ZFP57, a gene important in methylation and epigenetic regulation. This study demonstrates an improved strategy that can be used towards genetic dissection of diseases.Distinct regions of long-range genetic fixation in the human MHC region, known as conserved extended haplotypes (CEHs), possess unique genomic characteristics and are strongly associated with numerous diseases. While CEHs appear to be homogeneous by SNP analysis, the nature of fine variations within their genomic structure is unknown. Using multiple, MHC-homozygous cell lines, we demonstrate extensive sequence conservation in two common Asian MHC haplotypes: A33-B58-DR3 and A2-B46-DR9. However, characterization of phase-resolved MHC haplotypes revealed unique intra-CEH patterns of variation and uncovered 127 single nucleotide variants (SNVs) which are missing from public databases. We further show that the strong linkage disequilibrium structure within the human MHC that typically confounds precise identification of genetic features can be resolved using intra-CEH variants, as evidenced by rs3129063 and rs448489, which affect expression of  TRIM27 locus to the SYNGAP1 locus121415The human Major Histocompatibility Complex (MHC) region on chromosome 6p21.3 is the most gene-dense region in the human genome1357Attempts to quantify the level of polymorphism within MHC haplotypes have been carried out on eight European MHC haplotypes using bacterial artificial chromosome cloning (BACs) and shotgun sequencing171921Here, we report the characterization of two Asian conserved extended haplotypes, A*33:03-C*03:02-B*58:01-DRB1*03:01 (A33-B58-DR3) and A*02:07-C*01:02-B*46:01-DRB1*09:01 (A2-B46-DR9), which are present in the Singapore Chinese population at relatively high frequencies 23Using an MHC-specific screening panel of 1877 single-nucleotide polymorphisms (SNPs) in combination with HLA typing data of 247 unrelated Chinese individuals, two distinct patterns of the extended MHC region HLA-A33-B58-DR3 (n = 31) and HLA-A2-B46-DR9 (n = 36) were clearly identified . Three sThe use of high-density SNP typing (average 1/665 bp) showed that CEHs of HLA-identical, unrelated individuals appeared to be indistinguishable. However, it is not yet known if this conservation is maintained at the nucleotide level. To address this question, the HLA homozygous cell lines, B58AL, B58SC, B58CF, B46BM, B46ZS, and B46CM, were subjected to whole-genome sequencing (WGS) using the Complete Genomics (CG) platform. Raw reads were processed by the Complete Genomics Standard Sequencing Pipeline 2.0, and assembled according to the Genome Reference Consortium Human genome build 37 (GRCh37)To assess the quality of data generated from the CG platform, genotype calls between 25 and 35 Mb from the Illumina Human 1 M-Duo BeadChip SNP array were compared with the CG sequencing data. The results showed a high concordance rate (99.5% for A33-B58-DR3 and 99.6% for A2-B46-DR9) between SNP genotyping and the CG data . A randoNext, the range of genomic conservation in each sample was assessed with nucleotides from the 25–35 Mb region of chromosome 6 binned into windows of 5 kb, and the number of homozygous or heterozygous RefSeq single nucleotide variation (SNV) calls within each bin were examined. Stretches of homozygosity were defined to be regions with no more than four consecutive windows with a zygosity SNV ratio  of less than 0.95 . The res−5 and 8.75 × 10−5, respectively. These values are at least 38-fold lower than that found between the two common European MHC haplotypes, PGF and COX (3.4 × 10–3)26ZFP57 and HLA-F genes , were found in the A2-B46-DR9 haplotype but not in the A33-B58-DR3 haplotype , while the number of intra-CEH variations in the A2-B46-BR9 over this region was distinctly lower  located 16.43 kb centromeric to rs29228ZFP57. A more recent study revealed four additional eQTL SNPs , located in the ZFP57 introns and promoter region, that were associated with the expression of ZFP57ZFP57 mRNA levels in B58AL, B58SC, B58CF, B46BM, B46ZS, B46CM, as well as COX and QBL. Noticeably, the COX, B46BM, and B46CM (both A2-B46-DR9) cell lines which possess the “A” allele of rs29228, “T” allele of rs375984, “A” allele of rs416568, “C” allele of rs365052, and “T” allele of rs2747431 expressed ZFP57 while B46ZS (A2-B46-DR9), B58AL, B58SC, B58CF , and QBL which possess the alternate allele at these positions have no ZFP57 expression  that was reported to exert a cis-acting effect on the expression level of the Zinc Finger Protein 57 homolog . A total of 202 A2-B46-DR9 intra-haplotypic SNVs were found within this genomic segment, the majority of which were localised to the 5′ end of the ZFP57 gene , the HLA-B region (31.1–31.6 Mb), and the HLA-DRB1 region (32.3–32.8 Mb) , the 32.8 Mb) . PhylogeHLA-DRB1, -DQA1, and DQB1 genesThe COX and QBL cell lines were previously reported to have almost identical genomic sequences across the In this study, we have identified the presence of Asian conserved extended haplotypes in the A33-B58-DR3 and A2-B46-DR9 MHC haplotypes through SNP genotyping as well as deep sequencing of the extended MHC region. This provides an in-depth description of the extent and pattern of variation over several megabases within a certain CEH. In total, we have uncovered more than 200 haplotype-specific SNVs residing in each CEH, up to a third of which are not annotated in any public archives for genetic polymorphism. However, the use of the common SNP genotyping platform to interrogate the CEHs was unable to reveal the true extent of polymorphisms embedded within the conserved region. The results of this study suggest there could be additional CEH-specific private variants in other CEHs. The ability to reveal private variants that are enriched at the CEH level, but not at the population level, allows fine discrimination between individuals carrying the same CEH.Our study demonstrated that the pattern of variations in each CEH is unique with two varieties of sequence differences. The first is the rare SNVs that spread randomly across the conserved region of the CEH; such variations have been reported in previous publications albeit at significantly smaller numbers2135373836cis-regulatory effects by variants on nearby genes, affecting the expression of target genes, is prominent within the MHC region, adding further strength to this notion4647ZFP57 gene differentially. The presence of these MHC-residing variants offers the possibility to refine association studies for a number of MHC disease associations, which have not yet been explained, particularly the contribution of SNVs to disease phenotypes. In our study, up to 99% of intra-haplotypic variations were located within the non-coding region; these variants could be employed as a reference panel to infer the effects of non-coding variants on diseases.Genome-wide association studies have identified more than 100 diseases that are implicated by variants or genes within the MHC region4445−8 and a given generation is 20 years, we calculate the age of the Asian A33-B58-DR3 and A2-B46-DR9 haplotypes to be approximately 21,460 and 26,500 years old, respectively. These values are compatible with the age of the A1-B8-DR3-DQ2 haplotype, lending support to the theory that these MHC CEHs are likely to have resulted from recent expansion. In addition, it has been suggested that the expansion of CEHs is due to a single HLA allele under strong positive selection rather than epistatic selection of a specific HLA allelic combinationThere are two plausible explanations for the generation and maintenance of MHC CEHs. The first is that these long sweeps of conserved sequence haplotypes may have been driven to high frequency by positive selection over a relatively short period of time and have yet to be disrupted by recombination events49In conclusion, we demonstrated genetic fixity in two common MHC conserved extended haplotypes of Asian ancestry, A33-B58-DR3 and A2-B46-DR9, and assembled phase-resolved MHC sequences representative of these haplotypes. The availability of these Asian MHC sequences complements the eight European MHC haplotypes sequenced by the MHC Haplotype Project and provides a framework to study MHC diversity, variation, and evolution. In addition, these MHC conserved extended haplotype sequences are valuable as a resource for future disease studies on populations enriched in these haplotypes, which include many Asian populations2324HLA-A, -B, -C, and -DRB1 genes were typed by a sequence-based approach. This was achieved by interrogating the hyper-variable exons 2 and 3 of the HLA-A, -B, and -C genes by PCR amplification using specific primers, followed by direct DNA sequencing of the PCR products using ABI BigDye Terminator v3.1 chemistry run on an ABI Prism 3100 Genetic Analyser . HLA-DRB1 was sequenced and typed as described previouslyCells and DNA: The B-lymphoblastoid cell lines (B-LCLs), COX and QBL, were purchased from the Research Cell Bank, Fred Hutchinson Research Centre, Seattle, WA. Peripheral blood lymphocytes were isolated from healthy blood donors with informed consent. Genomic DNA from the six samples  was subjected to genome-wide SNP genotyping using the Illumina Human 1 M-Duo BeadChip Kit. SNP coordinates were mapped to the Human Reference Sequence Assembly 37.2 (NCBI build 37.2), and all samples had overall SNP call rates of more than 95%. SNP loci that were not called in any of the samples or that deviated from Hardy Weinberg equilibrium at a significance level of 0.05 were not included in the downstream analysis. SNPs in chromosome 6 from 25 MB to 35 MB, covering the extended MHC region, were selected and screened for Runs of Homozygosity (ROH) segment analysis using PLINK packageet al. and the SNPs haplotype reconstruction within the MHC region was performed as describedMHC panel SNPs and HLA typing data consisting of 247 individuals from the Singapore Chinese population were obtained from Lam B58AL, B58SC, B58CF, B46ZS, B46BM, and B46CM genotype data from the Illumina Human 1 M-Duo BeadChip from were used for IBD analysis. Independent SNPs  not in linkage disequilibrium with one another were randomly selected from the genome-wide SNPs data and were used to test for IBD among the samples. The PLINK package was used to estimate the following IBD parameters in each sample pair: probability of genetic markers sharing 0 allele (IBD = 0), probability of genetic markers sharing 1 allele IBD (IBD = 1), and probability of genetic markers sharing 2 alleles (IBD = 2).Genomic DNA was extracted from B-LCLs and subjected to whole genome sequencing using next generation technology implemented by Complete Genomics . Massive parallel short paired-end sequencing was performed on self-assembling DNA nanoball arrays, generated from fragmented DNA using combinatorial probe-anchor ligation chemistryFor intra-CEH comparisons, only nucleotides at positions with high quality score metrics generated from the CG assembly protocols were considered for comparison; nucleotides at positions with low confidence scores were considered ambiguous and were not used for comparison.For inter-haplotype comparisons, consensus sequences for each of the two Asian MHC haplotypes were first established. For each haplotype, each of the three genomes would give two haploid chromosomes, and the six resulting haploid chromosomes were then compared. If two or more chromosomes had ambiguous or low confidence calls at a position, it was no-called in the consensus sequence. A variant was called if two or more haploid chromosomes showed an alternate nucleotide call.http://www.ucl.ac.uk/cancer/medical-genomics/mhc/#HaplotypeData and the coordinates of these BED files were aligned to the Human Reference Sequence Assembly 37.2 (NCBI build 37.2). Genetic variants found between the MHC haplotypes were annotated using the ANNOVAR softwareTo compare the two Asian haplotypes against the eight European MHC haplotypes sequenced by the MHC Haplotype Project, BED files of the eight European MHC haplotypes917Primer pairs to assess the variants were designed using NCBI Primer-BLAST and checked against the Human Reference Sequence Assembly 37.2 (NCBI build 37.2). Only primer pair sequences matching the Asian MHC haplotypes were selected. The sequences of these primer pairs and the position of variants of interest can be found in The PCR template generated from primer sequences AGCAGTCACAAGTCACAGGG and CAGCCCATCGCATGCTCAAT was selected and subjected to TA-cloning. TA-cloning was then performed using pGEM®-T Easy Vector Systems , according to the manufacturer’s instructions. Colonies were selected and plasmid DNA was extracted using the QIAprep® Miniprep Kit (Qiagen), and sequenced with the T7 promoter and SP6 promoter primers.6 cells/ml were treated with 200 nM phorbol 12-myristate 13 acetate  and 125 nM ionomycin for 6 h. Total RNA was extracted using the RNeasy Mini Kit (Qiagen), from which cDNA was generated using the Maxima® First Strand cDNA Synthesis Kit (Thermo Fisher Scientific). qPCR by KAPA SYBR® FAST Roche LightCycler® 480 2X qPCR Master Mix  was performed in triplicate for each of the two biological replicates on the Roche LightCycler® 480 System (Roche Applied Science). Ct value calculations using the second derivative maximum method and melting curve analysis were carried out with gene-specific primer pairs. ZFP57 expression was normalized to Hypoxanthine Phosphoribosyltransferase 1 (HPRT1) and determined using the ΔCt method. Primer pair sequences were as follows: ZFP57 forward - TGAGGATGTGGCAGTGAATTT, ZFP57 reverse - GTGTTTGGGAGATGGACAAAC, HPRT1 forward - GTAATGACCAGTCAACAGGGGAC, HPRT1 reverse - CCAGCAAGCTTGCGACCTTGACCA.Cultures at 1 × 10The SNP sequences of the Asian and European haplotypes were comprised of 18,781 common SNPs annotated in dbSNP build 137. SNP positions with heterozygous calls in the Asian haplotypes were denoted as missing data. Phylogenetic trees were constructed based on the maximum likelihood statistical method, and the Kimura 2-parameter substitution model was used to calculate the likelihood on a given tree. To evaluate the reliability of branching points, a bootstrap test of phylogeny was performed (n = 500). The tree building process was implemented in MEGA52 was used as a measurement of LD between variants, and an LD block was defined as a region with an SNP pairwise r2 value >0.8. The computation of r2 and the haplotype analysis within each LD block were performed using HaploviewThe LD structure in this region was established based on the genotype information of 163 intra-CEH variants in 105 unrelated Southern Han Chinese individuals (1000 Genome Project)30ZFP57 transcription, a luciferase reporter assay was designed to test the effect of 41-bp DNA sequences bearing the different allele of each intra-CEH variant flanked by spanning sequences of 20 bp. The 41-bp DNA fragments were annealed and directly cloned between Sac I and Xho I restriction sites upstream of the minimal SV40 promoter in the pGL3-Promoter vector (Promega). Annealing primers are listed in To evaluate the potential regulatory role of intra-CEH variants in Genomic DNA was extracted from the six cultured cell lines  using the QIAGEN® DNeasy Blood and Tissue Kit, according to the manufacturer’s protocol. The assay used to determine the RCCX modular duplication in each cell line was developed from a modified version of the real-time PCR assay, as previously describedAccession codes: Data reported in this study will be available at NCBI SRA (Accession ID: SRP053276).How to cite this article: Lam, T.H. et al. Intrahaplotypic Variants Differentiate Complex Linkage Disequilibrium within Human MHC Haplotypes. Sci. Rep.5, 16972; doi: 10.1038/srep16972 (2015).


In this report, we used C1galt1 Mutant  and Control  mice to investigate how cumulus expansion is affected by oocyte-specific deletion of core 1-derived O-glycans without adversely affecting oocyte quality. Mutant cumulus–oocyte complexes (COCs) are smaller than Controls, with fewer CCs. Interestingly, the CCs in Mutant mice are functionally normal as each cell produced normal levels of the ECM molecules HA, TSG-6, and PTX3. However, HC levels were elevated in Mutant COCs. These data reveal that oocyte glycoproteins carrying core 1-derived O-glycans have a regulatory role in COC development. In addition, our study of Controls indicates that a functional COC can form provided all essential components are present above a minimum threshold level, and thus some variation in ECM composition does not adversely affect oocyte development, ovulation or fertilisation. These data have important implications for IVF and the use of cumulus expansion as a criterion for oocyte assessment.During follicle development, oocytes secrete factors that influence the development of granulosa and cumulus cells (CCs). In response to oocyte and somatic cell signals, CCs produce extracellular matrix (ECM) molecules resulting in cumulus expansion, which is essential for ovulation, fertilisation, and is predictive of oocyte quality. The cumulus ECM is largely made up of hyaluronan (HA), TNF-stimulated gene-6 , pentraxin-3 (PTX3), and the heavy chains (HCs) of serum-derived inter-α-inhibitor proteins. In contrast to other  The process of follicle development begins with the activation of a quiescent primordial follicle and culminates with the ovulation of a single fertilisable egg. During the early stages of follicle development, the granulosa cells that surround the oocyte proliferate to form multiple layers of cells. As the follicle develops, antral fluid is deposited between the granulosa cells which facilitates the physical separation and differentiation of the granulosa cell population into mural granulosa cells  and cumulus cells (CCs) that are associated with the oocyte.Prior to ovulation, an extracellular matrix (ECM) is assembled between the CCs leading to expansion of the cumulus mass that surrounds the oocyte. The expanded cumulus mass is believed to facilitate efficient capture of an ovulated egg by the oviductal fimbriae and transport into the oviduct , which initiates two signalling events. First, mGCs secrete epidermal growth factor-like (EGF-L) peptides that bind to EGF receptors on CCs . AlthougFollowing the LH surge, the activation of EGF- and OSF-mediated signalling pathways induces CCs to express hyaluronan synthase 2 (HAS2) and synthesise the glycosaminoglycan hyaluronan (HA) (bikunin (and hence unable to assemble IαI) or Tnfaip6 fail to support COC expansion , which is responsible for the synthesis of core 1-derived O-glycans  specifically in oocytes from the primary follicle stage onwards , our first hypothesis was that the altered cumulus mass was due to molecular changes in the cumulus ECM. Changes in any of the cumulus molecules would indicate either redundancy or plasticity in the function of the cumulus complex. In addition, considering the importance of the different cumulus ECM molecules (evident from the knock-out mouse models described previously) our novel intra-follicular approach, comparing ECM molecules of individual COCs, enabled us to determine the degree of correlation between these molecules in Control cumulus complexes.Therefore, on the basis that the cumulus expansion defect in C1galt1 Mutant COCs results predominantly from the reduced Mutant cumulus size brought about by fewer CCs with additional minor changes in cumulus ECM proteins. These data have wider implications in the field of assisted reproductive technologies (ARTs) since selection of developmentally competent eggs should not be judged solely by the size of the cumulus complex and the number of CCs surrounding an egg. In addition, using this novel method of correlating the levels of cumulus ECM molecules within individual cumulus complexes, we provide evidence that considerable variations exist in the composition of the cumulus ECM, which are tolerated without adverse effects on fertility in both the Control and the Mutant, as long as all components are present above a threshold level.In this study, we demonstrate that the modified cumulus matrix of C1galt1FF:ZP3Cre male mice (Mus musculus) were mated with C1galt1FF female mice to generate C1galt1FF:ZP3Cre (Mutant) and C1galt1FF (Control) female mice (2 solution (Bioline), 0.5 μl of 10 mM dNTP , 0.5 μl of each primer , 1 μl of genomic DNA (ear) and 0.15 μl of Taq polymerase (Bioline) for the detection of floxed C1galt1 or 0.5 μl of Taq polymerase for the detection of the Cre transgene. The primers used to detect the C1galt1 floxed allele were either TS1  for 8 h and washed in 70% ethanol. The ovaries were embedded in paraffin, sectioned at 5 μm and mounted on glass slides.2O2  in PBS for 5 min. The sections were blocked with 2% FCS (Sigma–Aldrich) in PBS for 1 h before HA detection, or with 1.5% normal goat serum  in TBS for 1 h before TSG-6, Ki-67 and pSMAD2 detection, or with 10% dry milk  in PBS for 1 h before PTX detection, or with 5% dry milk in PBS for 2 h before HC detection, or with 5% BSA (Fisher Scientific) in PBS for 1 h before pSMAD1/5/8 detection. The sections were then incubated with either 0.25 mg/ml biotinylated HA binding protein  and pSMAD1/5/8 (0.01 M citrate buffer). Endogenous peroxidase was blocked with 3% HAll immunohistochemistry slides were incubated with biotinylated anti-rabbit IgG secondary antibody  for 1 h at room temperature, followed by ABC solution (Vectastain ABC Elite Kit) for 30 min at room temperature. Antigen-specific detection was revealed using a DAB Kit (Vector Labs). The slides were then dehydrated and mounted with Depex  and imaged using the same light microscope . Experiments to detect HA and protein antigens using bHABP or antibodies, respectively, were all performed a minimum of three times.Molecules detected in CCs and cumulus ECM were quantified using ImageJ Software . In ImageJ, each pixel is given an intensity value from 0 (black) to 255 (white); based on this, total pixel intensity and mean pixel intensity are calculated. The values for total pixel intensity, number of pixels, and mean pixel intensity for each cumulus complex were all calculated and exported from ImageJ. The pixel values were then inverted, therefore 0=white and 255=black, to facilitate data interpretation. Finally, mean pixel intensity was expressed as a percentage . To normalise to CC numbers, the value of total pixel intensity was simply divided by CC numbers.To analyse the size of cumulus complexes, the section closest to the centre of the oocyte was chosen. Cumulus area was quantified by selecting the space occupied by CCs A, and avs.d. and data were analysed using Student's t-test . A P value of <0.05 was considered to be statistically significant. For correlations of ECM molecules and cumulus expansion, the coefficient of determination (r2) was calculated (GraphPad Prism) to establish the degree of association between the variables. An r2 value of >0.8 was considered to indicate a strong association.All bar graphs values are presented as mean±C1galt1 Mutant female mice are surrounded by modified cumulus masses compared with Controls . Analysis of these sections revealed that cumulus mass area ; i.e., the SMAD2 and SMAD1/5/8 pathways. Localisation of pSMAD2 was cell-associated as expected  nor did levels of OSF-induced pSMAD2 per CC correlate with cumulus size . Furthermore, in C1galt1 Mutant mice, the lack of oocyte glycoproteins with core 1-derived O-glycans did not alter the relationship between ECM molecules or pSMAD2 with cumulus size, as no correlations were observed  similar to Controls.Although the requirement for HA, PTX3, TSG-6 and HCs in cumulus expansion and their inter-dependence during ECM deposition are well described, the relationship between the available concentrations of these molecules and the extent of expansion have not been analysed in detail. In this study, we tested whether there was any correlation between the amount of each molecule present in the cumulus mass (as determined by staining intensity) and the cumulus area  or Mutants .In light of the interdependencies between ECM components during cumulus expansion  within individual COCs. These analyses reveal that there are no strong correlations either between the amount of any one of these ECM molecules and the size of the cumulus mass or between