Pisum sativum L.) is an important temperate legume crop for food, feed and fodder, and many breeding programs develop cultivars adapted to these end-uses. In order to assist pea development efforts, we assembled the USDA Pea Single Plant Plus Collection (PSPPC), which contains 431 P. sativum accessions with morphological, geographic and taxonomic diversity. The collection was characterized genetically in order to maximize its value for trait mapping and genomics-assisted breeding. To that end, we used genotyping-by-sequencing—a cost-effective method for de novo single-nucleotide polymorphism (SNP) marker discovery—to generate 66 591 high-quality SNPs. These data facilitated the identification of accessions divergent from mainstream breeding germplasm that could serve as sources of novel, favorable alleles. In particular, a group of accessions from Central Asia appear nearly as diverse as a sister species, P. fulvum, and subspecies, P. sativum subsp. elatius. PSPPC genotypes can be paired with new and existing phenotype data for trait mapping; as proof-of-concept, we localized Mendel’s A gene controlling flower color to its known position. We also used SNP data to define a smaller core collection of 108 accessions with similar levels of genetic diversity as the entire PSPPC, resulting in a smaller germplasm set for research screening and evaluation under limited resources. Taken together, the results presented in this study along with the release of a publicly available SNP data set comprise a valuable resource for supporting worldwide pea genetic improvement efforts.Globally, pea ( Pisum sativum L.) is a globally important food, feed and cover crop in temperate environments. In 2014, green and dry peas had worldwide productions of 17.4 and 11.2 million tonnes, respectively, making pea the fourth largest legume crop after soybean, groundnut and common bean.1 The nutritive benefits associated with pea have prompted the USDA to specify ‘beans and peas’ as one of five distinct vegetable subgroups recommended for regular consumption (http://www.choosemyplate.gov/), a decision supported by dietary studies showing that consumption of these legumes is correlated with higher intakes of fiber, protein and an array of vitamins and minerals.3 Comprising ~25% protein, pea seed can be used as a protein source in many animal feeds.5 In addition, as a cool-season and non-transgenic substitute for soybean, pea has the potential for organic systems and in short-season areas where local feed sources are prioritized but where soybean production is limited.6–8 As a rotation or cover crop, in association with Rhizobium bacteria, pea can fix atmospheric nitrogen at levels sufficient to produce subsequent vegetable and cereal crops with reduced application of additional fertilizers.10Pea  collection.24 The under-representation of genetically distinct Chinese accessions12 within the PSP collection led us to modify and augment this collection to form the USDA PSP Plus Collection (PSPPC), first reported here. The PSPPC includes 344 accessions from the PSP collection,23–25 accessions from the Chinese core collection and field, snap and snow peas from US public pea-breeding programs. Taxonomically, the PSPPC contains accessions from the primary cultivated subspecies, Pisum sativum subsp. sativum, as well as from each of the two currently accepted wild subspecies, P. sativum subsp. elatius and P. sativum subsp. abyssinicum.26 These wild subspecies can be distinguished from the cultivated subspecies by a set of morphological characteristics, for example, early flowering and strongly serrated leaflets in P. sativum subsp. abyssinicum and deshiscent pods in P. sativum subsp. elatius, as well as a reciprocal translocation that is characteristic of P. sativum subsp. abyssinicum accessions and many but not all of P. sativum subsp. elatius accessions.26 Geographically, PSPPC accessions are diverse, with robust representation from the center of domestication, that is, the Near East and Mediterranean,26 and other centers of diversity, including Central Asia and Ethiopia.27In order to maintain novel alleles in non-elite germplasm, many pea germplasm collections have been assembled. Sixteen collections housed in Europe, Asia and North America contain over 1000 accessions.29 In pea, reduced-representation library sequencing was recently used to construct a genetic linkage map that included 64 263 SNP markers for a historically important ‘Baccara’ x PI 180693 RIL population.30 Here, we generated 66 591 high-quality SNPs for the 431 samples of the PSPPC. To demonstrate the utility of our SNP marker data set for varying end-use applications, we identified accessions genetically distant from cultivated germplasm as potential new sources of diversity for breeding programs. We also mapped a previously cloned gene that regulates flower color in close proximity to its known position, showing that our high-density marker data set represents a resource that can be rapidly used to allow breeders to connect genotypes to phenotypes at a higher resolution. Finally, we constructed a high utility, smaller core collection of 108 accessions that captures 97% of the SNP allelic diversity found in the PSPPC.The objective of this research was to use genotyping-by-sequencing (GBS), a reduced-representation library sequencing approach, to generate a publicly available, high-density marker data set for the PSPPC to maximize its value for trait mapping and genomics-assisted breeding. Reduced-representation library sequencing has been used in a number of crop plants to discover and simultaneously score numerous single-nucleotide polymorphism (SNP) markers across the entire genome.P. sativum accessions are included in the PSPPC, with descriptor information provided in www.gpsvisualizer.com) was used to assign position coordinates using Google Maps Geocoding API. The snap and snow pea accessions are from Oregon State University and the field pea accessions are from the USDA Agricultural Research Service Grain Legume Genetics and Physiology Research Unit at Washington State University. The ‘rworldmap’ package in R was used to plot accessions that were collected .The PSPPC accessions were sequenced using GBS. Leaf tissue was harvested from one individual seedling of each accession grown in a greenhouse, and total genomic DNA was extracted in a plate format using the DNeasy 96 Plant Kit . GBS libraries of pooled samples were prepared by the Genomic Diversity Facility at Cornell University as previously described.35 Non-reference pipelines were used because of a preliminary analysis that found that reference-based SNP-calling with alignment to the closest sequenced Pisum relative, Medicago truncatula, yielded fewer than half of the number of SNPs as the non-reference pipelines. This is presumably due to significant divergence between Pisum and Medicago since their split ~25 million years ago.36 To call SNPs, each of the pipelines (UNEAK and Stacks) were run twice: once on the PSPPC alone and once including P. fulvum accessions (data set hereafter referred to as PSPPC+P. fulvum). For the Stacks pipeline, reads with intact barcodes from fastq files were demultiplexed, stripped of barcodes and truncated to 80 base pairs (bp) with the process_radtags function (-t 80 -e apeKI -i fastq). SNPs were called using the denovo_map.pl function using the following described parameters (-m 4 -M 1 -N 3 -n 1 -t -X ustacks:--max_locus_stacks 2). At least four identical reads (m) from each individual were grouped into ‘stacks’. Highly repetitive reads were removed (t). Loci for each individual were assembled by allowing one mismatch (M) between a maximum of two stacks (-X ustacks:--max_locus_stacks). Secondary reads containing up to three mismatches (N) were added to primary loci and a consensus sequence with the identified SNP was called. A catalog of loci from all individuals was created with one mismatch (n) allowed between loci, and SNPs were called by matching individual loci against the catalog loci. For the UNEAK pipeline, reads from fastq files with intact barcodes and no ‘N’s in the first 64 bp were demultiplexed, stripped of barcodes and truncated to 64 bp using the UFastqToTagCountPlugin function (-e ApeKI). A ‘tag’ was defined as the consensus sequence of identical reads from a single individual. Using the UMergeTaxaTagCountPlugin function, only tags present in at least five accessions (-c 5) were retained in the analysis. With the UTagCountToTagPairPlugin function and an error tolerance rate (-e 0.03) of 0.03, a network filter was used to identify reciprocal tag pairs that comprised putative loci. Sequence reads from accessions that were sampled as biological replicates were combined and processed as a single accession.SNPs were identified from 100 base-pair sequence reads using the TASSEL 3.0 Universal Network Enabled Analysis Kit (UNEAK) and Stacks v1.19, two SNP-calling pipelines that do not require a reference genome for read alignment.38 A final data set for analysis was assembled using the union of SNPs from the UNEAK and Stacks pipelines. Individual genotypes at shared SNPs were those called by UNEAK.Custom Perl scripts were used to call marker genotypes and to filter loci. For each accession, marker genotypes at a locus were considered ‘homozygous’ if fewer than 5% of the total sequence reads for that locus were the less-sequenced ‘alternate’ allele, ‘missing’ if 5–10% of the total reads were the alternate allele and ‘heterozygous’ if 10% or more of the total reads were the alternate allele. In addition, SNP markers were excluded from the data set when they met at least one of the following conditions: their minor allele frequency was lower than 0.01, their accession call rate  was lower than 0.2 or their heterozygosity rate was greater than 0.25. This latter threshold on heterozygosity was chosen because it is above the level of heterozygosity expected for any locus in a mostly inbred collection, but sufficient to filter out paralogous SNP loci. In Stacks, for sequences with more than one SNP, only the first SNP in the sequence passing all filtering criteria was retained. The consensus sequences of retained SNP markers from Stacks was aligned to the consensus sequences of retained SNP markers from UNEAK using the BLASTN algorithm in the BLAST 2.2.28 stand-alone package with an E-value cutoff of 0.01.P. fulvum accessions were divided into groups based on specific and subspecific taxonomic classification  or in the case of the main cultivated subspecies, P. sativum subsp. sativum, from two previous studies that defined population structure for an overlapping subset of accessions.25 In these previous studies, two subpopulation groups for P. sativum subsp. sativum were defined by the program STRUCTURE. We assigned PSPPC accessions to either the primary cultivated group, which we termed ‘P. sativum subsp. sativum—Primary’ or the smaller group with phenotypic attributes resembling that of undomesticated accessions and from Central Asia, which we termed ‘P. sativum—Central Asia’. For each accession, group membership was assigned if STRUCTURE values were equal to or greater than 0.85 for the same group in both studies25  for randomly chosen individuals until the