Of these, 3625 and 2177 SNPs have been integrated into eight desi and kabuli chromosomes, respectively in order to construct ultra-high density (0.20–0.37 cM) intra-specific chickpea genetic linkage maps. One of these constructed high-resolution genetic map has potential to identify 33 major genomic regions harbouring 35 robust QTLs (PVE: 17.9–39.7%) associated with three agronomic traits, which were mapped within <1 cM mean marker intervals on desi chromosomes. The extended LD (linkage disequilibrium) decay (~15 cM) in chromosomes of genetic maps have encouraged us to use a rapid integrated approach  rather than a traditional QTL map-based cloning method to narrow-down one major seed weight (SW) robust QTL region. It delineated favourable natural allelic variants and superior haplotype-containing one seed-specific candidate embryo defective gene regulating SW in chickpea. The ultra-high-resolution genetic maps, QTLs/genes and alleles/haplotypes-related genomic information generated and integrated strategy for rapid QTL/gene identification developed have potential to expedite genomics-assisted breeding applications in crop plants, including chickpea for their genetic enhancement.We discovered 26785 and 16573 high-quality SNPs differentiating two parental genotypes of a RIL mapping population using reference  Cicer arietinum L.), the second most grown legume crop world-wide is an annual self-pollinated and diploid (2n = 2x = 16) pulse species with a genome size of ~740 Mb1Kabuli and Desi- types basing upon their plant characteristics and diverse gene pools-based geographical distribution. They are known to enrich the soil nutritional status and fertility by symbiotic nitrogen fixation. The seed and pod traits are considered the most prominent characteristics of chickpea defining its economic value as a diet for human race. Consequently, they draw major interest of researchers for yield and product quality improvement towards generation of high-yielding genetically tailored chickpea cultivars via genomics-assisted breedingChickpea 1516171819202122232415222325262728293031323334353637A large genome with a narrow genetic base, chickpea requires numerous informative and genome-wide well-distributed SNP (single nucleotide polymorphism) markers for construction of ultra-high density genetic linkage map (≤1 cM average map-density46789APeKI) have enhanced the efficiency of GBS assay in fast discovery and genotyping of genome-wide SNPs in larger mapping populations. This in turn made the GBS assay much more advantageous over other available high-throughput traditional genotyping assays for constructing ultra-high density genetic linkage maps and genome-wide high-resolution (major and minor effects QTLs) gene/QTL mapping in small diploid as well as large genome crop species, including rice, wheat, barley, sorghum, Medicago and soybean24414243444546474849224550Genotyping-By-Sequencing (GBS) is a simple, rapid and cost-effective next-generation sequencing (NGS)-based assay having potential for simultaneous large-scale discovery and genotyping of SNPs in diverse crop plants at a genome-wide scale39Over the past few years, a number of QTLs associated with important agronomic traits, including yield component and stress tolerance traits have been identified and mapped on low/high-resolution intra- as well as inter-specific genetic linkage maps of chickpea31314205253545556575859606162636465666768697071737475761314desi) and ICC 8261 (kabuli)] by their genotyping in a 275 F7 RIL (recombinant inbred line) mapping population (ICC 12299 × ICC 8261) using reference desi and kabuli genome-based GBS assay, respectively. A selected 3625 and 2177 high-quality SNPs of these, were integrated into eight desi and kabuli chromosomes, respectively to construct ultra-high density intra-specific genetic linkage maps in chickpea. These high resolution genetic maps were further used as a reference to identify and map the novel major genomic regions underlying robust QTLs associated with three important agronomic traits in chickpea. To delineate functionally relevant candidate gene(s) regulating seed weight, one of the strong trait-associated major genomic region harbouring robust QTL has been narrowed down by integrating comparative desi and kabuli genome-based QTL mapping with QTL region-specific haplotype (linkage disequilibrium)-based high-resolution trait association analysis, differential gene expression profiling and gene haplotype-based association mapping in chickpea.Keeping all above in view, we discovered 26785 and 16573 genome-wide SNPs showing differentiation between parental genotypes  differentiating two parental genotypes using the reference desi and kabuli genome-based GBS assay, respectively  and 4 , respectively. The desi chromosomes 7 and 8 (13.9 kb each) and kabuli chromosome 4 (17.2 kb) had maximum mean map-density (Table S1). The remaining 21027 SNPs, including 18817 and 2210 SNPs were physically mapped on scaffolds of desi and kabuli genomes, respectively  were more abundant compared with that of transversions (kabuli genome-based GBS assay have been submitted to NCBI dbSNP (http://www.ncbi.nlm.nih.gov/SNP) with SNP submission (SS) accession numbers 1067289484 to 1067306056 for unrestricted use [NCBI db SNP Build (B142) year 2014 (http://www.ncbi.nlm.nih.gov/SNP/snp_viewTable.cgi?handle=NIPGR)].We generated an average of 207.3 million high-quality sequence reads   by sequeectively . Of thesectively . The A/Gversions . Detail desi and kabuli genome-based GBS assay were targeted for their validation through resequencing of PCR amplicons (384 SNPs) and MALDI-TOF mass array (96 SNPs). It validated 437 (91%) SNPs successfully in parental genotypes and selected mapping individuals of a RIL population (ICC 12299 × ICC 8261) (Four hundred-eighty SNPs mined by reference CC 8261) , B. Remadesi and kabuli genome-based GBS assay, respectively (Table S3) in parental genotypes and 275 individuals of a RIL mapping population (ICC 12299 × ICC 8261) were used to construct ultra-high density intra-specific genetic linkage maps in chickpea. The linkage analysis of SNPs showing significant Mendelian segregation ratio (1:1) mapped 3625 and 2177 SNPs on eight desi and kabuli linkage groups (LGs) (LG1 to LG8) of intra-specific genetic maps, respectively according to their physical positions (bp) on corresponding chromosomes  and kabuli LG3 (463), while desi LG8 (281) and kabuli LG5 (104) contained minimum numbers of SNPs (desi ranged from 85.662 cM (386 SNPs) in LG2 to 97.782 cM (574) in LG4, while for kabuli, it varied from 81.444 cM (292 SNPs) in LG5 to 120.359 cM (386) in LG2 , whereas LG5 and LG8 were least saturated (0.32 cM). In kabuli, LG3 (mean inter-SNP marker distance: 0.18 cM) and LG5 (0.78 cM) had most and least saturated genetic maps, respectively  discovered by reference omosomes , S4, S5.ectively . Maximum of SNPs . The map) in LG2 . In desiectively .2) and extent of LD decay using all possible pair-combinations of 3625 and 2177 SNPs genetically mapped on eight desi and kabuli LGs of intra-specific genetic maps, respectively were determined. The average LD estimates in eight LGs of desi genome (r2: 0.62) was almost comparable with that of kabuli genome (0.59) (Table S4). The LG4 of both desi (r2: 0.78) and kabuli (0.72) genetic maps had highest LD estimates. Maximum proportion of SNP-pairs showed significant LD (P < 0.0001) on desi LG4 (42.8%) and kabuli LG2 (34.6%) (Table S4). We determined the LD decay of 3625 and 2177 SNP-pairs by pooling the r2 estimates across eight desi and kabuli LGs and plotting their average r2 against the 5 cM equal intervals of genetic distance (maximum up to 120 cM). A decreasing trend of LD decay (r2 < 0.3) was observed with increase in the genetic distance (cM) of SNP markers mapped on the desi and kabuli LGs  was observed near about 15 cM genetic distance (The genome-wide LD estimates (rbuli LGs . Remarkadistance  in LGs o−2) negative correlation of PN and SN with SW (r = −0.42 to −0.51) as well as positive correlation between PN and SN (0.95)  (51.1–141.2 with 79–80% heritability), seed number/plant (SN) (60.6–231.6 with 77–81%) and 100-seed weight SW  .desi-genome based SNPs mapped on an ultra-high density intra-specific genetic linkage map was integrated with phenotyping data of 275 RIL mapping population. It identified and mapped 33 genomic regions harbouring 35 major and significant (LOD: 7.25–14.5) QTLs associated with PN, SN and SW on seven LGs (except LG3) of chickpea  were identified and mapped on six LGs. Collectively, all these QTLs showed 36.4% PVE. The PN QTL regions covered with 57 SNP markers on LGs (0.015 cm on LG4 to 1.978 cM on LG1) showed mostly the positive additive gene effects indicating the effective contributions of ICC 12299 alleles at these loci for increasing pod number in chickpea. For SN, eight major genomic regions harbouring eight robust QTLs explaining 21.7–38.1% PVE (LOD: 7.44–12.6) were identified. These SN QTLs covered with 24 SNPs (0.072 cM on LG2 to 1.083 cM on LG5) were mapped on four LGs . The comdesi and kabuli GBS-SNPs mapped on intra-specific genetic maps revealed the presence of 3017 and 1349 SNPs in the intergenic regions, while 1431 and 1340 SNPs in the different sequence components of 635 and 760 genes, respectively (Table S6A and B). The detailed annotation of SNPs within genes identified maximum frequency  of SNPs in the exons and minimum in the 1000-bp downstream regulatory regions (DRR)   substitutions, respectively  and coding (1.9) sequence components of kabuli genes.The structural annotation of 4448 and 2689, including 3625 and 2177 high-quality 7%, 238)  and S12Aectively  and S12Bacements  and S12Bdesi and 760 kabuli genes with SNPs detected a higher proportion of SNPs belonging to growth, development and metabolism-related proteins  followed by transcription factors (16 and 18%) (desi and 31% in kabuli) (desi and kabuli genes showed a significant (P ≤ 5.9 × 10−3) overrepresentation/enrichment of GO terms in the genes associated with transport (15.4%) belonging to biological process  . The KOG kabuli) . The GO  process .2: 39.7%) QTL (qSW5.1) region [Ca_Desi_SNP2337 (23.805 cM)-Ca_Desi_SNP2338 (24.875 cM)] governing SW mapped on desi LG5 of intra-specific genetic map was selected for fine mapping through QTL target-specific haplotype (LD)-based high-resolution trait association mapping . In kabuli, the SW trait-influencing QTL region spanned with 1.6 cM genetic interval between the markers Ca_Kabuli_SNP1459 (34.6 cM) and Ca_Kabuli_SNP1462 (36.2 cM) was mapped on LG5  on chromosome 5  and 10 of each homozygous low and high seed weight RIL mapping individuals detected 804 high-quality SNPs (mean SNP density: 1/886.8 bp). It includes 435 intergenic SNPs and 369 SNPs derived from the diverse coding and non-coding sequence components of 64 genes. The decay of LD over longer genetic distance -based high-resolution trait association mapping. The genotyping of 804 SNPs identified and mapped on the 713 kb qSW5.1 QTL region in 244 accessions belonging to an SW-specific association panel (Table S7) enabled to constitute 12 haplotypes (−6 and R2: 37.6–40.3%) with SW in contrast to any other SNP combinations. This haplotype region covered a maximum of 22.5 kb (40710.6–40733.1 kb) physical distance between CaSNP1 and CaSNP2 at qSW5.1 QTL interval. The structural and functional annotation of this 22.5 kb sequenced short QTL region with kabuli genome annotation database identified five protein-coding candidate genes (−5 and R2 = 39.8%) with SW.The targeted resequencing of this 713 kb  ~15 cM,  particulplotypes . The SNPte genes . The comqSW5.1) was performed. The gene-based primers were amplified using the RNA isolated from three different vegetative and reproductive tissues and two seed developmental stages of low and high SW parental genotypes (ICC 12299 and ICC 8261) of a RIL mapping population through quantitative RT-PCR assay  as well as pronounced down-regulation (>7-fold) in parental genotypes during seed development (qSW5.1) was selected as target candidate for understanding its significance in seed weight regulation through high-resolution gene haplotype-specific association/LD mapping in chickpea.To infer the differential regulatory gene expression patterns, the expression profiling of five protein-coding candidate genes annotated in the 22.5 kb delineated SW-specific robust QTL interval (CR