the relative levels of any of the matrix components in both Control and Mutant COCs. This suggests a highly flexible system whereby the relative amounts of HA, HCs, TSG-6 and PTX3 can vary quite substantially but can still form a functional matrix provided that they are all present at, or above, the minimum level required. Furthermore, the degree of cumulus expansion does not predict the respective levels of cumulus ECM molecules.Our results reveal that reduced cumulus size does not prevent ovulation and subsequent fertilisation, as these processes are not compromised in the in vitro maturation (IVM)). The low success rate of ARTs  is partIVM <35% . The resC1galt1 Mutant, we examined two parameters that underlie the formation of the cumulus matrix during the periovulatory period: i) the number of CCs that make up the CC complex and ii) the ability of each individual CC to produce cumulus ECM molecules. Mutant COCs were shown to occupy ∼32% less area compared with the Control, which is accompanied by fewer CCs in the entire complex. Furthermore, the corona radiata CCs in the Mutant were more tightly packed and were also closer to the oocyte compared with Control indicating aberrant expansion. In addition, the area occupied per CC in the Mutant is ∼13% less than Controls, making the Mutant COC ∼13% more dense. As a result of this, the mean intensity of HA and PTX3 molecules was higher in the Mutant COC, although when analysed per CC, the intensity of these two molecules was similar between Control and Mutant, indicating that each individual CC in the Mutant functions as Control. Interestingly, the levels of HCs detected, which are the only cumulus ECM component not produced within the follicle, were increased in Mutants compared with Controls. An increased production of IαI, and thus HC, by the liver due to the oocyte modification is unlikely. However, it has been observed that the basal lamina of Mutant follicles is altered during follicle development  resulting from heterozygosity of a mutation in bone morphogenetic protein receptor 1B . In these sheep, the increased number of preovulatory follicles is accompanied by a smaller number of granulosa cells per follicle, resulting in fewer cells contributing to the cumulus complex  there was altered proliferation of CCs in earlier stages of cumulus expansion, or ii) there were fewer somatic cells associated with the oocyte from the outset, or iii) CC apoptosis is elevated in the Mutant resulting in fewer CCs at 9 h post-hCG. The second hypothesis is consistent with the characteristics of Booroola sheep that exhibit increased fertility (similar to  complex . HoweverO-glycans from oocyte-expressed glycoproteins has effects on the whole follicle that are evident from i) greater levels of HCs of IαI and Pαl in the follicle and ii) altered numbers of CCs. This highlights the critical role of the oocyte in follicle development. The effects of C1galt1 deletion on the cumulus mass surrounding the oocyte could be a direct result of changes in the OSFs that determine the proliferative potential of CCs or an indirect outcome relating to the effects of OSFs on EGF-ligands/EGF-receptors, whereby if these are altered, the LH signal is not properly transmitted to CCs. The observation that mouse COCs with reduced CC numbers can function normally without compromising the developmental potential of oocytes is intriguing and raises the question of why do Control COCs have an apparent excess of CCs? In addition, it will be interesting to investigate whether this observed CC redundancy in mouse COCs applies to human COCs, in which case, assessment of the cumulus complex as an indication of oocyte quality for IVF needs to be used with caution. Finally, the lack of any strong correlations between the levels of different ECM molecules relative to each other or to the size of the cumulus mass indicates that, providing the minimum requirements for matrix formation are met, this system possesses a high degree of flexibility. It remains to be determined whether the specific expression patterns of individual ECM molecules by human CCs might be predictive of oocyte quality.In conclusion, the absence of core 1-derived http://dx.doi.org/10.1530/REP-14-0503.This is linked to the online version of the paper at


G. hirsutum varieties based on the rationale that varietal SNPs can be more confidently called when flanked by subgenome-specific SNPs. Using transcriptome data, we successfully identified 37,413 varietal SNPs and, of these, 22,121 did not have an additional varietal SNP within their 20-bp flanking regions so can be used in most SNP genotyping assays. From restriction site−associated DNA sequencing data, we identified an additional 3090 varietal SNPs between two of the varieties. Of the 1583 successful SNP assays achieved using different genotyping platforms, 1363 were verified. Many of the SNPs behaved as dominant markers because of coamplification from homeologous loci, but the number of SNPs acting as codominant markers increased when one or more subgenome-specific SNP(s) were incorporated in their assay primers, giving them greater utility for breeding applications. A G. hirsutum genetic map with 1244 SNP markers was constructed covering 5557.42 centiMorgan and used to map qualitative and quantitative traits. This collection of G. hirsutum varietal SNPs complements existing intra-specific SNPs and provides the cotton community with a valuable marker resource applicable to genetic analyses and breeding programs.Varietal single nucleotide polymorphisms (SNPs) are the differences within one of the two subgenomes between different tetraploid cotton varieties and have not been practically used in cotton genetics and breeding because they are difficult to identify due to low genetic diversity and very high sequence identity between homeologous genes in cotton. We have used transcriptome and restriction site−associated DNA sequencing to identify varietal SNPs among 18  Molecular markers genetically linked to agronomically important traits or genes of interest are valuable tools for increasing the efficiency of crop genetic improvement through marker-assisted selection. A number of different types of molecular markers, including restriction fragment length polymorphism, random amplified polymorphic DNA, simple sequence repeat (SSR), diversity arrays technology, amplified fragment length polymorphism, and single-nucleotide polymorphism (SNP), have been developed and used successfully in genetic studies of both simple and complex quantitative traits. SNPs are the most abundant type of molecular markers in plants, even in species that are restricted in their genetic diversity such as many of our crops (Gossypium hirsutum (Upland cotton) and G. barbadense , the two cotton species with the largest areas of cultivation worldwide  are both allotetraploids. They originated from a relatively recent (1−2 million years ago) interspecific hybridization event between an A-genome−like ancestral African diploid species similar to modern G. arboreum or G. herbaceum and a D-genome−like Central American diploid species similar to modern G. raimondii  of tetraploid cotton have a very high (often >95%) sequence conservation between homeologous genes. This means that the genic contents of the two subgenomes often are difficult to distinguish from each other in short read sequences from tetraploid plants. Second, compared with varieties among other major crop species, Upland cotton varieties, including our Australian cotton varieties, have a relatively low DNA sequence diversity. The average frequency of SNPs in Upland cotton was reported to be from less than 0.01–0.04%  and G. arboreum (A2), the putative extant form of the contributor of the Dt and At genomes of the cultivated tetraploid cotton species, respectively, have recently been sequenced  lists some 56,961 G. hirsutum SNPs identified from Public expressed sequence tag (EST) data, no effort was put into identifying varietal variation and most are likely to be subgenome-specific SNPs. Few, if any, of those computationally predicted SNPs have been validated or mapped. The first dedicated effort in the public domain identified SNPs by sequencing a large number of amplicons from prescreened single copy loci using the traditional Sanger sequencing approach. More than a thousand SNPs were identified from a panel of diverse Gossypium germplasm, but only 245 SNPs from 124 loci were found among the 16 G. hirsutum accessions examined  at the extreme of the diversity within G. hirsutum. Only a small proportion (<7%) of a subset consisting of 277 codominant markers assayed on a diverse panel of germplasm were found to be different between other domesticated varieties and Acala Maxxa, so not many of these SNPs are likely to be polymorphic between different breeding lines. A SNP genetic linkage map was constructed using 367 of the total of 1052 SNPs developed from this resource, validating the utility of the SNPs for introgressions from wilder sources of G. hirsutum, but not necessarily for normal intervarietal cotton breeding. G. hirsutum.Using a hypomethylated restriction-based genomic enrichment strategy and the 454 pyro-sequencing technology, G. hirsutum varieties and complexity-reduced, DNA sequences from two of those varieties using a novel approach and validated a significant subset (1363 SNPs) using the Sequenom or GoldenGate genotyping platform. This collection of G. hirsutum-specific varietal SNPs provides the cotton community with a valuable marker resource applicable to applied breeding targets and genetic analyses alike. Usage of these SNPs was demonstrated by construction of a G. hirsutum genetic linkage map containing 1244 SNP markers and the mapping of QTL for leaf shape, leaf trichome density, and pollen color.In this study, we performed varietal SNP identification using transcriptomes of 18 G. hirsutum varieties were used for SNP discovery in this study , India , and China . All 18 varieties were used in transcriptome-based SNP identification, whereas MCU-5  and Siokra 1-4  were also used in RAD-based SNP identification. Seeds of these varieties were provided by the cotton breeders of CSIRO Plant Industry, Narrabri, Australia. An F7 recombinant inbred line (RIL) population with 244 lines derived from MCU-5 × Siokra 1−4  according to the manufacturer’s instructions. Approximately 500 ng of DNA was used in digestion and ligation. To sequence the six libraries together in a single lane, Adaptor 1 with two different index sequences  was used for MCU-5 and Siokra 1−4. Single end reads with a length of 100 bp were generated using a HiSeq2000 (Illumina) at the Australian National University .The RAD sequencing libraries were generated according to the procedures reported by 5 genome  using Biokanga (http://www.biokanga.sourceforge.net) with the following settings: ≤4 bp of substitutions, ≤5 bp of microInDels, splice-junction detection for introns up to 1 kb in size, with 5′ and 3′ ends trimmed until edge bases matched the reference, polymerase chain reaction (PCR) differential amplification artifact reduction applied, and no indeterminate bases allowed. Only the reads with a single best unique alignment were used in the following SNP identification processes. The alignment results were written in SAM format.After adaptor trimming and removal of low-quality reads, RNA-seq reads were stringently aligned against the cotton DA custom C++ application, developed in-house, was used to predict bialleic SNPs based on these alignments. The program used a sliding window approach to identify regions with enough read density for SNP calling. The observed SNPs within each variety and all possible permutations of their combinations in these regions were processed in an iterative strategy to identify well-supported, subgenome SNPs. These regions with discriminated genome SNPs were then used in varietal comparisons to identify varietal SNPs. The detailed bioinformatic algorithm will be described in another publication dealing with both the methodology and implementation .EcoRI, ApeKI, and SbfI. After further removing the index sequence, all reads