threshold 7.59 was reached, which was the total score of the group with the least amount of data, P. fulvum. The number of unique SNPs was calculated on the downsampled groups. This procedure was repeated 100 times and the number of unique SNPs in each group was obtained by averaging the number of unique SNPs over the 100 iterations. Genetic diversity of collected and developed accessions was visualized using principal component analysis (PCA). The ppca function from the pcaMethods package in R39 was used to calculate 10 principal components for both the PSPPC and the PSPPC+P. fulvum data sets  where 66 591 is the number of statistical tests conducted . Therefore, statistical significance of a SNP–trait association was set at 7.5e−7.To demonstrate the utility of GBS-derived SNPs for dissecting the genetic basis of phenotypic variation in  ‘white’ . For PSPM. truncatula and P. sativum in the region of the A locus,40 pea sequence reads containing SNPs statistically significant at a Bonferroni correction of 5% were aligned via BLASTN to the J. Craig Venter Institute M. truncatula genome 4.0 using an E-value cutoff of 1e−5 and blastn-short default parameters.46 To evaluate the proximity of these SNPs to the A locus, the 11 892 A locus nucleotide sequence (complete coding sequence) from the pea accession PI 269818 (GU132941.1) was also aligned to M. truncatula via BLASTN using the same parameters.Given the genomic collinearity between 47 The software CoreHunter 2.0 was used to determine a minimum set of individuals from the PSPPC from among those available in GRIN that retained at least 95% of the alleles present in the full PSPPC data set.48 To this end, CoreHunter was run iteratively with the sample intensity parameter decreasing from 0.95 to 0.05 by 0.05 for each iteration with the following parameters remaining constant: runtime: 10 min and CV =1. For each output, minor allele frequency was determined using a custom python script. A principal component analysis was conducted on the resultant PSPPC mini-core using the same methods as described for the PSPPC and PSPPC+P. fulvum data sets.Accessions in the USDA pea core collections were selected based on geographic and morphological diversity in order to preserve underlying levels of genetic diversity. With high-density marker data, genetic diversity can be evaluated directly, and an optimal core identified based on a number of thresholds including total number of alleles or genetic distance between individuals.P. fulvum accessions were included, the same pipeline and filters called a total of 67 400 SNPs in the 456 accessions of the PSPPC+P. fulvum data set  from P. sativum accession PI 269818 to M. truncatula  aligned 1244 bp from the nearest blastn-anchored, P. sativum A sequence fragment (A genome-wide association study (GWAS) of flower color was conducted with 66 591 SNP markers in the GAPIT software package.hreshold . Of thessome one . Importauncatula . Ten of f 2e−137 . Of the fragment .Using only the accessions from the PSPPC that are publicly available in GRIN, a PSPPC mini-core of 108 individuals was constructed that sampled 97.4% of the 133 182 alleles in the PSPPC. In addition, 97.0% of all 66 591 markers have minor allele frequencies equal to or greater than 0.01, the original threshold for the PSPPC SNP data set. The PCA structure of the PSPPC core closely resembles the original PSPPC .P. sativum accessions of the PSPPC, representing one of the largest marker data sets in pea to date. Without the current availability of a P. sativum reference genome sequence, we used two non-reference-genome-enabled SNP-calling pipelines, UNEAK and Stacks. Pipelines with differing methodologies for SNP calling can yield distinct sets of SNPs, to the extent that in some cases, less than 50% of SNPs are shared.49 The advantages of each of multiple pipelines can be leveraged to identify a larger number of SNPs for downstream analyses. For instance, UNEAK is better suited to call genotypes from low-coverage loci, whereas Stacks is better suited to call genotypes from loci characterized by more than one SNP, that is, haplotypes.A GBS procedure was used to score 66 591 SNP markers across 431 diverse 50 cluster distinctly from breeding accessions and most other P. sativum accessions , while accessions in the P. sativum subsp. sativum—Primary group were from 37 countries spanning six continents (62 Wild (sub)species may contain similar alleles with utility for breeding programs,64 although crossing barriers such as chromosomal rearrangements between wild species and cultivated material can inhibit the transfer of these alleles.65 On the contrary, no crossing barriers are known to exist between the Afghanistan types and other cultivated P. sativum, making this group a valuable source of alleles for improvement of breeding germplasm.66For randomly chosen subsets of taxonomic and genetic groups standardized to account for missing data, the Central Asia group contained more SNPs absent from the breeding germplasm than other ntinents . Alleles25 The PSPPC data set is available as a ‘GWAS-ready’ public resource. Derived primarily from the PSP collection, the PSPPC is highly inbred. By using inbred accessions for phenotyping, researchers can remove within-accession genetic variance common in genetically heterogeneous USDA accessions that are maintained in the way that they are received. Given the high level of linkage disequilibrium in pea,67 a marker data set consisting of tens of thousands of SNPs should be sufficient in most association studies to tag important major genes, given amenable minor allele frequencies and sufficient population sizes. As proof-of-concept, we genetically pinpointed the previously identified A gene with SNP markers generated in this study and flower color phenotypes available from GRIN. All of the most significant P. sativum SNPs aligned to the same M. truncatula genomic interval that contained the A gene homolog. In addition, one of the significant SNPs from our GWAS, TP100211, was located less than 1.5 kb from the A locus.Phenotype data for the USDA pea collections have enabled breeders to identify useful germplasm for breeding programs, but the dense molecular marker data needed to identify robust marker–trait associations have been lacking. Previous genetic mapping efforts for important physiological and agronomic traits, such as seed mineral concentration, nematode resistance, days to flowering and biomass production, have identified some marker–trait associations, but low marker densities have prevented the detection of tight linkage between markers and candidate genes.Fusarium wilt, ascochyta blight and pea rust, in addition to stringlessness, snap pods and cold tolerance.68–74 With the appropriate phenotype data, PSPPC SNPs can be used to map these and other important traits. In addition, as P. sativum genome sequences become available, the raw GBS sequences can be used to call additional SNPs with reference genome-based pipelines and thereby help improve statistical power for mapping relatively smaller effect genes controlling polygenic traits.75Numerous other Mendelian genes and major-effect quantitative trait loci control agronomic traits of importance for pea-breeding programs, but are yet to be fine-mapped and cloned. These include genes for resistance to powdery mildew, The PSPPC SNP data set facilitated the formation of a mini-core collection of 108 accessions that retained nearly all of the diversity of the larger PSPPC . The PSPA high-density SNP data set is now available for the PSPPC, a public resource with high utility for pea improvement. Genotype information will complement phenotype data already available to allow pea curators, breeders and geneticists to explore and utilize genetic diversity in pea.P. fulvum SNP data sets, hapmap and vcf files as well as corresponding FASTA sequences are available on the USDA Ag Data Commons—DOI: 10.15482/USDA.ADC/1347137 , the Cool Season Food Legume database (https://www.coolseasonfoodlegume.org/PubDatasets) and on GRIN-GLOBAL . SNP names that begin with a ‘TP’ are derived from the TASSEL SNP-calling pipeline, while SNP names that include ‘_’ are derived from the Stacks SNP-calling pipeline. SNPs for each of the PSPPC and PSPPC+P. fulvum groups were called independently; therefore, any SNP name that is shared between these groups should NOT be assumed to refer to the same locus. All raw sequencing data are available through the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) with BioProject number: PRJNA379298 and BioSample numbers: SAMN06604244-SAMN06604699 (http://www.ncbi.nlm.nih.gov/bioproject/379298) listed in 76For the PSPPC and PSPPC+


Genome sequences for hundreds of mammalian species are available, but an understanding of their genomic regulatory regions, which control gene expression, is only beginning. A comprehensive prediction of potential active regulatory regions is necessary to functionally study the roles of the majority of genomic variants in evolution, domestication, and animal production. We developed a computational method to predict regulatory DNA sequences  in production animals (cows and pigs) and extended its broad applicability to other mammals. The method utilizes human regulatory features identified from thousands of tissues, cell lines, and experimental assays to find homologous regions that are conserved in sequences and genome organization and are enriched for regulatory elements in the genome sequences of other mammalian species. Importantly, we developed a filtering strategy, including a machine learning classification method, to utilize a very small number of species-specific experimental datasets available to select for the likely active regulatory regions. The method finds the optimal combination of sensitivity and accuracy to unbiasedly predict regulatory regions in mammalian species. Furthermore, we demonstrated the utility of the predicted regulatory datasets in cattle for prioritizing variants associated with multiple production and climate change adaptation traits and identifying potential genome editing targets. Predicting functional features of the genome beyond protein-coding regions has been the primary focus of the post-genome sequencing era , 2. MoreHowever, in mammalian species other than the human and mouse, there are few data available at the genome level for discovery of regulatory elements. The recently established Functional Annotation of ANimal Genomes (FAANG) consortium has begun to address this deficiency in a coordinated fashion , 13. It C. elegans to D. melanogaster to humans, the architecture of gene regulatory networks, organization of chromatin topological domains, chromatin context at enhancer and promoter regions, and nucleosome positioning are remarkably conserved . Si. SiP-val traits)  39]. Th. ThP-valAs examples of the application of our resources to identify likely causative mutations from a large list of significantly associated SNPs, we applied the HPRS approach to analyse 2 well-studied genetic variants in cattle that were known to contribute to phenotypic variation, but their mechanisms of action were not known because they were located within noncoding regions.PLAG1) locus has been identified