assay . One regelopment . HencefoThe sequencing of 9286 bp cloned amplicon covering the entire coding and non-coding regions (including 2 kb URR) of a strong SW-influencing embryo defective gene  validat. It incl−7 and R2: 44.7%). The haplotype-pair-based LD estimation produced a significant high degree of LD (r2 > 0.70 and P < 0.0001) across entire 9286 bp sequenced gene (−6) and thus strong association potential of this gene for SW in chickpea is expected. The differential expression profiling using these high (TAC) and low (CGT) SW-specific haplotypes constituted by three SNPs in URR of an embryo defective gene revealed significant down-regulated expression (~6 times) specifically in two seed developmental stages of low and high SW parental genotypes and homozygous RIL mapping individuals as compared to leaf. It inferred that favourable natural allelic variants and superior haplotype (TAC) constituted in the URR of an embryo defective gene involved in decreased transcript expression and thus have significance in regulation of seed development and consequently seed weight in chickpea.The association analysis using the four SNP marker-based haplotypes in an embryo defective gene with SW (5.9 to 57.6 g)-specific multi-location replicated field phenotyping data of 244 accessions (association panel) revealed its strong association with SW  differentiating the two parental genotypes (ICC 12299 and ICC 8261) of a RIL mapping population with 100% reproducibility, respectively. Notably, 22331, including 7968 and 14363 SNPs of these, showed extensive genome coverage across eight desi and kabuli chromosomes with mean densities of 1/15.6 and 1/24.2 kb, respectively. A high experimental validation success rate (91%) of 480 randomly selected SNPs from a total of 43358 GBS-based SNPs by amplicon resequencing and MALDI-TOF mass array genotyping assay was evident. A number of studies in the past have accessed the potential of diverse NGS-based assay for efficient mining and genotyping of non-erroneous SNPs through experimental validation of randomly chosen smaller set (~1–2%) of SNPs from a whole genome SNP dataset in diverse crop plants, including chickpea122242455051798081desi and kabuli genome-derived GBS-SNPs with that of SNP allelic information available in various chickpea genotypes89122237in silico based on their congruent physical positions was clearly evident. These findings overall suggest the robustness and utility of GBS assay for rapid large-scale mining and high-throughput genotyping of valid and high-quality SNPs at a genome-wide scale in chickpea. The advantages of GBS-assay in simultaneous genome-wide discovery and genotyping of SNPs and their ability to expedite various large-scale genotyping applications have been demonstrated in many crop plants2440414243444546474849desi and kabuli genome-based GBS-SNPs (61% novel SNPs) developed by us (submitted to NCBI SNPdb for unrestricted use) could serve as a valuable genomic resource for their immense use in genomics-assisted breeding applications of chickpea.We utilized NGS-based 3 × 96-plex GBS assay for large-scale validation and high-throughput genotyping of genome-wide SNPs in a 275 RIL mapping population (ICC 12299 × ICC 8261) to construct ultra-high density intra-specific genetic linkage maps in chickpea. The reference desi (mean inter-marker distance 0.20 cM) and kabuli (0.37 cM) LGs constructed in our study are highly saturated compared to the previously reported intra- and inter-specific genetic linkage maps (0.50–8.01 cM) in chickpea67891022111213142241516171819202122232425152223252627282930313233343536An ultra-high density 3625 and 2177 SNP marker-based intra-specific genetic linkage maps (ICC 12299 × ICC 8261) with eight kabuli 0. cM LGs cdesi (0.62) and kabuli (0.59) LGs of constructed intra-specific genetic maps using mapped SNP markers reflected direct correlation of LD patterns with the density of SNPs required to cover the genomes. In spite of large differences in SNP marker map density between desi (0.20 cM) and kabuli (0.37) LGs of genetic maps, we observed higher LD estimates and extended LD decay (~15 cM) in LGs of both desi and kabuli genomes. This is much higher than the LD decay estimated in other self- and cross-pollinated crop plants8384858687888936749192A significant LD estimates across eight kabuli 0. LGs of gOur study utilized ‘α-lattice’ designs in multi-location experimental field trials to efficiently grow and comprehensively phenotype the 275 RIL mapping individuals (ICC 12299 × ICC 8261) along with their parental genotypes for three important yield component traits  in chickpea. The ‘α-lattice’ designs are more preferred than the commonly adopted ‘randomized complete block design (RCBD)’, since they are less prone to experimental errors, cost-effective and provide more precise (~35% higher) phenotyping information in field experiments aside high reproducibility. The ‘α-lattice’ designs are also associated with smaller coefficient of variation and error mean square, which collectively gives them an edge over the traditionally used RCBD designs specifically when phenotyping of yield component trait variables was conducted for very large numbers of diverse crop genotypes in field experiments with a smaller plot size9495desi LGs of an ultra-high density genetic map, which were well validated in multiple geographical locations/environments across years in field. The reliability and validity of the identified PN, SN and SW QTLs were determined by correlating/comparing their underlying genomic regions with that of earlier QTL mapping studies13525964697477979899100qPN8.1) and SW (qSW4.1) QTLs in line with previous QTL mapping studies using diverse intra- and inter-specific mapping populations of chickpea135964699798202148102103104312131420225172737475desi and kabuli LGs, including SW, PN and SN QTL intervals of ultra-high density intra-specific genetic maps will enable us to select functional gene-based SNPs (like non-synonymous coding and regulatory SNPs) for establishing marker-trait linkages and identifying genes/QTLs associated with important qualitative and quantitative traits through genetic and association mapping in chickpea.Thirty-three major genomic regions harbouring 35 robust QTLs (LOD: 7.25–14.5 and PVE: 17.9–39.7%) associated with three important agronomic traits  were identified and mapped on seven qSW5.1) mapped on desi LG5 of an intra-specific genetic map was compared/correlated with that of integrated genetic and physical maps of kabuli to fine map the major QTL region. The extended LD decay (~15 cM) in desi and kabuli LGs of genetic maps suggested that high-resolution haplotype (LD)-based trait association mapping at target QTL interval could be an attractive alternative approach for narrowing-down the QTL regions to candidate gene(s) regulating three said agronomic traits in chickpea. Henceforth, we combined haplotype (LD)-based high-resolution trait association mapping at target SW-specific QTL region with differential gene expression profiling , which delineated one regulatory SNP-containing embryo defective protein-coding gene governing seed weight in chickpea. Such haplotype-specific association analysis in presence of high-resolution significant LD at major QTL region have utility to scale-down the QTLs to specific candidate gene(s) regulating important agronomic traits in crop plants106107108One strong SW-associated major genomic region  harbouring a robust QTL  identified in the URR of this gene with 44.7% SW phenotypic variation in a constituted 244 association panel. This was further supported by the seed-specific expression and significant down-regulated expression of gene haplotype transcript during seed development in contrasting SW parental genotypes and RIL mapping individuals. The favourable natural allelic variants and optimal superior haplotype identified in an embryo defective gene will be helpful in dissection of gene regulatory networks underlying this complex quantitative SW trait in chickpea. This identified gene could eventually be used as a potential candidate in marker-assisted genetic enhancement of chickpea for increasing seed weight and higher yield. The embryo defective chickpea gene ortholog in 7 RIL mapping population (ICC 12299 × ICC 8261) comprising of 275 individuals derived from the crosses between two parental chickpea genotypes ICC 12299 and ICC 8261 was generated using a single seed descent method. The chickpea genotype ICC 12299 originated from Nepal is a low 100-seed weight (9 g) and high pod (103) and seed (158) number-containing desi (C. arietinum) landrace. In contrast, the genotype ICC 8261 (originated from Turkey) is a high 100-seed weight (30 g) and low pod (64) and seed (73) number-containing kabuli (C. arietinum) landrace.An intra-specific Fet al.74et al.The RIL mapping individuals along with their parental genotypes were grown in the field according to ‘alpha (α)-lattice’ design for three consecutive years (2011–2013) with at least two replications during crop growing season at two diverse geographical locations of India. These RILs were phenotyped individually for three important quantitative agronomic traits, namely PN, SN and SW. The PN and SN was estimated by counting the average number of fully formed pods and seeds per plant (from 10–15 representative plants from each RILs at maturity), respectively. The SW (g) was measured by taking the average weight (g) of 100-matured seeds at 10% moisture content by selecting 10–15 representative plants from each RILs. The mean, standard deviation, coefficient of variation, broad-sense heritability, frequency distribution, correlation coefficient and analysis of variance (ANOVA) of three agronomic traits in RIL mapping population were analyzed using SPSS v17.0 and following the methods of Kujur ApeKI, ligated to adapters containing one of 288 unique barcodes, constructed 288-plex GBS libraries and pooled together following the detailed procedures of Elshire et al.et al.phred Q-score of 10 across the first 72-bp nucleotide sequences were considered as high-quality, which were further rechecked for their quality using FASTQC v0.10.1. The retained good quality sequences were de-multiplexed using unique barcodes attached to each mapping individuals. The sequence reads of each individuals were aligned and mapped to reference draft desi  were digested with  ICC 4958 and kabuhttp://www.sequenom.com) following the methods of Pandit et al.et al.77To validate the genome-wide SNPs identified through GBS assay, the primers designed from the 200-bp sequences flanking either-side of 384 selected SNPs were PCR amplified using the genomic DNA of two parental chickpea genotypes and selected RIL mapping individuals. The amplified PCR products was resequenced, aligned the high-quality sequences and the SNPs were detected among individuals  showing differentiation between parental genotypes (ICC 12299 and ICC 8261) were analysed in 275 RIL mapping individuals using the χ2-test (p < 0.05). The SNP genotyping data showing goodness-of-fit to the expected Mendelian 1:1 segregation ratio were used further for linkage analysis using MAPMAKER/EXP3.0 and JoinMap 4.1 at higher LOD (logarithm of odds) threshold (>10.0) with Kosambi mapping function. The SNPs were integrated into eight desi and kabuli LGs of intra-specific genetic maps based on their centimorgan (cM) genetic distance using the methods of Kujur et al.74et al.The genotyping data of reference P-value ≤ 10−3) as average squared-allele frequency correlations (r2) among a pairs of SNP loci that are genetically mapped on eight desi and kabuli LGs of intra-specific genetic maps were analysed using the sliding window approach of TASSEL v5.0. The decay of LD with the genetic distance was measured by combining the r2 values of SNP-pairs mapped in a uniform genetic intervals of 5 cM (maximum up to 120 cM) across eight desi and kabuli LGs of genetic maps. The graph was plotted between pooled r2 and genetic distance (cM) based on nonlinear regression model considering the r2 value = 1 at marker genetic distance of 0 cM7499101desi and kabuli genomes.To determine genome-wide LD patterns in chickpea, the LD estimates (significant desi and kabuli genome-based SNPs mapped on the eight LGs of intra-specific genetic maps were annotated in diverse intragenic and intergenic sequence components of genomes (chromosomes/pseudomolecules) using the genome