Method to identify and characterize chromosomal translocations segregating in a plant mapping population

Abstract

The present invention provides a method for the identification and characterization of chromosomal rearrangements, particularly reciprocal translocations, segregating in a plant mapping population, including a biparental mapping population of a polyploid plant species, based on the identification of allelic dosage imbalances in SNP genotyping data. Further, the invention provides a method allowing the deciphering of true aneuploid genotypes and tracking segregation thereof in a given population. Finally, the invention provides plant cells and materials and the use of suitable SNP genotyping data sets corresponding to or allowing the identification of single individuals being a carrier of the translocation and / or in addition an interesting aneuploid genotype.

Description

Munich, 30 September 2025Our Ref.: BM 5711-02WO CMC / gasAppiicant / Proprietor: BASF Agricultural Solutions US LLC, BASF SE serial Number: Subsequent Application based on EP24203537.6BASF Agricultural Solutions US LLC2 TW Alexander Drive Research Triangle Park, 27713 NC USABASF SECarl-Bosch-StraBe 38, 67056 Ludwigshafen am RheinGermanyMethod to identify and characterize chromosomal translocations segregating in a plant mapping populationTECHNICAL FIELDThe present invention provides a method for the identification and characterization of chromosomal rearrangements, particularly reciprocal translocations, segregating in a plant mapping population, including a biparental mapping population of a polyploid plant species, based on the identification of allelic dosage imbalances in SNP genotyping data. Further, the invention provides a method allowing the deciphering of true aneuploid genotypes and tracking segregation thereof in a given population. Finally, the invention provides plant cells and materials and the use of suitable SNP genotyping data sets corresponding to or allowing the identification of single individuals being a carrier of the translocation and / or in addition an interesting aneuploid genotype.BACKGROUNDThe possibilities for modern plant breeding have changed significantly since the development of molecular marker technologies back in the 1980ies. Different classes of molecular markers have been developed, with single-nucleotide polymorphism (SNP) markers being the most important among them, and advancements in sequencingtechnologies have enabled the identification of a high density of these markers across genomes. Genetic mapping using SNPs has established itself as a powerful tool to track genetic variations across populations and assist in breeding programs by associating specific genetic variants with desired characteristics.Translocations in chromosomes have been shown to significantly impact crosses during breeding. A translocation heterozygote segregates unbalanced (aneuploid) gametes that might have decreased viability. Surviving aneuploid progeny of a translocation heterozygote exhibit unusual and / or unbalanced allelic dosage in affected chromosomal regions, which in turn may influence expression of dosage-sensitive genes and present as phenotypic effect. Structural variations, such as translocations, may also have a direct phenotypic effect, as when the reshuffling of sequences around breakpoints leads to the creation of fusion genes, or when an expression of a gene close to the breakpoint is altered (Lv et al., 2023; Schilbert et al., 2023).Further, a reciprocal translocation (RT) segregating in a mapping population presents technical issues for any downstream genetic analysis involving the affected chromosomes. The construction of their linkage map is neither trivial nor routine (Farre, et al., 201 1). When segmental aneuploidy segregates in the progeny, markers spanning the region show segregation distortion or a presence / absence-like pattern of genotypes and are normally discarded from analysis, which prevents accurate mapping of any QTL residing in the translocated regions (Stein, et al., 2017).The first hypothesis that translocations affect segregation and fertility in plant populations came from Barbara McClintock (McClintock, 1930). Traditionally, the presence of a translocation was and still is confirmed by cytogenetic methods, typically by examination of metaphase chromosomes. Later, molecular markers were used to query the allelic state at different position of the genome. Even though the first marker assays were qualitative in nature (RFLP, SSR), apparent deletions segregating in aneuploid progeny of a translocation heterozygote were detectable and allowed an approximate inference of the translocation breakpoint (Sharpe, et al., 1995; Parkin, et al., 1995; Gaeta, et al., 2007). Evidently, the dosage-free nature of these markers did not allow the identification of aneuploid progeny with nulliploid, monoploid, triploid, ortetraploid chromosomal segments.More recently, two methods that rely on uncovering regions of high or low total allelic dosage have been widely used to identify unbalanced chromosomal rearrangements: comparative genomic hybridization (CGH) and copy-number variations (CNV) detection bySNP genotyping, both typically applied in a high-density array format (Alkan, et al., 2011 ; Zhang, et al., 2017). CGH measures the total number of copies of a target sequence (i.e., total allelic dosage) and can thus detect apparent deletions and duplications relative to a reference line. SNP genotyping, as implemented on the Illumina™ and Affymetrix™ arrays, can also output total allelic dosage as well as the allelic ratio for each individual. Similarly to CGH, these metrics allow detection of apparent deletions and duplications. Thus, aneuploid individuals carrying unbalanced translocated chromosomes will be detectable and the prediction of breakpoints becomes possible. Balanced rearrangements, such as a RT, are invisible to both methodologies.Numerous methods have been developed for identification of CNVs based on array SNP genotype data in mammals (Winchester, et al., 2009). An implementation of one such method - circular binary segmentation (Olshen, et al., 2004) - has been reported for use in an allopolyploid plant species and shown to identify deletions and duplications in Brassica napus (Grand ke, et al., 2017).Still, CNV methods typically investigate each individual separately for apparent deletions and duplications, i.e., for changes in total allelic dosage, but cannot decide where on the genome any potential additional copy of the target sequence reside. In addition, while some of these methods also estimate allelic ratios, CNV and CGH as well as any kind of long read chromosome sequencing do not consider parental origin of each allele. This is a considerable disadvantage, as these methods cannot discriminate reciprocal aneuploid states and will thus give identical results. When used in isolation, CNV methods are more useful for genotyping of previously described translocations or in inferring homeologous exchanges in allopolyploids, where it is assumed that the most likely new location of the translocated segment is on a homeologous chromosome. CNV methods, however, do not identify and characterize novel rearrangements involving two chromosomes not yet previously described and mapped. Furthermore, balanced rearrangements do not result in copy number changes and will not be detected by CNV methods.Another method has been previously described that investigates simulated data of reciprocally translocated chromosomes and their segregation, and pseudo-linkage, in a population. However, this method ignored aneuploid individuals by considering them genotyping errors, identified the breakpoint only roughly (even in the simulated data), and has not gained wide acceptance (Durrant et al., 2006).Another method has been reported that seeks to identify homeologous exchanges (HE) in Brassica napus based on SNP genotyping data and genomic re-sequencing data (Stein et al., 2017). This method preserves presence / absence SNP markers and identifies deleted regions. Markers that would map into duplicated regions and would present with segregation distortion and / or unusual cluster patterns are not investigated at all beyond one specific case denoted by the authors as “hemi-SNP” (homozygotes: “heterozygotes” in 1 :1 ratio). Further genetic analysis ofthe progeny is confined to linkage map construction. Thus, while the output of the re-sequencing-based CNV analysis projected to the Brassica napus reference genome appears convincing, the linkage map construction disregarded the SNP data for chromosomes with HEs. The method does not address identification of breakpoints.US 2022 / 0411883 A1 discloses a method for verifying a previously known chromosomal translocation between the N07 and N16 chromosomes of Brassica napus (Charne et al., 2022). The disclosure departs from existing knowledge of the presence of an N07-N16 translocation in the Brassica gene pool and in any specific translocation-carrier line. The application further describes an ascertainment of pseudo-linkage between chromosomes N07 and N16 as a method to declare the presence of a translocation in a population. However, to be able to decide for a single individual whether it carries the translocation or not and / or whether it has the full genome complement or not (i.e., is a segmental aneuploid) additional methods are needed, which US 2022 / 0411883 A1 is completely silent on. Charne et al. (2022) identify, by sequencing of a known translocation carrier, the breakpoints of the two involved chromosomes, and develop marker assays for identifying these specific breakpoints in breeding material.The gold standard methods for identification and visualization of chromosomal rearrangements are fluorescent in situ hybridization (FISH) (Jiang & Gill, 2006) and genomic in situ hybridization (GISH) (Silva & Souza, 2013). These are arguably among the most laborious and technically challenging methodologies.None of the methods currently available considers recoding all genotyping data in aneuploid progeny, except at most for obvious deletions.Consequently, there is a pressing need to identify a novel and generally applicable method that would a) provide helpful information on the exact nature of a chromosomal rearrangement, b) identify which chromosome(s) are involved, c) decipher chromosomal configuration in the donor parent, and d) specify the location of translocation breakpoint(s)and, hence, the putative extent of segmental aneuploidy in the progeny. Further, such a method should allow the identification and characterization of RTs that segregate in biparental mapping populations of plant allopolyploid species using only SNP genotyping data (e.g., using an Illumina Infinium™ array) using available sets of markers, primers and / or probes.To reliably screen progeny of a cross in which a novel chromosomal translocation event segregates, methods are urgently needed that would identify which individuals carry nontranslocated chromosomes, which carry translocated chromosomes, and which carry both (segmental aneuploidy). Importantly, it is specifically segmental aneuploids that allow the refinement of breakpoint locations; in the absence of a full genomic sequence of translocation carrier it is the only tool to do so. Furthermore, knowledge of a possible presence of a chromosomal rearrangement is beneficial before deployment of individuals in breeding programs so that methods suitable to reliably and reproducibly characterize such chromosomal rearrangements are needed.SUMMARY OF THE INVENTIONThe present invention solves the above needs by providing, in a first aspect, a method for identifying at least one translocation chromosomal rearrangement in a plant mapping population, including a plant biparental mapping population, preferably wherein the method allows the identification of an aneuploid individual, including a segmental aneuploid individual, as well as the identification of the position of a translocation breakpoint in at least one chromosome, the method comprising the following steps: (i) providing a singlenucleotide polymorphism (SNP) genotyping data set for a set of markers, preferably a curated SNP genotyping data set, preferably wherein the SNP genotyping data set is obtained by or obtainable by a suitable genotyping technology, the data set representing individual genotype samples of the plant mapping population, including a biparental mapping population, of interest, wherein each marker has a known position on a reference map, including a physical map and / or a genetic consensus map; (ii) calculating a matrix of pairwise recombination frequencies for each of the markers of the set of markers of (i), and / or calculating a logarithm of the odds (LOD) score, to identify at least one pseudolinkage event fortwo chromosomes orfortwo chromosome segments, wherein the pseudolinkage event identifies translocation between two chromosomes or chromosome segments and provides at least one location suitable as a sorting position for fine-mapping of at least one putative translocation breakpoint; (iii) sorting the set of markers for each of two chromosomes or chromosome segments defined to be in an apparent linkage to eachother due to at least one pseudo-linkage event identified in step (ii) and sorting the individuals of the plant mapping population based on the genotype of