were 3′ trimmed to a length of 78 bp. For each variety, identical short reads were collapsed into a sequence tag, and then the unique sequence tags with a read depth ≥4 (we found that sequencing errors could be effectively removed while keeping a maximum number of informative sequence tags when using this read depth threshold) from each variety were together aligned to the G. raimondii genome using the CLC Genomics Workbench  with the following parameter settings: mismatch cost, 2; insertion and deletion cost, 3; length fraction, 0.5; similarity fraction, 0.95; and nonspecifically matched reads ignored.For the RAD sequencing data, after removing adaptor-ligated and low quality reads, we assigned the remaining reads to MCU-5 or Siokra 1−4 based on the index sequences, and the sorted reads were then further separated based on the restriction sites of t genome, and another from the Dt genome. This alignment result was then used to call the potential SNPs using the “quality-based variation detection” model implemented in the CLC Genomics Workbench with the following settings: default read quality filters ; minimum tag coverage, 4; minimum variant frequency, 25%; maximum expected alleles, 2. The results were then filtered using coverage (4) and allele frequency (25% and 75%) to get the first set of potential varietal SNPs. The rationale for using these filters are that the region with a potential varietal SNP should be covered by two sequence tags from each variety and that the potential varietal SNP should have a 1:3 ratio. The alignment status of the filtered SNPs were manually checked to further remove false-positive results and to make sure that a putative varietal SNP is always flanked by at least one genome-specific SNP because this information could not be obtained by filtering. All SNPs reported in this paper are shown in File S1 and File S2, and they have also been submitted to CottonGen (http://www.cottongen.org/).We found that in the majority of regions with sequence tags mapped, only four tags were aligned, two from each variety, potentially one from the AG. raimondii to amplify a ~200-bp PCR product using the varieties from which the SNP was identified. For each pair of primers, PCR conditions were optimized to amplify a single band. The PCR products were then digested with an appropriate restriction enzyme for 2−3 hr at the appropriate temperatures and fractionated on a 2% agarose gel to visualize the digestion products.Selected putative varietal SNPs initially were analyzed by converting them to cleaved amplified polymorphic sequence (CAPS) markers. Primers were designed based on the aligned reads or the genome sequence of G. raimondii genome sequence by Blastn to confirm their uniqueness. Illumina GoldenGate SNP assays were performed by Beijing Genomics Institute (BGI Hong Kong). Then, 100-bp flanking sequences of 1652 putative varietal SNPs (from both transcriptome and complexity-reduced DNA) identified between MCU-5 and Siokra 1−4 and evenly distributed on 13 G. raimondii scaffolds were submitted to Illumina for marker design suitability ranking, before the selection of SNPs to assay. Because the RAD reads were only 78-bp long, G. raimondii genomic sequence was used to extend the flanking sequences . Of these SNPs, 1632 SNPs were selected for Oligo Pool Assay synthesis and used to genotype MCU-5, Siokra 1−4, and 244 F7 RILs derived from MCU-5 x Siokra 1-4. Of these, 1572 and 60 had a suitability ranking score >0.6 and 0.4−0.6, respectively, and 1521 had a SNP call in ≥95% of the samples analyzed and were kept for further analysis.Sequenom SNP assays were performed using the standard procedure at the Australian Genome Research Facility . Primers were designed based on 100-bp flanking sequences of the putative varietal SNP and aligned against the 7 RIL population was classified as okra, intermediate, or normal. Pollen color was classified as yellow or creamy. For leaf trichome density, three 57-mm2 leaf discs were collected from the flanking area of the main vein of the eighth leaf, treated, and observed  as previously described   and the D5 reference genome  . AssignmG. hirsutum, we sequenced individual transcriptomes of 18 G. hirsutum varieties using messenger RNA isolated from undifferentiated callus derived from cotyledons. This tissue was chosen because a substantial proportion of the genome is transcribed in callus while avoiding the highly expressed genes involved in photosynthesis that would otherwise dominate sequence reads from most vegetative tissues. After trimming and filtering, 45.8−52.7 M high-quality sequence reads (90-bp paired-end) from each of the individual varieties were retained for alignment, except for Sicot F-1, for which 27.2 M processed single-end reads were used in alignment , with the parameters detailed in the section Materials and Methods. Approximately 13.5−22.2 M (36.6–49.8%) reads originating from transcripts of both At and Dt genomes were uniquely aligned and used in SNP identification.To identify varietal SNPs in lignment . Reads wt and Dt subgenomes but mostly nonpolymorphic between varieties; and ii) varietal or allelic SNPs , which are polymorphisms in only the At or Dt genome between two different varieties. Accurate identification of varietal SNPs in cotton using RNA-seq data, however, is complicated by: i) the lack of availability of transcriptome or genome reference sequences for both the subgenomes; ii) the presence of genome-specific SNPs at frequencies much higher than varietal SNPs; and iii) the possibility of unequal or differential allelic expression of homeologs from the At and Dt subgenomes, making it difficult to be certain that all alleles present have been observed for the purposes of robustly assigning a sequence difference to a varietal type.As an allotetraploid, cotton has two types of SNPs: i) the more abundant subgenome-specific SNPs, which are polymorphisms between homeologous loci from the At subgenome between variety 1 and 2, but only if there is definitely a G in this position in both the At and Dt alleles of variety 2. Alternatively, the nucleotide G detected in variety 2 may simply reflect the sequencing depth being by chance insufficient to detect the At allele or the Dt allele because of their differential expression and/or technical issues related to library preparation and sequencing. These two scenarios cannot be distinguished with any great confidence, unless the sequencing depth is very large, and would otherwise result in a very high false-positive rate for varietal SNP calls. If there is a genome-specific SNP .Using this rationale, we deployed an analytical approach to identify only the varietal SNPs among our RNA-seq data that were flanked by at least one subgenome-specific SNP. The approach was first tested using the short reads from four varieties , and this identified 4894 varietal SNPs. Ten of the predicted varietal SNPs and 10 equivocal SNPs without a supporting adjacent subgenome-specific SNP were selected for validation by converting them to CAPS markers. Of the 10 predicted varietal SNPs with flanking subgenome-specific SNP(s), 7 were confirmed to be polymorphic. Two such examples are shown in EcoRI, ApeKI, or SbfI. In total, 95.5 M single-end reads (100 bp in length) were generated. After adaptor trimming and removal of low-quality reads, the remaining reads were separated based on the index sequences and restriction sites into seven groups . Of the reads (76.7 M) with both index and restriction-site information, the majority (95.3%) were from EcoRI−digested DNA, with only 2.2 M (2.8%) and 1.4 M (1.9%) from ApeKI− and SbfI−digested DNA, respectively. This was probably because ApeKI and SbfI did not digest cotton DNA well in our hands and SbfI is a rare cutter restriction enzyme (8-bp cutter). In the following analysis, we used only reads from EcoRI digested DNA.Transcriptome-based varietal SNPs are limited to expressed regions of the genome that are likely to be less polymorphic as they are often constrained by purifying selection. To identify SNPs located within nontranscribed and intronic regions and to test the feasibility of using DNA instead of RNA of tetraploid cotton in SNP identification, we created a RAD sequencing library using genomic DNA isolated from MCU-5 and Siokra 1−4 and digested with Materials and Methods. An example is shown in G. raimondii genome on both sides of the EcoRI (GAATTC) restriction site. These tags from each genotype are distinguished from each other by the presence of subgenome-specific SNPs on the same sequence reads. The varietal SNP (C/T) shown has a 1:3 ratio among the four tags. Based on this type of alignment pattern and the filters described in Materials and Methods, we identified an additional 3,090 varietal SNPs between MCU-5 and Siokra 1-4 (File S2).We used the CLC Genomics Workbench to identify putative varietal SNPs in our RAD sequencing data. We investigated different combinations of the input sequence format and variation detection modules and found that potential varietal SNPs between two tetraploid cotton varieties could be quite accurately called using a nonredundant read set and the “quality-based variation detection” module by following the criteria detailed in the section G. raimondii chromosomes with an average density of 53 SNPs/Mb. Of the 13 chromosomes, Chr02 and Chr09 had the lowest (42 SNPs/Mb) and highest (78 SNPs/Mb) SNP density, respectively (G. raimondii (ftp://ftp.jgi-psf.org/pub/compgen/phytozome/v9.0/Graimondii/). Flanking sequences of the transcriptome- and RAD-derived varietal SNPs matched G. raimondii transcripts in 92.2% and 19.9% of cases, respectively. Only 43 (or 1.4%) of 3090 RAD-derived SNPs were overlapping with the transcriptome-derived SNPs. In addition, gene density was highly correlated with the density of transcriptome-derived SNPs but not with the density of RAD-derived SNPs that had a much more even distribution within each chromosome than transcriptome-derived SNPs , 689 (52.3%) of the verified SNPs were also found to be polymorphic between two standard varieties  commonly used in cotton genetics and generation of a number of mapping populations, suggesting that our SNPs should be useful more broadly in both cotton genetics and breeding.Some of the initial transcriptome based varietal SNPs were verified by conversion to CAPS markers as indicated previously. We extended the validation to two different high-throughput SNP genotyping platforms to verify a larger number of the predicted SNPs . First, , and Sequenom, were developed primarily for diploid species. For polyploid species such as cotton, these platforms can be problematic when the SNP assay amplifies both homeologous (At and Dt) copies of a locus, i.e., they act as “dominant” SNP markers and are unable to discriminate between a heterozygous plant and one that is homozygous for one of the two possible alleles at that locus being assayed, such as Genotype 1 in i.e., they act as codominant SNP assays as they would in a diploid species -containing primers on the conversion of dominant SNP assays to co-dominant SNP assays, we analyzed the number and positions of subgenome-specific SNP(s) in both allele-specific and universal primers for the subset of 258 RAD-based SNPs that had their primers designed based on the 78-bp long RAD reads. The relationship between the number of subgenome-specific SNP(s) in the SNP assay primers and the percentage of assays behaving as co-dominant SNP markers are shown in File S1). Given a large enough number of SNPs to choose from, it should be possible to bias the selection of SNPs to those that will behave as co-dominant assays by choosing those that have flanking genome-specific SNPs within 20 bp.Because most current available SNP genotyping platforms prefer that there be no additional varietal SNP(s) within the 20-bp flanking regions of the targeted nucleotide being assayed, we separated our transcriptome-derived varietal SNPs into three types: type I , with no additional varietal SNP(s) within the 100-bp regions either side of a varietal SNP; type II (6130), with one or more varietal SNPs within the 21-100-bp flanking