annotation information (GFF) of desikabulidesi and kabuli genomes using Circos v0.67-pre3. The functional annotation of SNP-containing genes was performed according to desi and kabuli genome annotation and PFAM database v27.0. The genes with SNPs were BLAST searched against the KOGnitor NCBI database. Gene Ontology (GO) enrichment analysis of genes with SNPs was performed using the BiNGO plugin of Cytoscape v2.6 based on Benjamini and Hochberg false discovery rate correction at 5% significance level.The reference desi genome-based high-quality GBS SNP genotyping data and genetic linkage map information of SNPs mapped on eight desi LGs of intra-specific genetic map were correlated with multi-location/years replicated field phenotyping data  of RIL mapping individuals and parental genotypes using single marker analysis, interval mapping and composite interval mapping functions of QTL Cartographer v2.5 and MapQTL v6.0. To identify and map the novel genomic regions harbouring the major QTLs associated with three agronomic traits  on LGs, the LOD threshold score of ≥5.0 at 1000 permutations was considered significant (p < 0.05). The significant major trait-influencing QTLs, which were validated across multiple environments (locations)/years were considered as “robust QTLs” controlling agronomic traits  with SW at significant cut-off P ≤ 10−4 were selected.A selected strong SW major genomic region underlying robust QTL was sequenced in parental genotypes (ICC 12299 and ICC 8261) along with 10 of each homozygous low and high SW RIL mapping individuals using the multiplexed amplicons sequencing protocol (following manufacturer's instructions) of TruSeq Custom Amplicon v1.5 in Illumina MiSeq next-generation sequencer. The high-quality amplicon sequence reads (> 90% bases covered at 0.5× mean coverage) were mapped to reference The SNPs-carrying genes annotated at the short delineated major genomic region underlying the robust SW-specific QTL were selected to infer their differential regulation during seed development through expression profiling. The RNA isolated from three different vegetative and reproductive tissues  and two seed developmental stages7499et al.74et al.et al.To perform high-resolution haplotype-based association/LD mapping, the fragments covering the entire coding and non-coding sequences, including 2 kb upstream regulatory region (URR) of one strong SW-associated gene  were amplified using the genomic DNA of 244 SW-specific association panel. The cloning and sequencing of amplicons, SNP mining, haplotype constitution and LD pattern determination of a gene including, its association potential with SW were estimated using the methods of Kujur Supplementary information


Next-generation sequencing (NGS) technologies have accelerated considerably the investigation into the composition of genomes and their functions. Genotyping-by-sequencing (GBS) is a genotyping approach that makes use of NGS to rapidly and economically scan a genome. It has been shown to allow the simultaneous discovery and genotyping of thousands to millions of SNPs across a wide range of species. For most users, the main challenge in GBS is the bioinformatics analysis of the large amount of sequence information derived from sequencing GBS libraries in view of calling alleles at SNP loci. Herein we describe a new GBS bioinformatics pipeline, Fast-GBS, designed to provide highly accurate genotyping, to require modest computing resources and to offer ease of use.Fast-GBS is built upon standard bioinformatics language and file formats, is capable of handling data from different sequencing platforms, is capable of detecting different kinds of variants . To illustrate its performance, we called variants in three collections of samples  that cover a range of different genome sizes, levels of genome complexity, and ploidy. Within these small sets of samples, we called 35 k, 32 k and 38 k SNPs for soybean, barley and potato, respectively. To assess genotype accuracy, we compared these GBS-derived SNP genotypes with independent data sets obtained from whole-genome sequencing or SNP arrays. This analysis yielded estimated accuracies of 98.7, 95.2, and 94% for soybean, barley, and potato, respectively.We conclude that Fast-GBS provides a highly efficient and reliable tool for calling SNPs from GBS data. Currently, genomics lies at the heart of an extraordinary number of discoveries, innovations and applications. This revolution is a direct result of the rise of next-generation sequencing (NGS) technologies –4. In thAdvanced sequencing technologies (NGS) have reduced both the cost and the time required to generate sequence data. The efficient and accurate computational processing, variant and genotype calling of large-scale NGS data is the new bottleneck in genomics. To meet this need, numerous bioinformatics pipelines have been developed –16 and ade novo-based and reference-based. In the presence of a reference genome, the reads from reduced-representation sequencing can be mapped to the reference genome and SNPs can be called [de novo-based approach are UNEAK and Stacks [Conventionally, bioinformatics pipelines for handling GBS data are categorized in two groups: e called , 20. Up e called –15, 21. d Stacks .Herein, we describe a new reference-based pipeline, Fast-GBS, and we benchmark the pipeline based upon a large-scale, species-wide analysis of soybean, barley and potato. It is easy to use with various species, in different contexts, and provides an analysis platform that can be run with different types of sequencing data and modest computational resources.To test the performance of Fast-GBS, we used existing sequence datasets for panels of 24 unrelated accessions/clones for three species covering a range of genomic situations: soybean , barley To estimate genotype accuracy for Fast-GBS calls, we compared the called SNPs with independently derived genotypic data resulting from either whole-genome resequencing (soybean and barley) or genotyping on a SNP array (potato) for the same samples. For soybean, we compared the GBS-called SNPs with whole genome resequencing data for the same 24 samples. In the case of barley, GBS-derived genotypic data for one of the 24 barley samples (cv. Morex) was compared to the barley reference genome produced using this same cultivar. For potato, we compared the GBS-derived genotypes with those obtained for the same 24 samples at a set of 122 SNPs that were in common with the SolCAP Infinium Chip (8.3 k SNPs) .The Fast-GBS analysis pipeline was developed by integrating public packages with internally developed tools. The public packages include Sabre (demultiplexing) , CutadapWe developed a Bash script to create the directory structure before running the Fast-GBS pipeline. This command line creates the directories for data (FASTQ files), barcodes (key file), reference genome, and results (Fast-GBS outputs).The input data are sequenced DNA fragments from any restriction enzyme–based GBS protocol. Fast-GBS handles raw sequencing data in FASTQ format.The parameter file is a text file containing key information about the analysis including the path to the FASTQ files, barcodes and reference genome. It also contains information about the type of sequence (paired or single-end), the adaptor sequence and the sequencing technology. In this file we can define critical filtering options such as the minimal quality scores for reads, minimal number of reads required to call a genotype, and maximal amount of missing data allowed. Number of CPU, names of output files are also defined in this file. This file comes with the Fast-GBS pipeline.The cost efficiency of GBS is partly due to the multiplexing of samples and the resulting pooled reads will need to be demultiplexed prior to SNP calling. Fast-GBS uses Sabre  to demulAfter demultiplexing, Fast-GBS uses Cutadapt to find and remove adapter sequences, primers, and other types of unwanted sequence from high-throughput sequencing reads .Fast-GBS uses the MEM  algorithm implemented in BWA that works by seeding alignments and then extending seeds with the Smith-Waterman (SW) algorithm using an affine gap penalty . This alAfter initial alignment, the mapped reads are further processed by Platypus  in orderIn Fast-GBS, variants are called using Platypus. Unlike alignment-based variant callers which focus on a single variant type (SNP or indel), Platypus uses multi-sample variant calling that helps to exploit information from multiple samples to call variants that may not look reliable in a single sample. This approach decreases the errors around indels and larger variants (MNPs). At first, the local assembler looks at a small window (~few kb) at a time and uses all the reads in the window to generate a colored de Bruijn graph, then using all candidate variants, it generates an exhaustive list of haplotypes. Candidate haplotypes are generated by clustering the candidate alleles across windows. Haplotype frequencies are estimated by the expectation-maximization (EM) algorithm. Then variants are called using the estimated haplotype frequencies. This approach works on the local haplotype level rather than on the level of individual variants and does well on highly divergent regions. This also decreases computational requirements.Platypus was originally designed and used to detect variants in human, mouse, rat and chimpanzee samples. To optimize Platypus options in the context of the analysis of GBS-derived single-end reads, we modified several options  derived from a 24-plex MspI/PstI library. Finally, in potato, 38 k SNPs were obtained from sequencing a 24-plex MspI/PstI library (43 million 100-bp Illumina reads).To assess the performance of the Fast-GBS analysis pipeline, we used it to analyze existing GBS-derived read data from sets of 24 soybean, barley, and potato samples. Table GBS was originally demonstrated for soybean by Sonah et al  using thMspI/PstI-digested library) and they called 34 k and 19 k SNPs using either the reference genome (with SAMtools) or a de novo pipeline (TASSEL), respectively. In this study we used Fast-GBS for SNP calling in barley and, as can be seen in Table Barley has one of the larger genomes (>5 Gb) among cultivated plant species. Because of the huge size and high level of complexity of its genome, complexity reduction is highly recommended in barley, an important crop species for which a draft genome has been published . MascherBecause of the high level of ploidy and heterozygosity, potato is a challenging species for genotyping. The most often used method for genotyping in potato is a SNP array. Two SNP arrays have been developed so far, the SolCAP 8 k and 20 k arrays , 34, 35.An important aspect to consider for any variant calling tool is the accuracy of called genotypes. In this study, we estimated the accuracy of genotypes called by Fast-GBS Table  by compaIn this study, to assess the performance of Fast-GBS, we used both Illumina and Ion Torrent reads. Soybean and potato samples were sequenced using an Illumina Hiseq platform and barley samples on an Ion Torrent (Proton) platform. Typically for GBS, Illumina sequencing generates reads of uniform length (100 bp), while Ion Torrent reads are in 50 to 150 bp. Ion Torrent sequencing usually leads to a higher rate of sequencing errors , 39. ThuGBS provides an extremely powerful and versatile tool for identifying and calling genetic markers to be used by researchers working in numerous species and fields of study. This genotyping approach, like all applications based on NGS, generates a huge amount of raw data. These data need to be analyzed as quickly and efficiently as possible, all the while yielding SNP data that is highly accurate. Fast-GBS showed itself to be a powerful pipeline to generate large numbers of highly accurate SNPs using sequence read data obtained from different sequencing platforms and diverse species characterized by different levels of ploidy, zygosity, and genome complexity. By combining efficiency and accuracy in this way, Fast-GSB constitutes a useful tool for a broad array of users in different research communities.Project name: Fast-GBSProject home page:https://bitbucket.org/jerlar73/fast-gbsOperating system: LinuxProgramming language: Bash and PythonLicense: GNU GPL vAny restrictions to use by non-academics: NoSequence data are available in NCBI Sequence Read Archive (SRA) with the SRP# Study accession, SRP059747.