the individual at the at least one putative translocation breakpoint as determined in step (ii); (iv) optionally: calculating the number of apparent crossovers for the at least one selected individual analyzed and optionally: repeating step (iii) and / or (iv) to refine the results; (v) inspecting the genotyping data set for identifying an unexpected pattern, and optionally: repeating step (iii) and / or (iv) and / or (v) to refine the results; and (vi) identifying at least one individual comprising said unexpected pattern and identifying the position of at least one translocation breakpoint based on the location of the unexpected pattern in said at least one individual, preferably including identifying the configuration of the at least one translocation in at least one parent, in at least one chromosome and optionally allocating the information related to the at least one translocation breakpoint to at least one carrier individual.In one embodiment, there is provided a method wherein step (vi) further comprises, for each individual in the biparental mapping population analyzed, specifically differentiating selected individuals and attributing the individuals into a group of individuals either (a) not carrying a translocation; (b) carrying a translocation; or (c) carrying a translocation and further carrying an aneuploid configuration in the chromosomes involved in the translocation; and optionally: assigning a specific phenotype to a specific individual of configurations (a) to (c) in a biparental mapping population; and optionally: selecting and / or obtaining at least one individual having an aneuploid, including a segmental aneuploid, genotype of interest.In another embodiment, there is provided a method wherein step (vi) of allocating the information related to the at least one translocation breakpoint to at least one carrier individual further includes determining the type of aneuploidy, including segmental aneuploidy, for each individual in the biparental mapping population analyzed, wherein the determination includes defining a gamete or progeny chromosome architecture as depicted in Figure 2 or 3, preferably to identify at least one aneuploid, including a segmental aneuploid, event or gamete and / or carrier individual.In yet another embodiment, there is provided a method wherein the SNP genotyping data are obtained by or obtainable by a suitable genotyping technology generating a quantitative or at least semi-quantitative result for SNP allele dosage, preferably wherein the assay is selected from a microarray, including a bead microarray, a hybrid-SNP microarray and a customized genomic DNA fragment microarray, a whole genome sequencing data set, a quantitative PCR data set, a next-generation sequencing data set, or an ampliconsequencing based data set, or any combination thereof, preferably wherein the assay is a microarray assay.In another embodiment, there is provided a method wherein the plant biparental mapping population is independently selected from a diploid or polyploid population, including an F2 population, doubled haploid (DH) population, a recombinant inbred line (RIL) population, a backcross population, and / or involves at least one synthetic polyploid parent.In a further embodiment, there is provided a method wherein the plant biparental mapping population is created from a polyploid or a synthetic polyploid parental plant.In another embodiment, there is provided a method wherein the plant is independently selected from Triticum spp., including Triticum durum and Triticum aestivum, Triticale, Brassica spp., including Brassica napus, Brassica carinata and Brassica juncea, Gossypium spp., including Gossypium hirsutum and Gossypium barbadense, and synthetic polyploids, such as artificial auto-tetraploid Zea mays.In yet another embodiment, there is provided a method wherein the method includes an additional step of regenerating and / or propagating a plant from a selected individual analyzed having at least one translocation chromosomal rearrangement in its genome.In one embodiment, there is provided a screening method identifying at least one translocation chromosomal rearrangement in a plant mapping population, including a plant biparental mapping population according to the first aspect, wherein step (vi) includes identifying the configuration of the at least one translocation in at least one of the parent by: (vii) comparing the arrangement of at least one translocated segment and the position information of the at least one translocation breakpoint with theoretical prediction of segregation of translocated genomic segments during meiosis, and optionally: pairing of gametes to assist identification of an aneuploid individual and / or linkage map construction, in the progeny of a translocation heterozygote in the plant biparental mapping population.In one embodiment, there is provided a method for identifying at least one translocation chromosomal rearrangement in a plant mapping population, including a plant biparental mapping population according to the first aspect, wherein step (vi) includes identifying the configuration of the at least one translocation in at least one of the parent by: (vii) comparing the arrangement of at least one translocated segment and the position information of the at least one translocation breakpoint with theoretical prediction of segregation oftranslocated genomic segments during meiosis, and optionally: pairing of gametes to assist identification of an aneuploid individual and / or linkage map construction, in the progeny of a translocation heterozygote in the plant biparental mapping population, wherein the method may include an additional step of regenerating and / or propagating a plant from a selected individual analyzed having at least one translocation chromosomal rearrangement in its genome.In another embodiment, there is provided a method wherein the method comprises an additional step of: (viii) determining allelic ratios from the data set considering the parental origin of each allele to identify and thus provide at least one reciprocal translocation event position.In yet another embodiment, there is provided a method further comprising a step of: (ix) selecting and / or obtaining at least one individual carrying the at least one reciprocal translocation positioned as the at least one translocation event position in step (viii) in its genome.In a second aspect, there is provided a cell, tissue, organ, material, seed orwhole organism obtained by or obtainable by a method according to the first aspect and the various embodiments related thereto.In a third aspect, there is provided a customized SNP genotyping technology configured for being suitable for performing a method according to the first aspect and the various embodiments related thereto.In a fourth aspect, there is provided a use of single-nucleotide polymorphism (SNP) genotyping data set as defined according to the first aspect and the various embodiments related thereto, or a use of a method according to the first aspect and the various embodiments related thereto for determining and optionally correcting genotyping errors or miscalled data in a reference genotyping data set by identifying the true allele dosage in the single-nucleotide polymorphism genotyping data set by considering both aneuploidy and diploid chromosomal segments in all individuals of a biparental mapping population.In a fifth aspect, there is provided a use of single-nucleotide polymorphism (SNP) genotyping data set as defined according to the first aspect and the various embodiments related thereto, or a use of a method according to the first aspect and the various embodiments related thereto, for precisely identifying the position of at least onetranslocation breakpoint representing the result of at least one translocation chromosomal rearrangement in at least one chromosome in a plant of a biparental mapping population, optionally including the precise determination of the orientation of the at least one translocation on the at least one chromosome, where the translocation occurred.BRIEF DESCRIPTION OF DRAWINGSFigure 1 (Fig. 1) shows a schematic overview of segregation of chromosomes in an RT heterozygote during meiosis and the resulting gametes. The two chromosomes involved in the translocation are indicated by white and black fill. An RT heterozygote carries a translocated and a non-translocated version of both chromosomes, which come together during meiosis to form a structure called “quadrivalent”. A quadrivalent can be resolved by three possible pathways: by an “alternate segregation”, which restores parental configuration (“balanced” gametes), or by “adjacent-1 ” and “adjacent-2” segregations, which result in gametes that do not carry the full genome complement (“unbalanced” gametes). The viability of unbalanced gametes and the zygotes they produce is typically compromised; this effect is less pronounced in polyploid species where additional copies may buffer the imbalances.Figure 2 (Fig. 2) shows gametes produced by an RT heterozygote and outcomes of their pairing in an F2 population at the level of genotypes. Possible female gametes are in rows (1 to 6), male gametes are in columns (1 to 6). Translocation breakpoints are shown by a slanted line separating black and white fill. F2 individuals in dashed-line boxes have a full genome complement, i.e., they have two copies of each segment of the two chromosomes. All others F2 individuals are segmental aneuploids, i.e., they carry at least one chromosomal segment with fewer than two copies or, conversely, more than two copies. Numbers above and below a horizontal divider correspond to the dosage of chromosomal segments (“white” / ”black”) above and below the translocation breakpoints, respectively. Possible aneuploid states may include monosomy (1), trisomy (3), tetrasomy (4) and nullisomy (0). Individuals exhibiting an aneuploid state in at least one chromosomal segment are herein referred to as “having (segmental) aneuploid architecture” and are individuals themselves are referred to as “(segmental) aneuploids”.Figure 3 (Fig. 3) shows possible gametes and resulting progeny in a DH (doubled haploid) population generated through another culture, thus male gametes (1 to 6) are shown. Similar to Figure 2, boxed individuals (shown in dashed-line boxes) have a full genome complement, i.e., they have two copies of each segment of the two chromosomes. All otherDH individuals are segmental aneuploids, i.e., they carry at least one chromosomal segment with fewer than two copies or, conversely, more than two copies. Translocation breakpoints are shown by a slanted line separating black and white fill. Numbers above and below a horizontal divider correspond to the dosage of chromosomal segments (“white” / ”black”) above and below the translocation breakpoints, respectively. Possible aneuploid states may include tetrasomy (4) and nullisomy (0). Individuals exhibiting an aneuploid state in at least one chromosomal segment are herein referred to as “having (segmental) aneuploid architecture” and are individuals themselves are referred to as “(segmental) aneuploids”.Figure 4 (Fig. 4) shows a linkage group (“LG”) showing pseudo-linkage between chromosomes 5B and 7B in a wheat F2 population. Bars depict chromosomes with markers, scale is in cM. The 5B / 7B pseudo-linkage group is drawn in the middle and is flanked by the 5B (left) and the 7B (right) consensus genetic maps. Collinearity is shown by lines connecting identical markers. Further details are provided in Example 1 below.Figure 5 (Fig. 5) shows 5B and 7B genotypes of a wheat F2 population. Relative genotypes (aa, bb, and ab) are represented by different shades of grey. Markers, in rows, were sorted according to the Chinese Spring v1.0 physical map. Individuals, in columns, were sorted according to genotypes at both putative translocation breakpoints (arrows). Identity of the expected pair of gametes that gave rise to each block of individuals is indicated by numbers above the plot (cf. Fig. 2). The few unlabeled individuals on the right side of the plot might represent products of rarer gamete types and / or their pairing (cf. Fig. 2) but were not investigated further because of their low number. For details, see Example 1 below.Figure 6 (Fig. 6) shows an example of a wheat cluster plot (marker SEQ ID NO: 24) of normalized intensity versus normalized theta (GenomeStudio). The darker grey area in the center represents heterozygous calls, as assigned by GenomeStudio, while the lighter grey areas on either side correspond to the two homozygous genotype classes. An oval-shape solid-line outline demarcates a GenomeStudio-calculated confidence region (GenCall boundary) for each of the 3 default genotype classes. A suggested split of a genotype class into sub-clusters is indicated by a dashed outline; the corresponding dosage of the two parental alleles (X and Y) is shown.Figure 7 (Fig. 7) shows N07 and N16 genotypes of an oilseed rape DH population. Relative genotypes (aa, bb, and ab) are represented by different shades of grey. Markers, in rows, were sorted according to Darmor-bz v10 physical map. Individuals, in columns, were sortedaccording to genotypes at both putative translocation breakpoints (arrows). Identity of the expected pair of gametes that gave rise to each block of individuals is indicated by numbers above the plot (cf. Fig. 3). The