in the control of stature (weight and height) by several independent GWAS studies in cattle [PLAG1 and the Coiled-coil-helix-coiled-coil-helix domain containing 7 (CHCHD7) gene, which are 540 bp apart  can be used for functional assays of SNP genotype. Measuring promoter activity of these 3 constructs by using the similar promoter luciferase assay and transcription factor binding assay employed by Karim et al. [The bovine Pleomorphic adenoma gene 1 . The SNP rs209821678 had a deltaSVM score of –5.99. The score was beyond the 95th percentile range of SVM scores for 97 million SNPs, suggesting that it may play an important regulatory role. Notably, the rs209821678 deletion of the (CCG)x11 to (CCG)x9 trinucleotide repeats lies in a predicted G-quadruplex and may cause changes in its structure, an event that could alter transcriptional activity . In contPLAG1 locus. We applied HPRS for mapping interactions defined by chromatin conformation capture data (5C and Hi-C in the ENCODE human datasets) to predict distal targets of the promoter regions in the PLAG1 locus [ 043 , with a predicted target region (chr14: 25 478 861–25 497 096) near the Inositol monophosphatase domain containing 1 (IMPAD1). Variants within IMPAD1 have been implicated in short stature and chondrodysplasia  replaces the 10 bp (chr1: 1 706 051–1 706 060)  [ 706 046–1 706 182, UMD3.1). A detailed transcription factor binding motif analysis of the polled mutation site suggests that a binding site for the Heart and neural crest derivatives expressed 1 (TF HAND1) is lost due to the 10-bp deletion in animals containing the Celtic mutation  and A_264 636  and interacts with genes hundreds of Kb away  , 47. We way Fig. . AlthougTherefore, from the 2 examples described above , we found that the HPRS regulatory database can be used to prioritize SNPs and genetic variants that were identified by GWAS studies and to draw hypotheses about biological mechanisms of a causative SNP.The main aim of the HPRS pipeline is to predict as many regulatory regions and as accurately as possible, so that the dataset could be applied for functional SNP analysis in the target species. However, given the uncertain nature of promoter and enhancer identification, the rate of false positives and negatives by HPRS is difficult to determine. In our analysis, all of the reference cattle liver enhancers were included in the initial unfiltered datasets, although ∼25% were lost during the filtering process. Similarly, 96% of reference cattle liver dataset promoters were covered by the unfiltered dataset, with less than 3% lost in the filtering process. A limitation of the HPRS filtering process is the requirement to use a species-specific dataset. Nevertheless, compared with the large number of datasets and biochemical assay types that are required to create comprehensive coverage of regulatory regions, the number of species-specific datasets needed for HPRS is small. In this paper, for each of the 3 species , we used data from only 3 biological replicates of the H3K27Ac assay, which was generated within a scope of 1 project, as reported in Villar et al. , to succIn contrast to the HPRS pipeline prediction of regulatory regions, the prediction of causative genetic variation within regulatory regions is much more challenging. The current approach relies on the enrichment of sequence motifs within regulatory regions relative to nonregulatory regions. At least some of the motifs are TFBS, but there are likely to be other types of motifs, such as G-quadraplexes, present in regulatory regions. While the predicted datasets can be useful for generating relevant hypotheses, the identification of causal variants still requires considerable future refinement and validation.We have developed the HPRS pipeline using a large collection of existing human genomics data and a limited number of cattle-specific datasets to predict a database of cattle regulatory regions that covers a large number of active promoters, enhancers, and TFBS. The database generated here is not a final product because HPRS is capable of readily integrating new cattle-specific datasets into its mapping and filtering pipeline to expand, refine, and validate the databases. Moreover, the HPRS pipeline can be applied to data of other mammalian species and by scientists without computer programming skills. We anticipate that the pipeline will be used to integrate large-scale datasets from the FAANG consortium, when they become available, with complementary data from human research. The immediate application of the regulatory database is to complement the current species-specific GWAS analysis by (1) discovery of potential regulatory mechanisms of SNPs lying outside gene coding regions, (2) prioritizing SNPs that are statistically significant at a genome-wide level but located within regulatory regions, (3) prioritizing SNPs that are at low allele frequency but have potential for large effects, and (4) suggesting possible causative SNPs as targets for precise genome editing or selective breeding practices.The complete HPRS pipeline is divided into 3 modules: mapping, filtering, and SNP analysis. The whole pipeline and documentation are available from the CSIRO BitBucket .We developed a mapping strategy based on 4 elements: (1) selecting a suitable combination of human databases as HPRS inputs; (2) finding an optimal sequence identity threshold in the target genome; (3) finding options to remove less confident mapped results; and (4) adding multiple mapped regions that meet a high sequence similarity threshold. Depending on the species, targeted tissues, or regulatory categories of interest, users can select suitable human databases using the following suggested criteria: types of regulatory regions , biochemical assays, computational models for combining data, and data sources . Second, by applying the UCSC liftOver tool , regionsIn addition to the customized minMatchMain and minMatchMulti parameter inputs, the Main_Mapping_Pipeline.py script also takes user-specified chain files for target species, which can be any of the mammalian species with chain files available from the UCSC databases or generated in-house. The HPRS mapping pipeline enables fast mapping of as many databases as necessary. The script PostHPRSMapping_MergeDifferentDatabaseTypes.py (available in the CSIRO BitBucket ) can be P and RatioE  for each filter and for a range of filter parameters so that the optimal parameters are used in the main filtering pipeline. The filtering pipeline was written in a such a way that it is simple to add or remove filter layers depending on availability of species-specific data.Detailed description of the 7 filters is presented in the Supplementary Materials and Methods section. Briefly, the HPRS filtering pipeline was written in R and contains 7 filtering steps Fig. , Table 4The HPRS dataset can be applied for the selection of top candidate SNPs in regulatory regions, which are present in existing genotyping SNP chips. The selected SNPs form a small set of SNPs that are more likely to be causal or associated to phenotypes. Using these SNPs for GWAS analysis may reduce noise compared with using a large number of SNPs that are noncausal but in high linkage disequilibrium to causal SNPs. The top candidate SNPs can be selected by the identification of SNPs belonging or not belonging to the following categories: the Universal Dataset, the Filtered Dataset, the TFBS of the predicted regulatory regions, and regulatory regions active in tissues related to the trait of interest. In addition, deltaSVM scores can be used as 1 of the indicators for potential SNP effects, as discussed in the Supplementary Methods section. Alternatively, the dataset can be used for post-GWAS analysis, in which significant SNPs in noncoding regions that are identified from GWAS can be assessed for potential effect on gene regulatory activity. We have discussed examples of applications for the cases of pleiotropic SNPs, climatic adaptation–associated SNPs, and associated SNPs’ milk-production traits , and of We developed an implementation pipeline of the gkm-SVM model to estimate SNP effects on enhancer activities in cattle by adapting the model to the case where very limited species-specific ChIP-Seq data are available for model training .GigaScience database, GigaDB [We have made all HPRS Python and R scripts publically available with usage instruction from the CSIRO BitBucket . These c, GigaDB .All human databases used for prediction are publically available . ResultsAdditional file 1: Figure S1: Assessing mapping results for different human tissues and cell line datasets onto other species. The counts and percentages are for mapped regions before the HPRS filtering pipeline. A, Possible cross-species mapping results, with 3 scenarios: (i) reciprocal mapping with a low identity threshold (minMatch = 0.20) but that requires exact back mapping to the human genome; (ii) multiple mapping allowing multiple targets, but requiring a stringent minMatch = 0.80; and (iii) features that do not fall into the 2 categories above, such as species-specific enhancers, are not included in the prediction. B, Coverage of the cattle Villar enhancer reference dataset by predicted and random feature datasets. Features were mapped from 42 human enhancer datasets or 42 random datasets  to the bovine genome and then compared for percent overlap with the cattle Villar reference liver enhancer dataset. For all 42 datasets, the regulatory datasets produced 5–10 times higher coverage of the reference bovine liver enhancers than the random datasets. C, Coverage of predictions by 12 common ENCODE human cell lines (x-axis). The numbers of recovered regions using minMatch = 0.2 and minMatch = 0.95 are shown. D, Percentage of additional features that had multiple mapped targets but met the high sequence similarity threshold for reciprocal mapping from the bovine genome to the human genome (minMatch = 0.80).P-values) ≥7 in any of the 10 separate phenotypes. GWAS P-values for each trait ≤10-7 are considered significant with multiple test correction . SNPs were selected for each of the 10 phenotypes separately and were pooled into 1 set, and density plots of SNP counts and corresponding –log for each phenotype are shown. The x-axis shows –log values from 0 to 20, and the y-axis shows density of the SNP counts according to the –logP distribution. B, Significant SNPS were selected based on combined criteria: –log >2 and abs(effect size) greater than or equal to the third quartile effect size value for each of the 10 phenotypes. The x-axis shows the name IDs of the 10 phenotypes.Additional file 2: Figure S2: Enrichment of GWAS-associated SNPs in HPRS filtered regulatory regions. Data are from a GWAS dataset for 2112 cattle measured for 10 different climatic change adaptation–related phenotypes . A, NumbAdditional file 3: Figure S3: Promoter prediction. A, We selected a random large region of the chromosome to evaluate promoter prediction. We observed consistent overlapping of predicted promoters with known transcription start sites (TSS). The higher and denser numbers of predicted promoters compared with annotated TSS suggest that the HPRS prediction potentially led to