During the last two decades, many QTL (quantitative trait locus) mapping experiments in pigs have been conducted using F2 crosses established from two outbred founder breeds. The founder breeds were frequently chosen from the Asian and European type breeds. A combination of next-generation sequencing, SNP (single nucleotide polymorphism) genotyping technology using SNP-chips, and genotype imputation techniques, can be used to infer the sequence information of all F2 individuals in a cost-effective way. The aim of the present simulation study was to analyze the power and precision of genome-wide association studies (GWASs) with whole-genome sequence data in several types of F2 crosses, including pooled crosses.Based on a common historical population, three breeds representing two European type breeds (EU1 and EU2) and one Asian type breed (AS) were simulated. Two F2 designs of 500 individuals each were simulated. The cross EU1xEU2 (ASxEU2) was simulated using the phylogenetically closely related breeds EU1 and EU2 (or distantly related breeds AS and EU2) as the founder breeds. The simulated genomes comprised ten chromosomes, each with a length of 1 Morgan and whole-genome sequence information. A polygenic trait with a heritability of 0.5, which was affected by approximately 20 QTL per Morgan, was simulated. GWASs were conducted using single marker mixed linear models, either within the crosses or in their pooled datasets. Additionally, the studies were conducted in the breed EU2, which was a founder breed in both simulated crosses.The power to map QTL was high (low) in the ASxEU2 (EU1xEU2) cross and was highest when the data of both crosses were analyzed jointly. By contrast, the mapping precision was the highest in the EU1xEU2 cross. Pooling data led to a precision that was in between the precision of the EU1xEU2 cross and the ASxEU2 cross. A higher mapping precision was observed for QTL segregating within a founder breed.These results suggest that the existing F2 crosses are promising databases for QTL mapping when the founder breeds are closely related or several crosses can be pooled. This conclusion is particularly applicable for QTL that segregate in a founder breed. QTL (quantitative trait locus) mapping and the identification of causative single nucleotide polymorphisms  is still of high importance in animal breeding. The results of genome-wide association studies (GWASs) provide knowledge about the evolution and genetic architecture of traits and may improve the accuracy of genomic prediction , especiaSince the availability of dense SNP maps and the possibility to conduct large-scale SNP genotyping with SNP-chips, QTL mapping is usually performed in genome-wide association studies (GWASs) within breeding populations or in admixed populations . For exaThe aim of the present study was to analyze the power and precision of GWASs with whole-genome sequence data in several types of F2 crosses, including pooled crosses. Particular emphasis was paid to founder breed segregating QTNs because these are of interest for breeding purposes. The crosses were established using distantly or closely related founder breeds using stochastic simulations. The results were compared with those obtained from pooled F2 crosses, which increased the sample size and putatively reduced the LD. For comparison purposes, we also simulated one of the founder breeds and conducted GWASs within this breed.ST, using the formula (8) in Weir and Cockerham ).t-distribution with four degrees of freedom and were assumed to be the same for the two single crosses. This result roughly resembled the distribution of additive effects in porcine F2 crosses [aj being the simulated additive effect, pj the frequency of the mutant allele, and Q the number of simulated QTN. The additive genetic variance was calculated as the variances of the breeding values within the pooled dataset. Hence, the additive genetic variance differed slightly between the crosses due to different gene frequencies at the QTNs. The gene frequencies were more intermediate in the ASxEU2 cross compared to the EU1xEU2 cross, because in the former cross the two founder breeds were less related. However, in general the difference of the gene frequencies in both crosses were small. A random residual was added to the breeding values to complete the phenotypes of the individuals, assuming a heritability of 0.5 in the pooled dataset. In addition, 1000 EU2 individuals were simulated using the same procedures. Note that the LD structure of these types of simulated F2 crosses were investigated in detail in an earlier study [Ten chromosomes of one Morgan (M) length each were simulated. The pig genome consists of more than ten chromosomes, but we restricted this number for computational reasons. Recombination events were simulated according to the Haldane mapping function. The mutation rate was adjusted in a simplified manner so that two mutations per chromosome (20 per genome), on average, were expected to occur per meiosis. All SNPs were generated solely by the mutations within the evolution of the simulated populations. This protocol was repeated ten times. For each population, five traits were simulated, which resulted in 50 replicates in total. For each trait, 20 SNPs per chromosome were randomly selected to become a QTN, which resulted in 200 QTN to mimic the polygenic nature of quantitative traits . Because crosses . Breedin crosses . For an er study , and henAll SNPs and QTNs with a minor allele frequency (MAF) below 0.05 in the individual crosses were removed from the following analyses. GWASs were conducted for each SNP  separately by using the following regression model and the software GCTA  :yi denotes the phenotypic value of individual i, k to which individual i belongs, xij denotes the number of copies of a randomly chosen allele of SNP j  and bjis the regression coefficient for SNP j. The random polygenic effect of the individual (gi) was fit to capture population stratification effects. The covariance structure of the polygenic effects was modeled using a genomic relationship matrix (GRM) [Here, ix (GRM) . To avoiix (GRM) . This apall segregating genes (ASG) scenario, the aim was to map all available QTNs. Association mapping was performed on the full set of SNPs and QTNs. However, from a breeder’s perspective, GWAS results are most important for QTNs segregating in the breed of interest. As economically relevant breeds  were often used as founder breeds in F2 crosses, a second scenario, the so-called founder segregating genes (FSG) scenario was considered. The aim was to map QTNs that segregated in the common founder breed EU2. Consequently, all SNPs and QTNs that did not segregate in the common founder breed EU2 were removed from the simulated datasets because they could be excluded beforehand as putative QTNs. The association analyses were subsequently conducted using these reduced data sets. Consequently, the number of tests were much smaller and so were the levels of multiple testing corrections using Bonferroni. Both GWAS scenarios (ASG and FSG) were applied to all simulated F2 crosses.Two GWAS scenarios were considered. In the For comparison purposes, the simulated purebred EU2 data set was also analyzed.S. In the ASG scenario, S contained all segregating SNPs (MAF > 0.05 in the respective cross), but in the FSG scenario, the set included only SNPs also segregating in the common founder breed (MAF > 0.05 in EU2). Thus, Qα ⊆ Q ⊆ S, where Q is the set of QTNs, and Qα contains all simulated QTNs with a Bonferroni corrected p-value less than α. We calculated the power to map QTNs as the proportion of QTNs with a Bonferroni corrected p-value smaller than α, i.e., Q denotes the number of elements in set Q. This definition is in agreement with classical statistical test theory. QTL power was defined as the proportion of QTNs, which are either mapped per se or by a significant SNP in LD with the QTN To determine whether a QTN i can be detected through a SNP in high LD, a window Wi was defined spanning 1 cM with the QTN in the center. This window defined the QTL region. If the SNP with the smallest p-value was significant in such a QTL window, the QTN was indicated by this SNP and, therefore, was mapped. Hence, the QTL power was calculated as Wα being the number of windows, which contained a QTN and at least one significant SNP within these windows.The set of SNPs was denoted as The windows were also considered to specify the precision of mapping QTNs. For each window containing a significant QTN, the proportion of SNPs showing a higher significance than the QTN itself was computed. The QTN mapping precision was then calculated by subtracting this proportion from 1. This step ensured that the maximum achievable mapping precision was one, which implied that the QTN showed the highest significance among all SNPs in the window. By contrast, a precision close to 0.5 indicated that 50% of the significant SNPs were more significant than the causal mutation.The parameters QTN power, QTL power, and QTN precision were calculated for each analyzed data set and then averaged across the simulated replicates.Maximum marker density was simulated, which resembles a situation where the whole genome sequence variants are known from each F2 individual. In real porcine F2 crosses, sequencing all F2 individuals is still unaffordable, but the Illumina PorcineSNP60 BeadChip  with appST value calculated between the two simulated European breeds EU1 and EU2 was FST = 0.02, and between the Asian breed AS and the European breed EU2 it was FST = 0.36. These values implied a small (large) genetic differentiation between EU1 and EU2 (AS and EU2) [The Fand EU2) . Hence, The average number of SNPs across all replicates in the ASG scenario with an MAF > 0.05 within the respective populations is given in Table For the FSG scenario, the average numbers of SNPs with an MAF > 0.05 in the EU2 and the F2 crosses are given in Table The power to detect a QTN or at least one significant SNP within a 1 cM window around a QTN  is given in Table A low mapping precision was observed in F2 crosses with phylogenetically strongly divergent founder breeds Table  and, theIn the FSG scenario, the EU2 was the breed of interest, and the aim was to map QTNs segregating in this breed. The results for QTL and QTN power in this scenario are shown in Table As shown in Table The precision in the FSG scenario was always above the precision in the ASG scenario. This result is in agreement with  for whicThe general pattern of the mapping power and precision results in the simulated populations and scenarios as described above is visualized by a comparison of the Manhattan plots for a randomly chosen replicate in Fig. Based on the results of this simulation study, in can be concluded, that the existing F2 crosses are promising databases for gene mapping in the era of genomics when the founder breeds are closely related or when crosses can be pooled. For the fine-mapping of QTNs, F2 crosses from distantly related founder breeds should be pooled with data from additional populations in which the QTNs of interest are segregating. This step could substantially increase the precision. By contrast, the mapping precision could be even higher in F2 crosses from closely related founder breeds than in the purebred population; thus, no pooling would be required if the sample size and the numbers of founders in the F0 are sufficiently high. This conclusion is particularly true for QTNs that segregate in a founder breed.


