctacaaaa   120     t   121       <210>   103   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   103   Atcattaatt   Gtcatcctaa   Ccaaacactt   Tcttgatcag   Ttccttttct   Tggtttctga   60     Yatccaaatc   Ttccatgaca   Caattaaaga   Aaatcaagaa   Tgaacagata   Atctcatgtt   120     t   121       <210>   104   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP   For   Bunch   Number   (BNP)     <400>   104   Atgggtcatt   Tcttctaaga   Tccttttttt   Tcaactactc   Cattttgttg   Tggtattttt   60     Katgctaaaa   Agttgggttg   Aatcccattt   Tcattattga   Aattttcaaa   Atttttattt   120     t   121       <210>   105   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   105   Taacagttaa   Tgagtcccct   Caatccacct   Gattggattc   Tgtttttcca   Agctaccggg   60     Ycttaaattg   Ggatgccttc   Tattaaagtt   Gtgcaataat   Ctgagattta   Aattagtatc   120     t   121       <210>   106   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   106   Aaatgtgtct   Gacttatcca   Aaacatctct   Taggctgcat   Aatgagctcc   Agatggccag   60     Ytgaaaaact   Catcaccgt   Tcccgacatc   Aagtattgga   Tgatgtcagt   Aaccacatga   120     g   121       <210>   107   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   107   Tttcatcgat   Atatattata   Tggttggatg   Attcatgctt   Gttgattttc   Tgatatatac   60     Ygcaagtttc   Tttggtatat   Actcagtagc   Aattcaatac   Acacctcgga   Taaatcgata   120     a   121       <210>   108   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   108   Gaggcctaaa   Cgatttcctg   Tttccattga   Tgatgactta   Catcatgatg   Aaaggagatg   60     Wataatggtt   Taaaaatata   Tattgagtgc   Tgtcaataat   Cagggtcacc   Ttctagaggg   120     t   121       <210>   109   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   109   Ccactcctta   Gttaagcttg   Acccgccgtt   Cacaggcagg   Tgtgaagccg   Tcattgagtc   60     Yctcagacaa   Cacattcggg   Ccaacctcat   Ctttaccctg   Gagcaactct   Ttgctagtaa   120     a   121       <210>   110   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   110   Ctgatctgat   Cagtcttctc   Gggagtgcta   Gtttgcatga   Agacttctt   Cttaactgat   60     Ytggatcttt   Ttctcaaatt   Tttaaaattt   Ttttagagat   Tgaagataga   Cttctagagg   120     a   121       <210>   111   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   111   Gaagaaaaga   Aagaaccaga   Tgaaaaatct   Gaagggtact   Aagcatctga   Agaggtcaat   60     Raatagtgct   Gctcgcttgc   Gatgaacatt   Cccacctctg   Actcatttta   Aatacttatc   120     t   121       <210>   112   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   112   Caattcatgc   Gccagcttgt   Aatcccccaa   Agtcgaagcc   Tcaagcttcg   Ggccagttga   60     Ygcaatccca   Aaagcaccag   Ctgtctcgac   Tgatgcctct   Tctagggaaa   Ggtcgacaac   120     t   121       <210>   113   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   113   Ctatgatgag   Cttcattgat   Tttactttat   Ttctttgggg   Acatgtctca   Cttatcgtaa   60     Kgcatatttt   Aaataggatt   Ccttctaaat   Ccattcctac   Tataccatat   Gagatatggc   120     a   121       <210>   114   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   114   Ctttgtcact   Gaggccagga   Tctcttgcct   Catacttcag   Cagttctaaa   Acatccacag   60     Yggagacacc   Acctagtgtt   Agtgctgcaa   Tagctggtgc   Acgggctgca   Gctgcacagt   120     t   121       <210>   115   <211>   121   <212>   DNA   <213>   Artificial sequence     <220>   <223>   SNP of the number of strings     <400>   115   Cgttctattt   Taaataaata   Aataaataaa   Tatttaggga   Ggcaagacta   Cgctgagtcg   60     Yggtgtcggt   Ttggtagatt   Acgtgcggta   Taccgcaggc   Aaaactggaa   Ccaggcgagc   120     t   121

Claims (25)

A method of predicting or determining a phenotype of a crop of interest, wherein the method comprises detecting one or more polymorphic genes selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 115 Whether the tag exists or not. The method of claim 1, wherein the crop is a palm family. The method of claim 1, wherein the crop is an oil palm. The method of claim 2, wherein the oil palm is from the genus Oil palm. The method of claim 3, wherein the oil palm is composed of a tenera type. The method of claim 3, wherein the oil palm is composed of a variation selected from the group consisting of pisifera and dura . A method according to any one of the preceding claims, wherein the oil palm is selected from the group consisting of a sub-group of Deli, Nigerian and East Sumatra rubber plantation associations. (Algemene Vereniging van Rubberplanters ter Oostkust van Sumatra, AVROS) cultivated background composition. The method according to any one of the preceding claims, wherein the oil palm has at least one of at least one selected from the group consisting of a background of a joint general meeting of the rubber plantation of Delhi, Nigeria and East Sumatra. One or three genetic backgrounds of the subgroup. The method of claim 1, wherein the presence or absence of the one or more polymorphic gene markers is determined in a crop