the identification of unannotated promoters, including alternative promoters within annotated transcripts and promoters of unannotated transcripts such as those for long noncoding RNAs. B, HPRS promoters also predict bidirectional promoters with high accuracy . C, HPRS-predicted alternative promoters are supported by cattle expression sequencing tag (EST) data. The predicted promoters overlap the start sites of EST transcripts within the full-length ZC3H14 gene.Additional file 4: Figure S4: Enrichment of TFBS within enhancers and promoters. The promoters and enhancers were mapped from the human FANTOM enhancer  and the Additional file 5: Figure S5: Tissue specificity of predicted regulatory regions. A, Counts of HPRS-predicted enhancers (using 88 ROADMAP human enhancer datasets) that overlap with the Villar cattle reference enhancers. B, We then defined a tissue-specific enhancer dataset by identifying HPRS regions that overlap with Villar reference enhancers for cattle and are unique to each of the 88 tissues. The datasets that yielded the highest overlap are those from the liver cell line (liver hepatocellular cells - HepG2) and the human liver tissue. C, We mapped 101 RNA sequencing datasets, collected from more than 79 tissues (Table S5), to the predicted regulatory regions. The mapped RNA signal was used to compare the similarity between different tissues. Strong enrichment of brain, muscle, and liver tissues was observed.ALDOB) gene in cattle. Single nucleotide resolution scores within the ALDOB enhancer are shown. Negative scores indicate loss of function (or TF binding), while positive scores indicate increases in activities. Computational predictions of transcription factor binding sites  and prediction of TFBS (in humans and cattle).Additional file 6: Figure S6: Large-scale gapped k-mer support vector machine (LS-gkm-SVM) scores for enhancers and deltaSVM scores for SNPs. A, The LS-gkm-SVM model was used to calculate the gkm-SVM scores for all enhancers in the Villar dataset. Red, enhancers scored on “enhancers versus background matrix”; green, random regions (selected by shuffling through the genomes to sample genomic regions of the same length as the Villar reference bovine enhancers) scored on “enhancers versus background matrix”; blue, enhancers scored on a “background versus background” matrix. The positive background was selected from the Villar reference enhancer dataset, as described in the Supplementary Materials and Methods section. Training datasets using human (HHb) and cattle (BBb) and liftOver enhancer regions from human to cattle (LOBHb) yielded consistent and comparable results, which predicted higher scores for enhancer regions  than the predictions for promoter (pmtr_Enh) and random regions . B, deltaSVM for scoring SNP effects on enhancer activity. The LS-gkm-SVM model was used to score every possible SNP across the enhancer of the aldolase B fructose bisphosphate  genome. Predicted regulatory regions are shown in blue, with annotations  marked on the left. For regions with multiple annotations, users can display the annotations by selecting the region on the browser. The example shows the Additional file 8: Table S1: Enrichment of GWAS SNPs in common phenotypes measured for dairy cattle.Additional file 9: Table S2: Hi-C targets and gkm-SVM predict causative SNPs and gene targets.Additional file 10: Table S3: Predicted Hi-C interaction regions at the putative Callipyge locus (chr21:67339968:67340027).Additional file 11: Table S4: Regulatory datasets used for optimizing mapping parameters.Additional file 12: Table S5: Data sources for publically available human datasets used as inputs for the HPRA pipeline in this paper.bp: base pair; CAGE: Cap Analysis of Gene Expression; CDS: coding sequence; ENCODE: Encyclopedia of DNA Elements; FANTOM: Functional Annotation of the Mammalian genome; FD: Filtered Dataset; Gb: Giga base; GWAS: Genome-Wide Association Studies; HPRS: Human Projection of Regulatory Regions; kb: kilo base; Mb: Mega base pair; RDEs: Regulatory DNA Elements; ROADMAP: Roadmap Epigenomics Mapping Consortium; SNP: Single Nucleotide Polymorphism; SRA: Sequence Read Archive; SVM: Support Vector Machine; TE: Transposable Elements; TFs: Transcription Factors; UCSC: University of California Santa Cruz; ENSEMBL: Ensemnbl database; TSS: Transcription Start Sites; TFBS: Transcription Binding Sites; PWM: Position Weight Matrix; UD: Universal Dataset.The authors declare that they have no competing interests.Q.N. and M.N.S. were supported by CSIRO OCE Postdoctoral Fellowships.B.D., R.T., J.K., B.B., and Q.N. conceived the project. Q.N. and B.D. designed the HPRS algorithm. Q.N. wrote the pipeline. Q.N., M.N.S., L.P.N., A.R., and B.H. contributed to the analysis. Q.N. and B.D. wrote the manuscript, with input from all other co-authors.GIGA-D-17-00310_Original_Submission.pdfClick here for additional data file.GIGA-D-17-00310_Revision_1.pdfClick here for additional data file.Response_to_Reviewer_Comments_Original_Submission.pdfClick here for additional data file.Reviewer_1_Report_ -- Christiaan Henkel04 Dec 2017 ReviewedClick here for additional data file.Reviewer_2_Report_ -- Ole K Tørresen06 Dec 2017 ReviewedClick here for additional data file.Supplemental materialClick here for additional data file.


Jinchuan yak, a newly discovered yak breed, not only possesses a large proportion of multi-ribs but also exhibits many good characteristics, such as high meat production, milk yield, and reproductive performance. However, there is limited information about its overall genetic structure, relationship with yaks in other areas, and possible origins and evolutionary processes. In this study, 7,693,689 high-quality single-nucleotide polymorphisms were identified by resequencing the genome of Jinchuan yak. Principal component and population genetic structure analyses showed that Jinchuan yak could be distinguished as an independent population among the domestic yak population. Linkage disequilibrium analysis showed that the decay rate of Jinchuan yak was the lowest of the domestic yak breeds, indicating that the degree of domestication and selection intensity of Jinchuan yak were higher than those of other yak breeds. Combined with archaeological data, we speculated that the origin of domestication of Jinchuan yak was ∼6000 yr ago . The quantitative dynamics of population growth history in Jinchuan yak was similar to that of other breeds of domestic and wild yaks, but was closer to that of the wild yak. No significant gene exchange between Jinchuan and other domestic yaks occurred. Compared with other domestic yaks, Jinchuan yak possessed 339 significantly and positively selected genes, several of which relate to physiological rhythm, histones, and the breed’s excellent production characteristics. Our results provide a basis for the discovery of the evolution, molecular origin, and unique traits of Jinchuan yak. Bos grunniens), also known as “plateau ship,” is a bovid species mainly distributed in the Qinghai–Tibetan Plateau of China and its adjacent alpine or subalpine regions, such as Pakistan, Nepal, Bhutan, India, and Afghanistan  were selected from published local breeding genomic data specific to the geographical location of Jinchuan yaks and compared with those of yak species from other places  in >50% of the read length. High-quality and clean data were obtained through stringent filtering of the sequencing data.To avoid raw reads with artificial bias during library construction and sequencing, or low-quality reads resulting from base-calling duplicates and adapter contamination, we filtered the original Jinchuan yak genome sequence and chose relevant data from local yak breed genome sequences in accordance with the following unified criteria. Reads containing adaptor sequences were filtered. Paired reads were removed when the content of After uniform filtering was completed, the obtained high-quality clean reads were mapped to the reference yakgenome1.1  with defi.e., coverage depth ≥3, root mean square mapping quality ≥20, miss ≤0.2, minor allele frequency (MAF) ≥0.05] for subsequent analysis. In addition, raw SNPs were further filtered to meet the following criteria. (1) The sum of read depths across all populations was 31,000. (2) The mapping quality score was >20. (3) Multi-nucleotide polymorphisms were ignored. (4) The average Phred scaled base quality of the variant allele was ≥20. (5) The missing variant (miss) from a population scale was detected at ≤2%. (6) The MAF was >0.05.After aligning the reads, we performed SNP calling on a population scale using a Bayesian approach implemented in the SAM tool package. The reference genome was subdivided into segments and analyzed in parallel. We then calculated genotype likelihoods from the reads of each individual at each genomic location and allele frequencies in the sample using the Bayesian approach. The “mpileup” command was used to identify SNPs with the parameters “-q 1 -C 50 -t SP -t DP -m 2 -F 0.002.” To exclude SNP calling errors caused by incorrect mapping or InDels, we retained only high-quality SNPs [i.e., SNPs overlapping with a coding exon), intronic regions , splicing sites (SNPs within 2 bp of a splicing junction), upstream and downstream regions (SNPs within a 1 kb region upstream or downstream of a transcription start site), and intergenic regions. SNPs in coding exons were further grouped into synonymous SNPs, which do not cause amino acid changes, and nonsynonymous SNPs, which cause amino acid changes. Mutations that induced stop gain and loss were also classified in this group. Only high-quality SNPs were annotated.SNP annotation was performed according to the yak reference genome using the package ANNOVAR (Version: 2013-05-20) (http://treesoft.sourceforge.net/treebest.shtml). We also conducted principal component analysis (PCA) to evaluate the genetic structure by utilizing the GCTA software (http://cnsgenomics.com/software/gcta/mlmassoc.html). The Tracy–Widom test was conducted to determine the significance level of the principal components. After the SNP data were transformed to PLINK files using PLINK  (http://www.cog-genomics.org/plink/2.0/), the population genetic structure was further inferred using ADMIXTURE (https://www.genetics.ucla.edu/software/admixture/). Presumed ancestral populations (K) between two and three were run with 10,000 iterations.We used all high-quality SNPs of the individuals to infer the population structure. To clarify the phylogenetic relationship from a genome-wide perspective, we constructed an individual-based neighbor-joining tree with 1000 bootstrap values based on p-distance using the TreeBest software (r2) between pairwise SNPs with MAF > 0.05 were computed using the Haploview software . The MCMCTree package in PAML 4.5 (http://abacus.gene.ucl.ac.uk/software/paml.html) was used to estimate divergence times on the basis of the evolutionary tree and to further describe the differentiation time between Jinchuan yak and other groups. We used the putative sequence of a coding region based on the allele frequency of each population, which was concatenated to one supergene for each species. The Bayesian estimate of species divergence times was determined