Genome-wide association studies and genomic predictions are thought to be optimized by using whole-genome sequence (WGS) data. However, sequencing thousands of individuals of interest is expensive. Imputation from SNP panels to WGS data is an attractive and less expensive approach to obtain WGS data. The aims of this study were to investigate the accuracy of imputation and to provide insight into the design and execution of genotype imputation.We genotyped 450 chickens with a 600 K SNP array, and sequenced 24 key individuals by whole genome re-sequencing. Accuracy of imputation from putative 60 K and 600 K array data to WGS data was 0.620 and 0.812 for Beagle, and 0.810 and 0.914 for FImpute, respectively. By increasing the sequencing cost from 24X to 144X, the imputation accuracy increased from 0.525 to 0.698 for Beagle and from 0.654 to 0.823 for FImpute. With fixed sequence depth (12X), increasing the number of sequenced animals from 1 to 24, improved accuracy from 0.421 to 0.897 for FImpute and from 0.396 to 0.777 for Beagle. Using optimally selected key individuals resulted in a higher imputation accuracy compared with using randomly selected individuals as a reference population for re-sequencing. With fixed reference population size (24), imputation accuracy increased from 0.654 to 0.875 for FImpute and from 0.512 to 0.762 for Beagle as the sequencing depth increased from 1X to 12X. With a given total cost of genotyping, accuracy increased with the size of the reference population for FImpute, but the pattern was not valid for Beagle, which showed the highest accuracy at six fold coverage for the scenarios used in this study.In conclusion, we comprehensively investigated the impacts of several key factors on genotype imputation. Generally, increasing sequencing cost gave a higher imputation accuracy. But with a fixed sequencing cost, the optimal imputation enhance the performance of WGP and GWAS. An optimal imputation strategy should take size of reference population, imputation algorithms, marker density, and population structure of the target population and methods to select key individuals into consideration comprehensively. This work sheds additional light on how to design and execute genotype imputation for livestock populations.The online version of this article (10.1186/s40104-018-0241-5) contains supplementary material, which is available to authorized users. Genotype imputation  has becoThe availability of next generation sequencing (NGS) techniques has made it possible to obtain WGS SNP markers at a reasonable cost. However, sequencing thousands of individuals of interest is still too costly for routine implementation in livestock breeding programs. Recently, many studies have recommended the imputation of low-density SNP markers to WGS SNP markers. Genotype imputation is a widely used method that utilizes LD knowledge from haplotypes in a known reference panel to predict genotypes at missing or un-genotyped markers.P = 3.3 × 10− 8), rs711830 at 2q31.1 (P = 7.5 × 10− 12) and rs688187 at 19q13.2 (P = 6.8 × 10− 13). Pausch et al. 8], horseh et al.  detectedMany factors affect the accuracy of genotype imputation, such as imputation algorithms , geneticIn this study, we genotyped a Chinese indigenous chicken population using a chicken 600 K SNP chip, and we sequenced 24 selected key individuals. We systematically investigated the impacts of reference population size, key individual selection strategies, imputation algorithms, marker density of the target panel, sequencing depth, and the total cost of genotyping on the accuracy of genotype imputation when imputing array data to sequence data. Our results provide insight into the designing and executing of genotype imputation.rd batch of the 25th generation of this chicken population. These birds came from a mixture of full sib and half sib families with the mating of 30 males and 360 females from the 24th generation. After hatching, all birds were maintained in a closed building under controlled environmental conditions and provided with a standard diet till the end of 4 wk of age. Then they were randomly assigned to six pens by gender  for growth performance testing from 5 to 13 wk of age. They received food and water ad libitum in all stages. Finally, slaughter was performed at 91 d of age and carcass traits recorded. In total, 1,338 birds  were systematically phenotyped for further study.The chicken population used in this study was derived from a Chinese indigenous breed and maintained for 25 generations by Wens Nanfang Poultry Breeding Co. Ltd. . The population consisting of 1,600 birds , was the 3A total of 450 birds were selected for genotyping. These birds were 15 sires and 435 male offsprings. The average sire family size was 13.5 which ranged from 7 to 23. Genomic DNA of 450 individuals was extracted from blood samples using the NRBC Blood DNA Kit  according to the manufacturer’s instructions. DNA concentrations of samples were quantified and genotyped using the 600 K Affymetrix® Axiom® high-density genotyping array . This SNG matrix to replace the A matrix (pedigree-based genetic relationship matrix), to maximize the expected genetic relationship between key individuals and the remaining population, while maximizing the proportion of unique genomes sequenced in the population. Following previous studies [G matrix in this study was defined as M was an adjusted marker genotype matrix including m SNPs in columns and n individuals in rows. Here, the genotypes were coded as 0, 1, and 2 representing the copy number of the second allele, and then adjusted by 2pi in each column, where pi was the allele frequency of the second allele at the ith locus in the base population. Because the use of different allele frequencies pi did not affect the accuracy of prediction [pi = 0.5 for all SNPs to build all genomic relationship matrices, as in our previous studies [The key individuals for re-sequencing were selected by maximizing the expected genetic relationship (REL), as described in detail by Druet and Hayes (2014) . We util studies , 24, theediction –27, we u studies .N reads (where N > 10% of one read) to produce clean reads. Then the clean reads were aligned to the chicken reference genome  using the Burrows-Wheeler Alignment tool  [Key individuals selected from the previous procedures were re-sequenced with 150 bp paired-end reads on the Illumina HiSeq 3000 platform. The sequencing was performed by RiboBio Co., Ltd. . Briefly, the initial quality of raw reads was checked using FastQC , with a  0.7.12) . After t 0.7.12) . Lastly, 0.7.12)  to call  0.7.12) , 35. Aft 0.7.12) .For each key individual, the concordances of SNPs called by GATK were evaluated by SNP genotypes obtained from the 600 K array. As proposed by Baes et al. , four meTo investigate the influences of the imputation algorithms, SNP density of the target panel, the number of sequenced individuals, selection strategies, sequencing depth, and the total cost of genotyping were assessed on the imputation accuracy from SNP chip data to WGS data. Four scenarios were considered in genotype imputation section .Scenario one S1) was designed to investigate the effect of target panel density on imputation accuracy from SNP chip data to WGS data. S1 contained three imputation sections: direct imputation from low-density chip (60 K) to WGS data, direct imputation from high-density chip (600 K) to WGS data, and indirect imputation from 60 K to 600 K data and then from 600 K to WGS data (two-step imputation approach). The supposed 60 K chip data was generated by sampling the first SNP in each bin of adjacent 10 SNPs from the 600 K SNP chip. Scenario two (S2) was designed to study the effects of the number of sequenced individuals and selection strategies on imputation accuracy from 600 K to WGS data. S2 was performed by adding sequenced individuals to the reference population one by one with optimized or random rank, respectively. In S2, the optimized rank was defined as the priority queue of key individuals determined by REL. Also we fixed the sequence read depths (X) of sequenced individuals as 12X. Scenario three (S3) was designed to study the effects of sequenced depths on imputation accuracy. We fixed the number of sequenced individuals (N) as 24 and changed the sequence read depths from 1 to 12X. Scenario four (S4) was designed to study the effects of different WGS data costs on imputation accuracy and to assess optimal sequencing strategies. The total cost of WGS data was defined as that the number of sequenced individuals multiplied by the sequence depth of each sequenced individual (Table  was desiImputations were executed by FImpute (Version 2.2) and Beagle (Version 3.3.2) with default parameter settings. FImpute was based on an overlapping sliding window method in which information from close relatives (long haplotype match) was first utilized, and information from distant relatives was subsequently used by shortening the window size . Beagle The quality control criteria of SNP chip data were minor allele frequency (MAF) > 0.005, individual genotyping call rate > 95%, and SNP genotyping call rate > 97%. Finally, 462,092 SNPs and 444 birds remained for further investigation. We randomly masked 2% of SNPs from the 600 K SNP panel of each individual to be imputed, and then compared the imputed genotypes with array-derived genotypes to calculate the imputation accuracy. The imputation accuracy was defined as the average genotype concordance from five replicates. To improve computational efficiency, chromosomes 1, 3, 6, and 28  were selected.We calculated the marker-based genetic relationship matrix (G matrix) of 450 genotyped chickens with 600 K array data. All genotyped individuals were ordered by maximizing the expected genetic relationship (REL), using G matrix, between the group of sequenced birds and the whole population. The 24 birds that showed the highest values were selected as key individuals. These 24 birds from 21 sire family were three male parents  and 21 male offspring, and their relatedness is shown in Additional file 1: Table S1. The cumulative genetic diversity of selected key individuals increased as the numbers of selected key individuals increased, but the rate of increase gradually slowed  of SNP panels with different densities to WGS data for four chromosomes  are shown in Fig. With all 24 key individuals as a reference, imputation accuracies under different sequencing depths are shown in Fig. To investigate the influences of different selection strategies and reference size on imputation accuracy, we randomly selected different numbers of individuals from these 24 key individuals using random rank and optimized rank  to WGS SNPs 11645758) , while the loss of imputation accuracy was only less than 4% for FImpute and 9% for Beagle using a two-step imputation approach Fig. . This reKey individual selection was effective for genotype imputation. We selected 24 representative chickens for re-sequencing using the REL model. The sequenced individuals were added to the reference population one by one using optimized or random rank. We predicted that the advantage of optimized rank over random rank would be observed at the middle stage of this comparison. This was because the individuals used for random rank were the same 24 key selected chickens with a random resampling rank, and these 24 key individuals were the most representative ones in the population. Our observation met well with our prediction  [We compared the performance of Beagle and FImpute for all scenarios. Generally, FImpute outperformed Beagle in the present population. The superiority of FImpute over Beagle was also observed in previous studies , 46, 47. = 1652) . In thisThere are three versions of Beagle . We compared the imputation accuracy with Beagle and FImpute with or without pedigree in these recent versions . These values were similar to those observed in 43 Fleckvieh cattle [The comparison of array-derived genotypes versus sequence-derived genotypes provides an objective quality measurement for NGS experiments and the variant calling pipeline. Our results revealed that both GC and NRS were very high, =ranging from 0.928 to 0.992 and from 0.947 to 0.995, respectively. But the average NRD of 24 key individuals was 0.037. Moreover, Pearson’s correlation coefficient between NRD and the depth coverage of 24 key individuals was − 0.18  with different sequencing depth under the different total cost of genotyping, respectively. (XLSX 25 kb)Additional file 2:Figure S1. Imputation accuracy in different total X obtained with FImpute and Beagle against 4 chromosomes  among 5 replications, respectively. Figure S2 Imputation accuracy in different X with fixed N (N = 24) obtained with FImpute and Beagle against 4 chromosomes  among 5 replications, respectively. Figure S3 The average imputation accuracy of direct imputation from 600 K to WGS data obtained with FImpute, Beagle v.3.3.2, Beagle v.4.0, and Beagle v.4.1 against four chromosomes  among 5 replications. (DOCX 1755 kb)