sample comprising a genetic material selected from the group consisting of leaves a group consisting of tender stems, stems, saplings, roots, buds, flowers, seeds, fruits, and fragments. The method of claim 1, wherein the crop phenotype of interest is selected from the group consisting of fresh fruit bunches, string size, fruit size, oil extraction ratio, number of strings, and combinations thereof. One of the group consisting of production components. The method of claim 10, wherein the crop phenotype of interest is related to the yield of fresh fruit bunches or the number of strands. The method according to any one of the preceding claims, wherein the polymorphic gene marker is SNPs (single nucleotide polytype), SSRs (simple sequence repeat), AFLPs (amplified fragment length polytype), RAPDs (random amplified polymorphic DNA) and combinations thereof. The method of claim 9, wherein the one or more polymorphic gene markers are single nucleotide polymorphism. A method according to any one of the preceding claims, wherein the single nucleotide polymorphism associated with increased yield of fresh fruit bunches is selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO 79 One, two or more gene markers. The method of claim 14, wherein the single nucleotide polymorphism associated with increased yield of fresh fruit bunches is selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 3, and SEQ ID NO: 6. 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, SEQ ID NO: 51, SEQ ID NO: 52, SEQ ID NO: 53, SEQ ID NO: 54, SEQ ID NO: 56, SEQ ID NO: 57, SEQ ID NO: 58, SEQ ID NO: 60, SEQ ID NO: 62, SEQ ID NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, SEQ ID NO: 66, SEQ ID NO 67, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ ID NO: 73, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, and SEQ ID NO: 79 One of the groups formed, Or more genetic markers. The method of claim 15, wherein the single nucleotide polymorphism associated with increased yield of fresh fruit bunches is selected from the group consisting of SEQ ID NO: 8, SEQ ID NO: 10, and SEQ ID NO: 13. a gene marker of one, two or more of the group consisting of SEQ ID NO: 19, SEQ ID NO: 20 and SEQ ID NO: 34. A method according to any one of the preceding claims, wherein the single nucleotide polymorphism associated with an increase in the number of strands is selected from the group consisting of 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, and SEQ ID NO: 80 to SEQ ID NO: 115 One, two or more gene markers. The method of claim 17, wherein the single nucleotide polymorphism associated with an increase in the number of strands is selected from the group consisting of 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, one or two or more gene markers of the group consisting of SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 105, SEQ ID NO: 106, and SEQ ID NO: 113. The method of claim 18, wherein the single nucleotide polymorphism associated with an increase in the number of strands is selected from the group consisting of SEQ ID NO: 80, SEQ ID NO: 90, SEQ ID NO: 99, A gene signature of one, two or more of the groups consisting of SEQ ID NO: 105 and SEQ ID NO: 106. A method according to any one of the preceding claims, wherein the method is selected from the group consisting of the marker number 1 (SEQ ID NO: 10), the marker number 2 (SEQ ID NO: 20), and the marker number 3 (SEQ ID NO: 13) The presence of a marker of at least one or all of the group consisting of marker number 4 (SEQ ID NO: 8), marker number 5 (SEQ ID NO: 19), and marker number 6 (SEQ ID NO: 34) Increased production of fresh fruit bunches. The method of any one of the preceding claims, wherein the method is selected from the group consisting of the marker number 1 (SEQ ID NO: 10), the marker number 3 (SEQ ID NO: 13), and the marker number 5 (SEQ ID NO: The presence of a marker of at least one or all of the group consisting of 19) and marker number 6 (SEQ ID NO: 34) is associated with a decrease in the yield of fresh fruit bunches. A method according to any one of the preceding claims, wherein the presence of a combination of marker number 8 (SEQ ID NO: 105) and marker number 11 (SEQ ID NO: 80) is associated with a change in the number of strands. The method of any of the preceding claims, wherein the combination of marker number 7 (SEQ ID NO: 90), marker number 9 (SEQ ID NO: 106), and marker number 10 (SEQ ID NO: 99) The existence is related to the change in the number of strings. A method according to any one of the preceding claims, wherein the presence of the marker number 11 (SEQ ID NO: 80) is related to a change in the number of strands. Use of a gene marker according to any one of the preceding claims, as a criterion for selecting a crop, wherein the selection is based on the presence or absence of one or more polymorphic gene markers .