using soft fossil constraints under various molecular clock models. A molecular clock is an effective tool with which to estimate species divergence times. Available information on species divergence times from fossil or geological records can be utilized to calibrate phylogeny and estimate divergence times for all nodes in a tree. We used a pairwise sequentially Markovian coalescent (PSMC) model to infer the demographic history. The history of changes in population size over time was reconstructed by applying the distribution of the most recent common ancestor between two alleles in a sample. The parameters used for the analysis were “-g3 -μ5.84 × 10−9-N25 -t15 -r5 -p “4+25∗2+4+6,” similar to those used in a previous yak study  and nucleotide diversity (θπ) were calculated with 40 kb sliding windows that had a 20 kb overlap between adjacent windows. A log function was used to Z-transform the distribution of FST and the θπ ratio. Windows at the top 5% of log-odds ratios for FST and θπ were considered as putative selection target regions. The functions of the selected genes were annotated by submitting them to the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases for enrichment analyses. A binomial distribution probability approach corrected by the false discovery rate was used to test significantly enriched gene functions at File S1.The raw data files obtained in this study by Illumina sequencing have been submitted to the Sequence Read Archive Database of the National Center for Biotechnology Information. The accession numbers of sequence read archive runs of six samples are SRR5641605, SRR5641604, SRR5641601, SRR5641603, SRR5641602, and SRR5641600. Genomic data selected from published local breeding genomic data of yaks are available in Table S1 in Raw sequencing data (400.72G) from six Jinchuan yaks were obtained after sequencing. The yak genome is typically 2.66G, whereas its effective genome data are 2.54G. Genome resequencing yielded an average depth of 25× in each sample. After quality check and filtering were completed, 394.08G high-quality clean reads were obtained for further analysis. After mapping the valid filtered high-quality sequence data to the genome, we observed that the average mapping ratio of Jinchuan yak samples was 98.10%, with an average coverage of 99.14% and sequencing depth of 21.87×. To compare Jinchuan yaks with the population distributed in other regions, we screened the original genome sequencing data of Jinchuan yaks and breeds in other regions on the basis of a unified standard. The available genome data of the yak breeds in other regions showed an average sequencing depth of 6×. Thus, we adjusted the sequencing data of the screened Jinchuan yak sample to obtain an average sequencing depth similar to that of the genome data of yak breeds in other regions. The average mapping ratio of the population sample, sequencing depth of the genome, and coverage were 97.39, 6.77, and 97.66%, respectively.File S1) indicated that SNPs in the intergenic region accounted for a major proportion (76.31%) and the inclusive area corresponded to 21.73%. By comparison, the exon area was merely 0.62%, which comprised 24,812 synonymous mutations and 22,418 nonsynonymous mutations. Furthermore, the ratio of transition mutations  to transversion mutations  was 2.57.After filtering, we obtained 7,693,689 valid quality SNPs for further analysis. These SNPs were then annotated. Results  between pairs of SNPs. As shown in To examine the genetic population structure and relationships between Jinchuan yaks and other yak groups, we conducted a genome-wide population genetic polymorphism analysis. The neighbor-joining tree based on genome-wide data revealed that the yak population in China is largely divided into wild and domesticated yaks. Of these categories, Jinchuan yak is an independent branch and has a close genetic evolutionary relationship with certain yaks in the Kangding, Jianzha, Dangxiong, and Hezuo districts . Using tThe bioinformatic analysis of gene flow  indicateFile S1), the GO category related to physiological rhythm accounted for 50% (5/10), followed by two GO categories associated with carbohydrate metabolism (2/10). KEGG analysis revealed the involvement of these genes in many pathways. Among the top 10 pathways  and reproduction . The two skeletal development-related pathways involved three genes, namely, ETV6, Mitf, and Ifnar1. ETV6 and Mitf are transcription-regulating factors, whereas ETV6 is a transcription-repressing factor implicated in hematopoiesis and malignant transformation (In addition to the previously stated top pathways, certain pathways associated with the special traits of Jinchuan yaks were enriched. Examples included pathways related to skeletal development (ormation . Hence, In summary, the possible origin, evolutionary history, molecular phylogeny, and selection evidence of Jinchuan yaks were explored in this study. The results provided a basis for further enrichment of the background information of these yaks and evaluated their uniqueness.www.g3journal.org/lookup/suppl/doi:10.1534/g3.118.300572/-/DC1.Supplemental material is available online at Click here for additional data file.


RPS10 and VASN), lipid metabolism (GSK3), male fertility (INSL6) and developmental processes (TBX19). These findings provide a window into the potential genetic mechanism underlying development of desirable phenotypes in Enshi black pigs during domestication and subsequent artificial selection. Thus, our results illustrate how domestication has shaped patterns of genetic variation in Enshi black pigs and provide valuable genetic resources that enable effective use of pigs in agricultural production.Identification of genomic signatures that help reveal mechanisms underlying desirable traits in domesticated pigs is of significant biological, agricultural and medical importance. To identify the genomic footprints left by selection during domestication of the Enshi black pig, a typical native and meat-lard breed in China, we generated about 72-fold coverage of the pig genome using pools of genomic DNA representing three different populations of Enshi black pigs from three different locations. Combining this data with the available whole genomes of 13 Chinese wild boars, we identified 417 protein-coding genes embedded in the selected regions of Enshi black pigs. These genes are mainly involved in developmental and metabolic processes, response to stimulus, and other biological processes. Signatures of selection were detected in genes involved in body size and immunity ( Guided by the reference genome of the domestic Duroc pig, considerable attempts have been made to unveil the genetic bases of phenotypes by using whole-genome wide SNP chip and resequencing approaches123th century and are characterized by their average-sized head, concave and wrinkled face, well-developed limbs, concave back, tilted haunch and big belly5Enshi black pigs, which comprise a typical native black breed in China, are best known for their fat storage ability and cold-wet tolerance. They are mainly raised in mountainous regions with an average altitude of 800 m above sea level in southwest ChinaTo identify signatures of selection resulting from domestication, we performed whole-genome pooled resequencing of three representative populations of Enshi black pigs . Together with 96 publicly available genomes of pig breeds found in Asia  and using a total of ~4.00 trillion bases of resequencing data78910Pooled paired-end sequencing of the three populations of Enshi black pigs generated 70.64, 67.86 and 63.71 Gb data for the genomes of the Lvcongpo (LCP), Yetinglu (YTL) and Zhongbaozhen (ZBZ) populations, respectively. To accurately detect genomic footprints left by selection, we downloaded the publicly available genome data of 13 Chinese wild boars . To redu79A total of 248.91 Gb high-quality sequencing data from three Enshi black pig populations and a Chinese wild boar population were aligned against the reference pig genome (Sscrofa10.2) using BWA (v0.7.8)We identified ~14.09 M SNPs in four populations that were concurrently cataloged by two currently dominant algorithms (i.e. SAMtoolsCompared with the 60.92% SNPs (~10.70 M of 17.57 M) that were shared between the Enshi black pigs and Chinese wild boar, more than three-quarter of SNPs  were shared among three Enshi black pig populations , which in ≥ 7) of nonsynonymous SNPs were mainly over-represented in the highly variable sensory perception categories . We identified 1,051–1,078 regions (195.15 M to 198.16 M) in the Enshi black pig genome with extremely low levels of heterozygosity (Z(HP) < −2) and 1,043–1,067 regions (208.94 M to 210.96 M) with strongly elevated FST values (Z(FST) > 2). In total, 463–487 unique candidate domestication regions (CDRs) (from 81.77 M to 87.52 M) containing 858–1,046 genes were identified in three populations by focusing our analysis of putatively selected regions on those that fall at least two standard deviations away from the mean (Z(HP) < −2 and Z(FST) > 2, HP and FST regions indicated that the two statistical methods detected the same events. SNPs from these CDRs formed two distinct clusters (i.e. three Enshi black pig populations and a Chinese wild boar population)  .P = 1.39 × 10−11; cell differentiation, P = 2.27 × 10−9; system development, P = 3.36 × 10−2), metabolic processes , and response to stimulus  , which may have resulted from selection driven by a demand for energy-rich food during the development of fatty Enshi black pigs in China. Consistent with previous reports1UBC, JAK3, RAC1, RELA and MAPK1 as the central node genes (We detected 417 candidate selected genes (CSGs) in 185 CDRs 30.91 M, , which w × 10−4) . These rde genes . The cent test) using the identity score (IS) of the two conditions (IS between the Enshi black pigs and the Chinese wild boar breeds or IS among three Enshi black pig populations). We then selected top 10 genes  with the lowest P value for further study.To identify the key genes that play an important role in shaping the domestication of Enshi black pigs, we performed statistical analysis , 13 Chinese wild boars, and 10 Korean wild boarsTo analyze the signs of selection in detail, we detected SNPs from the regions spanning these genes with highly significant effects (SNPs located in untranslated regions (UTRs), exon, and downstream/upstream of the gene). We found 34 SNPs with significantly different mutation frequency between Enshi black pigs and Chinese wild boars  in RPS10RPS10), which harbors three mutations in the 3′ UTR, and has also been reported to be commonly mutated in Diamond-Blackfan Anemia16RPS10 may be associated with body height, body length, and longissimus muscle weight in pigs19RPS10 may be associated with limb bone length (which is associated with body height and body length)VASN), which has three nonsynonymous mutations. This gene may be involved in modulating arterial response to injury by inhibiting the TGF-β signaling pathway2223VASN is highly expressed in vascular smooth muscle cells (hence the name) and the developing skeletal systemVASN may indirectly influence the body size of pigs during embryonic development.Among these five genes, two