We demonstrate that the Wheat Breeders’ Array is also suitable for generating high‐density genetic maps of previously uncharacterized populations and for characterizing novel genetic diversity produced by mutagenesis. To facilitate the use of the array by the wheat community, the markers, the associated sequence and the genotype information have been made available through the interactive web site ‘CerealsDB’.Targeted selection and inbreeding have resulted in a lack of genetic diversity in elite hexaploid bread wheat accessions. Reduced diversity can be a limiting factor in the breeding of high yielding varieties and crucially can mean reduced resilience in the face of changing climate and resource pressures. Recent technological advances have enabled the development of molecular markers for use in the assessment and utilization of genetic diversity in hexaploid wheat. Starting with a large collection of 819 571 previously characterized wheat markers, here we describe the identification of 35 143 single nucleotide polymorphism‐based markers, which are highly suited to the genotyping of elite hexaploid wheat accessions. To assess their suitability, the markers have been validated using a commercial high‐density Affymetrix Axiom By making the array and resulting data available to the global community, we hope to demonstrate the utility of this platform for researchers worldwide. Developing global resources such as these promote rapid germplasm exchanges to boost genetic diversity and facilitate targeted breeding.® HD 820K wheat array (Table S1). Overall SNP markers were selected as described in methods to include those that were evenly spaced throughout the genome (according to genetic map position) and showed higher levels of polymorphism  in the test range of hexaploid accessions, which included 108 elite hexaploid accessions of which 48 were suggested by a number of commercial wheat breeders  generated from the F6 generation or F7 generation , and one was produced by single seed descent to the F5 generation (Apogee × Paragon). To maximize the number of genetically mapped SNPs, a diverse selection of parental material was used to generate these populations which included spring and winter varieties, a synthetic hexaploid, the model variety Chinese Spring and a ‘super‐dwarf’, ‘rapid cycling’ cultivar . The number of SNPs on the array polymorphic between these specific crosses ranged from 6772 to 11 720, suggesting an average of 8793 SNPs on the array (25%) are predicted to be polymorphic between any two varieties.Five mapping populations were genotyped using the Wheat Breeders’ Array. Of the 35 143, SNP markers selected 22 001 (62.6%) were placed on one of five genetic maps (Table S3). The five different mapping populations differed in parental accessions, size of population and crossing strategy as detailed in Table Markers with greater than 20% missing data were removed prior to map construction. Of the SNPs polymorphic between the parents of the crosses, 86%, 90%, 88% and 80% of markers were able to be assigned to a linkage group on the Avalon × Cadenza, Savannah × Rialto, Opata × Synthetic and Chinese Spring × Paragon maps, respectively Table . The numP < 0.005) distortion of segregation was Chinese Spring × Paragon (317 SNPs), then Avalon × Cadenza (86 SNPs), Apogee × Paragon (54 SNPs) and Savannah × Rialto (38 SNPs). The Opata × Synthetic population had no SNP loci exhibiting significant distortion of segregation. The distorted loci were unevenly distributed across the genome with clusters of SNPs in specific locations  and 2D (P = 3.13e−6) which equates to parent1 : parent2 ratios of 31 : 83 and 77 : 29, respectively. On the Apogee × Paragon genetic map, the most distorted markers were located on chromosomes 2D (P = 5.17e−13), 3B (P = 5.38e−11), 6A (P = 2.0e−10) and 6B (P = 1.89e−6). For the Savannah × Rialto population, the most highly distorted SNP was located on chromosome 3B (P = 6.7e−4). The markers exhibiting the highest level of distortion on the Chinese Spring × Paragon map were located in the largest clusters of SNPs on chromosomes 2D and 6B . The direction of distortion in relation to the parental genotype in the Avalon × Cadenza population appeared biased towards Cadenza with 8 of 11 clusters and 95% SNPs being distorted in favour of the Cadenza genotype. In the Savannah × Rialto map, 24 SNPs in three locations were distorted towards Rialto, while 14 SNPs in one location were distorted towards Savannah. For the Apogee × Paragon population, 57% of the significant SNPs were distorted towards Paragon in 7 of the 14 chromosome locations. A significant bias was seen in the Chinese Spring × Paragon with 81% of SNPs in 23 locations on 16 chromosomes distorted in favour of the Chinese Spring genotype.The markers with the most significant distortion of segregation in the Avalon × Cadenza population were mapped to chromosomes 2A . These unique accessions included an elite collection of 505 breeding lines derived from 17 countries in Africa, Australia, the Americas, the Middle East and Europe; 436 lines from the Gediflux collection (representing Western Europe winter wheat diversity from 1920 to 1990) and 790 accessions from the Watkins global landrace collection assembled from 33 countries in the 1930s . The accession type with the highest average call rate was mutation lines (98.1%), and the lowest was synthetic hexaploids (96.3%). The relationship between call rate and heterozygous call rate per accession was investigated. A trend was observed where samples with low call rates tended to have a higher than average het rate . The DNA samples for these lines are predicted to be of lower quality as the increase in AB calls and lower call rate represents a higher number of outlier calls from the main clusters. For use as a high‐throughput genotyping platform, reproducibility is an important consideration. The call rate among duplicate samples was highly similar (ranging from 99.3% to 99.8%); however, the call rate for replicate samples prepared from the same named accessions, but from different sources, showed more variation (97.5–99.4); this is likely to reflect true within‐cultivar variation.The total number of polymorphic SNPs was 33 326 94.8%) of the entire array based on the screen of the collection of lines described above. A summary of the numbers of polymorphisms present unique to and shared between germplasm collections is presented in Table .8% of thet al., A. tauschii accessions used in the creation of these lines.The minor allele frequencies of SNPs within different germplasm collections were calculated as a measure of allelic diversity Figure a. The laThe relationship between accessions was visualized by calculating a pairwise similarity matrix that was used to perform multidimensional scaling (MDS) and create principal coordinate (PCoA) plots Figure . With thTo further examine the diversity present in different elite germplasm collections, they were defined by geographical region of origin. The separate components of the collections are summarized in Table FST value, which is not surprising given that they are both of Western European origin. The Western Europe and Australian collections appeared to have a high degree of similarity with considerable overlaps in polymorphic markers, although this is may also be attributed to both collections having the highest number of polymorphic SNPs overall. Overall, the majority of polymorphic SNPs were shared among populations, suggesting that there is a high transferability of SNP markers across global elite germplasm collections. High FST measures between populations of different geographical origin is likely to be caused by the usage of different founders or by allele frequency divergence during the development of locally adapted populations. High FST measures were seen in particular between Western European and Central American subpopulations. Conversely, the lowest FST measures were seen between Middle Eastern, Southern American and Southern Europe subpopulations suggesting use of similar founders or overlap in these breeding programmes.The relationship between different subpopulations was further examined by calculating the number of shared polymorphisms and genetic differentiation between subcollections Table . The numTo further examine the relationship between cultivars of different geographical origins, samples plotted by the first two principal co‐ordinates were coloured by region of origin Figure b. Three 6 generation resulting in ~3% residual heterogeneity in the final cultivar. Unique SNPs to each accession were mostly randomly distributed throughout the genome and are likely to reside in mutated regions specific to each line.The collection of lines screened on the Wheat Breeders’ Array included 40 accessions with known deletions of various types; monosomic (missing one chromosome of a pair), nullisomic (both chromosomes of a pair are deleted but substituted with those from a homoeologous genome), ditelosomic (missing part of an end of a chromosome) and gamma‐irradiated deletions . In the case of the gamma‐irradiated lines, we were able to identify between 299 and 796 polymorphic markers compared to the Paragon control representing between 1.86% and 1.95% of the markers scored. For the five lineages examined, the majority of variable markers mapped to specific regions of which most were associated with the known deleted regions. For the Cadenza EMS mutagenized lines, for the 15 lines examined, there were between 424 and 709 polymorphic markers compared to the Cadenza control representing between 2.64% and 4.41% of the markers scored. A number of these polymorphic markers appeared to be common to all or most of the mutagenized lines, suggesting that some polymorphisms might have existed in the original stock used for mutagenesis. The breeding schedule for cv. Cadenza shows that cultivar is based on a single individual from the FWe used copy number