few unlabeled individuals on the right side of the plot might represent rarer gamete types (cf. Fig. 3) but were not investigated further because of their low number.Figure 8 (Fig. 8) shows an example of an oilseed rape cluster plot (marker SEQ ID NO: 39) of normalized intensity versus normalized theta (GenomeStudio). The darker grey area in the center represents heterozygous calls, as assigned by GenomeStudio, while the lighter grey areas on either side correspond to the two homozygous genotype classes. An oval-shape solid-line outline demarcates a GenomeStudio-calculated confidence region (GenCall boundary) for each of the 3 default genotype classes. Individuals that are tetrapioid or nulliploid at the SEQ ID 39 locus in the DH population under study are represented by black-filled circles at the top and bottom of the middle section of the plot, respectively. Control heterozygous genotypes (ab) are denoted by dash-dot oval-shape outline. The estimated dosage of the two parental alleles (X and Y) is shown for each cluster.DEFINITIONSThe term “allelic dosage” or “allele dosage” as used herein refers to the number of copies of an allele in a given individual. The “total allelic / allele dosage” refers to the combined number of copies of all alleles present in an individual. The “SNP allele dosage” in turn refers to the allele or allelic dosage as determined in view of the quantitative or semi- quantitative determination of SNP information related to an allele of interest. The “allelic ratio” is the ratio of the dosage of the two alleles at a single site in an individual.The terms “aneuploidy” or“aneuploid individual” refers to a state or to an individual with an abnormal number of chromosomes, or an individual in which some chromosomal segments are represented by an unusual number of copies, called “segmental aneuploidy”. Generally, “aneuploidy” refers to the loss or gain of whole chromosomes, or in a broader sense as used herein parts of chromosomes, relative to an established karyotype. Herein, the terms segmental (synonym: partial, in a portion of a chromosome) aneuploidy is implied when “aneuploid individuals” are mentioned. Different conditions of aneuploidy as commonly understood are: (1) nullisomy, i.e. the loss of both pairs of homologous chromosomes. Individuals are called nullisomics and their chromosomal composition is 2N-2. (2) Monosomy refers to the loss of a single chromosome. The resulting individuals are calledmonosomies and their chromosomal composition is 2N-1 . (3) Trisomy denotes the gain of an extra copy of a chromosome. The resulting individuals are called trisomics and their chromosomal composition is 2N+1 . (4) Tetrasomic as further type of aneuploidy means the gain of an extra pair of homologous chromosomes. The resulting individuals are called tetrasomics and their chromosomal composition is 2N+2. “Aneuploidy” thus means the presence of an abnormal number of whole chromosomes, caused by the gain or loss of one or more (complete) chromosomes, leading to an imbalance compared to the normal set. “Segmental aneuploidy” means the gain or loss or rearrangement of only part or segment of a chromosome (thus segmental), rather than the whole chromosome, leading to local gene dosage imbalances. The segment, as it is shown in Figures 2 and 3, may be translocated.A “biparental mapping population” or simply “biparental population” as used herein refers to a set of plant individuals derived from a cross between two parents, wherein the parents may belong to different species of a botanical genus and differing in terms of phenotype and traits to share.The term “carrier” as used herein specifically in the context of a chromosomal rearrangement, including a (reciprocal) translocation, refers to any individual that carries at least one (partially) translocated chromosome(s). This can occur in balanced and unbalanced configurations, i.e., a balanced donor parent as well as an unbalanced progeny can be a carrier.A “chromosomal rearrangement” as used herein refers to a change in structure of a native chromosome. A chromosomal rearrangement can be balanced or unbalanced (see RT below). No loss or gain of genetic material results from a balanced chromosomal rearrangement.The term “crossing” as used herein refers to the fertilization of a female plant (or a gamete thereof) by a male plant (or a gamete thereof). In this context, the term “gamete” refers to the haploid reproductive cell (egg or sperm) produced during meiosis from a gametophyte and involved in sexual reproduction, during which two gametes of the opposite sex fuse to form a diploid zygote. The term thus generally includes reference to pollen (including the sperm cell) and an ovule (including the ovum). Based thereon, “crossing” refers to the fertilization of ovules of one individual with pollen from another individual, whereas “selfing” refers to the fertilization of ovules of an individual with pollen from the same individual.The term “genetic mapping” or “mapping” as used herein refers to the grouping of molecular markers and / or genes, determining their relative order, and calculating the genetic distances between them based on linkage observed in one or multiple populations. Genetic map is the result of genetic mapping and is a scaled representation of the linear order of molecular markers and / or genes on a given chromosome, where distances reflect recombination frequency between the mapped entities.Historically, genetic mapping is an important tool for genomic studies and for molecular breeding of economically important cultivars. A genetic linkage map with high density and resolution is a critical and indispensable tool in a wide range of genetic and genomic research applications. Highly saturated genetic linkage maps are extremely helpful to breeders and are an essential prerequisite for many biological applications such as the identification of marker-trait associations, mapping Quantitative Trait Loci (QTL), candidate gene identification, development of molecular markers for Marker-Assisted Selection (MAS) and comparative genetic studies. Molecular markers are the basis for high- resolution genetic linkage map construction and Quantitative Trait Loci (QTL) fine-mapping, which provide powerful tools for genetic analyses of economic traits.A ‘homeologous exchange” or “HE” as used herein refers to a chromosomal rearrangement occurring in allopolyploids, whereby a segment of one chromosome is replaced by homeologous sequences from its counterpart.“Identity” and / or “homology”, as used in the context of comparison of two or more nucleic acid or amino acid molecules, refers to the degree of similarity between the sequences of said molecules, where similarity is understood to mean the proportion of the sequence that is identical. For example, enzyme variants may be defined by their sequence identity when compared to a parent enzyme. Sequence identity is usually provided as “% sequence identity” or “% identity”. To determine the perce nt- id entity between two amino acid sequences, a pairwise sequence alignment between the two sequences is first generated, wherein the two sequences are aligned over their complete length (i.e., a pairwise global alignment). The alignment is generated with a program implementing the Needleman and Wunsch algorithm (see, e.g., http: / / dx.doi.Org / 10.1016 / 0022-2836(70)90057-4), preferably by using the program “NEEDLE” (The European Molecular Biology Open Software Suite (EMBOSS)) with the programs default parameters (gapopen=10.0, gapextend=0.5 and matrix=EBLOSUM62). The preferred alignment for the purpose of this invention is that alignment from which the highest sequence identity can be determined.The following example illustrates calculation of %-identity between two nucleotide sequences (also applicable to marker or gene sequences as used herein); the same calculations apply to protein sequences:Seq A: AAGATACTG; length: 9 basesSeq B: GATCTGA; length: 7 basesHence, the shorter sequence is sequence B.Producing a pairwise global alignment which is showing both sequences over their complete lengths results in:Seq A: AAGATACTG-Seq B: - - GAT- CTGAThe “I” symbol in the alignment indicates identical residues (which means bases for DNA or amino acids for proteins). The number of identical residues is 6.The symbol in the alignment indicates gaps. The number of gaps introduced by alignment within the Seq B is 1 . The number of gaps introduced by alignment at borders of Seq B is 2, and at borders of Seq A is 1 .The alignment length showing the aligned sequences over their complete length is 10.Producing a pairwise alignment which is showing the shorter sequence over its complete length according to the invention consequently results in:Seq A: GATACTG-Seq B: GAT- CTGAProducing a pairwise alignment which is showing sequence A over its complete length according to the invention consequently results in:Seq A: AAGATACTGSeq B: - - GAT- CTGProducing a pairwise alignment which is showing sequence B over its complete length according to the invention consequently results in:Seq A: GATACTG-Seq B: GAT- CTGAThe alignment length showing the shorter sequence over its complete length is 8 (one gap is present which is factored in the alignment length of the shorter sequence).Accordingly, the alignment length showing Seq A over its complete length would be 9 (meaning Seq A is the sequence of the invention).Accordingly, the alignment length showing Seq B over its complete length would be 8 (meaning Seq B is the sequence of the invention). After aligning two sequences, in a second step, an identity value is determined from the alignment produced. For purposes of this description, percent identity is calculated by %-identity = (identical residues I length of the alignment region which is showing the respective sequence of this invention over its complete length) *100. Thus, sequence identity in relation to comparison of two amino acid sequences according to this embodiment is calculated by dividing the number of identical residues by the length of the alignment region which is showing the respective sequence of this invention over its complete length. This value is multiplied with 100 to give “Coidentity”. According to the example provided above, %-identity is: for Seq A being the sequence of the invention (6 / 9) * 100 = 66.7 %; for Seq B being the sequence of the invention (6 / 8) * 100 =75%.“Linkage” as used herein and as used in the scientific field of genetics denotes the tendency of two or more markers and / or genes that are in physical proximity to each other to be inherited together. Generally, the closer together the markers are, the lower the probability that they will be separated by a recombination event during meiosis. Two markers and / or genes are herein referred to as “linked” when they are inherited together at a frequency that is statistically significantly higher than would be expected by chance, i.e., the null hypothesis of an independent assortment can be rejected. The exact threshold value for said frequency depends on the type of cross and the size of the population. Because physical proximity of two genes or markers is directly related to the probability that they will be passed together to individuals in the next generation, the term “linked” may also loosely be used to indicate that two genes and / or markers are on the same chromosome, usually within 100 Mb of each other for wheat and 10 Mb for oilseed rape, often much closer, the exact value depending not only on the species but also on the chromosomal region, as it is known and understood by one skilled in the art. Two “linked” genes or markers, for example, but not limited to Brassica, may be separated by about 2.3 Mb; 2.00 Mb; about 1.95 Mb; about 1.90 Mb; about 1.85 Mb; about 1.80 Mb; about 1.75 Mb; about 1.70 Mb; about 1 .65 Mb; about 1 .60 Mb; about 1 .55 Mb; about 1 .50 Mb; about 1 .45 Mb; about 1 .40 Mb; about 1.35 Mb; about 1 .30 Mb; about 1.25 Mb; about 1.20 Mb; about 1.15 Mb; about 1.10 Mb; about 1.05 Mb; about 1.00 Mb; about 0.95 Mb; about 0.90 Mb; about 0.85 Mb; about 0.80 Mb; about 0.75 Mb; about 0.70 Mb; about 0.65 Mb; about 0.60 Mb; about 0.55 Mb; about 0.50 Mb; about 0.45 Mb; about 0.40 Mb; about 0.35 Mb; about 0.30 Mb; about 0.25 Mb; about 0.20 Mb; about 0.15 Mb; about 0.10 Mb; about 0.05 Mb; about 0.025 Mb; about 0.012 Mb; and about 0.01 Mb. A gene of interest may be “linked” to a marker that resides within an exon or intron of the gene. In this case, the separation between the linkedgene and marker is 0.00 Mb. Further, genes and / or markers may also be “linked” to a phenotype, for example, a phenotype in which the linked gene or gene linked to the linked marker is involved. As will be understood by those of skill in the art, the length of this marker will vary if nucleotides are added or subtracted from the span of genomic DNA located between the distal ends of the particular primers used when annealed to each other. As used herein, the term “tightly I closely linked” may refer to one or more genes or markers that are located within about 0.5 Mb of one another on the same chromosome. Thus, two “tightly / closely linked” genes or markers may be separated by about 0.6 Mb; about 0.55 Mb; 0.5 Mb; about 0.45 Mb; about 0.4 Mb; about 0.35 Mb; about 0.3 Mb; about 0.25 Mb; about 0.2 Mb; about 0.15 Mb; about 0.12 Mb; about 0.1 Mb; about 0.05 Mb; and about 0.00 Mb.