genes may be associated with body size and immunity. The first gene is ribosomal protein S10 (GSK3). GSK3 is a constitutively active, proline-directed serine/threonine kinase that participates in a number of physiological processes that range from glycogen metabolism to gene transcriptionGSK3A and GSK3BGSK3A knockout experiment in mice displayed improved glucose tolerance in response to glucose load and elevated hepatic glycogen storage and insulin sensitivity29GSK3A Hin1I and MspI polymorphisms were significantly associated with loin muscle area, average back fat thickness, thorax-waist fat thickness, and buttock fat thickness32GSK3 gene may contribute to enhanced fat storage ability and relatively high intramuscular adipose content.As a typical meat-lard pig breed, Enshi pigs exhibit strong selection signals in the obesity-related gene glycogen synthase kinase 3 (TBX19 (known as TPIT) is a member of the T-box family of transcription factors, which are involved in the regulation of developmental processes35363739INSL6), is a member of the insulin superfamily, which comprises a group of structurally related proteinsINSL6 is predominantly expressed in the testis, and it is necessary for the progression of spermatogenesis. Deficiency in INSL6 will cause varying levels of male infertility42434445The remaining two genes were involved in developmental processes and male fertility. These positively selected genes may serve as important genetic foundation of the evolutionary scenarios triggered by artificial selection for agricultural production of Enshi black pigs. Further studies are required to define the signaling cascades associated with these genes and their regulatory mechanisms in order to facilitate a better understanding of their roles in the formation of economically important native breeds.RPS10 and VASN), obesity (GSK3), male fertility (INSL6), and early development (TBX19). In addition, important mutations within these genes were also identified to enrich the pool of markers that can be used to further refine selection in these pigs in the future. Our research methods and findings should also be helpful in deciphering genomic footprints left by selection and domestication in other livestocks4748This study detected the genomic signatures that may have shaped the domestication of Enshi black pigs in China. The genes found to be positively selected in Enshi pigs are involved in crucial biological processes such as body size and immunity .Three representative populations of Enshi black pig were raised in counties of Enshi Tujia and Miao Autonomous Prefecture in Hubei Province, China  with 150 bp paired-end reads according to the manufacturer’s instructions. We also downloaded 96 publicly available pig genomes in Asia , of whicTo avoid reads with low-quality, we removed the following types of reads: (a) reads with ≥10% unidentified nucleotides (N); (b) reads with >10 nt aligned to the adapter, allowing ≤10% mismatches; and (c) reads with >50% bases having phred quality <5; and (d) putative PCR duplicates generated by PCR amplification in the library construction process. After the low-quality reads were excluded, the remaining high-quality reads were aligned against the Sscrofa10.2 reference sequence by using Burrows-Wheeler Aligner , with the BWA command of “mem -t 4 -k 19 -M -w 200”. The uniquely aligned reads with ≤5 mismatches were used for SNP calling.To obtain highly confident SNPs, we employed both SAMtools (v0.1.19) and GATK tool (v3.3) variant calling pipelines to process each pool of samples respectively. In SAMtools, base calling was conducted by using the “mpileup” command and the “-q 1 -C 50 -S -D -m 2 -F 0.002” parameters of SAMtools. The “view” command of BCFtools was used to convert the BCF files to VCF files. The VCF files were then filtered by the “vcfutils.pl” script with the use of the “varFilter -Q 20 -d 4 -D 1000” option and vcftools using the “–thin 4” option, and high-quality SNPs  were retained for subsequent analysis. For GATK, the settings were used according to the GATK best practice online documentation. Results that were obtained by the two pipelines were compared using the BEDTools (v2.21.0) “intersectBed” moduleTo determine novel variants in our sequence data, we compared the identified SNPs with the dbSNP (Build 143) data using BEDTools and annotated the detected genetic variants using ANNOVARHP , and (2) the average fixation index, FST, between Enshi black pig and Chinese wild boar. We calculated FST using PoPoolation2HP as follows:For each population, we used allele counts at variable sites to identify signals of selection in 100 kb windows (with a step size of 50 kb) through two approaches: for each window, we calculated (1) the average pooled heterozygosity, where nMAJ is the most frequently observed allele, and nMAJ is the least frequently observed allele.Z-transformed HP and FST distributions by applying a threshold of two standard deviations  < −2, Z(FST) > 2). We disregarded the selection signals on chromosome X because of the extremely low rate of recombinationPutatively selected regions in each group were located by extracting windows from the tails of the FPopulation1 − FPopulation2|) (2), with SNPs assessed only if a minimum of one read was obtained in each population. The IS value for a gene was the mean of all IS values observed in the gene for a specific comparison.We calculated ISs to visualize haplotype sharing in pairwise comparisons at the selected genes. For each identified SNP, we determined the fraction of reads that corresponded to the reference genome allele, F, in each pig population. The IS values of individual SNPs were then calculated as IS = 1 − To explore the genetic relationship of the 4 pooled group, we also constructed another two phylogenetic trees by using SNPs in whole genome and SNPs in selected regions respectively. Homologous regions among different pig populations were identified and extracted by SNPhylo (v20160204)Accession codes: The genome resequencing reads have been deposited into the NCBI sequence read archive (SRA) under the accession SRP071318.How to cite this article: Fu, Y. et al. Genomic analysis reveals selection in Chinese native black pig. Sci. Rep. 6, 36354; doi: 10.1038/srep36354 (2016).Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Because of their abundance and their amenability to high-throughput genotyping techniques, Single Nucleotide Polymorphisms (SNPs) are powerful tools for efficient genetics and genomics studies, including characterization of genetic resources, genome-wide association studies and genomic selection. In wheat, most of the previous SNP discovery initiatives targeted the coding fraction, leaving almost 98% of the wheat genome largely unexploited. Here we report on the use of whole-genome resequencing data from eight wheat lines to mine for SNPs in the genic, the repetitive and non-repetitive intergenic fractions of the wheat genome. Eventually, we identified 3.3 million SNPs, 49% being located on the B-genome, 41% on the A-genome and 10% on the D-genome. We also describe the development of the TaBW280K high-throughput genotyping array containing 280,226 SNPs. Performance of this chip was examined by genotyping a set of 96 wheat accessions representing the worldwide diversity. Sixty-nine percent of the SNPs can be efficiently scored, half of them showing a diploid-like clustering. The TaBW280K was proven to be a very efficient tool for diversity analyses, as well as for breeding as it can discriminate between closely related elite varieties. Finally, the TaBW280K array was used to genotype a population derived from a cross between Chinese Spring and Renan, leading to the construction a dense genetic map comprising 83,721 markers. The results described here will provide the wheat community with powerful tools for both basic and applied research.


The initiation of growth cessation and dormancy represent critical life-history trade-offs between survival and growth and have important fitness effects in perennial plants. Such adaptive life-history traits often show strong local adaptation along environmental gradients but, despite their importance, the genetic architecture of these traits remains poorly understood.Populus tremula). A single genomic region containing the PtFT2 gene mediates local adaptation in the timing of bud set and explains 65% of the observed genetic variation in bud set. This locus is the likely target of a recent selective sweep that originated right before or during colonization of northern Scandinavia following the last glaciation. Field and greenhouse experiments confirm that variation in PtFT2 gene expression affects the phenotypic variation in bud set that we observe in wild natural populations.We integrate whole genome re-sequencing with environmental and phenotypic data from common garden experiments to investigate the genomic basis of local adaptation across a latitudinal gradient in European aspen  contains supplementary material, which is available to authorized users. Most species are distributed over heterogeneous environments across their geographic range and spatially varying selection is known to induce adaptation to local environments . Local aMany perennial plants, such as forest trees, have wide geographic distributions and are consequently exposed to a broad range of environmental conditions, making adaptation to diverse environmental and climate conditions crucial in these species –7. NaturPopulus tremula). P. tremula is a dioecious and obligately outbreeding tree species; both seeds and pollen are wind-dispersed and usually show weak population genetic structure ) between genetic variants and specific PCs without any prior definition of populations. As only the first PC was significant from the PCA . Values were collected from the years 2002–2012 for the original sample coordinates of each SwAsp individual and the average values over years were then calculated. The environmental variables include latitude, longitude, altitude, the number of days with temperatures > 5 °C, UV irradiance, the photosynthetic photon flux density (PPFD), sunshine duration, monthly and annual average precipitation, and temperature. Due to the high degree of correlation among these environmental variables  in the range of 1–3, using 5000 iterations as burn-in followed by 10,000 iterations to compute LFMM parameters for all SNPs. This was performed five times for each value of K; we observed identical results across both different values of K and across independent runs within each value of K (data not shown). We only showed the results using K = 2 to account for the background population structure. LFMM outliers were detected as those SNPs with FDR < 0.05 after using the method of Storey and Tibshirani [Second, we tested for the presence of candidate SNPs that exhibited high correlations with environmental gradients. To do this, a total of 39 environmental variables were analyzed  and residual term (εijklm) were modeled as random effects, whereas the site (si), site/block (bj(i)), and site/year (yk(i)) were treated as fixed effects. The genetic value of each individual was then used as the dependent trait in a univariate linear mixed model for SNP-trait