regions of a varietal SNP; and type III , with additional varietal SNP(s) within the 20-bp flanking regions of a varietal SNP (G. hirsutum genetic linkage map with 1244 SNP markers (not including 25 redundant SNP markers) was constructed based on an F7 RIL population derived from MCU-5 × Siokra 1−4 (File S3). This map contains 54 linkage groups with a total genetic distance of 5557.42 cM. These linkage groups were first assigned to a corresponding G. raimondii chromosome based on the localization of the majority of SNPs within the linkage group and then assigned to At or Dt subgenome based on the origin (A2 or D5) of the majority of the RNA-seq derived SNPs of each linkage group. This was determined by comparison of SNP containing reads with the transcriptome data from the A2 genome  and the published G. raimondii (D5) genome sequence (t or Dt) origin was bioinformatically determined, 905 (99%) were correctly mapped to a corresponding At or Dt linkage group. Of the 1244 SNP markers, 526 (42.28%) and 718 (57.72%) were mapped to the At and Dt linkage groups, respectively. Each of the 26 chromosomes of G. hirsutum was represented by 1−4 linkage groups. Generally, colinearity of the SNP markers between G. raimondii and G. hirsutum was observed for the majority of linkage groups although minor intrachromosomal inversions existed in some of the linkage groups. In total, 80 SNP markers (6.43%) did not group with their corresponding chromosome (those highlighted in pink in File S3). For example, SNP marker Chr10_48842108 was mapped to G. raimondii Chr02 [C15(Dt)_LG04], and SNP markers Chr09_32106785, Chr09_28064206, and Chr09_29035534 were mapped to G. raimondii Chr12 [C04(At)_LG15] , which is a result of chromosome arm translocation between C02(At) (G. raimondii Chr05) and C03(At) (G. raimondii Chr03) in G. hirsutum (G. hirsutum was between C04(At) and C05(At)  and C22 (G. raimondii Chr12) because all RNA-seq derived SNP markers mapped to G. raimondii Chr09 and Chr12 in LG16 were from the Dt subgenome (File S3).A t)_LG15] . Some ofqLS1, 62% phenotypic contribution) was mapped to an 8.8-cM interval in linkage group C15(Dt)_LG04, consistent with previous results achieved in other populations  were identified in linkage groups 11C(At)_LG43 and C06(At)_LG21. qLT2 has been previously reported in other population  for pollen color, was mapped to a 19-cM interval in linkage group C04(At)_LG15. In a recent report, several QTL for pollen color were mapped to C05 (At subgenome) in a region centered on SSR marker CIR253  rather than C05 (G. raimondii Chr09), consistent with our result.Leaf shape in cotton affects yield, insect resistance, lint trash, and the efficacy of foliar chemical application . A majorhttp://www.illumina.com/applications/agriculture/consortia.ilmn).In this study, we showed that the rationale we proposed worked well for the identification of genuine varietal or allelic SNPs in tetraploid cotton as opposed to the mostly subgenome-specific SNPs that have dominated many earlier SNP discovery projects in cotton based on the analysis of cotton ESTs in database collections. The validity of our approach was demonstrated by the high verification rate (72.6–91.7%) obtained with a reasonably large number of the identified varietal SNPs (~1700) and several different SNP genotyping platforms . Our anaG. hirsutum, and both used NGS and the complexity-reduced DNA sequencing approach  were selected for validation and it is still unclear what proportion are reliable allelic SNPs rather than just subgenome-specific SNPs. Further verification using more SNPs and a broader range of germplasm is required to establish the reliability of those predicted SNPs. In the second study, where SNPs between a wild and a cultivated cotton were determined, less than 40% of the 1052 SNPs genotyped were found to amplify or segregate in an expected ratio in an F2 population derived from the two G. hirsutum accessions used in SNP identification . A second strategy was to separately align the At and Dt genome reads from two varieties using very stringent alignment parameters or a precharacterized genome-specific SNP index to separate short reads into their subgenome of origin before varietal SNP identification (2: G. arboreum and D5: G. raimondii) have remained unaltered since polyploidy formation and can be used as diagnostic subgenome-specific SNPs in modern tetraploid cotton  genome sequence . This makes our approach more adaptable in polyploids without reference genome sequences. The constraints we have imposed through our strategy would necessarily underestimate the number of varietal SNPs between genotypes because our approach i) cannot identify varietal SNPs located in regions unique to either the At or Dt genome, as our SNP calling relies on the presence of both At and Dt reads; and ii) will miss the genuine varietal SNPs without a flanking subgenome-specific SNP. Nevertheless, in terms of ease of automation and reliability of prediction, our approach is a considerable improvement in SNP detection in the absence of a full tetraploid genome sequence and even when that sequence is available, will still offer some advantages over other automated approaches.More importantly, our approach did not specifically need to distinguish which allele is from the AComplexity-reduced DNA-sequencing strategies, such as RAD , contribG. hirsutum transcriptome reads could be aligned to the G. raimondii genome, suggesting that the remaining reads could derive from genes either unique to the At genome, or that have diverged significantly from those of their common ancestor with G. raimondii. When allowing only a single best aligned position (≤4 mismatches), 37–50% of G. hirsutum transcriptome reads could still be aligned to G. raimondii , and the genes annotated in the intervals can then be screened and investigated to single out potential candidate(s) contributing to the QTL. There are 87, 74, 87, and 151 annotated genes in the regions corresponding to qLS1, qLT1, qLT2, and qPC1 in G. raimondii, respectively (File S4). Recently, the region corresponding to qLS1 has been further narrowed down to containing only 34 annotated genes, from which Gorai.002G244000 and Gorai.002G244200 that encode HD-Zip transcription factors were suggested to be the possible candidates for the leaf shape trait (G. raimondii should be able to speed up the procedure of QTL fine mapping and identification of candidate genes underlying QTL of interest. Our full set of markers will have great utility in mapping more complex traits in cotton such as disease resistance or fiber yield and quality that are often conferred by many genes of small effect and will advance the use of marker-assisted selection in cotton breeding.Cotton genetic maps so far used in QTL mapping were mainly constructed using non-SNP markers, mostly SSR markers, although a genetic map with 1104 markers, including 414 SNP markers, has recently been used in mapping of QTL resistant to Verticillum wilt (ulations . In addiG. hirsutum varieties based on a robust protocol that relied on adjacent subgenome-specific SNPs to increase the confidence of SNP assignment to single alleles. A verification rate of 72.6–91.7% was achieved and ~25,000 of these SNPs satisfy the criteria for use in a number of common SNP genotyping platforms. Our pool of SNPs span a range of commercial and elite germplasm of G. hirsutum and so provides valuable marker resource for the cotton community as demonstrated by mapping of QTL for several traits of interest. The SNP identification rationale described here should be applicable to other polyploids.Using transcriptome and complexity-reduced-DNA sequencing, we identified a large number of varietal SNPs among 18


Single Nucleotide Polymorphism (SNP) panels recently developed for the assessment of genetic diversity in wheat are primarily based on elite varieties, mostly those of bread wheat. The usefulness of such SNP panels for studying wheat evolution and domestication has not yet been fully explored and ascertainment bias issues can potentially affect their applicability when studying landraces and tetraploid ancestors of bread wheat. We here evaluate whether population structure and evolutionary history can be assessed in tetraploid landrace wheats using SNP markers previously developed for the analysis of elite cultivars of hexaploid wheat.We genotyped more than 100 tetraploid wheat landraces and wild emmer wheat accessions, some of which had previously been screened with SSR markers, for an existing SNP panel and obtained publically available genotypes for the same SNPs for hexaploid wheat varieties and landraces. Results showed that quantification of genetic diversity can be affected by ascertainment bias but that the effects of ascertainment bias can at least partly be alleviated by merging SNPs to haplotypes. Analyses of population structure and genetic differentiation show strong subdivision between the tetraploid wheat subspecies, except for durum and rivet that are not separable. A more detailed population structure of durum landraces could be obtained than with SSR markers. The results also suggest an emmer, rather than durum, ancestry of bread wheat and with gene flow from wild emmer.SNP markers developed for elite cultivars show great potential for inferring population structure and can address evolutionary questions in landrace wheat. Issues of marker genome specificity and mapping need, however, to be addressed. Ascertainment bias does not seem to interfere with the ability of a SNP marker system developed for elite bread wheat accessions to detect population structure in other types of wheat. Triticum aestivum ssp. aestivum) is, together with rice and maize, one of the main staple food crops of the world; in 2012 some 675 million tones were produced worldwide [Bread wheat , arose as an allotetraploid 300,000–500,000 years ago [de novo domestication of durum from wild emmer has not yet been ruled out [Aegilops tauschii[The progenitor species of all domesticated wheat, wild emmer , the most widely grown tetraploid wheat, is cultivated to a far lesser extent than hexaploid bread wheat. The cultivation of other domesticated tetraploid wheats, such as emmer (T. turgidum ssp. dicoccum) and rivet (T. turgidum ssp. turgidum), is very limited. These relict crops have little agricultural importance, which has also lead to them being studied to a lesser extent than bread wheat. However, some SSR [Durum wheat  and two rivet landraces  were genotyped with 5386 SNPs in a panel developed by Allen et al. . DNA wasOf the SNPs assayed, 444 SNPs produced clear calls in at least five of the six accessions and were chosen for further analysis  were estimated according to Nei [ST, significance values were determined by permutation tests (1000 permutations). Principal component analysis (PCA) was carried out with R software  using the prcomp command. In the PCA, data was analysed on an accession level where the number of copies of each allele at each locus were treated as independent variables.Genetic diversity  and Wright’s Fg to Nei  using pug to Nei . For theK = 1) to 20 (K = 20) clusters were used to test different datasets, using 50,000 MCMCs and 20,000 burn-in runs with the “admixture” model. Ten replicate runs were performed for each value of K. The best-fit model was determined by calculating ΔK[K < 4, whereas the Greedy algorithm was used for K = 4 to K = 6 and the LargeKGreedy algorithm for higher Ks. We also evaluated the “no admixture” model using the same settings. CLUMPP was further used to compare the output of Structure analysis of durum SNP and SSR data. Graphical representation of results of CLUMPP runs was obtained using the DISTRUCT software v 1.1 [Publicly available wheat SNP data for both tetraploid and hexaploid accessions , was addlating ΔK and fromlating ΔK. In CLUMre v 1.1 . We alsore v 1.1   was used to fit a non-linear regression line to the LD between pairs of linked SNPs in order to explore the decay of LD over distance.Linkage disequilibrium (LD), measured as D’ and retic map ) map disThe full SNP panel developed by the Functional Genomics Group at