variation (CNV) analysis to characterize the various deletion lines; using this procedure, we were able to highlight SNP loci associated with each of the deleted regions where a reduced hybridization signal was observed Figure . Interes5 population containing heterozygotes in addition to the more traditional DH populations. This may be a more cost‐effective mapping population development strategy than DHs for some purposes.We designed the Wheat Breeders’ Array to be a high‐throughput platform for the cost‐efficient generation of genetic maps between a range of parental lines and for genotyping hexaploid wheat derived from a variety of sources. To confirm the utility of the platform, we screened several mapping populations, generated from a range of hexaploid lines via different crossing strategies, to produce five high‐density genetic maps. The relatively high level of codominant SNP assays on the array enabled a map to be produced from an FA. tauschii compared to that from tetraploids into the A and B genomes  or genes of large effect. In contrast, the Apogee × Paragon population had numerous regions of distorted segregation, typically consisting of relatively small numbers of SNPs with a high level of significance, with no significant bias towards either parent. The difference in pattern of distorted loci may partially reflect how each population was produced. The DH populations were in effect ‘fixed’ at the FFST was observed between populations of different geographical origin, suggesting that there has been an overlap of germplasm used within these breeding programmes. The principal co‐ordinate plots reflect low FST measures with overlaps in particular between (i) Western Europe, Northern Europe and Gediflux accessions; (ii) Australian and Central American accessions; (iii) Northern American, Southern American, Southern Europe, Middle Eastern and Southern African accessions. Cluster 1 is unsurprising given the overlap between the geographical origins of these collections. Cluster 2 reflects the significant impact the CIMMYT developed lines have had on Australian breeding programmes since 1965 . By first using a collection of lines containing known deletions of different sizes and locations, we were able to characterize a range of deletions in terms of both their size and nature, that is monosomic or nullisomic. Examination of a number of lines of the variety Paragon, which had undergone gamma irradiation, allowed us to identify previous uncharacterized deletions and in addition show that a number of these irradiated lines also potentially carry duplicated regions. Finally, we used the fact that the SNP markers on the Wheat Breeders’ Array were specific for hexaploid wheat, to screen a range of hexaploid lines for the evidence of either introgressions, such as the 1RS introgression from rye, or deletions. This screen generated evidence that numerous lines probably carry deletions and introgressions, and hence, our analysis suggests that further work is needed to characterize the extent of copy number variation within the hexaploid gene pool.The hexaploid nature of the bread wheat genome means that it is amenable for both crossing with a range of wheat relatives and large‐scale mutagenesis such as gamma irradiation. As both of these procedures can increase the diversity of the hexaploid gene pool, they are becoming more widely employed by breeders and academics alike. Hence, we investigated the ability of the Wheat Breeders’ Array to characterize such material via the use of the CNV tool developed by Affymetrix  and genomic selection. To further increase the utility of the array, we have screened five mapping populations and constructed a consensus genetic map to allocate a position to over 63% of the markers on the array. Further analysis has indicated that the markers on the array may be successfully used to identify regions of CNV and distorted segregation in the wheat genome, which in turn point towards chromosomal rearrangements and the presence of introgressions. To facilitate the use of the array by the global wheat community, the markers, the associated sequence and the genotype information have been made available through the interactive web site ‘CerealsDB’  for inclusion on the Wheat Breeders’ Array, each SNP was assigned to an IWGSC scaffold via BLAST  were grown in peat‐based soil in pots and maintained in a glasshouse at 15–25 °C with 16‐h light, 8‐h dark. Leaf tissue was harvested from 6‐week‐old plants, immediately frozen on liquid nitrogen and then stored at −20 °C prior to nucleic acid extraction. Genomic DNA was prepared from leaf tissue using a phenol–chloroform extraction method . Genomic DNA samples were treated with RNase‐A , according to the manufacturer's instructions and purified using the QiaQuick PCR purification kit .® Wheat Breeders’ Array was used to genotype 2713 samples (Table S4) using the Affymetrix GeneTitan® system according to the procedure described by Affymetrix (Axiom® 2.0 Assay for 384 samples P/N 703154 Rev. 2). Allele calling was carried out using the Affymetrix proprietary software package Axiom Analysis Suite, following the Axiom® Best Practices Genotyping Workflow .The Axiom® Wheat Breeders’ Array  method, cmdscale.et al., I is the number of markers, pij is the frequency of ith marker in a group of cultivars j, and Pi is the frequency of ith marker in the total dataset.Summary statistics were calculated using StAMPP v1.0 .The authors declare no conflict of interests.Table S1 SNP markers selected for the Wheat Breeders’ Array and associated informationClick here for additional data file.Table S2 Distribution of SNP loci exhibiting significant distortion of segregationClick here for additional data file.Table S3 Genetic maps and consensus map dataClick here for additional data file.Table S4 Accessions assayed using the Wheat Breeders’ Array and genotype data Click here for additional data file.Table S5 Summary of copy number analysis for accessions screened using the Wheat Breeders’ ArrayClick here for additional data file.


Drought tolerance is a complex trait that involves numerous genes. Identifying key causal genes or linked molecular markers can facilitate the fast development of drought tolerant varieties. Using a whole-genome resequencing approach, we sequenced 132 chickpea varieties and advanced breeding lines and found more than 144,000 single nucleotide polymorphisms (SNPs). We measured 13 yield and yield-related traits in three drought-prone environments of Western Australia. The genotypic effects were significant for all traits, and many traits showed highly significant correlations, ranging from 0.83 between grain yield and biomass to -0.67 between seed weight and seed emergence rate. To identify candidate genes, the SNP and trait data were incorporated into the SUPER genome-wide association study (GWAS) model, a modified version of the linear mixed model. We found that several SNPs from auxin-related genes, including auxin efflux carrier protein (PIN3), p-glycoprotein, and nodulin MtN21/EamA-like transporter, were significantly associated with yield and yield-related traits under drought-prone environments. We identified four genetic regions containing SNPs significantly associated with several different traits, which was an indication of pleiotropic effects. We also investigated the possibility of incorporating the GWAS results into a genomic selection (GS) model, which is another approach to deal with complex traits. Compared to using all SNPs, application of the GS model using subsets of SNPs significantly associated with the traits under investigation increased the prediction accuracies of three yield and yield-related traits by more than twofold. This has important implication for implementing GS in plant breeding programs. Cicer arietinum L.) is ranked second after soybean in terms of global legume production, reaching ∼13 million tons in 2014 (FAOSTAT 2017). Consisting of 25% of the total exports worldwide, Australia was the second-largest producer and the largest exporter of chickpea in 2014 (FAOSTAT 2017). Chickpea is an important component of the farming system in Australia, serving as a disease break crop and nitrogen fixer  and faba bean (Vicia faba L.). Thanks to the advance of next-generation sequencing (NGS) technology and a relatively small genome size, chickpea has well-developed genomic resources. Chickpea reference genomes for kabuli, desi, and wild Cicer species are available and there are ongoing efforts to improve the assemblies and annotation of the genomes  and sparse simple sequence repeat (SSR) markers, identified 15 and 93 QTL associated with different drought-related traits . HoweverA matrix) with the genomic relationship matrix (G matrix), which is obtained from genome-wide markers  was estimated using the following formula:The three sites were first analyzed separately for each trait by fitting a linear mixed model (LMM), which included spatial effects (row and column effects). The resulting best linear unbiased estimator (BLUE) values for each genotype were used to fit a multiple-environment LMM in which environments were treated as random effects. Statistical significance of fixed and random effects were assessed using Wald’s test  and the t and r are the numbers of environments and replications within an environment, respectively. All phenotypic analysis was done using GenStat, 16th edition.where PRJNA375953. Paired-end reads for each genotype were trimmed, filtered, and mapped to the kabuli reference genome 2.6.31 using SOAP2. Homozygous SNPs were called using the SGSautoSNP pipeline (DNA of the 132 genotypes was extracted from young leaf tissues using the Qiagen DNeasy Plant Mini Kit following the manufacturer’s instruction. Paired-end sequencing libraries were constructed using the TruSeq library kit for each genotype with an insert size of 500 bp. The procedure was implemented according to the Illumina manufacturer’s instruction. Paired-end short reads (150 bp) were generated using the Illumina HiSeq 2000 platform. Sequence data is available from the NCBI Short Read Archive under BioProject accession pipeline .r2 using SNPs with high confidence (minimum five reads per genotype). An r2 = 0.2 was used as a threshold to determine LD extent. The method to estimate the LD-decay curve under the mutation-drift-equilibrium model was described in detail in To correct for confounding effects in the association studies, population structure was estimated based on 144,777 SNPs (MAF > 0.05) using ADMIXTURE (v1.23) software . Similarp-value cut-off was set as p = 3.45e-07, equivalent to the α level of 0.05 after Bonferroni correction. The two genes flanking the significant SNP are reported.Genome-wide association analysis was done using BLUE values of the 132 genotypes with 12 traits and 144,777 SNPs (MAF > 0.05). Adjusting the confounding effects of population structure and kinship, the SUPER GWAS method, implemented in the GAPIT software, was used to estimate each SNP effect . This meGenomic predictions were performed using three different models: RR-BLUP, Bayesian least absolute shrinkage and selection operator (Bayesian LASSO or BL), and Bayesian ridge regression (BRR). The RR-BLUP model is written as:y is the adjusted entry means of phenotypes, μ is the overall mean, 1n denotes n × 1 vector of 1s, Z is an incident matrix for random genotype effect, and g is genotype effect with normal distribution N . Heritabilities (h2) of the 12 traits ranged from 0.11 for GY to 0.91 for 100SW. Many traits showed highly significant correlations, ranging from 0.83 between GY and DW to -0.67 between 100SW and EM (Table 2). GY was positively correlated with 100SW, SN, DW, FT, PT, EM, EV and negatively correlated with EPR, MA, and PH. 100SW was positively correlated with FT, DW, MA and negatively correlated with SN, PT, and EM. GY has a highly positive correlation with DW (r = 0.83), which is not surprising, given that a strong and healthy plant with sufficient biomass is advantageous under drought to retain yield . The number of SNPs on each chromosome ranged from 25,323 on Ca4 to 4,740 on Ca8, partially reflecting the length of the chromosomes in the Kabuli 2.6.3 reference assembly. The extent of LD on each chromosome ranged from 4,000 kb on Ca3 to 150 kb on Ca6 with an average of 700 kb . The most likely number of groups (K) in the 132 genotypes was estimated to be two using a cross-validation method from the ADMIXTURE software. The red group in Figure 1 is mainly the DICC lines (selected from ICRISAT breeding lines) consisting of progenies from the crosses of ICCV98503 × Moti, ICCV96836 × PBG5, ICCV96836 × ICC12004, and ICCV96836 × ICC3996. The green group in Figure 1 consists of Australian-released varieties and advanced lines. Genotypes with a mixture of red and green have mixed ancestry from ICRISAT, ICARDA, and Australia.A total of 144,777 homozygous SNPs were discovered in 132 genotypes (p < 3.45e-07) associated with six traits: GY, 100SW, EPR, PT, EM, and EV . One SNP, located in Ca3: 18,924,965, was significantly associated with GY . The closest gene near this SNP encodes a protein belonging to the ABC transporter B family/p-glycoprotein (PGP). Nine SNPs, located on Ca3, Ca4, Ca5, and Ca6, were significantly associated with 100SW . Candidate genes located near these SNPs include two sugar transporters, two nodulin MtN21/EamA-like transporters, one Lateral Organ Boundaries (LOB) domain protein and several uncharacterized genes.In total, 38 SNPs were significantly . One was located on Ca4: 35,589,599 near a gene encoding a major latex protein (MLP), which promotes vegetative growth and delays flowering in Arabidopsis . One of the significant SNPs was located on Ca5: 11,580,061, near the nodulin MtN21/EamA-like transporter which has been shown to be involved in auxin homoeostasis . One of the significant SNPs  was located near a gene encoding MLP, which promotes vegetative growth in Arabidopsis as described above . One of the significant SNPs  was located near a gene encoding the transcriptional factor SPL9. It has been shown that SPL9 regulates leaf initiation negatively in Arabidopsis, leading to a shorter leaf plastochron, which is the time interval between two successive events of plant growth (p < 3.45e-07) associated with SN, HI, DW, FT, or PH. This could be attributed to the lack of statistical power due to the small sample size (62–93 genotypes) employed in this study.There were 12 SNPs significantly associated with MA, including one SNP on Ca2, three SNPs on Ca4, six SNPs on Ca5, and two SNPs on Ca6 . For example, a genomic region of ∼274 kb on Ca3  contains a SNP significantly associated with GY and two SNPs weakly  associated with SN. A ∼437 kb genomic region on Ca4  contains eight SNPs, significantly associated with four traits: EM, maturity, PT, and 100SW. Four auxin-related genes, encoding one auxin efflux carrier protein (PIN3) and three nodulin MtN21/EamA-like transporters, are located in this region on Ca4. Another ∼587 kb genomic region on Ca5  contains two SNPs, which were significantly associated with 100SW and MA. Five auxin-related genes, including one auxin influx transporter (LAX3) and four nodulin MtN21/EamA-like transporters, are located in this region. Two SNPs, significantly associated with both 100SW and maturity, were located in a 197 kb region on Ca6  which contains two sugar transporters.Some regions contain SNPs significantly associated with several different traits, which is an indication of a pleiotropic effect . The increments plateaued in all four traits using subsets of SNPs with p-values between 0.05 and 0.01 and dropped dramatically at p-values of 3.45e-07 . The lowest prediction accuracies in three traits occurred when using all SNPs, which was probably due to noise introduced by non-causal variants as RR-BLUP shrikes each marker effect equally , ROS metabolism, and metabolic homeostasis in Arabidopsis  is a well-known phytohormone that plays a pivotal role in plant growth, seed development, and abiotic stress response . A recenbidopsis . PIN3, bbidopsis , 2013. Abidopsis . Severalbidopsis . Arabidobidopsis . Severalbidopsis .Using a bi-parental QTL mapping population, a “QTL-hotspot” region on Ca4 13,239,546 to 13,547,009 (based on the kabuli reference genome v1.0) was associated with at least seven traits including root traits, 100SW, PH, and days to flowering . In thisFigure 2). The kinesin-4 and self-incompatibility (SI) proteins were adjacent to two of the three significant SNPs. The kinesin-4 family plays an important role in cell elongation and has been shown to affect the length of siliques and seeds produced per silique in Arabidopsis  associated with traits. Several computer simulation studies  such as disease resistance . This stYL and TS conceived the study; YL performed the GWAS and GS analysis; JB contributed to sequencing; PR analyzed the sequencing data; DE supervised sequencing-data analysis; KS, JP, TK, and TC contributed to phenotyping; YL wrote the manuscript and all the authors read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Coding/functional SNPs change the biological function of a gene and, therefore, could serve as “large-effect” genetic markers. In this study, we used two bioinformatics pipelines, GATK and SAMtools, for discovering coding/functional SNPs with allelic-imbalances associated with total body weight, muscle yield, muscle fat content, shear force, and whiteness. Phenotypic data were collected for approximately 500 fish, representing 98 families (5 fish/family), from a growth-selected line, and the muscle transcriptome was sequenced from 22 families with divergent phenotypes (4 low- versus 4 high-ranked families per trait).GATK detected 59,112 putative SNPs; of these SNPs, 4798 showed allelic imbalances (>2.0 as an amplification and <0.5 as loss of heterozygosity). SAMtools detected 87,066 putative SNPs; and of them, 4962 had allelic imbalances between the low- and high-ranked families. Only 1829 SNPs with allelic imbalances were common between the two datasets, indicating significant differences in algorithms. The two datasets contained 7930 non-redundant SNPs of which 4439 mapped to 1498 protein-coding genes (with 6.4% non-synonymous SNPs) and 684 mapped to 295 lncRNAs. Validation of a subset of 92 SNPs revealed 1) 86.7–93.8% success rate in calling polymorphic SNPs and 2) 95.4% consistent matching between DNA and cDNA genotypes indicating a high rate of identifying SNPs with allelic imbalances. In addition, 4.64% SNPs revealed random monoallelic expression. Genome distribution of the SNPs with allelic imbalances exhibited high density for all five traits in several chromosomes, especially chromosome 9, 20 and 28. Most of the SNP-harboring genes were assigned to important growth-related metabolic pathways.These results demonstrate utility of RNA-Seq in assessing phenotype-associated allelic imbalances in pooled RNA-Seq samples. The SNPs identified in this study were included in a new SNP-Chip design (available from Affymetrix) for genomic and genetic analyses in rainbow trout.The online version of this article (doi:10.1186/s12864-017-3992-z) contains supplementary material, which is available to authorized users. Fish growth rate, muscle yield and fillet quality are major traits affecting profitability of aquatic food animal production. As feed cost is a major factor influencing the profitability, efficiency of growth is important and related to growth rate and muscle yield and composition. Skeletal muscle constitutes about 50–60% of the fish weight . Given tGenomic selection tools have been created to improve economically important traits in plants and livestock. Genetic maps, which characterize the linkage or co-inheritance patterns of genetic markers, have been developed for a wide range of species, including fish, with the aim of discovering allelic variation affecting traits; and ultimately identify DNA sequences underlying phenotypes , 4. MarkRainbow trout is the most cultivated cool and cold freshwater fish in the U.S. , and it R2 = 0.56 and 0.50 respectively, data not shown). Our previous reports showed that fast growth may be genetically associated with improved muscle yield, paler fillets (affected by intramuscular fat content) and firmer texture , data not shown). However, despite the similarity in SNP density distributions, most of the identified SNPs were unique to each trait. From the 7930 SNPs with allelic imbalances, only 27 were shared by all five traits, 161 were shared by four traits, 680 were shared by three traits and 1783 were shared by two traits. In agreement with our results, a recent GWAS study identified two windows with effect on fillet yield located on chromosome 9 and explaining 1.0–1.5% of genetic variance in the same fish population 1/2 [81].Fresh fillet surface color was measured with a Chroma meter  calibrated using a standard white plate No. 21333180 . L* (lightness), a* (redness), and b* (yellowness) values were recorded at three locations above the lateral line along the long axis of the right fillet, and these values were used to calculate a fillet whiteness index according to the following equation: Whiteness = 100 – [(100 – L)For RNA-Seq analyses, out of 98 families measured for phenotypic data, eight families (5 fish each) showing opposite phenotypes for each of the 5 traits were analyzed (4 high ranked families versus 4 low ranked families on average for each trait). Since some fish families were common between the traits, the total number of selected families for RNA-Seq was 22 families. Total RNA was isolated from each fish muscle sample using TRIzol™ . Equal masses of total RNA from 5 samples of each family were pooled and used for RNA-Seq sequencing. cDNA libraries were prepared and sequenced on an Illumina HiSeq  using multiplexing standard protocols as previously described . Brieflyp-value <0.05 to determine if SNPs that are showing allelic imbalances are statistically significant.For each trait , sequence reads from each family were aligned to the rainbow trout genome using STAR . After rFor the GATK pipeline , reads fP < 0.01) in the two mapping families. Those SNPs were reported as assay-failed SNPs.Flanking sequences (up to 250 bp on each side) of putative SNPs were extracted from the reference genome . Some SNFor the Sanger sequencing validation of the SNPs showing potential mon-allelic gene expression, flanking sequences (up to 250 bp on each side) of each SNP were PCR amplified from DNAs and cDNA from the same 35 fish samples that were used for RNA-Seq high versus low muscle analyses. PCR amplicons were Sanger sequenced and manually inspected for consistency between DNA and cDNA genotypes or mono-allele specific gene expression as explained in the results section.SNP annotation by functional class (genic/intergenic etc.) for different SNP sets and their genome distributions were conducted using in-house Perl scripts. The gff file of the rainbow trout genome reference  was used