association analyses performed with GEMMA [MAF is the effect size estimate and minor allele frequency for the SNP, N is sample size, and th GEMMA . This meth GEMMA . Given tth GEMMA  to contrth GEMMA :2\documeP. tremula assembly to 19 pseudo-chromosomes according to synteny with the P. trichocarpa genome. We then performed pairwise genome alignment between scaffolds of P. tremula and the 19 pseudochromosomes using the BLAST algorithm  and, finally, > 99% of the SNPs  were anchored on the 19 pseudochromosomes.For some haplotype-based selection tests, imputed and phased datasets were needed. We therefore used BEAGLE v4.1  to perfoTo test for the accuracy of imputation, and its relationship with the MAF cutoff and the missing rate of genotypes in our dataset, we selected 346,821 SNPs with a rate of missing genotypes < 10% from the pseudo-chromosome 2 (~ 32.6 Mb) for the simulation analysis. We randomly masked out varying proportions (5–50%) of SNPs, which were treated as missing. BEAGLE v 4.1 was then used to impute genotypes at the masked positions. We found high imputation accuracy (> 0.97) across a wide range of MAF when rates of missing genotypes were < 30%  and one P. trichocarpa (SRA ID: SRR1571343) individual from the NCBI Sequence Read Archive (SRA) [P. tremula assembly  and used UnifiedGenotyper in GATK to call SNPs at all sites (−-output_mode EMIT_ALL_SITES). For each SNP, two procedures were performed to define their ancestral states: (1) because P. trichocarpa is more distantly related to P. tremula compared to P. tremuloides [P. tremula and P. trichocarpa [P. trichocarpa allele at sites where the P. trichocarpa individual was homozygous and matched one of the P. tremula alleles; otherwise, (2) we inferred the ancestral state as the P. tremuloides allele at sites where the P. tremuloides individual was homozygous and matched one of the P. tremula alleles. If the above two requirements were not met, the ancestral state was defined as missing. In total, we obtained information of ancestral states for 96.3% of all SNPs.Since the ancestral states of SNPs are usually used for selection detection, for each SNP, we classified alleles as either ancestral or derived on the basis of comparisons with two outgroup species: ve (SRA) . We indimuloides  and fromchocarpa , we infeP. tremula and P. trichocarpa using default settings. The output was then converted and filtered into GBrowse synteny compatible format that was available at http://popgenie.org [P. tremula assembly that contain SNPs inferred to be associated with local adaptation were completely or partially mapped to a single region on chromosome 10 of P. trichocarpa genome  detected by at least two of the three methods  were clustered in a single genomic region on pseudo-chromosome 10, we performed several further steps to refine the anchoring and orientation of these SNPs within this region. First, we used ColaAlignSatsuma from the Satsuma packages  to alignenie.org . Based or2) between all pairwise common SNPs  in the ~ 700-kbp region using PLINK 1.9 [r2) between all pairs of SNPs that were within a distance of 100 kbp. The decay of LD against physical distance was estimated using non-linear regression of pairwise r2 vs the physical distance between sites in base pairs [To explore and compare patterns of LD between the ~ 700-kbp region on chromosome 10 and genome-wide levels, we first calculated correlations  to obtain a set of causal variants that capture all the causal variants with the probability > 99%.We utilized CAVIAR   to identL) [L values were then normalized in frequency bins across the whole genome (we used 100 bins). To test for whether there is significant concentration of selection signals on the region surrounding the PtFT2, we divided the 19 pseudo-chromosomes (without the seven scaffolds around the PtFT2 locus) into non-overlapping windows of 700 kbp and calculated the proportion of SNPs with |iHS| > 2 or with |nSL| > 2 in each window. Statistical significance was assessed using the ranking of genome-wide windows, with windows having < 100 SNPs being excluded.We measured two haplotype-based tests, integrated haplotype score (iHS)  and the L) , to testL)  with itsFST, which measures genetic divergence between pairs of three groups of populations, South, Mid, and North, was calculated using a sliding-window approach with window size of 10 kbp and moving step of 5 kbp by VCFtools; (3) a combination of H12 and H2/H1 [r2) in P. tremula decays to < 0.1 within 10 kbp . As recommended by Huber et al. [PtFT2 gene on chromosome 10 and genome-wide estimates, we use the same strategy to divide the genome into the windows with the same size for each test and calculated the above statistics across the genome  pairwise nucleotide diversity (π) , which ind H2/H1 , which mCFtools;  a combinr et al. , we onlyeμ) of 0.0081/bp, which corresponds to the average observed diversity in the North populations. Similarly, we set the scaled population recombination rate (4Ner) to 0.0019 to match the genome-wide ratio of r/μ = 0.229 in P. tremula [To assess the scale of a genomic region that is affected by a selective sweep, we ran coalescent simulations modeling a selective sweep in the Northern populations. Simulations were run assuming that the selected site was located at the center of the simulated region. Parameters for the simulations were taken from ABC calculations dating the selective sweep inferred in the North populations (as shown below). Briefly, we used a scaled population mutation rate  and T  assuming a model of selection from a de novo mutation (hard selective sweep). We simulated 5 × 105 independent selective sweep events using the coalescent simulation program msms [e = 92,000, a mutation rate of μ = 3.75 × 10−8 per base pair per generation [r = 0.729 × 10−8 per base pair per generation. Together these parameters yielded a scaled population mutation rate equal to Θ = 4NeμL = 86.27 and a scaled population recombination rate ρ = 4NerL = 19.76. For each simulation, values for both s and T were drawn from uniform prior distributions, log10(T)~U and log10(s)~U.To date the inferred selective sweep in the North populations, we used the ABC method described in Ormond et al.  to jointram msms . For theneration , and a rPtFT2 were collected from both climate chamber and the field  conditions. For treatment in the climate chamber, two southern clones  and two northern clones  were selected. These plants were selected to represent the northern-most and southern-most populations of the SwAsp collection that are experiencing the most diverged photoperiodic conditions. Plants were grown under 23-h day lengths for one month and then transferred to 19-h day-length conditions for two weeks before the start of sampling. Leaves were harvested at 2-h intervals for a total period of 24 h using three biological replicates of each genotype. Samples were subsequently flash-frozen in liquid nitrogen and stored at − 80 °C until sample preparation.Samples used for the expression analysis of Field samples were collected in the Sävar common garden in early July 2014 and samples were taken from two southern clones  and two northern clones . Leaves were harvested from three different clonal replicates planted in the common garden to serve as biological repeats. Leaf samples were flash-frozen in in liquid nitrogen and stored at − 80 °C until sample preparation. Samples were collected at 2-h intervals for a total period of 24 h.FT2 and qUBQF (5’-GTTGATTTTTGCTGGGAAGC-3′) and qUBQR (5’-GATCTTGGCCTTCACGTTGT-3′) for UBQ as the internal control. We assessed the presence of transcription of both PtFT2 (Potra001246g10694) and PtFT2β by digesting RT-PCR products with SacI that distinguish the two transcripts , and βj is the effect of block j and εij are individual residual errors.Construction of the etail in . Briefly lengths  and this lengths . TransfoAdditional file 1:Table S1. Geographical details of the 94 P. tremula samples used in this study and the summary statistics of Illumina re-sequencing data per sample. (DOCX 155 kb)Additional file 2:Table S2.. Tracy-Widom statistics for the first three eigenvalues in PCA. (DOCX 31 kb)Additional file 3:Figures S1–S19. (PDF 7335 kb)Additional file 4:Table S3. List of the 1615 candidate SNPs associated with local adaptation. (XLSX 234 kb)Additional file 5:Table S4. Summary statistics  for five selective sweep measures across the ~ 700-kbp region around PtFT2 gene on chromosome 10 and genome-wide level. Pairwise nucleotide diversity (π), genetic divergence between groups of populations (FST), H12, H2/H1, and composite likelihood ratio (CLR) test are compared for three groups of populations, South (pop 1–6), Mid (pop 7–8), and North (pop 9–12) corresponding to Fig. Additional file 6:Table S5. ANOVA tables for analyses of gene expression in greenhouse and common garden experiments. (DOCX 51 kb)Additional file 7:Table S6. Average values of 39 environmental variables over the years 2002–2012 for the original sample location of 94 P. tremula individuals used in this study. (XLSX 64 kb)


GWAS) using the wheat 90K genotyping assay, characterized in 163 bread wheat cultivars. Agronomic traits were investigated in 14 environments at three locations over 3 years. After filtering SNP data sets, GWAS using 20 689 high‐quality SNPs associated 1769 significant loci that explained, on average, ~20% of the phenotypic variation, both detected already reported loci and new promising genomic regions. Of these, repetitive and pleiotropic SNPs on chromosomes 6AS, 6AL, 6BS, 5BL and 7AS were significantly linked to thousand kernel weight, for example BS00021705_51 on 6BS and wsnp_Ex_c32624_41252144 on 6AS, with phenotypic variation explained (PVE) of ~24%, consistently identified in 12 and 13 of the 14 environments, respectively. Kernel length‐related SNPs were mainly identified on chromosomes 7BS, 6AS, 5AL and 5BL. Plant height‐related SNPs on chromosomes 4DS, 6DL, 2DS and 1BL were, respectively, identified in more than 11 environments, with averaged PVE of ~55%. Four SNPs were confirmed to be important genetic loci in two RIL populations. Based on repetivity and PVE, a total of 41 SNP loci possibly played the key role in modulating yield‐related traits of the cultivars surveyed. Distribution of superior alleles at the 41 SNP loci indicated that superior alleles were getting popular with time and modern cultivars had integrated many superior alleles, especially for peduncle length‐ and plant height‐related superior alleles. However, there were still 19 SNP loci showing less than percentages of 50% in modern cultivars, suggesting they should be paid more attention to improve yield‐related traits of cultivars in the Yellow and Huai wheat region. This study could provide useful information for dissection of yield‐related traits and valuable genetic loci for marker‐assisted selection in Chinese wheat breeding programme.Bread wheat is a leading cereal crop worldwide. Limited amount of superior allele loci restricted the progress of molecular improvement in wheat breeding. Here, we revealed new allelic variation distribution for 13 yield‐related traits in series of genome‐wide association studies ( Triticum aestivum L.) is one of the most