the University of Bristol and collaborators  was assaSince these results were generated, many of the SNPs in the Allen et al.  panel haOf the 5386 SNPs assayed in the six test individuals, 444 were chosen for further genotyping. These were genotyped in an additional 99 tetraploid wheats creating a dataset consisting of 105 tetraploid wheats . Of the 444 SNPs assayed seven were later revealed to map to the D genome and were subsequently removed from further analyses . This could be due to either variation among individuals within an accession , heterozygous individuals or a combination of the two. Among the different wheat types, durum and rivet exhibited more heterozygous loci (6.4 and 5.7% respectively) than landrace emmer and wild emmer (3.4 and 1.2% respectively)  was still only 50.2% of that of the 369 SNPs used for the extended set of accessions.It has been suggested that the effects of ascertainment bias can be alleviated by combining SNPs into haplotypes . To inve2. As both measures gave similar results only the latter is reported below. For pairs of unlinked loci (only pairs of loci on different chromosomes were used) the r2 values for most pairs showed fairly low levels of LD with a skewed tail of higher values of LD , located on different chromosomes but in complete LD was detected.LD was measured both as D’ and r2 of more than 0.4 could be detected over several tens of cM  while CLUMPP H’ values were similarly high for K = 2 to K = 4 (highest for K = 3 for the “no admixture” model). Neither the PCA nor any level of clustering showed a strong geographic structure. Instead, different accessions from the same country or region clustered together already at K = 2 in the Structure analysis  from all other accessions along the first PC  and CLUMMP H’ values (supporting K = 2 and K = 9), mainly introduced mixed ancestry to the accessions . The degree to which accessions were assigned to a Structure cluster was highly correlated at K = 2  but with marked differences for some accessions. For example, the accession CItr15519 was assigned almost completely to one group based on SNP markers, but to the opposite group based on SSRs. A PCA of the SSR data showed lower explanatory power of the two first PCs (6.0 and 4.4% for SSRs compared to 8.3 and 7.8% for SNPs).Many accessions, such as most of those included in the Italian cluster described above, clearly demonstrated similar clustering using the two marker systems; however, this was not the case for all accessions .Seventy-one SNPs mapping to the D genome were successfully genotyped in the test panel of six tetraploid wheats, where only the A and B genomes occur. This is a cause of concern as it suggests some markers may have been incorrectly mapped or are not genome specific. To minimize the number of such markers, we excluded all markers mapping to the D genome. We also removed putative cross-amplifying markers showing within-accession heterozygosity in five genotyped improved durum varieties, since these accessions can be expected to be genetically monomorphic. High heterozygosity in SNP loci in wheat lines selfed for many generations has also been noted by Würschum et al.  as an unOur background level of LD was lower than that previously detected in bread wheat using other marker systems, but with LD between linked loci decaying on a similar scale as in previous studies of bread wheat -38 and dPopulation structure studies based on SNP data can potentially be affected by ascertainment bias when the SNP panel used has been developed for populations or species other than those analysed ,22. HübnIn this study we find strong support for independent evolutionary trajectories for tetraploid and hexaploid wheats. Structure analysis first separates hexaploid wheats from durums and rivets, with intermediate clustering of wild and landrace emmers Figure a as woulMore surprisingly, along the third PC, wild emmer assumes an intermediate location compared to bread wheats and domesticated emmer landraces, tentatively suggesting gene flow from wild emmer into the progenitor of hexaploid wheat or directly to the A and B genomes of hexaploid wheat. The location of durum along the third PC likewise suggests a role of wild emmer in the formation of the durum gene pool. Further studies directly targeted at clarifying the evolutionary origin of domesticated wheat and the role of gene flow between different types of wheat are needed.ST value between the two was the lowest of the pairwise FST values calculated . As in Oliveira et al. [Durum and rivet have traditionally been classified as different taxa, based on different ear morphology and by the latter’s broader tolerance to moist and cold environments. Neither the PCAs presented here nor the Structure analysis separate durum and rivet from each other. The Fa et al.  no genetGiven the number of markers used in this study it is unlikely that our genome-wide values of Tajima’s D are caused by selection acting on all the different markers. More likely they reflect components of population history that affect the whole genome equally, such as population subdivision or population growth for example following a bottleneck. Ascertainment bias resulting from the marker selection in this study should lead to an underrepresentation of rare alleles . As a laAlthough the Tajima’s D of landrace durum was not significantly positive, it was markedly higher than those of the other types of wheat. This, together with the somewhat higher LD found in durum wheats compared to the complete set of tetraploid wheats, suggests that a certain level of population subdivision is present in the landrace durum analysed here, possibly resulting in the observed genetic structuring. The increased number of markers allowed for the detection of higher levels of genetic structuring than those found by Oliveira et al.  . Markers that were removed before the final analysis are highlighted in red.ed from ). MarkeClick here for fileAccessions used in this study. Tetraploid landrace accessions were originally screened for this work. The remaining accessions were screened by the Crop Improvement Research Club, as described in [ribed in  and in [ribed in  and genoClick here for fileList of SNPs mapping to the D genome that successfully amplified in some or all of the tetraploid test panel accessions described in this work.Click here for fileDistribution of linkage disequilibrium values calculated between pairs of loci located on different chromosomes. a) All values of r2, b) Values of r2 from 0.2 and higher.Click here for fileLinkage disequilibrium (r2) between linked markers plotted against genetic distance with a non-linear regression line fitted to the values. a) All tetraploid wheats; b) all durum landraces.Click here for fileResults of Structure analysis of wheat accessions based on 369 SNPs using the “admixture” option (top panel) and the “ no admixture” option (bottom panel) for a) K = 2 model with the durum accessions; b) K = 4 model with durum accessions; c) K = 2 model with the complete set of accessions; d) K = 5 model with the complete set of accessions.Click here for fileResults of Structure analysis of the K = 3 model for the tetraploid wheat set.Click here for fileResults of Structure analysis of the set of durum accessions for the K = 2 model based on a) 369 SNPs and b) 29 SSRs respectively.Click here for fileResults of Principal Component Analysis of the complete set of wheat accessions. The distinctiveness of Yumai 34, Anahuac 75 and Ukrainka is evident (top left corner). Black = wild emmer; purple = landrace emmer; red = durum; orange = rivet; blue = landrace bread wheat; green = commercial bread wheat.Click here for fileResults of Structure analysis of the complete set of wheat accessions for the a) K = 8 model and b) K = 9 model.Click here for file


Many genetic and environmental factors are involved in the etiology of nicotine dependence. Although several candidate gene variations have been reported by candidate gene studies or genome-wide association studies (GWASs) to be associated with smoking behavior and the vulnerability to nicotine dependence, such studies have been mostly conducted with subjects with European ancestry. However, genetic factors have rarely been investigated for the Japanese population as GWASs. To elucidate genetic factors involved in nicotine dependence in Japanese, the present study comprehensively explored genetic contributors to nicotine dependence by using whole-genome genotyping arrays with more than 200,000 markers in Japanese subjects.p value of 0.0005921 in the two-stage GWAS. This possible association was replicated for the remaining 374 samples. This SNP was also associated with postoperative pain, the initiation of methamphetamine use, schizotypal personality traits, and susceptibility to goiter.The subjects for the GWAS and replication study were 148 and 374 patients, respectively. A two-stage GWAS was conducted using the Fagerström Test for Nicotine Dependence (FTND), Tobacco Dependence Screener (TDS), and number of cigarettes smoked per day (CPD) as indices of nicotine dependence. For the additional association analyses, patients who underwent major abdominal surgery, patients with methamphetamine dependence/psychosis, and healthy subjects with schizotypal personality trait data were recruited. Autopsy specimens with various diseases were also evaluated. After the study of associations between more than 200,000 marker single-nucleotide polymorphisms (SNPs) and the FTND, TDS, and CPD, the nonsynonymous rs2653349 SNP (located on the gene that encodes orexin [hypocretin] receptor 2) was selected as the most notable SNP associated with FTND, with a p value did not reach a conventional genome-wide level of significance in our two-stage GWAS, we obtained significant results in the subsequent analyses that suggest that the rs2653349 SNP  could be a genetic factor that is related to nicotine dependence and possibly pain, schizotypal personality traits, and goiter in the Japanese population.Although the The online version of this article (doi:10.1186/s13041-015-0142-x) contains supplementary material, which is available to authorized users. Despite the worldwide spread of cigarette smoking, many tobacco users are reportedly unaware of the harmful chemicals in tobacco products and tobacco smoke and the wide spectrum of specific illnesses that are caused by tobacco use . They alCHRNA5–CHRNA3–CHRNB4, which encodes neuronal nicotinic acetylcholine receptor subunits , Diagnostic and Statistical Manual of Mental Disorders, 3rd edition, revised [DSM-III-R], and DSM-IV), which consists of 10 questions [The results of the questionnaire, especially questions related to smoking, were used in the analysis of the severity of nicotine dependence. Some of them included the Fagerstrӧm Test for Nicotine Dependence   and Tobauestions . The queA total of 300 DNA samples from the patients with smoking behavior data who visited Iwata City Hospital were used for genotyping. Whole-genome genotyping was performed using the Infinium assay II  with HumanCytoSNP-12 v2.0  BeadChips  was adopted for a total of 112 and 203 DNA samples from patients who underwent major abdominal surgery and patients with METH dependence/psychosis, respectively. Additional file To genotype the rs2653349 SNP for a total of 311 and 2,305 DNA samples from healthy subjects with schizotypal personality trait data and autopsy specimens, respectively, array-based genotyping was conducted. The genotype data for this SNP were extracted from the overall results. In the former case, genotyping was performed using Affymetrix Genome-Wide Human SNP Array 6.0  as previously described . In the A two-stage GWAS was conducted for patients with smoking behavior data to initially investigate the association between genetic variations and the severity of nicotine dependence as an exploratory study to narrow down candidate SNPs. Among the 300 subjects genotyped, approximately half lacked smoking behavior data that were related to nicotine dependence. Therefore, a total of 148 subjects were used for our two-stage GWAS . In our two-stage GWAS, 225,591 SNPs were selected for the analyses.http://pngu.mgh.harvard.edu/purcell/plink/; accessed April 19, 2014) [The FTND, TDS, and CPD data were used as an index of nicotine dependence for the GWAS. Prior to the analyses, the subjects were divided into two subgroups: low dependence  and high dependence . The association with SNPs was explored using Cochran-Armitage trend tests. All of the statistical analyses were performed using gPLINK v. 2.050, PLINK v. 1.07   and Hapl9, 2014) .p < 0.05 were selected as candidate SNPs for the second-stage analysis among the 225,591 SNPs. For these SNPs, the second-stage analysis was conducted with 80 samples, and the SNPs that showed p < 0.05 for the single analysis of this stage and combined analysis of the first and second stages were considered potent candidates. The SNPs for further replication analysis were selected from these SNPs after linkage disequilibrium (LD)-based SNP pruning to remove redundant SNPs due to strong LD with each other  p-value plot based on the results of the GWAS for the combined samples was subsequently drawn to check the pattern of the generated p-value distribution (Additional file The GWAS procedure is summarized in Additional file  reports , 73. The2 test, and the absence of significant deviation from the theoretical distribution expected from Hardy-Weinberg equilibrium was confirmed (Additional file U-test, the χ2 test, and Fisher’s exact test were performed. The statistical methods and software used in the analyses for the candidate SNP are provided in the Additional file p < 0.05.For all of the genotype data used in the additional analyses after the GWAS, the distributions were checked using the χThe databases, power analysis, and imputation analysis, which were based on previous reports –76, are