[0108] As used herein, the term “extremely tightly linked” may refer to one or more genes or markers that are located within about 100 kb of one another on the same chromosome. Thus, two “very tightly I closely linked” genes or markers may be separated by about 125 kb; about 120 kb; about 115 kb; about 110 kb; about 105 kb; 100 kb; about 95 kb; about 90 kb; about 85 kb; about 80 kb; about 75 kb; about 70 kb; about 65 kb; about 60 kb; about 55 kb; about 50 kb; about 45 kb; about 40 kb; about 35 kb; about 30 kb; about 25 kb; about 20 kb; about 15 kb; about 12 kb; about 10 kb; about 5 kb; about 1 kb; and about 0 kb. Linked, tightly linked, and extremely tightly genetic markers may be useful in marker-assisted breeding programs to identify individuals comprising linked phenotypes and / or gene types, and to breed these traits and / or genes into available varieties.The terms “nulliploid”, “monoploid”, “triploid”, and “tetrapioid” are herein used interchangeably with the terms “nullisomics”, “monosomic”, “trisomic”, and “tetrasomic”, respectively.The term “plant” as used herein encompasses whole plants, ancestors and progeny of the plants and plant parts, including seeds, shoots, stems, leaves, roots (including tubers), flowers, and tissues and organs. The term “plant” also encompasses plant cells, suspension cultures, callus tissue, embryos, meristematic regions, gametophytes, sporophytes, pollen and microspores.A plant cell, tissue, organ, material, or whole organism as used herein includes an algal cell, tissue, organ, material or whole organism, respectively.“Pseudolinkage” or “pseudo-linkage” as used herein refers to the behaviour of two genes and / or markers known to reside on two different chromosomes in the reference genome that show an apparent linkage, i.e., are inherited together more often than expected bychance. In a stricter sense, it refers to the characteristic of an RT heterozygote, in which genes and / or markers located near the translocation breakpoints behave as if they are linked to both chromosomes at the same time.A “reciprocal translocation” or “RT” as used herein is a type of chromosomal rearrangement (cf. supra), in which two chromosomes exchange chromosomal segments. No loss or gain of genetic material results from the exchange and, therefore, an RT is a balanced chromosomal rearrangement. When a line carrying an RT is crossed to a non-translocated line, a translocation heterozygote is produced. Most viable gametes produced by selffertilization of a translocation heterozygote are balanced gametes that reconstruct the two parental haplotype configurations; they are the products of alternate segregation during meiosis (Fig. 1). A minority of viable gametes, primarily the products of adjacent-1 segregation, inherit one translocated and one non-translocated chromosome and are, therefore, unbalanced (Griffith, et al., 2005). The ratio of balanced vs. unbalanced progeny is variable, reflecting variable survival rates of unbalanced gametes. In diploid organisms, few gametes and / or zygotes with genetic imbalances survive, while in polyploids they are more viable because homeologous sequences often compensate for the loss of genetic material.A “translocation” as used herein is a subtype of a chromosomal rearrangement (cf. supra), in which chromosomal segments have changed position between chromosomes (interchromosomal translocation) or within a chromosome (intrachromosomal translocation). A translocation can be reciprocal (cf. supra, reciprocal translocation).A “translocation breakpoint” as used herein in the context of a chromosomal translocation refers to the position of breakage and subsequent joining of a chromosomal segment to a new chromosomal location. Each breakpoint is unique to a particular translocation.Regarding further terms as used herein, and unless otherwise specifically defined above, all technical or scientific terms used herein have the same meaning as commonly understood by one skilled in the art to which this disclosure belongs. Definitions of common terms in molecular genetics can be found in, for example: Griffith, et al., 2005.DETAILED DESCRIPTIONThe present invention describes a novel and generally applicable method for a de novo identification of translocations. The method explains irregularities in genotyping data ofplant biparental populations of different origins in terms of an underlying chromosomal rearrangement (translocation), allows curation of genotyping data of aneuploid individuals, provides information on configuration of chromosomes involved in the rearrangement, and precisely identifies translocation breakpoints. Importantly, the tools of the present invention overcome the shortcomings of the prior art in that they allow, for the first time, the identification of allelic dosage imbalances in SNP genotyping data. Since genotypes affected by allelic dosage imbalances are often misread and disregarded because of segregation distortion, chromosomal rearrangement often remain unnoticed during routine genetic mapping. Furthermore, the method allows the deciphering of the true aneuploid (not the original, miscalled data) genotypes and tracking of their segregation in the population.To this end, the present invention provides, in a first aspect, a method for identifying at least one translocation chromosomal rearrangement in a plant mapping population, including a plant biparental mapping population, preferably wherein the method allows the identification of an aneuploid individual, including a segmental aneuploid individual, as well as the identification of the position of a translocation breakpoint in at least one chromosome, the method comprising the following steps: (i) providing a single-nucleotide polymorphism (SNP) genotyping data set for a set of markers, preferably a curated SNP genotyping data set, preferably wherein the SNP genotyping data set is obtained by or obtainable by a suitable genotyping technology, the data set representing individual genotype samples of the plant mapping population, including a biparental mapping population, of interest, wherein each marker has a known position on a reference map, including a physical map and / or a genetic consensus map; (ii) calculating a matrix of pairwise recombination frequencies for each of the markers of the set of markers of (i), and / or calculating a logarithm of the odds (LOD) score, to identify at least one pseudolinkage event fortwo chromosomes orfortwo chromosome segments, wherein the pseudolinkage event identifies translocation between two chromosomes or chromosome segments and provides at least one location suitable as a sorting position for fine-mapping of at least one putative translocation breakpoint; (iii) sorting the set of markers for each of two chromosomes or chromosome segments defined to be in an apparent linkage to each other due to at least one pseudo-linkage event identified in step (ii) and sorting the individuals of the plant mapping population based on the genotype of the individual at the at least one putative translocation breakpoint as determined in step (ii); (iv) optionally: calculating the number of apparent crossovers for the at least one selected individual analyzed and optionally: repeating step (iii) and / or (iv) to refine the results; (v) inspecting the genotyping data set for identifying an unexpected pattern, and optionally: repeatingstep (iii) and / or (iv) and / or (v) to refine the results; and (vi) identifying at least one individual comprising said unexpected pattern and identifying the position of at least one translocation breakpoint based on the location of the unexpected pattern in said at least one individual, preferably including identifying the configuration of the at least one translocation in at least one parent in at least one chromosome and optionally allocating the information related to the at least one translocation breakpoint to at least one carrier individual.An “unexpected pattern” as used in this context refers to a) an unusual number of apparent crossovers observed in the genotyping data and / or b) an increased number of NoCalls, indicative of a possible deletion, and / or c) unexpected heterozygous genotypes. An unexpected pattern thus represents a deviation from the usual, expected pattern, where the expectation depends on the type of population being analyzed and the deviation depends on the nature of the rearrangement and the allelic dosage in the affected individuals at all segments of the involved chromosomes. An unexpected pattern can be identified, for example, by visual inspection.The position of a translocation breakpoint is delimited by the last marker exhibiting the usual genotypic pattern, i.e. a pattern normally observed for diploid genotypes for the population type under study, and the first marker showing an unexpected pattern (cf. supra). Thus, the two closest markers flanking the interval in which the transition between diploid genotypes and aneuploid genotypes occurs define the translocation breakpoint.Using the methods and analyses proposed here, the present inventors identified unexpected patterns in genotyping data (as exemplary shown in Figures 5 and 7). Based on the methods of the present invention, these patterns can be analyzed in silico, visualized by a graphical output, and correlated with pseudo-linkage between chromosomes and / or the presence of a potential chromosomal rearrangement, including a reciprocal translocation. In addition, the methods allow the identification of aneuploid individuals, because genotypes of aneuploid individuals are enriched for unexpected patterns, including NoCalls, wherein this pattern is specific for the type of population (DHs or F2) and / or the type of aneuploidy (nulliploids vs. triploids). In essence, the unexpected patterns were not qualified as errors, but were deeper analyzed, which allowed to arrive at the methods as disclosed and claimed herein.In certain embodiments, there is thus provided the use of an observed unexpected pattern to identify at least one aneuploid individual based on the identification and characterization of NoCall events that may, inter alia, indicate an aneuploid individual.In certain embodiments, the first and original assignment of genotypes for an array of interest may be agnostic of any consideration of aneuploidy. During identification of chromosomal rearrangements, including reciprocal translocations, the presence of, for example, NoCalls, or of any other distinct and unexpected pattern, will be further investigated to make a possible correlation with a true event of (segmental) aneuploidy. In an iterative manner, anyunexpected pattern is noted and the underlying allelic dosage is ascertained on assay cluster plots., “NoCall” as used herein is understood to mean either a true lack of signal (nulliploidy) or a skewed ratio of the two alleles that was deemed ambiguous during the original genotype assignment (e.g., in the case of triploidy). The ascertainment of allele dosage on cluster plots thus distinguishes a true absence of the locus from an artefact of the original genotype assignment.The method of the first aspect of the present invention has the significant advantage to allow the identification of novel translocations, including translocations not yet previously described and mapped.This method of the present invention also has the considerable advantage in that it allows the identification of translocation breakpoints as the points on the map where genotypic pattern changes in segmental aneuploid individuals, enables a comparison of segregation of both aneuploid and diploid genotypes against theoretical predictions, identifies the parent carrying the translocation, and allows deciphering of the configuration of the translocated chromosomes (head-to-tail or head-to-head). Finally, the method has the advantage to be able to identify multiple types of chromosomal rearrangements including non-reciprocal translocations, deletions, insertions, and duplications.Advantageously, the method as disclosed herein exclusively utilizes SNP genotyping data, e.g., from arrays, including beadchip arrays, as a basis to identify and moreover also to assign specific genotypes to an aneuploid progeny of a translocation heterozygote. Therefore, the method of the present invention, for the first time, considers the segregation of both aneuploid and diploid chromosomal segments in all individuals of a mapping population, preferably a biparental mapping population, in order to understand characteristics of the chromosomal rearrangement under investigation, which may be balanced or unbalanced (cf. Fig. 2 and 3), to obtain individuals having an aneuploid, including segmental aneuploid, genotype, preferably being associated with a beneficial phenotype.In one embodiment of the first aspect, step (vi) may further comprise, for each individual in the biparental mapping population analyzed, specifically differentiating selected individuals and attributing the individuals into a group of individuals either (a) not carrying a translocation; (b) carrying a translocation; or (c) carrying a translocation and further carrying an aneuploid configuration in the chromosomes involved in the translocation.In a further embodiment, the method may further comprise assigning a specific phenotype to a specific individual of configurations (a) to (c) in a biparental mapping population; and, optionally, in yet a further embodiment: selecting and / or obtaining at least one individual having an aneuploid, including a segmental aneuploid, genotype of interest.Individuals, including carrier individuals of a reciprocal translocation event in the genome, and gametes according to categories (a) to (c) are shown in Figures 2 and 3. wherein step (vi) of allocating the information related to the at least one translocation breakpoint to at least one carrier individual further includes determining the type of aneuploidy, including segmental aneuploidy, for each individual in the biparental mapping population analyzed, wherein the determination includes defining a gamete or progeny chromosome architecture as depicted in Figure 2 or 3, preferably to identify at least one aneuploid, including a segmental aneuploid event (as identified in silica in a data set) or gamete (as traced back in a population) and / or carrier individual. In one embodiment, an aneuploid or segmental aneuploid gamete or progeny chromosome architecture is as depicted in Figure 2 or 3 and may be selected from the group consisting of an allele dosage of “white” to “black” chromosomes above the translocation breakpoint of 0:4, 1 :3, 2:2, 3:1 , 4:0 and / or below the translocation breakpoint of 0:4, 1 :3, 2:2, 3:1 , 4:0, or a combination thereof, wherein an aneuploid or segmental aneuploid is an individual having less than one complete white and one complete black allele for each chromosome (shown as boxed individuals in Figures 2 and 3) and / or having at least one segmental translocation either above or below the translocation breakpoint, and thus carries a segmental aneuploid event. Any individual carrying a gamete or a chromosome architecture other than 2:2 above the centromere and 2:2 below the centromere at least for one set of chromosomes (for polyploid individuals), shown as boxed individuals in Figures 2 and 3, thus carries an aneuploid or segmental aneuploid event of particular interest.In certain embodiments, the methods of the present invention can thus also be used to identify, vice versa, gametes and individuals in a breeding pool not carrying a putative chromosomal rearrangement, including a reciprocal translocation, in scenarios, where a rearrangement occurred, but led to a deteriorated phenotype. In these embodiments, themethods disclosed herein can help to identify the origin of the translocation in the breeding pool.In a further embodiment of the above methods of the first aspect, the SNP genotyping data are obtained by or obtainable by a suitable genotyping technology generating a quantitative or at least semi-quantitative result for SNP allele dosage, preferably wherein the assay is selected from a microarray, including a bead microarray, a hybrid-SNP microarray and a customized genomic DNA fragment microarray, a whole genome sequencing data set, a quantitative PCR data set, a next-generation sequencing data set, or an amplicon sequencing based data set, or any combination thereof, preferably wherein the assay is a microarray assay.The “total allelic / allele dosage” refers to the combined number of copies of all alleles present for a given site in an individual. The “SNP allele dosage” in turn refers to the allele or allelic dosage as determined in view of the quantitative or semi-quantitative determination of SNP information related to an allele of interest.In yet a further embodiment of the above methods, the mapping population, preferably a biparental mapping population, can be one of various types of populations of a diploid or polyploid plant species, including an F2 population, doubled haploid (DH) population, a recombinant inbred line (RIL) population, a backcross population, and / or a population derived from at least one synthetic polyploid parent. Examples of segregation patterns in an F2 and DH populations of polyploids are shown in Figures 2 and 3, respectively.A “synthetic polyploid” as used herein refers to the product of resynthesizing or artificially inducing a polyploid stage as a common strategy to increase genetic diversity. Polyploidy is known to be one of the drivers of speciation and evolution in the plant kingdom, increasing the biological diversity in many plants, including crops, spices, vegetables, etc. Polyploidy, either through natural means or non-natural artificial induction, allows to increase the scope of plant improvement. The non-natural induction of polyploidy is usually done via antimitotic chemicals, duplicating the complete chromosomal set and allowing for genetic alterations and rearrangements that result in phenotypic changes. Consequently, increasing ploidy in cultivated plants often results in improved yield, biomass, vigour, biotic and abiotic stress tolerance, and secondary metabolite production, all of which can contribute to the increased economic success of these plants, including major crop plants.According to a further embodiment of the above method of the first aspect, the plant biparental mapping population is created from a polyploid or a synthetic polyploid parental plant.In another embodiment, the plant may be independently selected from Triticum spp., including Triticum durum and Triticum aestivum, Triticale, Brassica spp., including Brassica napus, Brassica carinata and Brassica juncea, Gossypium spp., including Gossypium hirsutum and Gossypium barbadense, and synthetic polyploids, such as artificial auto- tetraploid Zea mays.In yet another embodiment of the above method, the method includes an additional step of regenerating and / or propagating a plant from a selected individual analyzed carrying at least one translocation chromosomal rearrangement in its genome. The skilled person is well aware of several technologies applicable to various plant species for regeneration and / or propagation of the relevant material, including regeneration from plant cells, plant protoplasts, plant cell tissue cultures from which plants can be regenerated, plant DNA, plant calli, plant clumps, and plant cells that represent whole intact plants or parts of such plants, including embryos, meristematic tissues, pollen, ovules, flowers, seeds, leaves, roots, root tips, anthers, and the like. The term “regeneration” in this context means the process of growing a plant from at least one plant cell (e.g., from a plant protoplast cell, or from an explant of a plant, or, of course, from a seed of a plant) via culturing and cultivation in vitro and / or in a greenhouse or in the field.In certain embodiments of the various aspects of the present invention, the methods will include a step of determining the orientation of the at least one translocation observed, preferably a reciprocal translocation.In yet another embodiment of the first aspect, there is provided a screening method for identifying at least one translocation chromosomal rearrangement in a plant mapping population, including a plant biparental mapping population according to the first aspect, wherein step (vi) includes identifying the configuration of the at least one translocation in at least one of the parent by: (vii) comparing the arrangement of at least one translocated segment and the position information of the at least one translocation breakpoint with theoretical prediction of segregation of translocated genomic segments during meiosis, and optionally: pairing of gametes to assist identification of an aneuploid individual and / or linkage map construction, in the progeny of a translocation heterozygote in the plantbiparental mapping population. Chromosomal configuration of parental gametes and products of their pairing are shown in Figures 2 and 3.The above methods of the present invention allow an unprecedented assignment of a specific phenotype to a particular individual, hence correctly distinguishing whether a desired or an unwanted phenotypic effect is caused by a translocation and / or aneuploidy on an exemplary “chromosome no.1 ”, and / or whether it is caused by aneuploidy on an exemplary “chromosome no.2” involved in a translocation event This differentiation has not been performed by methods of the prior art.In an additional embodiment, the method may comprise an additional step of: (viii) determining allelic ratios from the data set considering the parental origin of each allele to identify and thus provide at least one reciprocal translocation (RT) event position.In yet an additional embodiment, the method may comprise an additional step of: (ix) selecting and / or obtaining at least one individual carrying the at least one reciprocal translocation positioned as the at least one translocation event position in step (viii) in its genome.Additional step (viii) and optionally (ix) may be suitable to characterize an RT event to evaluate its potential biological impact and to map the position precisely as starting point for subsequent analysis of a given individual.In a second aspect, there is provided a cell, tissue, organ, material, seed orwhole organism obtained by or obtainable by a method according to the first aspect, preferably wherein the plant or seed is not obtained by an essentially biological process and / or wherein the cell, tissue, organ, material, seed or whole organism further comprises at least one technically induced mutation in its genome, including an EMS (ethyl methanesulfonate) induced mutation, a modification in the genome created with genome editing technologies, or a transgene, or a combination thereof.In certain embodiments of the second aspect, said cell, tissue, organ, material, seed or whole organism comprises aneuploidy, including segmental aneuploidy, in its genome as a potentially beneficial characteristic suitable for breeding purposes, as the progeny significantly differs from the parents, but is viable and carries a potentially valuable chromosomal rearrangement, preferably, wherein said cell, tissue, organ, material, seed or whole organism comprises a translocation event identified and / or mapped, including denovo identified and mapped in a population, using the screening, identification and characterization methods as used herein.Abnormalities in chromosomal structure are known as an important source of genetic variability with a direct impact on phenotypic variation and disease susceptibility and, particularly in polyploid organisms, also on disease or stress resistance. For plant breeding, the methods of the present invention can help to identify and trace back individuals within the breeding pool that carry a chromosomal rearrangement and are viable and vital.In one embodiment of the first aspect, curated SNP data are provided by (ia) a removal of technical failures in both marker and sample dimension, wherein in bioinformatics, the classification of “failure” depends on the used technology: markers and samples with large amount of missing data are typically removed, and (ib) a removal of markers without a confident placing on the reference genome I reference map. As it is known to one skilled in the art, the data curation strategy of choice will depend on the specific data set; for example, the type of a population analyzed and / or the technology used need to be considered.Habitually, during routine SNP genotyping, additional filters are used to curate the data, e.g., allele frequencies and proportion of heterozygosity are required to conform to expectations. According to the methods of the present invention, relaxed filters are used for the de novo discovery of translocation chromosomal rearrangement events, since these events in the data set are particular and may be interpreted as artefacts in a prior art setting for analyzing SNP genotyping data in view of the different focus and purpose of the analysis.In a third aspect, there is provided a customized SNP genotyping technology configured for being suitable for performing a method of the various embodiments of the first aspect above. In one embodiment, the SNP genotyping technology can be configured for being suitable for performing a method of the various embodiments of the first aspect depending on (i) the starting material to be analyzed and / or (ii) the SNP data and information of interest. The skilled person, based on the teachings provided herein, can easily configure a customized SNP genotyping technology assay accordingly.In certain embodiments, the SNP genotyping data set has a minimum marker “coverage” or “density”, wherein for a given plant chromosome, at least 50 markers, at least 55 markers, at least 60 markers, at least 70 markers, at least 80 markers, at least 90 markersor at least 100 markers should be available as the minimum marker coverage. For example, depending on the size of the translocation event that occurred and that is to be identified, the density has to be sufficiently high to obtain at least a certain number of markers in each translocated segment. As further detailed in the Examples below, arrays with 12-15K of markers per genome may be used for high resolution analysis when the translocated segments are large -- in the detailed examples close to half of the chromosome. In other cases, lower density may be enough. In order to have a sufficient resolution, a lower limit of about 5K markers per genome may be preferred. In certain embodiments, this threshold will be lower for large rearrangements and correspondingly higher for smaller events in other embodiments.A collection of sequences representing a suitable and exemplary set of SNPs for the analysis presented in the Examples below are shown in SEQ ID NOs: 1 to 60, wherein SEQ ID NOs: 1 to 30 show markers specific for wheat (Triticum aestivum) SNPs (cf. Example 1) and SEQ ID NOs: 31 to 60 show markers specific for oilseed rape (Brassica napus) SNPs (cf. Example 2). For both crops, each marker sequence is composed of 5’flank + SNP (IUPAC code) + 3’flank. The oilseed rape markers of SEQ ID NOs: 31 to 60 have uniform-length flanks (150bp for both the 5’ and 3’ flanks). For wheat, a more variable flank sequence length (50bp, or 60bp, or 100bp) is shown, which may also be suitable according to certain embodiments. Usually, a SNP will be located in the middle of a marker sequence. For both crops, the 4 exemplary markers that flank each of the two translocation breakpoints on each side are shown (see explanation in the Examples below). These markers, or markers having at least 75%, 76%, 77%, 78%, 79%, 80%, 81 %, 82%, 83%, 84%, preferably at least 85%, 86%, 87%, 88%, 89%, , more preferably at least 90%, 91 %, 92%, 93%, 94%, and most preferably at least 95%, 96%, 97%, 98%, or at least 99% sequence identity to the respective marker of any one of SEQ ID NO: 1 to 30 and 31 to 60, respectively, can thus be used.As it is well known to a skilled person, the genomic information used for the methods of the present invention can be obtained from data bases and knowledge of existing physical and / or consensus genetic maps can be used. Additional markers can be identified and deemed equivalent to the above exemplary marker sequences, for example, by determining the frequency of recombination between the additional marker and the exemplary SNP markers). Such determinations may utilize an improved method of orthogonal contrasts based on the method of Mather (The Measurement of Linkage in Heredity, Methuen & Co., London, 1931) followed by a test of maximum likelihood to determine a recombination frequency.In one embodiment, the sorting of markers in step (iii) is performed based on the known position of said markers on either a physical map or, alternatively, on a consensus genetic map. Optionally, in another embodiment, a re-sorting can be included, wherein individuals can be sorted based on genotypes at a specific region, namely in the vicinity of the point of the highest pseudo-linkage, e.g., in a further round, in case the relevant position information is available. Sorting is usually performed in two different dimensions. Usually, in the case of an optional re-sorting, the order of markers (and also the assignment of individuals into groups) can be manually curated to refine the breakpoints in this optional step.Sorting of markers according to the present invention can be performed by a variety of means, also depending on the maps, data and arrays available. A minimum requirement to perform sorting is the knowledge of an established and characterized order of markers along a genome as represented by a physical or by a consensus map. Therefore, a variety of assays and technologies can be applied that build upon the marker-based positioning information within a given genome, or a part thereof.Markers used during refinement will have an existing map position. All unmapped markers should be removed from the data set.In one embodiment of the first aspect, the method comprises a preceding step of providing a biological sample, preferably from a plant cell or plant germplasm, and obtaining genetic information from this biological sample of interest, wherein a sample is usually obtained from a representative number of cells. The genetic information of this biological sample can then be used further for the in silico method of the first aspect, so that specific information regarding the biological sample of interest can be obtained; for example: a putative translocation chromosomal rearrangement can be identified (or confirmed) in a progeny individual and / or a parent and analyzed in detail, and the relevant information can subsequently be used in breeding studies.In certain embodiments, the LOD score of linkage, or another measure / metric quantifying the non-independent assortment of alleles during meiosis, can be calculated for all pairs of markers in a data set, preferably genome-wide, to identify regions of pseudo-linkage, i.e., regions where an apparent linkage can be observed between markers that are normally known to reside on different chromosomes, thus suggesting a possible unusual physical proximity. The LOD score is a measure of linkage between two markers and is calculated from the entire population. For example, for markers ml and m2, the LOD score is calculated from across-the-population genotypes for both ml and m2.For all embodiments of the present invention, markers and assays based on publicly available data and commercially available arrays can be used. In certain embodiments, customized arrays can be used in case specific markers should be additionally included for a given plant genome. For example, the Illumina Infinium™ array can be used in either a standard or in a customized way, wherein customization may include a custom selection of public assays and / or the inclusion of user’s own assays to increase SNP coverage in a target region. Meanwhile, high density arrays, e.g. like Illumina Infinium™, are commercially available and can be purchased off-the-shelf from the vendor. These arrays are suitable for conducting the methods of the present invention for detecting chromosomal rearrangement, namely at least one reciprocal translocation.During analysis and sorting as suggested by the methods as disclosed herein, a pseudolinkage event is considered a “significant” pseudo-linkage event when the magnitude of the pseudo-linkage between the two chromosomes involved in a putative reciprocal translocation is comparable to the magnitude of linkage observed between markers on the same chromosome across the translocation breakpoint. Specifying a threshold value for a significant pseudo-linkage is difficult, as significance strongly depends on both the type and the size of the population, but a qualitative significance can be determined, as detailed above, to identify pseudo-linkage events.The order of markers in the vicinity of translocation breakpoints cannot be ascertained by routine genetic mapping in a population in which the reciprocal translocation segregates.. Producing a genetic map by standard methods would lead to “confused” results for the two involved chromosomes and would be thus uninformative. According to the methods of the present invention, a physical position of each marker on a reference genome is used as reference. This is obtained by aligning in silica a small segment of sequence containing the marker to a reference genome and noting the position of the best match. In another embodiment, a consensus genetic map may be used, which map is derived from multiple previously analyzed and curated mapping populations. Preferably, if available, a physical map will be used for positioning of markers on the genome and, after analysis according to the method of the present invention, for fine mapping of a putative chromosomal translocation rearrangement, including a reciprocal translocation.Giving the absolute numbers of markers needed to perform the methods disclosed herein (i..e., in exact numeric values) is difficult or even impossible, as this number depends on other factors, such as the type and the size of the population. The magnitude of the pseudolinkage between the two chromosomes involved in the RT is comparable to the magnitudeof linkage observed between markers on the same chromosome across the translocation breakpoint. For each marker or an additional marker in a set of markers suitable for the methods of the present invention, it is important that all markers relied upon have a known position on a reference map, including a physical map or a consensus genetic map.According to the various embodiments of the method of the first aspect, and using SNP- based technology and information, the refinement of translocation breakpoints, the identification of the donor parent, and the determination of the architecture of translocated chromosomes were enabled by decoding of the true aneuploid genotypes, an important step of which was analysis of cluster plots of Illumina Infinium™ assays. The last step of the method was a comparison of the observed data to the theoretical predictions, followed by a selection of a scenario that best fits the data.In certain embodiments, the methods of the present invention may contain a step of obtaining a segregation ratio for alleles and or genotypes of one or more marker(s) within a group of a plant biparental mapping population for determining the ratio of parental alleles in at least one selected individual to define the presence or absence of balanced and unbalanced gametes in said selected individual (cf. Fig. 2 and 3).In a fourth aspect, there is provided a use, preferably a computer implemented use, of single-nucleotide polymorphism (SNP) genotyping data set as defined in any of the various embodiments of the first aspect, or a use, preferably a computer implemented use, of a method of any one of the various embodiments of the first aspect for determining and optionally correcting genotyping errors or miscalled data in a reference genotyping data set by identifying the true allele dosage in the single-nucleotide polymorphism genotyping data set by considering both aneuploid and diploid chromosomal segments in all individuals of a biparental mapping population. In one embodiment of the above use, a plant biparental mapping population is created or provided as first step followed by the subsequent step of determining and optionally correcting genotyping errors or miscalled data in a reference genotyping data set.In a fifth aspect, there is provided a use, preferably a computer implemented use, of single- nucleotide polymorphism (SNP) genotyping data set as defined in any of the various embodiments of the first aspect, or a use, preferably a computer implemented use, of a method of the various embodiments of the first aspect, for precisely identifying the position of at least one translocation breakpoint defining at least one translocation chromosomal rearrangement in at least one chromosome in a plant of a biparental mapping population,optionally including the precise determination of the orientation of at least one translocation segment on the at least one chromosome, where the translocation occurred. In one embodiment of the above use, a plant biparental mapping population is created or provided as first step followed by the subsequent step of precisely identifying the position of at least one translocation breakpoint defining at least one translocation chromosomal rearrangement in at least one chromosome in a plant of a biparental mapping population.The uses of the fourth and the fifth aspects are particularly suitable for explaining irregularities in genotyping data that would normally be classified as genotyping errors or missing data but may instead be a manifestation of a chromosomal rearrangement, including a reciprocal translocation in a biparental mapping population.The methods and uses of the first, fourth and fifth aspect will usually be conducted with the help of a computer program or a computer program product executed by a computer, on a physical means, e.g., a computer, smartphone, a specialized apparatus and as artificialintelligence supported use and the like, wherein the specific knowledge about the SNPs and the SNP genotyping data set can be provided manually and / or via computer implemented means, i.e., as read-out from stored data and information generated before and finding a biological basis in the biological material as analyzed and building the origin of the genotyping data.In one embodiment of the first, fourth and fifth aspects, an additional step of refining the identification on an aneuploid, including a segmental aneuploid, individual by fine-mapping, i.e., by using even more, more specific and / or alternative marker sequences, may be included, wherein preferably on each side of a (putative) translocation breakpoint in a subset of selected individuals new markers as well as corresponding primers and / or probes are used to refine the results.The refinement, in one embodiment, may include (a) identifying and discriminating individuals with balanced versus unbalanced allele dosage on the two chromosomes according to a pattern specific to a reciprocal translocation event, as detailed in Figure 2 and 3.In one embodiment of the first, fourth and fifth aspects, the method may include a step of determining the type of aneuploidy, including segmental aneuploidy, in a subset of selected individuals in a population, preferably including, inter alia, identifying an originally unexpected pattern by, for example, identifying an increased NoCall events to identify atleast one carrier gamete and / or individual carrying an aneuploid, including segmental aneuploid, chromosome architecture in its genome.In one embodiment, the methods and uses provided herein can thus be used for amending and / or correcting reference genome data by the information obtained related to at least one chromosomal rearrangement identified and characterized.Even though the methods of the present invention do not primarily rely upon sequencing, various methods and methodologies can be used to verify and supplement the result related to a chromosomal rearrangement, preferably a reciprocal translocation, as disclosed herein. For example, polymerase chain reaction (PCR) can be used. PCR detection makes use of two oligonucleotide primers flanking the region of interest, which allows DNA amplification of said region. The amplification involves repeated cycles of heat denaturation of DNA followed by annealing al lower temperatures of primers to their complementary sequences and extension of the annealed primers with DNA polymerase. Size separation of DNA fragments on agarose or polyacrylamide gels typically follows amplification and is used to confirm the consistency of the expected DNA fragment size as well as to quantify the amount of DNA obtained.Such selection and screening methodologies are well known to those skilled in the art. Molecular methods that can be used to confirm the presence of a specific nucleotide sequence in a genome of a plant are known to those skilled in the art. Several exemplary methods are further described below. Isothermal methods of DNA amplification, including LAMP (loop-mediated isothermal amplification), RPA (recombinase polymerase amplification) and similar methods, as they are known to the person skilled in the art, can be used as well. Further, the so called molecular beacons have been described for use in sequence detection, wherein a FRET (fluorescence resonance energy transfer) oligonucleotide probe is designed that spans the junction(s) between the targeted insert and the flanking genomic regions. Another probe-based assay, Taqman (Life Technologies), can be used for detecting and / or quantifying the target DNA sequence or SNP.For embodiments, where no transgenic event, but naturally occurring translocations are tracked, methods like, for example, PCR-based method, such as KASP, TaqMan or amplicon sequencing may be preferred to verify the results of the methods of the present invention.In certain embodiments, a fluorescent signal emitted by a fluorescent dye may be used for detection and verification of a result, e.g. in fluorescent in situ hybridization (FISH). In another embodiment, an amplification reaction for verification of the results obtained herein can be performed using a suitable secondary fluorescent DNA dye capable of staining cellular DNA at a concentration range detectable by flow cytometry and emitting at a wavelength detectable by a real time thermocycler. Those skilled in the art understand that other nucleic acid signals I dyes exist, in addition to the ones specified above, are known and can be used. Generally, any suitable nucleic acid dye with appropriate excitation and emission spectra can be employed.In yet another embodiment, Next Generation Sequencing (NGS) can be used for verification and detection of a SNP and / or translocation breakpoint, as used and / or identified by the methods of the present invention, by sequencing genomic regions of interest. The sequence of the target region can be obtained by amplifying a DNA fragment spanning the region of interest, isolating it, and sequencing it. Using older technologies, the amplified fragments can be isolated and sub-cloned into a vector and sequenced using chain-terminator method (also referred to as Sanger sequencing) or Dye-terminator sequencing. NGS technologies do not require the sub-cloning step, and multiple sequencing reads can be completed in a one and the same reaction. Several NGS platforms are commercially available, including Genome Sequencer FLX™ from 454 Life Sciences / Roche, Illumina Genome Analyser™ from Solexa and Applied Biosystems’ SOLiD™. Further, single molecule sequencing methods can be used, including a true Single Molecule Sequencing (tSMS) system from Helicos Bioscience and a Single Molecule Real Time sequencing (SMRT) from Pacific Biosciences. The SMRT Next Generation Sequencing system uses a real time sequencing by synthesis. This technology can produce reads of up to 1 ,000 bp because it is not limited by reversible terminators. Raw read throughput that is equivalent to one-fold coverage of a diploid human genome can be produced in a single day using this technology.In certain embodiments, an Illumina Infinium™ assay, or preferably a customized version thereof, whenusing an optimized set of assays, can be used for the methods of the first aspect.lllumina Infinium™ assays are capable of detecting copy number variations (e.g., duplicaton, deletions, higher copy number) because one of the outputs of the Infinum™ assay is the intensity of signal for each of the two queried alleles. To enhance the effectiveness for copy number variation (CNV) identification, Illumina’s high density Infinium HD beadchip platform in combination with the Infinum™ assay technology allows unconstrained marker design. If customization of assays is not preferred, Illumina offersready-made whole-genome panels for several crop species that consist of uniformly distributed SNP markers to create the fewest possible number of large gaps across the genome; this allows high-resolution detection of potential anomalies, including potentialbreakpoint mapping. The present invention will be further described below by non-limiting Examples detailing key aspect of the method disclosed herein as well as its possible applications.ReferencesAlkan, C., Coe, B. P., & Eichler, E. E. (2011). Genome structural variation discovery and genotyping. Nature reviews genetics, 12(5), 363-376.Badaeva, E. D., Dedkova, O. S., Gay, G., Pukhalskyi, V. A., Zelenin, A. V., Bernard, S., & Bernard, M. (2007). Chromosomal rearrangements in wheat: their types and distribution. Genome, 50(10), 907-926.Charne, D.G., Fengler, K.A., Jetty, S.A., Jobgen, S.C. (2022). Composition and methods for the detection of a chromosomal translocation in Brassica napus (US patent application US 2022 / 0411883 A1). US Patent and Trademark Office.Durrant, J. D., Gardunia, B. W., Livingstone, K. D., Stevens, M. R., & Jellen, E. N. (2006). An algorithm for analyzing linkages affected by heterozygous translocations: QuadMap. Journal of Heredity, 97(1), 62-66.Farre, A. et al., 2011 . Linkage map construction involving a reciprocal translocation. Theor Appl Genet 122, 1029-1037.Gaeta, R. T., Pires, J. C., Iniguez-Luy, F., Leon, E., & Osborn, T. C. (2007). Genomic changes in resynthesized Brassica napus and their effect on gene expression and phenotype. The Plant Cell, 19(1 1), 3403-3417.Grandke, F., Snowdon, R., & Samans, B. (2017). gsrc: an R package for genome structure rearrangement calling. Bioinformatics, 33(4), 545-546.Griffith, A. J. F., Wessler, S.R., Lewontin, R.C., Carroll, S.B. (2005). Introduction to genetic analysis (9th edition). W. H. Freeman & Company, New York, 583-586.International Wheat Genome Sequencing Consortium (IWGSC), Appels, R., Eversole, K., Stein, N., Feuillet, C., Keller, B. ... & Singh, N. K. (2018). Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science, 361 (6403), eaar7191 .Jiang, J., & Gill, B. S. (2006). Current status and the future of fluorescence in situ hybridization (FISH) in plant genome research. Genome, 49(9), 1057-1068.Lv, R., Gou, X., Li, N., Zhang, Z., Wang, C., Wang, R., ... & Liu, B. (2023). Chromosome translocation affects multiple phenotypes, causes genome-wide dysregulation of gene expression, and remodels metabolome in hexapioid wheat. The Plant Journal, 115(6), 1564-1582.McClintock, B. (1930). A cytological demonstration of the location of an interchange between two non-homologous chromosomes of Zea mays. Proceedings of the National Academy of Sciences, 16(12), 791-796.Olshen, A. B., Venkatraman, E. S., Lucito, R., & Wigler, M. (2004). Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics, 5(4), 557-572.Osborn, T. C., Butrulle, D. V., Sharpe, A. G., Pickering, K. J., Parkin, I. A., Parker, J. S., & Lydiate, D. J. (2003). Detection and effects of a homeologous reciprocal transposition in Brassica napus. Genetics, 165(3), 1569-1577.Parkin, I. A. P., Sharpe, A. G., Keith, D. J., & Lydiate, D. J. (1995). Identification of the A and C genomes of amphidiploid Brassica napus (oilseed rape). Genome, 38(6), 1 122- 1131.Rousseau-Gueutin, M., Belser, C., Da Silva, C., Richard, G., Istace, B., Cruaud, C., ... & Aury, J. M. (2020). Long-read assembly of the Brassica napus reference genome Darmor- bzh. GigaScience, 9(12), giaa137.Schilbert, H. M., Holzenkamp, K., Viehbver, P., Holtgrawe, D., & Mollers, C. (2023). Homoeologous non-reciprocal translocation explains a major QTL for seed lignin content in oilseed rape (Brassica napus L.). Theoretical and Applied Genetics, 136(8), 172.Sharpe, A. G., Parkin, I. A. P., Keith, D. J., & Lydiate, D. J. (1995). Frequent nonreciprocal translocations in the amphidiploid genome of oilseed rape (Brassica napus). Genome, 38(6), 1112-1121.Silva, G. S., & Souza, M. M. (2013). Genomic in situ hybridization in plants. Genet Mol Res, 12(3), 2953-2965.Stein, A., Coriton, O., Rousseau-Gueutin, M., Samans, B., Schiessl, S. V., Obermeier, C., & Snowdon, R. J. (2017). Mapping of homoeologous chromosome exchanges influencing quantitative trait variation in Brassica napus. Plant biotechnology journal, 15(1 1), 1478- 1489.Teo, Y. Y., Inouye, M., Small, K. S., Gwilliam, R., Deloukas, P., Kwiatkowski, D. P., & Clark, T. G. (2007). A genotype calling algorithm for the Illumina BeadArray platform. Bioinformatics, 23(20), 2741 -2746.Van Ooijen, J. W. (2006). JoinMap 4. Software for the calculation of genetic linkage maps in experimental populations. Kyazma BV, Wageningen, Netherlands.Winchester, L., Yau, C., & Ragoussis, J. (2009). Comparing CNV detection methods for SNP arrays. Briefings in functional genomics and proteomics, 8(5), 353-366.Zhang, C., Cerveira, E., Romanovitch, M., & Zhu, Q. (2017). Array-based comparative genomic hybridization (aCGH). Cancer Cytogenetics: Methods and Protocols, 167-179.EXAMPLESExample 1 Identification and Characterization of a 5B / 7B Translocation in WheatDuring analysis of a wheat biparental F2 population genotyped on an Illumina Infinium™ array, a reproducible sequence of steps, applicable to different plant species, identified a reciprocal translocation involving chromosomes 5B and 7B. While a 5B / 7B translocation was described before (Badaeva et al., 2007), neither of the parents of the cross had any clear pedigree relationship to the previously identified translocation carriers.A population of 282 F2 plants from a biparental cross was sown and grown to the seedling stage. Leaf discs were sampled and plant DNA was extracted according to standard protocols. The population was genotyped on a custom-designed Illumina Infinium™ SNP array with 15K single nucleotide polymorphisms (SNPs) that had been optimized for an even genome coverage. The array was manufactured by Illumina Inc. (San Diego, CA, USA). Normalization of sample DNA concentration, DNA amplification, hybridization to array beadchip, single-base extension, and fluorescence staining were performed according to the manufacturer’s recommendations. Stained beadchips were scanned using an Illumina iScan system. Raw data from the iScan were processed using Illumina GenomeStudio software to call genotypes.A genetic map was constructed using JoinMap version4.0 (Van Ooijen, 2006). Figure 4 shows a linkage group harbouring the majority of 5B and 7B markers and its alignment to the consensus genetics maps for chromosomes 5B and 7B. In the F2 population, the two chromosomes appear to be not only linked but also enmeshed in an unexpected way.The apparent inter-chromosomal linkage between 5B and 7B in the F2 population was further supported by calculating the recombination fraction and the LOD score of linkage for all pairs of markers. High LOD score values were observed for markers in physical proximity of each other, i.e., on the same chromosome, but also for 5B / 7B marker pairing, indicating non-independence of assortment of some segments of 5B and 7B chromosomes.All markers that map to chromosomes 5B and 7B of the reference genome Chinese Spring v1 .0 (International Wheat Genome Sequencing Consortium [IWGSC], 2018) were collected and ordered according to the physical map. Unlike in routine genotype processing, markers whose genotype frequencies deviated significantly from the ratio expected in an F2 population were kept.In the next step, individuals were sorted based on their genotypes on chromosomes 5B and 7B, at putative translocation breakpoints, i.e., at positions that had the highest inter- chromosomal linkage (Fig. 5). This sorting divided the population into roughly 4 groups: a group with a reconstituted genotype of parent 1 (aa) across both breakpoints, a group with a reconstituted genotype of parent 2 (bb) across both breakpoints, a group heterozygous (ab) across both breakpoints, and a heterogeneous group of individuals with unexpected genotypic pattern above or below each breakpoint. The first 3 groups are products of pairing of 2 games formed through alternate segregation during meiosis (cf. Fig. 1 and Fig. 2). Individuals in the last group fell into multiple subtypes (cf. Fig. 2 and 5).Surprisingly, it could be observed that the point of transition from consistent genotype calls (aa, bb, or ab) to the unexpected, alternating pattern (e.g., aalab) occurred at the same position for all subtypes (Fig. 5). The last, heterogenous group comprised segmental aneuploids, originating by pairing of a gamete produced by the alternate segregation with a gamete produced by the adjacent-1 segregation during meiosis (cf. Fig. 1 and Fig. 2). The abrupt change of pattern in segmental aneuploids occurs exactly at each translocation breakpoint (Fig. 5). Moreover, the apparent symmetry of pattern on 5B versus 7B indicates that the translocation is likely to be reciprocal (Fig. 2).The possibility of the above method to reliably identify putative aneuploid individuals is decisive in refining the position of translocation breakpoints in the next step, as the rest of the population, which is also its majority, exhibits seamless continuation of genotypes across the putative junctions (Fig. 5). This specific refinement has so far not been performed by standard methods described in the literature. The only observable effect of the translocation on genotypes of the majority group - the diploid individuals, i.e., the individuals with a full genome complement - is the pseudo-linkage between 5B and 7B.The correspondence of the unexpected genotype pattern with segmental aneuploidy was confirmed by analyzing allele dosage for markers in relevant chromosomal segments. For a selection of assays on each side of each putative breakpoint, plots of normalized intensity vs. normalized theta (‘cluster plots') were inspected in GenomeStudio (Teo et al., 2007). An example of a cluster plot is in Figure 6. The cluster of called heterozygous genotypes in the middle of the plot splits into 3 sub-clusters: a cluster of diploid heterozygous genotypes (XY) and two clusters of triploid genotypes each carrying an additional copy of one parental allele (XXY and XYY). Separation of haploid genotypes (X- and Y-) from diploid homozygous genotypes is typically less clear; in Figure 6 it is visible for the Y allele (YY and Y-). Importantly, the grouping of samples into clusters and sub-clusters on the plotshows an excellent agreement with groups and subgroups identified in the previous step, during inspection of graphical genotypes. Additionally, we performed CNV analysis on total allelic dosage and allelic ratio (Log2R and B-allele-frequency values, as exported from GenomeStudio), and reached similar conclusions. The two closest markers flanking the interval where the transition between diploid genotypes and aneuploid genotypes occurs define the location of the respective translocation breakpoint. In the wheat F2 population, the breakpoints were delimited by markers SEQ ID NO: 7 and SEQ ID NO: 8 on 5B, and by markers SEQ ID NO 22 and SEQ ID NO: 23 on 7B.The following theoretical scenarios regarding the origin of a reciprocal translocation segregating in a biparental population are possible: 1) the donor could have been parent 1 or it could have been parent 2, and 2) the exchanged chromosome arms were in the same orientation or they were in the opposite orientation, where the default orientation is understood to be the orientation in the Chinese Spring reference genome. For each of these cases, we made theoretical predictions detailing a) the gametes produced by the F1 translocation heterozygote, b) the possible products of gamete pairing, and c) the expected genotype transitions in segmental aneuploids. This allowed us to identify the donor of the translocation as well as to confirm that the orientation of the translocated segment in the donor is the same as reported in the literature for the 5B / 7B translocation (Badaeva et al., 2007).Example 2 Identification and Characterization of an N07 / N16 Translocation in oilseed rapeWhen analyzing an oilseed rape biparental DH population genotyped on an Illumina Infinium array, the same protocol that had been used to identify the 5B / 7B RT in wheat (cf. Example 1 above) was applied:it identified a reciprocal translocation involving chromosomes N07 and N16. This translocation was indistinguishable from the known N07 / N16 reciprocal translocation - which was originally described by Osborn et al. (2003) and recently elaborated on in detail by Charne et al. (2022) - and is relatively common in some germplasm pools of the annual form of B. napus. Even though the parents of the cross that gave rise to the DH population analyzed herein had no clear pedigree relationship to any known carrier of the translocation, their geographical origin suggested that the translocation segregating in the population analyzed in this experiment was likely the same event.A population of 139 DH plants from a biparental cross was sown and grown to the seedling stage. Leaf discs were sampled and plant DNA was extracted according to standardprotocols. The population was genotyped on a custom-designed Illumina Infinium™ SNP array with 12K single nucleotide polymorphisms (SNPs) that had been optimized for an even genome coverage. The array was manufactured by Illumina Inc. (San Diego, CA, USA). Normalization of sample DNA concentration, DNA amplification, hybridization to array beadchip, single-base extension, and fluorescence staining were performed according to the manufacturer’s recommendations. Stained beadchips were scanned using an Illumina iScan system. Raw data from the iScan were processed using Illumina GenomeStudio software to call genotypes.A genetic map was constructed using JoinMap version4.0 (Van Ooijen, 2006). Chromosomes N07 and N16 were partially merged in an unexpected configuration (data not shown). The apparent inter-chromosomal linkage between N07 and N16 was further supported by calculating the recombination fraction and the LOD score of linkage for all pairs of markers. High LOD score values were observed for markers in physical proximity of each other, i.e., on the same chromosome, but also for N07 / N16 marker pairing, indicating non-independence of assortment of some segments of N07 and N16 chromosomes.All markers that map to chromosomes N07 and N16 of the reference genome Darmor-bz v10 (Rousseau-Gueutin et al., 2020) were collected and ordered according to the physical map. Unlike in routine genotype processing, markers whose genotype frequencies deviated significantly from the ratio expected in a DH population were kept.Next, individuals were sorted based on their genotypes on chromosomes N07 and N16, at putative translocation breakpoints, i.e., at positions that had the highest inter-chromosomal linkage (Fig. 7). This sorting divided the population into roughly 4 groups: a group with a reconstituted genotype of parent 1 (aa) across both breakpoints, a group with a reconstituted genotype of parent 2 (bb) across both breakpoints, and two groups in which genotypes at the breakpoints transitioned to either predominantly null allele - an apparent deletion - or to predominantly heterozygous calls. The first 2 groups are products of doubling of gametes formed through the alternate segregation during meiosis (Fig. 1 and Fig. 3). The last 2 groups comprise segmental aneuploids, originating by doubling of gametes produced by the adjacent-1 segregation during meiosis (Fig. 1 and Fig. 3). The abrupt change of pattern in segmental aneuploids occurs exactly at each translocation breakpoint (Fig. 7). Moreover, the apparent symmetry of pattern on N07 versus N16 indicates that the translocation is reciprocal.Similarly to the wheat use example, where an F2 population was studied (cf. Example 1 above), also here, in the oilseed rape DH population, the putative aneuploid individuals were decisive in refining the position of translocation breakpoints. This methodology has not yet been described in the literature. The rest of the population, which represents its majority (but, in contrast to the wheat F2s, only by a small margin), exhibits seamless continuation of genotypes across the putative junctions (Fig. 7). The only observable effect of the translocation on genotypes of the majority group - the diploid individuals, i.e., the individuals with a full genome complement - is the pseudo-linkage between N07 and N16.The correspondence of the predominantly NoCall and predominantly heterozygous genotype calls with segmental aneuploidy was confirmed by analyzing allele dosage for markers in relevant chromosomal segments. For a selection of assays on each side of each putative breakpoint, cluster plots were inspected in GenomeStudio (Teo et al., 2007). Figure 8 shows an example of a cluster plot combining data from several DH populations. Data points representing individuals nulliploid at the queried locus have very low intensity od signal (‘null’ allele, ‘NoCall’) and are all collected at the bottom of the plot. Conversely, data points representing individuals tetrapioid at the locus (aabb) can be found at the same theta as heterozygous controls (ab) but at a higher intensity.Additionally, a CNV analysis was performed on total allelic dosage and allelic ratio (Log2R and B-allele-frequency values, as exported from GenomeStudio) to independently verify the results; this analysis reached similar conclusions.The two closest markers flanking the interval where the transition between diploid genotypes and aneuploid genotypes occurs define the location of the respective translocation breakpoint. In the oilseed rape DH population, the breakpoints were delimited by markers SEQ ID No 37 and SEQ ID No 38 on N07, and by markers SEQ ID NO 53 and SEQ ID NO 54 on N16. When comparing coordinates of these intervals on the Darmor-bz reference genome with the N07 / N16 breakpoint coordinates reported by Charne et al. (2022), they are indistinguishable.The following theoretical scenarios regarding the origin of a reciprocal translocation segregating in a biparental population are possible: 1) the donor could have been parent 1 or it could have been parent 2, and 2) the exchanged chromosome arms were in the same orientation or they were in the opposite orientation, where the default orientation is understood to be the orientation in the Darmor-bz reference genome. For each of these cases, we made theoretical predictions detailing a) the gametes produced by the F1translocation heterozygote, and b) the expected genotype transitions in segmental aneuploids. This allowed us to identify the donor of the translocation as well as to confirm that the orientation of the translocated segment in the donor is the same as reported in the literature for the N07 / N16 translocation (Osborn et al., 2004).

Claims

1. CLAIMS1 . A method for identifying at least one translocation chromosomal rearrangement in a plant mapping population, including a plant biparental mapping population, preferably wherein the method allows the identification of an aneuploid individual, including a segmental aneuploid individual, as well as the identification of the position of a translocation breakpoint in at least one chromosome, the method comprising the following steps:(i) providing a single-nucleotide polymorphism (SNP) genotyping data set for a set of markers, preferably a curated SNP genotyping data set, preferably wherein the SNP genotyping data set is obtained by or obtainable by a suitable genotyping technology, the data set representing individual genotype samples of the plant mapping population, including a biparental mapping population, of interest, wherein each marker has a known position on a reference map, including a physical map and / or a genetic consensus map;(ii) calculating a matrix of pairwise recombination frequencies for each of the markers of the set of markers of (i), and / or calculating a logarithm of the odds (LOD) score, to identify at least one pseudo-linkage event for two chromosomes or for two chromosome segments, wherein the pseudo-linkage event identifies translocation between two chromosomes or chromosome segments and provides at least one location suitable as a sorting position for fine-mapping of at least one putative translocation breakpoint;(iii) sorting the set of markers for each of two chromosomes or chromosome segments defined to be in an apparent linkage to each other due to at least one pseudo-linkage event identified in step (ii) and sorting the individuals of the plant mapping population based on the genotype of the individual at the at least one putative translocation breakpoint as determined in step (ii);(iv) optionally: calculating the number of apparent crossovers for the at least one selected individual analyzed and optionally: repeating step (iii) and / or (iv) to refine the results;(v) inspecting the genotyping data set for identifying an unexpected pattern, and optionally: repeating step (iii) and / or (iv) and / or (v) to refine the results; and(vi) identifying at least one individual comprising said unexpected pattern and identifying the position of at least one translocation breakpoint based on the location of the unexpectedpattern in said at least one individual, preferably including identifying the configuration of the at least one translocation in at least one parent, in at least one chromosome and optionally allocating the information related to the at least one translocation breakpoint to at least one carrier individual.

2. The method of claim 1 , wherein step (vi) further comprises, for each individual in the biparental mapping population analyzed, specifically differentiating selected individuals and attributing the individuals into a group of individuals either(a) not carrying a translocation;(b) carrying a translocation; or(c) carrying a translocation and further carrying an aneuploid configuration in the chromosomes involved in the translocation; and optionally: assigning a specific phenotype to a specific individual of configurations (a) to (c) in a biparental mapping population; and optionally: selecting and / or obtaining at least one individual having an aneuploid, including a segmental aneuploid, genotype of interest.

3. The method of claim 1 or 2, wherein step (vi) of allocating the information related to the at least one translocation breakpoint to at least one carrier individual further includes determining the type of aneuploidy, including segmental aneuploidy, for each individual in the biparental mapping population analyzed, wherein the determination includes defining a gamete or progeny chromosome architecture as depicted in Figure 2 or 3.

4. The method of any of the preceding claims, wherein the SNP genotyping data are obtained by or obtainable by a suitable genotyping technology generating a quantitative or at least semi-quantitative result for SNP allele dosage, preferably wherein the assay is selected from a microarray, including a bead microarray, a hybrid-SNP microarray and a customized genomic DNA fragment microarray, a whole genome sequencing data set, a quantitative PCR data set, a next-generation sequencing data set, or an amplicon sequencing based data set, or any combination thereof, preferably wherein the assay is a microarray assay.

5. The method of any of the preceding claims, wherein the plant biparental mapping population is independently selected from a diploid or polyploid population, including an F2 population, doubled haploid (DH) population, a recombinant inbred line (RIL) population, a backcross population, and / or involves at least one synthetic polyploid parent.

6. The method of any one of the preceding claims, wherein the plant biparental mapping population is created from a polyploid or a synthetic polyploid parental plant.

7. The method of claim 6, wherein the plant is independently selected from Triticum spp., including Triticum durum and Triticum aestivum, Triticale, Brassica spp., including Brassica napus, Brassica carinata and Brassica juncea, Gossypium spp., including Gossypium hirsutum and Gossypium barbadense, and synthetic polyploids, such as artificial auto-tetraploid Zea mays.

8. The method of any of the preceding claims, wherein the method includes an additional step of regenerating and / or propagating a plant from a selected individual analyzed having at least one translocation chromosomal rearrangement in its genome.

9. A cell, tissue, organ, material, seed or whole organism obtained by or obtainable by a method according to any one of claims 1 to 8.

10. The method of claim 1 , wherein step (vi) includes identifying the configuration of the at least one translocation in at least one of the parent by:(vii) comparing the arrangement of at least one translocated segment and the position information of the at least one translocation breakpoint with theoretical prediction of segregation of translocated genomic segments during meiosis, and optionally: pairing of gametes to assist identification of an aneuploid individual and / or linkage map construction, in the progeny of a translocation heterozygote in the plant biparental mapping population.11 . The method of claim 10, wherein the method comprises an additional step of(viii) determining allelic ratios from the data set considering the parental origin of each allele to identify and thus provide at least one reciprocal translocation event position.

12. The method of claim 11 , further comprising a step of:(ix) selecting and / or obtaining at least one individual carrying the at least one reciprocal translocation positioned as the at least one translocation event in step (viii) in its genome.

13. A customized SNP genotyping technology configured for being suitable for performing a method of any one of claims 1 to 8, or 10 to 12.

14. A use of single-nucleotide polymorphism (SNP) genotyping data set as defined in any one of claims 1 to 8, or 10 to 12, or a use of a method of any one of claims 1 to 8, or 10 to 12 for determining and optionally correcting genotyping errors or miscalled data in a reference genotyping data set by identifying the true allele dosage in the single-nucleotide polymorphism genotyping data set by considering both aneuploidy and diploid chromosomal segments in all individuals of a biparental mapping population.

15. A use of single-nucleotide polymorphism (SNP) genotyping data set as defined in any one of claims 1 to 8, or 10 to 12, or a use of a method of any one of claims 1 to 8, or 10 to 12 for precisely identifying the position of at least on translocation breakpoint representing the result of at least one translocation chromosomal rearrangement in at least one chromosome in a plant of a biparental mapping population, optionally including the precise determination of the orientation of the at least one translocation on the at least one chromosome, where the translocation occurred.

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