important crops worldwide. Given the gradual decrease in farmland and rapid increase in global population, high yield is always one of the most important targets in wheat breeding. Wheat yield consists of three key components: thousand kernel weight (TKW), spike number/m2 and kernel number per spike (KNS). Moreover, other agronomic traits also play important roles in determination of wheat yield, for example plant height (PH), spike length (SL), kernel weight per spike (KWS), kernel length (KL) and kernel width (KW). Larger kernels are not only directly related to wheat yield but also have favourable effects on seeding vigour and early growth  and a number of genes been isolated by in silico cloning in bread wheat  were revealed to be associated with TKW in Chinese cultivars in multiple environments  constructed an integrated genetic map containing 926 molecular markers for einkorn wheat A genome and provided valuable information for QTL mapping and genome sequence anchoring. However, the standard QTL mapping methods only locate associated genomic regions with low resolution, and not take diverse genetic background into consideration , provide the opportunity to methodically analyse the genetic architecture of complex traits in crop plants . After frequency and genotyping pruning, there are 20 689 SNPs. No individual was removed for low genotyping (MIND > 0.1). Finally, 163 cultivars with 20 689 SNPs were retained for further analysis.Before GWAS, the 90K SNP genotyping assay was filtered in PLINK software, about 36 916 SNPs failed frequency test (MAF < 0.02), 4104 SNPs failed missing test (GENO > 0.1), and 2990 SNPs were excluded based on HWE test (k) was plotted against the delta k calculated from the STRUCTURE software .We verified the candidate repetitive SNPs in the natural population based on their polymorphism Table , and theAmong the significant SNPs, some of them were associated with more than one trait. We identified the significant SNPs for pairwise traits and summarized the numbers Table . On the In this study, superior allele means regulating high TKW and KWS, long kernel length and spike length, more KNS and FSNS, short plant height and peduncle length and small FLBA and FLD. Based on repetivity and PVE of all significant SNPs in the 14 environments surveyed, some SNPs were considered as important genetic loci to possibly play the key role in modulating agronomic traits of wheat cultivars in the Yellow and Huai wheat region. For TKW, 12 genetic loci were selected to be important in the cultivars from this wheat region Table , and theFor kernel length, seven SNPs were selected as important genetic loci Table . PhysicaFor KNS, only two SNPs were selected as important genetic loci Table . FurtherFor spike length, six SNPs were selected as important genetic loci Table . Seven cFor plant height and peduncle length, totally eight SNPs were selected as important genetic loci Table . HoweverFor FLBA and FLD, totally six SNPs were selected as important genetic loci, and three of them simultaneously regulated FLBA and FLD Tables . All sixBased on released time, the natural population used in this study could be divided into three eras, that is before 1980s, 1980s–2000s and after 2000s, and their percentages were 41.1%, 30.1% and 28.8%, respectively. Analysis of distribution of superior alleles indicated that percentages of total superior alleles at the above‐selected 41 SNP loci are 36.6%, 39.9.0% and 40.6% in the cultivars of before 1980s, 1980s–2000s and after 2000s Figure , suggestet al.  in PC population, regulating peduncle length and kernel length, which indicated this SNP may be significantly linked to a new yield‐related genetic locus on chromosome 6BS. BS00060460_51 and Jagger_c4951_122 on chromosome 5BL were located in a pleiotropic region, affecting KWS, kernel length and spike length. QTL mapping results in UP population in the current study also indicated that Jagger_c4951_122 was located in a pleiotropic QTL interval (Jagger_c4951_122~wms118) affecting five new agronomic traits, including kernel width, germination index, germination energy, germination rate and germination rate in the 1st day. A positive ‘additive effect’ indicated that an increasing effect derived from cv UC1110 with allele T and cultivars with allele TT in Jagger_c4951_122 also showed higher TKW in the association population, which was consistent with the GWAS results. Besides, Ain et al.  also det et al., . Therefoet al., et al., Another pleiotropic SNP Excalibur_c39508_88 affecting TKW, KWS, kernel width and FSNS was revealed on chromosome 3AL. It was mapped to a QTL interval , affecting spike length in the PC population consistently in two environments. A negative ‘additive effect’ indicated that an increasing effect derived from cv Chaja with allele A and cultivars with allele AG in Excalibur_c39508_88 also showed higher TKW, KWS and more FSNS in the association population. This SNP may also closely link to genetic locus on chromosome 3A. Earlier studies reported a QTL for yield in chromosome 6AS in winter wheat  and found that all of the phenotype differences reached extremely significant level when integrating three superior alleles, which provided a guidance for multilocus pyramid breeding Table .Popular wheat cultivars have always been the major contributors to wheat yield in the Yellow and Huai wheat region. Most of the 32 selected popular cultivars had possessed superior alleles at the loci BS00060460_51, Jagger_c4951_122 and BS00021705_51 for TKW, Kukri_c2951_2574 for kernel length, Ra_c72517_981 for KNS, BS00085688_51 and BS00046263_51 for spike length, Tdurum_contig27385_131, RAC875_c48703_148, Tdurum_contig29563_183, Kukri_c5282_622 and RAC875_c829_611 for plant height and peduncle length  and six FLBA‐related and FLD‐related loci  will be helpful for decrease in plant height and compact plant type in most cultivars from the Yellow and Huai wheat region. In summary, to increase wheat yield, superior alleles for yield‐related traits should be integrated properly by marker‐assisted selection for multiloci pyramid breeding.et al., et al., Based on their pedigree, released regions, agronomic performance, importance (backbone parents or not) and cultivated area, a total of 163 bread wheat cultivars collected from the Yellow and Huai Valley of China, including Henan, Hebei, Shaanxi, Shanxi and Shandong provinces, were selected as association panel (Chen 10 RIL populations UC1110 × PI610750 (UP) with 187 lines and Proteo × Chaja (PC) with 97 lines were used to validate significant SNPs revealed by GWAS. The two RIL populations grew well at Anyang and Zhengzhou in the 2013–2014 and 2014–2015 cropping seasons. The field experiments were designed as mentioned above.Two FFor each cultivar surveyed, ten spikes from ten different single plants were marked with red ribbon and were used to investigate agronomic traits including plant height, spike length, peduncle length (PL), fertile spikelet number per spike (FSNS), sterile spikelet number per spike (SSNS), total spikelet number per spike (TSNS), flag leaf base angle (FLBA) and flag leaf direction (FLD) before harvest. Plant height was measured after physiological maturity by measuring the distance between the stem base and the top of the spike excluding awns. Flag leaf base angle means the angle between the wheat stem and the flag leaf base and was divided into four hierarchical levels with angle ranges of 0°–45°, 45°–90°, 90°–135° and 135°–180°. Flag leaf direction means the angle between the wheat stem and the flag leaf tip and was labelled as 1 for up and 2 for down. Other traits of the above‐mentioned ten marked spikes were further investigated after harvest, including kernel weight per spike, kernel number per spike, kernel length, kernel width and thousand kernel weight. Kernel length and kernel width were calculated from five replications of ten random kernels. TKW was measured by weighing 500 randomly selected kernels from each plot. For the UP and PC populations, except for the 13 agronomic traits, we also investigated germination‐related traits, including germination rate (GR), germination rate in the 1st day (GR_1st), germination energy (GE: germination rate in the third day) and germination index (GI).Kernel diameter was measured by the Perten Single Kernel Characterization System (SKCS) 4100, following the manufacturer's operation procedure , using 300 random kernels. This system can calculate the average kernel diameter automatically.http://www.bjcompass.com/), using the 90K wheat genotyping assay   among markers was calculated for the A, B and D genomes in the PLINK software. The window size for linkage disequilibrium calculation was set based on the number of SNPs located in each genome. Pair‐wise linkage disequilibrium was measured using the squared allele frequency correlations, according to Weir  using STRUCTURE software 2.3.4  in a putative range of 1–10. To estimate the robustness of inferred population structure, ten replications were conducted for each k. The subpopulation number was estimated using an ad hoc statistic delta k based on the rate of change in log probability of data between successive values  according to the method of Li et al.  was given.GWAS were implemented by GAPIT packages . Genome‐specific primers were designed using Primer3 . Multiple alignments of the candidate sequences were performed using DNAMAN software with default parameters .Candidate gene sites were aligned and downloaded from the ViroBLAST database . In ICIM, the background markers were selected only once using the standard stepwise regression by considering all marker information simultaneously, and the phenotypic values were then adjusted by all markers retained in the regression equation except for the two markers flanking the current mapping interval. The adjusted phenotypic values were finally used for interval mapping. ICIM‐ADD model was selected for QTL mapping, and other parameters used the default designs. The threshold of LOD score was set as 2.5 for declaring the presence of QTL.Genetic linkage maps were constructed and associated SNPs were mapped to the two RIL populations, using the inclusive composite interval mapping (ICIM) method  plots for 13 agronomic traits.Click here for additional data file.Figure S3 Genetic linkage maps of the two RIL populations. (a‐c) Linkage maps for chromosome 3A, 6A and 6B in the PC population; (d) linkage map for chromosome 5B in the UP population.Click here for additional data file.Figure S4 The superior allele loci distributions in the surveyed natural population. Red colours represent superior allele.Click here for additional data file.Table S1 Primers used for detecting significant SNP sites in two RIL populations.Click here for additional data file.Table S2 Summary of SNPs significantly associated with 13 agronomic traits.Click here for additional data file.Table S3 Significant SNP numbers in pairwise traits.Click here for additional data file.Table S4 The distribution of focused SNPs superior alleles in the natural population.Click here for additional data file.Table S5 P value of t‐test for averaged TKW grouped by taking different number of superior alleles.Click here for additional data file.