In the whole genome sequencing, genetic map provides an essential framework for accurate and efficient genome assembly and validation. The main objectives of this study were to develop a high-density genetic map using RAD-Seq (Restriction-site Associated DNA Sequencing) genotyping-by-sequencing (RAD-Seq GBS) and Illumina GoldenGate assays, and to examine the alignment of the current map with the kabuli chickpea genome assembly.Cicer arietinum and Cicer reticulatum genotypes. Subsequently, an Illumina GoldenGate assay for 1,536 SNPs was developed. A total of 1,519 SNPs were successfully assayed across 92 recombinant inbred lines (RILs), of which 761 SNPs were polymorphic between the two parents. In addition, the next generation sequencing (NGS)-based GBS was applied to the same population generating 29,464 high quality SNPs. These SNPs were clustered into 626 recombination bins based on common segregation patterns. Data from the two approaches were used for the construction of a genetic map using a population derived from an intraspecific cross. The map consisted of 1,336 SNPs including 604 RAD recombination bins and 732 SNPs from Illumina GoldenGate assay. The map covered 653 cM of the chickpea genome with an average distance between adjacent markers of 0.5 cM. To date, this is the most extensive genetic map of chickpea using an intraspecific population. The alignment of the map with the CDC Frontier genome assembly revealed an overall conserved marker order; however, a few local inconsistencies within the Cicer arietinum pseudochromosome 1 (Ca1), Ca5 and Ca8 were detected. The map enabled the alignment of 215 unplaced scaffolds from the CDC Frontier draft genome assembly. The alignment also revealed varying degrees of recombination rates and hotspots across the chickpea genome.Genic single nucleotide polymorphisms (SNPs) totaling 51,632 SNPs were identified by 454 transcriptome sequencing of A high-density genetic map using RAD-Seq GBS and Illumina GoldenGate assay was developed and aligned with the existing kabuli chickpea draft genome sequence. The analysis revealed an overall conserved marker order, although some localized inversions between draft genome assembly and the genetic map were detected. The current analysis provides an insight of the recombination rates and hotspots across the chickpea genome.The online version of this article (doi:10.1186/1471-2164-15-708) contains supplementary material, which is available to authorized users. Cicer arietinum L., 2n = 16) is the second most widely grown food legume crops after common bean, with annual production of 11.6 M tons [http://ndb.nal.usda.gov/ndb/foods/show/4778). The chickpea crop helps to restore and maintain soil fertility through symbiotic nitrogen fixation. Globally more than 90% of chickpea production occurs in the semi-arid tropics of Asia and Africa [Chickpea . Limited genomic resources and low levels of genetic variability in the primary gene pool, however, have restricted the practical application of genetic mapping in chickpea . During Eucalyptus species [Next generation sequencing (NGS) technology has become powerful tool for detecting large numbers of SNPs in a relatively short time frame . SNPs ar species  to one S species . In chic species  to one p species  have bee species  makes SN species , 21.In parallel to the development of sequencing technologies, several new technologies for large scale SNP genotyping have been developed. These technologies can integrate up to one million SNPs and several folds of multiplexing per assay. Among these, Illumina GoldenGate and Infinium genotyping platforms have been widely used in many crops including soybean , wheat , maize , rice 2, sunflowThe efficiency of genome-wide marker-trait association mostly depends on SNP marker density and distribution. Therefore, it is important to develop SNP based genotyping platforms that allow association study to be conducted in chickpea. Cost involved in the development of an array-based genotyping platform mostly depends on initial SNP discovery, SNP selection and development of array platforms.Recent advances in DNA sequencing technologies have generated several cost-effective SNP discovery and genotyping platform such as genotyping-by-sequencing (GBS) , complexWhole genome re-sequencing of more than 90 chickpea cultivars of desi type , kabuli type (large owl's or ram's head shaped seeds with cream-colored seed coat) and wild accessions has been completed recently , 37. A hCicer arietinum and C. reticulatum due to the low polymorphism among the cultivated chickpea genotypes. In the present study, we generated one of most comprehensive and high density chickpea genetic maps from intraspecific population available to date. We also demonstrated the potential use of this map as tool for improving the whole genome assembly. Comparison of this high density genetic map with the whole genome sequencing data revealed the recombination landscape in the current population. The identified SNP markers with anchored positions on the genetic and physical maps can be used for fine scale QTL mapping and candidate gene identification.In the early phase of chickpea genomic research, many linkage maps were developed using interspecific crosses between The schematic outline of the experimental protocol is given in Figure Cicer arietinum L.), including desi and kabuli market classes, as well as wild species (Cicer reticulatum) accessions . SNPs present in at least two of the eight accessions were filtered for further analysis. In order to design oligos for the Illumina GoldenGate array , sequences with a minimum of 60 bp flanking the SNP were selected. Further, SNPs were selected based on the Illumina Assay Design Tool (ADT) score  and even distribution across the Medicago genome. This strategy was designed and implemented prior to the availability of the chickpea pseodochromosomes (Cicer_arietinum_GA_v1.0 pseudochromosomes). The same strategy has been successfully implemented in lentil SNP genotyping assay design [Medicago genome and sequences located within a single exon were selected. Finally, 1,536 SNPs were chosen for Illumina GoldenGate assay for the production of an Oligo Pooled Array (Ca1536 GoldenGate OPA). Twenty SNPs were randomly selected for validation using allele-specific PCR assays .Plant tissue for RNA extraction was collected from each genotype individually at various developmental stages, including seedling emergence, 8–10 node stage, early flowering stage, early pod stage and early senescence. Total RNA was extracted using the RNeasy Plant Mini Kit (Qiagen) and treated with DNase I (Invitrogen) to remove DNA contamination. Two micrograms of total RNA at each developmental stage were pooled. escribed , 41. Eacescribed  and Titaescribed  to produy design . Intron-Arabidopsis thaliana and Oryza sativa data sets [http://snpeff.sourceforge.net/) [Gene ontology (GO) analysis was conducted on SNPs containing transcript sequences using Blast2GO program . These tata sets . A SnpEfge.net/) .SSR identification was done using the QDD software program  with thehttp://www.illumina.com/technology/goldengate_genotyping_assay.ilmn). Products generated by this assay were read with an Illumina HiScan  and the resulting data were clustered for allele calling using GenomeStudio software version 2010.3 . Allele calls and genotype clusters were visually inspected for errors in automated SNP genotype clustering algorithm and corrected based on the expected segregation ratio in the RIL population [SNP genotyping was performed using the Illumina GoldenGate platform, following the standard assay protocol (pulation .https://www.wiki.ed.ac.uk/display/RADSequencing/Home). Briefly, genomic DNA from each RIL was digested with EcoRI (New England Biolabs) and then ligated to a P1 adapter containing a six-bp index identifier unique to each individual. The adapter-ligated fragments were subsequently pooled, randomly sheared and size-selected between 300–500 bp on an agarose gel. The obtained DNA fragments were then ligated to a P2 adapter. To enrich RAD tags, the adapter-ligated DNA was subjected to 18 cycles of PCR enrichment followed by gel purification of the 300 to 500 bp DNA fragments. Up to 24 RAD libraries, each representing an individual RIL, were pooled for 100 bp pair-end sequencing using established v3 chemistry methodologies on single lanes of an Illumina HiSeq 2000 flow-cell (Illumina Inc.). Following sequencing and Illumina data processing (Casava v1.8.0), the valid raw pair-end reads were separated into pools using custom Perl scripts to identify reads associated with individual RILs.High quality genomic DNA from ICCV 96029, CDC Frontier and 92 inbred lines from CPR-01 was extracted following the procedure described in Saghai-Maroof et al. . IndividPaired-end Illumina reads were demultiplexed using the FASTX toolkit's barcode splitter and PCR duplicates were removed with Samtools rmdup. Reads from each line were aligned to the chickpea genome with Bowtie , allowinhttp://www.isbreeding.net/) software. Before linkage analysis, genotypic scores were filtered for missing data (genotypic score missing in more than 25% RILs and 30% per marker) and distorted allele frequency. The marker loci with allele frequencies of < 0.2 for one parent and > 0.8 for the other parent were removed from further analysis. The filtered SNP markers were clustered into linkage groups using MadMapper with recombination value (haplotype distance) cut-off of 0.2 and a BIT score of 100. Linkage groups were assigned using the position of SNP markers on the pseudochromosomes of the chickpea genome. Marker order was determined using the RECORD algorithm of RECORD_win and QTL Icimapping V3.2 software with a setting of 30 cM gap size, Kosambi mapping function and 0.1 recombination fraction allowed. Rippling was done by permutation of a window of 5 markers using COUNT rippling criteria. Following the initial map construction, double recombinants or singletons were identified as potential genotyping errors using the color genotypes feature of the MapDisto tool [Genotypic data generated using the RAD-seq and Illumina GoldenGate were used to create the genetic linkage map of CPR-01. Linkage analysis between the markers and the best possible linear order of the loci were determined using MadMapper , RECORD sto tool . The potThe average genome-wide average recombination frequency in cM/Mb was calculated by dividing the total genetic map length by 323 Mb genome size flanked by the most distal markers on each linkage group. The average recombination frequency in genes/cM was calculated by dividing the total number of genes  by the map length. Recombination rates for individual chromosomes were calculated by dividing the genetic length (cM) by the sequence length (Mb) between the first and last marker placed on each chromosome. The estimated genome length was calculated using the method 4 of Chakravarti et al. , in whicIn order to identify SNPs in the genic region of chickpea, eight diverse chickpea genotypes Table  were selWe investigated the functional and structural impact of the identified genic SNPs selected for assay design by comparing the position of the SNPs relative to the annotated chickpea genome. The majority 2,536 SNPs) were distributed across the eight chickpea pseudochromosomes, whereas 153 SNPs were distributed in 93 unplaced scaffolds. Only 40 SNPs could not be located in the CDC Frontier genome sequence. The average frequency of SNPs across the chickpea genome was one SNP per 138.7 Kb. The distribution of the genic SNPs was not equal for all chromosomes. The highest number of SNPs was identified on Ca4 (696) followed by Ca7 (390), Ca6 (324) and Ca1 (315), whereas the lowest number of SNPs were identified on Ca5 (137) followed by Ca8 (139), Ca3 (263) and Ca2 (272). The ratio of transitions to transversions (Ts/Tv) was 1.5, which is expected for genic regions. 53.3% of these SNPs resided downstream of an open reading frame, 37.4% in coding regions of which 18.7% were synonymous coding, 17.8% were non-synonymous coding and 0.8% generated a stop codon. Interestingly, 6.7% of SNPs resided in intergenic regions, that are located at least 5 kb up- or downstream of a gene. Further 23 SNPs in coding sequence that introduced a TAG, TAA, or TGA stop codon could potentially alter the function of these genes . The KO represents 241 pathways with maximum hits in the biosynthesis of secondary metabolites, followed by metabolic pathways. Annotation of SNP containing transcript sequences into GO and KEGG could serve as important and valuable resources for gene identification and functional analysis of some important traits in chickpea.Cicer arietinum) genotype group and its wild progenitor (Cicer reticulatum) genotype group. A further 95 SNPs were found polymorphic between desi and wild progenitor, and 227 SNPs between kabuli and wild type chickpea. We identified 104 SNPs that were present between desi and kabuli groups.To investigate SNP diversity in the gene coding region that has been generated or lost during domestication and breeding, we compared SNPs between the cultivated chickpea  and NJ-phylogenetic tree. The first three principal components explained 56.3% of the total variation and showed clustering of chickpea lines into cultivated and wild groups Figure -A. The pin silico analysis of SSR containing sequences showed 153 polymorphic SSRs across the eight chickpea genotypes. Di-nucleotide SSRs were more polymorphic than tri-nucleotide repeats. The number of alleles ranged from 2–4 and PIC value ranged from 0.2 to 0.6 , followed by tri- 42.6%), tetra- (1.3%) and penta-nucleotides (0.4%). The TA and GAA repeats represented the most di- and tri-nucleotide repeats, respectively. PCR primer pairs were designed for 585 SSRs. An .6%, tetrA total of 2,729 SNPs were processed for custom OPA design with Illumina Assay Design Tool (ADT). On the basis of ADT, 2,562 93.8%) SNPs were assigned ADT score of ≥0.6, indicating a high success rate for the conversion of a SNP into a successful GoldenGate assay. In the SNP validation process, 18 (90%) out of 20allele-specific PCR assays resulted in the identical genotype to the transcriptome sequence based SNPs  were identified. After further imputation of missing data, 12,012 high-quality SNPs were selected and clustered into 626 bins with identical segregation patterns. Single SNP representing each of the 626 bins was used for linkage analysis.The number of RAD tags sequenced per Mb showed uniform genome-wide distribution across the chickpea genome Figure . On aver7-derived RIL population.A total of 92 RILs belonging to the CPR-01 population were used to construct the linkage map. A total of 1,336 SNPs out of 1,387 SNPs were mapped into eight linkage groups  of the total mapped markers . Among the distorted markers, the majority (93.4%) deviated towards the female parent ICCV 96029 and 6.6% marker deviated towards the male parent CDC Frontier. This pattern of preferential transmission of ICCV 96029 alleles occurred in all linkage groups; except LG3, LG6 and LG8. LG4 showed the highest proportion of ICCV 96029 alleles , whereas LG3 showed the highest proportion of CDC Frontier alleles . Most of the distorted markers were clustered in specific regions on linkage groups ranging in size from 2.9 to 34.1 cM and consisting of 2 to 200 markers  marker loci into eight pseudochromosomes and 215 (16.1%) markers into unplaced scaffolds. 12 (0.9%) markers could not be placed on the chickpea genome sequence using the BLAST search indicating that the corresponding genome sequence was missing from the published draft chickpea genome assembly. The details of mapped markers are presented in Table _arietinum_GA_v1.0 pseudochromosomes  in several crops. Over the last few years NGS platforms have accelerated the process of large scale SNP discovery see review by  leading entation , 40. TheERECTA, ASR, DREB, CAP2 and AMDH) were found significantly associated with morphological, phenological, yield and yield related traits [acetohydroxyacid synthase 1(AHAS1) gene which confers resistance to imidazolinone (IMI) herbicide and also developed a breeder friendly allele-specific SNP marker for use in marker-assisted selection (MAS) for IMI-resistant in chickpea [One of the important reasons behind the utilization of genic SNP markers in genetic mapping is its potential to establish a direct link between important agronomical traits to the functional SNPs and also to find candidate genes underlying traits of interest. Several genic SNPs markers with significant association with traits have been identified in many plant species , 59. In d traits . Recentlchickpea . These fWe used three genotypes of each desi and kabuli market class and two wild chickpea accessions for genic SNP discovery. The average allele frequency of the identified SNPs was 4.9% for kabuli accessions and 8.8% for desi accessions. This difference reveals that desi accessions used in this study were more genetically diverse then the kabuli accessions. In contrast Upadhyaya et al.  observedThe effectiveness and suitability of the Illumina GoldenGate SNP genotyping platform has been well demonstrated in several crops, including chickpea . Our stuEcoRI for RAD-Seq yields a uniform distribution of sequence reads across the genome that enables a greater coverage of the assembly. In contrast, GBS methodologies commonly utilized methylation-sensitive restriction enzymes that cannot cleave the repetitive regions of the genome. This provides not only a targeted coverage of the gene rich regions of the genome but also potentially missed portions of the assembly. A comparison of the two methodologies for the purposes of anchoring a genome assembly has been provided recently for Brassica oleracea[Genetic mapping with gene-based SNP markers in chickpea genome consist of >45% repetitive elements of the total nuclear genome may result in uneven coverage of the genetic map. Therefore, in addition to genic SNP based GoldenGate genotyping arrays, we used RAD-seq to construct a high-density genetic linkage map. For the precise anchoring of the newly sequenced genome assembly, a high genome-wide marker density is necessary. The RAD-seq methodology utilized in this study has advantages over other GBS methods in this regard. The use of oleracea.Cicer arietinum) and PI 489777 (Cicer reticulatum), thus far is comprised of 1,291 markers, spanning 846 cM with an average inter-marker distance of 0.65 cM [This study generated one of the most comprehensive intraspecific linkage maps of chickpea to date. The map spans 653 cM and is divided into eight linkage groups corresponding to the number of chickpea chromosomes, with average inter-marker distance of 0.5 cM. In comparison, earlier intraspecific linkage maps , 66 were 0.65 cM . The int2 and RIL mapping populations also observed several distortion regions on LG4 [Segregation distortion is a common phenomenon in mapping populations which the frequency of genotypes skews from the expected Mendelian ratio . In the s on LG4 . Howevers on LG4 , 72, 73.The recombination rate varies by an order of magnitude among species and between individuals , 75. SevCicer arietinum X Cicer reticulatum for the genetic anchoring of the chickpea pseodochromosomes (Cicer_arietinum_GA_v1.0). In spite of substantial morphological similarities and crossability between C. arietinum and C. reticulatum, some chromosomal rearrangements such as reciprocal translocation, a paracentric inversion or location of chromosomal satellites have been reported [C. arietinum X C. reticulatum genetic map using common SSR markers also detected a few inversions in marker order possibly due to inversion of DNA sequences and minor chromosomal translocation [C. arietinum and C. reticulatum. Further analysis is needed to test this hypothesis.Alignment of the CPR-01 map with the chickpea pseudochromosomes indicated overall high co-linearity between the genetic and the physical order. However, the alignment also showed some inconsistency in localized order on Ca1, Ca5, Ca6, Ca7 and Ca8. This could be due to the incorporation of small scaffolds and/or too little recombination to allow exact placement or correct orientations of scaffolds during the chickpea genome assembly. Another possible reason of marker order inconsistency could be due to the utilization of an interspecific genetic map generated by using a cross between reported . Also, clocation , 82. TheNGS has dramatically increased the rate of the completion of new genome sequences across different species. Most of the recently released plant genomes were sequenced using NGS platforms see “The First 50 Plant Genomes” review by . SequencThis study generated a high-density intraspecific linkage map of chickpea using genic SNP based genotyping assay and RAD-seq GBS. The map allowed addressing some issues with marker alignment in the corresponding chromosome and inconsistency in marker order within the physical map. The high-density CPR-01 map helped in assigning large number of previously unplaced scaffolds from the version 1.0 of the CDC Frontier draft genome sequence. The alignment analysis also revealed the varying degrees of recombination rates and hotspots across the chickpea genome. On average the estimated genome-wide recombination rate in the current population is 2.0 cM/Mb ranging from 1.6 to 4.8 cM/Mb per chromosome.The CPR-01 is one of the key genetic materials derived from an intraspecific cross segregating for some important agronomic traits in chickpea, such as photoperiod sensitivity, ascochyta blight resistance and double podding. Therefore the high-density CPR-01 map will help to precisely map and estimate the effects of quantitative trait loci for these traits. Furthermore, the information on the genome wide recombination rates in CPR-01 may provide the basis for designing effective marker-assisted selection strategies.The data sets supporting the results of this article are included within the article as additional files.http://www.ncbi.nlm.nih.gov/sra) accessions SRP044343 and SRP044316.Sequence data under the study has been deposited into NCBI Sequence Read Archive Additional file 1:List of Identified SSR, providing details on SSR motifs, primer sequences, expected PCR product size andin silicoPolymorphism survey in eight chickpea genotypes.(XLSX 1 MB)Additional file 2:List of SNPs selected for KASP assays, and the results of these assays on a panel of 8 chickpea lines.(XLSX 16 KB)Additional file 3: