Ovis aries) may facilitate our efforts towards further improvement. Here, we report the deep resequencing of 248 sheep including the wild ancestor , landraces, and improved breeds. We explored the sheep variome and selection signatures. We detected genomic regions harboring genes associated with distinct morphological and agronomic traits, which may be past and potential future targets of domestication, breeding, and selection. Furthermore, we found non-synonymous mutations in a set of plausible candidate genes and significant differences in their allele frequency distributions across breeds. We identified PDGFD as a likely causal gene for fat deposition in the tails of sheep through transcriptome, RT-PCR, qPCR, and Western blot analyses. Our results provide insights into the demographic history of sheep and a valuable genomic resource for future genetic studies and improved genome-assisted breeding of sheep and other domestic animals.Understanding the genetic changes underlying phenotypic variation in sheep ( Genetic resources for livestock are valuable for understanding their demography and past selection, and may inform future breeding programs and agricultural improvement. Here, the authors present deep resequencing of 248 wild and domestic sheep, with associated data and analysis. Ovis aries) is an important livestock species, which has provided meat, wool, skin, and milk for humans since the Neolithic. Characterization of genome-wide sequence variation and identification of phenotype-associated functional variants are essential steps for guidance of genome-assisted breeding in the near future. The impact of domestication and subsequent selection on genomic variation has recently been investigated in sheep2, and a number of quantitative trait loci (QTLs) and functional genes have been associated with phenotypic traits3. However, most of these investigations focused on a few phenotypes and involved a limited number of molecular markers and breeds/populations. So far, whole-genome resequencing has allowed the identification of genomic variants involved in domestication and genetic improvement for several domestic plants 5 and animals 6.Sheep  identify a number of selected regions and genes potentially affected by domestication and associated with several important morphological and agronomic traits. Moreover, we conduct a survey of non-silent single-nucleotide polymorphisms (SNPs) and gene-containing copy number variations (CNVs), which are part of the selective signatures. These data provide a valuable genomic resource for facilitating future molecular-guided breeding and genetic improvement of domestic sheep, potentially valuable in the face of ongoing climate change and consequent impacts in agricultural practice. In addition, our findings contribute to further understanding of the demographic history of sheep and the molecular basis of distinct phenotypes in the species and other animals.The completion of a sheep reference genomeP > 0.05). Of the Asiatic mouflon and domestic sheep sequencing reads, 99.04% were mapped to the O. aries reference genome for both datasets. We obtained a total of 67,314,959 and 91,772,948 SNPs after mapping with SAMtools and GATK, respectively, of which 50,520,459 were identified by both methods (Supplementary Data P < 0.001) along with 4.80 million insertions and deletions   for SNP calling, we identified 28.75 million SNPs in Asiatic mouflon, which was higher than that based on the sheep reference genome Oar v.4.0 (23.27 million).The 28.36 million SNPs were analyzed across the three groups of sheep . A majority up to 23.27 million SNPs were observed in Asiatic mouflon at 7.77–9.16 million per individual (12.06 million to be unique for this group), followed by 14.38 million in landraces at 5.62–8.92 million per individual (1.06 million to be unique) and 14.01 million in improved breeds at 5.90–6.90 million per individual (1.08 million to be unique)  was less than that between Asiatic mouflon and landraces  or between Asiatic mouflon and improved breeds . The genomic diversities (π) in Asiatic mouflon, landraces, and improved breeds based on the SNPs with <10% missing data were 0.00127, 0.00113, and 0.00109, respectively. The distribution of SNPs at various regions near or within genes was similar in Asiatic mouflon, landraces, and improved breeds  or between the mouflon and improved breeds (9.98 million)  and SVs  per individual among the three groups  on CNVs and SVsely Fig. . This yiely Fig. . The abuely Fig. . The numely Fig. . In contFrom a total of 6,929 common CNV regions , we found 946 functional genes overlapping with 1,999 CNV regions (Supplementary Data π) in European, Asian, African, and Middle Eastern populations based on the SNPs with <10% missing data were 0.00105, 0.00118, 0.00113, and 0.00114, respectively  decreased to half of its maximum value at 2.8 kb in Asiatic mouflon but at 12.1 kb and 17.1 kb in landraces and improved breeds, respectively  test, we scanned for genomic regions with extreme allele frequency differentiation. The top 1% XP-CLR values identified 302 putative selective sweeps in the five old landraces after annotation and removing repeats  along their genomes. Selecting the windows with the top 1% diversity ratios, i.e., low diversity in the five old landraces but high in the Asiatic mouflon, we found 529 putative selective sweeps  analysis (top 5% outliers) and the Hudson-Kreitman-Aguadé (HKA) test  identified 899 and 1,503 putative selective sweeps, respectively  or three  methods, 36 were also identified to be the targets of selection in the comparison of Asiatic mouflon with domestic sheep in two recent studies (Supplementary Table 2. In the same selection tests (XP-CLR and π ratio) between the Asiatic mouflon and the five old landraces, none of the 48 selected genes in the Asiatic mouflon  to be associated with known functions  in sheep in previous studies  were previously found to be influenced by selection in other species including cattle, goat, horse, pig, dog, chicken, rabbit, and mice  and the genotype pattern in LOC101108252 showed significant differences  between the Asiatic mouflon and the five old landraces 15, milk production (JAK2)16, wool production (KIF16B)17, adipogenesis (TCF7L1 and BCO2)19, and spleen size, oxygenated red blood cells and consequently high tolerance to hypoxia (PDE10A)20  and three translocations  between the Asiatic mouflon and domestic sheep 20 . Also, w21. In this context, we further compared the genomes of domestic breeds  was located near the gene PAPPA2, which has been reported to be associated with fat deposition in humans23 and has been identified as a candidate gene for milk, reproduction, and body size traits in cattle24. Comparison of allele frequencies at non-synonymous SNPs in candidate selected genes revealed four  with significantly differentiated allele frequencies among domestic sheep breeds   have been shown to be under selection in sheep, humans, and other domestic animals in previous investigations  and novel  genes.We implemented genome-wide selection tests between domestic breeds representing contrasting phenotypes for several traits that are relevant for sheep husbandry  between the fat-tailed  and thin-tailed  and Finnsheep (FIN)) breeds  = 3.08; Padj = 0.045) between the fat-tailed/fat-rumped and the thin-tailed sheep , followed sequentially by the small-tailed Han sheep (SXW), the large-tailed Han sheep (HDW), and fat-rumped Altay sheep (ALS)  Fig. .VST25, and identified a set of trait-associated CNVs and their associated functional genes as part of the selective signatures, which are known to be responsible for the phenotypic traits  = 6, 6, 4, respectively  with informative phenotypic records 26, annotation of the significant GWAS signals revealed that those for litter size were mapped to a number of novel genes, such as NOX4, IRF2, PDE11A, ZFAT, ZFP91, TENM1, BICC1, LRRTM3, CTNN3, SMYD3, KCNN3, and CD96 . For the number of horns, several GWAS peaks were situated in HOXD1, HOXD3, and RXFP2  reported previously30. In particular, we detected seven novel genes  identified in this study, we detected 80 SNPs to explain 1.2‒16.8% phenotypic variation in litter size, 106 SNPs to explain 7.0‒16.1% variation in number of nipples, and three SNPs to explain 14.1‒17.0% variation in number of horns , 87 signals in one QTL associated with horns , and 31 signals in four QTLs related to teat placement, udder depth, udder shape and udder attachment   . Severals: GPC5) .In this study, we re-sequenced the whole genomes of 248 wild, landrace, and improved sheep with an emphasis on local breeds with distinct phenotypes that have not been studied previously at the genomic level. Our exploration of the sheep variome and selective sweeps focused specifically on domestication and selective breed formation.34 and recently also in sheep2. These studies were based on genome sequences of low-to-medium coverages . We have complemented this work using a hierarchically structured breed panel and high-depth whole-genome sequencing (mean depth of 25.7×).Deciphering the genetic basis of animal domestication is an active research area. The availability of whole-genome sequences provides an opportunity to study this at the gene mutation level. Such genomic studies have been implemented in other domestic animals9. Similar patterns of changes in genomic diversity through domestication have been observed in yak35 and soybean5. The estimates of the ratio of non-synonymous and synonymous substitutions in wild (0.55) and domestic sheep (0.53–0.54) were lower than those in a previous study with a lower sequencing coverage 22. The low values in both wild and domestic sheep suggested a strong impact of purifying selection.We observed a lower level of genomic diversity in domestic breeds than their wild ancestors. This suggested that a substantial proportion of the genomic variation has been lost during and after domestication, whereas the genomic diversity in landraces has been largely retained in improved breeds. It should be noted that the nearly identical nucleotide diversity between landraces and improved breeds was also consistent with the observation that very strong positive selective pressure on modern breeds has only been in practice over the last ~200 generations2. Furthermore, functions of the domestication-related genes and significant differences in non-synonymous SNP allele frequencies between Asiatic mouflon and domestic sheep provided additional evidence for signatures of selection on these genes and associated traits  brought about by domestication. Nevertheless, by annotation of the 260 fixed SNPs  in the 209 putative selective sweeps, we detected 206 SNPs located in the non-coding regions but only two SNPs in the exon regions including a single non-synonymous mutation. This finding suggested domestication as a quantitative trait  to be affected mostly by mutations in non-coding regions37, whereas very few mutations were non-synonymous. In addition to the functional non-synonymous SNPs, we observed differentiated frequencies in SVs between Asiatic mouflon and domestic sheep (Supplementary Tables BCO2)19 and proteostasis (LOC101112255)38 have been under selection during domestication. Interestingly, as SVs in GTF2I have been found to be linked to Williams-Beuren syndrome in humans39 and hypersocial behavior  in domestic dogs41, the specific SV present in GTF2I may account for some behavioral differences between Asiatic mouflon and domestic sheep gained or lost during domestication.By contrasting Asiatic mouflon to the most primitive sheep landraces in our panel, we focused on the early stage of domestication. Results of functional enrichment analysis of the 261 candidate genes were in agreement with previous evidence that metabolic process and olfactory transduction were involved as primary functional categories in sheep domesticationOvis orientalis gmelini, O. o. ophion, and O. o. laristanica)42 and are distributed in various geographic areas such as Iran, Turkey, Azerbaijan, and Cyprus, therefore a comprehensive sampling of all of them would be necessary in future investigations. It would also be interesting to compare genetic mechanisms responsible for specific domestication traits, and identify general patterns across different species of domestic animals6.This work may inform ongoing and future analysis of ancient DNA in order to pinpoint more accurately the origin and time of sheep domestication and associated impact at the genomic level. We noted that Asiatic mouflon have several subspecies , reproduction (FAM184B), photoreceptor development (PDE6B), and tail configuration (PDGFD) during the development of specific breeds  and production traits in sheep towards unique breeds. It is worth noting that the most significantly enriched pathway for the 391 selected genes identified from FST analysis   or unique traits that were rarely studied in sheep and other livestock (tail configuration and numbers of horns and nipples). Moreover, our findings will help to narrow down the functional sub-regions within the QTLs and pinpoint the causal genes associated with these breeding-related traits. In particular, we identified several previously reported and a few novel genes to be associated with the tail configuration in sheep (Supplementary Data PDGFD (cysteine/tyrosine) have an important role in fat metabolism and adipogenesis in humans43. We envisage that non-synonymous variants with deviating allele frequencies between wild and domestic sheep and large effects on specific phenotypes in domestic sheep might be useful, for instance, as targets in CRIPSR/Cas experiments. Transcriptome analysis showed significant differential expressions of PDGFD transcripts among populations with different tail configurations  and 16 wild sheep . The domestic sheep samples represent 36 landraces  and six improved breeds  with different geographic origins from Asia, Europe, Africa, and the Middle East  with the Burrows-Wheeler Aligner v.0.7.8 (ref. 47) using the default parameters. Mapping results were then converted into the BAM format and sorted with SAMtools v.1.3.1 (ref. 48). Duplicate reads were removed using SAMtools. If multiple read pairs had identical external coordinates, only the pair with the highest mapping quality was retained.We obtained ~82.86 Gb of raw sequences for each sample, giving an average depth of 25.7× coverage for clean reads (17.2‒37.0×) (Supplementary Data 49), with all individuals in each set simultaneously. One set included the 228 wild/domestic sheep with at least five samples per breed/population, which was used in all analyses, whereas the other set consisted of the 20 domestic sheep from the Middle East and Africa with one individual per breed, which was used to explore population structure and demographic history of domestic sheep in the Old World and to identify selective signatures associated with domestication and improvement . Only SNPs detected by both methods were kept for further analyses. The detailed processes were as follows: (i) For the GATK, the UnifiedGenotyper parameters -stand_emit_conf and -stand_call_conf were both set as 30. The same aligned BAM files were used in SNP calling through the SAMtools mpileup package; and (ii) For filtering using the command parameters –mis 0.1–maf 0.05 –qd 2 –fs 60 –mq 40 –dp_min 6 –dp_max 120 –DP_min 100 –DP_max 30000 –gq 20 –MQRankSum -12.5 –ReadPosRankSum -8.0, the common sites were first identified by the GATK and SAMtools using the SelectVariants package, and then SNPs with missing rates ≥0.1 and minor allele frequencies (MAF) <0.05 from the three groups  were filtered out from further analysis. For Asiatic mouflon and each breed of domestic sheep, we estimated the site frequency spectrum  with the parameters –dosaf 1 –fold 1 –maxIter 100.After mapping, we performed SNP calling separately for the two sets of samples (see below) using the Bayesian approach implemented in SAMtools and Genome Analysis Toolkit (GATK) v.3.7 . Next, we compared the genotypes of the called SNPs with those on the Ovine Infinium HD BeadChip array (~600 K SNPs)  for 223 samples with available chip data (Supplementary Methods). In addition, to assess the performances of the GATK and SAMtools variant calling methods, we employed a false-positive measure by determining the rate of the monomorphic reference loci on the Ovine Infinium HD BeadChip array that were erroneously called as variant loci by the variant calling methods. We calculated the false-positive rates as the number of false heterozygous SNPs divided by the total number of homozygous reference loci51.To validate the SNPs detected, we first compared the identified set with the 52) based on the sheep reference genome Oar v.4.0 and then categorized as variations in exonic regions, splicing sites, intronic regions, upstream and downstream regions, and intergenic regions. Those in exons were further classified into synonymous or non-synonymous SNPs.SNPs were annotated using the ANNOVAR v.2013-06-21 (ref. 53) and DELLY v.0.7.9 (ref. 54). For CNVnator, the analyses were performed on the BAM files with a bin size of 100 bp and with the length >200 bp (Supplementary Methods). For DELLY, the analyses were conducted with default parameters, and deletions and duplications were considered to be CNVs (Supplementary Methods). Only the CNV calls with >50% of their lengths being overlapped between the two approaches were retained in the final set of CNVs. The CNVs that overlapped with gaps or genomic repeats were removed, and the remaining CNVs were segregated into short CNV bins (≥100 bp) across the genomes among the 248 individuals for subsequent analyses25.Similar to SNP calling, the calling of INDELs was conducted using SAMtools with minimum depth ≥4 and GQ >20, and only INDELs <100 bp were retained. CNVs were detected using both CNVnator v.0.3.2 (ref. 55) and DELLY v.0.7.9 (ref. 54). The two software called SVs by performing mapped paired-end reads and split reads analyses, and were run with default parameters to detect deletions (DEL), inversions (INV), duplications (DUP), and translocations (TRA) (Supplementary Methods). The SURVIVOR v.1.0.6 (ref. 56) was implemented to detect the overlapping SVs identified by the two approaches with the command line ./SURVIVOR merge sample_files 1000 2 1 1 0 50 sample_merge.vcf.SVs were identified through the Manta v.1.6.0 (ref. p-distance matrix using the TreeBeST v.1.9.2 (ref. 57). The NJ tree was rooted with the outgroup of 16 Asiatic mouflon and visualized using the FigTree v.1.4.3 (http://tree.bio.ed.ac.uk/software/figtree/). PCA of whole-genome SNPs for all 248 individuals was performed with the GCTA v.1.24.2 (ref. 58). Furthermore, population structure was assessed using the default setting in the ADMIXTURE v.1.23 (ref. 59). The number of assumed genetic clusters K ranged from 2 to 7. To construct a NJ tree for the four subgroups of domestic sheep , 1–2 individuals from each landrace and two individuals from each sampling site of Asiatic mouflon were selected, totaling 50 individuals . To eliminate the potential influence of selective SNPs, we only retained the SNPs located 150 kb away from genes and without missing genotypes. Eventually, a final set of 59,943 SNPs for the 50 individuals were kept for the construction of NJ tree. Reynolds genetic distances (ref. 61) among the five groups were calculated using Arlequin v3.5.2.2 (ref. 62) (Supplementary Table 63) and visualized using FigTree v.1.4.3. The parameter r2 (ref. 64) for LD was calculated for pairwise SNPs within each chromosome using PLINK v.1.0760 with the parameters (–ld-window-r2 0 –ld-window 99999 –ld-window-kb 500). The average r2 values were calculated for each length of distance and the whole-genome LD was averaged across all chromosomes. The LD decay plot was depicted against the length of distance using the R script (http://www.r-project.org). Nucleotide diversity (π) and global FST were calculated using the vcftools v.0.1.14 (ref. 65). The FST values between populations were estimated using the ARLSUMSTAT implemented in the Arlequin v3.5.2.2 (ref. 62), with a sliding window of 50 kb. The genomic SNP data of variant call format (VCF) were converted into the Arlequin format (arp) using the VCF2Arlequin python script62. Statistical significance  of the FST values were tested through 100,000 Markov chains following 10,000 burn-in steps. The average FST and associated P values over all sliding windows were regarded as the values at the whole-genome level.After filtering, we generated a set of SNPs for the following analyses. First, an individual-based neighbor-joining (NJ) tree was constructed for all the samples based on the nucleotide 66 method to estimate changes in effective population size (Ne) over the last one million years. The PSMC analysis was implemented in each of the 248 samples. The parameters were set as follows: -N30 -t15 -r5 -p ‘4 + 25*2 + 4 + 6’, with the filtering criteria of read depth for each SNP as six at the individual level. An average mutation rate (μ) of 2.5 × 10−8 per base per generation and a generation time (g) of 3 years67 were used for the analysis. We also inferred recent Ne using the SNeP v.1.0 (ref. 68) with default settings. SNPs with missing data and a MAF smaller than 5% were excluded from the analysis. The different SNP marker distance bins for r2 analysis were used to obtain different estimates of Ne at t = 1/2c generations ago.We used the PSMC69), and by the comparison of π ratios calculated using the vcftools v.0.1.14 (ref. 65). To detect genomic regions under selection during domestication, we calculated the ln/ln(2). Also, we estimated the ln(π-Control/π-Target)/ln(2) between populations of domestic sheep with contrasting phenotypes for a specific target trait. The specific populations involved in comparisons between wild and domestic sheep and pairwise comparisons between domestic populations for detecting the signals associated with particular traits are shown in Supplementary Table π were calculated with a 50 kb sliding window and a 25 kb sliding step. For the XP-CLR approach, a 0.5 cM sliding window with a spacing of 2 kb across the whole genomes were used for scanning, and 200 SNPs were assayed in each window with the parameters -w1 0.005 200 2,000 chrN -p0 0.95. To assess the statistical significance of the XP-CLR value for each window, we first estimated the proportion of SNPs with extreme XP-CLR values  in the sliding windows, and then calculated the P values from the empirical distribution of the proportion scores obtained with these windows. In each comparison, the genomic regions in the top 1% XP-CLR values and ln(π ratio)/ln(2) values across the whole-genome were considered to be the selective sweeps.For SNPs, we performed tests for selective sweeps during domestication and breeding using two approaches based on the SNPs with less than 10% missing data: the XP-CLR approach implemented in the XP-CLR v.1.0 (ref. iHS across the genomes of Asiatic mouflon and different groups of domestic sheep populations using the Selscan v.1.2.0 (ref. 70) after filtering all missing data, with 50 kb sliding windows and 25 kb stepwise, a recombination rate of 1 cM Mb−1 (ref. 9) and default parameters –max-extend 1,000,000 –max-gap 200,000 –cutoff 0.05 (Supplementary Methods). We computed the proportions of SNPs with normalized |iHS | >2 in non-overlapping windows, and identified those windows within the top 5% empirical cutoff 71 in the tested group as the signals of positive selection. We also employed the HKA test72 to identify the selective signals associated with domestication and specific traits using Asiatic mouflon as an outgroup after filtering all missing data (Supplementary Methods). We calculated the χ2 statistic in 50 kb sliding windows and shift of 25 kb across the genome to find potential selective signals deviating from genome-wide neutral expectations. Two loci were analyzed each time, one was the 50 kb window taken from the tested genome and the other was the virtual neutral 50 kb window in terms of the average value of nucleotide statistics in the whole genome. After application of the HKA test for each sliding window, the χ2 statistic used to measure the goodness-of-fit was obtained and subsequently used to identify the selective signals.Moreover, we estimated the VST, an analog to FST. VST estimates population differentiation based on the quantitative intensity data and varies from 0 to 1 (ref. 25). The statistic VST of each CNV region was calculated to detect the selective signals between different comparisons25. VST is defined as (VT − VS)/VT, where VT is the variance of all the CNVs among all unrelated individuals in the target and control populations while VS represents the average variance in the target and control populations weighted for population size. The CNVs with the top 1% VST values were considered as the selective CNVs.For CNVs, we calculated a statistic 73) based on a panel of 109, 146, and 123 samples collected from 11, 15, and 13 breeds, respectively . To avoid potential false positives in multiple comparisons, the whole-genome significance threshold was adjusted via the Bonferroni test75. For SNPs, we set the thresholds as −log10 = 6, 6, and 4 for litter size, numbers of horns and nipples, respectively. For CNVs, we set the thresholds as −log10 = 5.28, 5.29 for litter size and number of horns, respectively, but −log10 = 4 for number of nipples using GEMMA v.0.96 based on the genotypes of CNVs selected by the DELLY and CNVnator. In addition, the quantile–quantile (Q-Q) plots of the MLM for individual traits were implemented in R Bioconductor.Association analyses of litter size, numbers of horns and nipples were performed using the MLM in the GEMMA v.0.96 .We performed permutation test to check if the overlaps between selective sweeps/GWAS hits and QTL regions were significantly different from those expected at random. To this end, we used BEDTools v2.26.0 shuffle to generate simulated data sets by randomly selecting genomic regions of equal number and size to the observed selective sweeps/GWAS hits in the sheep genome, and we replicated this process 10,000 timesTM RNA Library Prep Kit  and sequenced using the Illumina HiSeq X Ten System to generate 150-bp paired-end reads. We used the HISAT v2.1.0, StringTie v2.0, and Ballgown package in R version 3.5.3 (ref. 77) to map the paired-end reads to the sheep reference genome, assemble the reads, and estimate the gene expression levels, respectively. The number of reads matched to an expressed gene was standardized as fragments per kilobase of exon per million mapped fragments (FPKM) values. We employed the stattest function in the Ballgown package77 to search for transcripts that were differentially expressed between the thin-tailed breed (Chinese Merino sheep) and fat-tailed/fat-rumped breeds , following correction for any differences in expression owing to population variables. This allowed us to get the confounder-adjusted fold changes between the two tested groups. The genes that exhibited |log2(fold change)| ≥ 2 and adjusted P ≤ 0.05 in the comparisons between fat-tailed/fat-rumped and thin-tailed individuals were considered as differentially expressed genes.We collected tail adipose tissues from thin-tailed, short fat-tailed, long fat-tailed, and fat-rumped sheep for RNA-Seq analysis  and qPCR. The three biological replicates of adipose samples from different individuals for each tail type were used. The total RNA was extracted using the Trizol RNA Reagent  and were treated with RNase-free DNase I to remove DNA using the RapidOut DNA Removal Kit . The first-strand cDNA was synthesized using the RevertAid First Strand cDNA Synthesis Kit . Following the manufacturer’s instruction, 500 ng of RNA was reverse transcribed as the template for RT-PCR in 40 μl volume , 8 μl 5× Reaction Buffer (including 250 mM Tris-HCl (pH 8.3), 250 mM KCl, 20 mM MgCl2, 50 mM DTT), 2 μl RiboLock RNase Inhibitor (20 U μl−1), 4 μl 10 mM dNTP Mix, 2 μl RevertAid RT (200 U μl−1) and 2 μl nuclease-free water) and a thermocycling condition at 25 °C for 5 min, 42 °C for 60 min, and 70 °C for 5 min. Subsequently, the qPCR with SYBR Green  was performed on the QuantStudioTM 6 Flex Real-Time PCR System  using the first-strand cDNA and the primers designed based on the 5′- and -3′ end sequences of PDGFD gene (PDGFD F: 5′-GCGGATGCTCTGGACAAA and PDGFD R: AAGGAGGCAGCGTGGAAA-3′). The qPCR reactions and conditions were set as those described above. Each qPCR was run three times for one sample as technical replicates. Based on the qPCR results, the expression level was calculated using the 2Ct−ΔΔ method78 and normalized according to the internal control β-actin gene. The primers for β-actin were β-actin F (5′-CCAACCGTGAGAAGATGACC) and β-actin R (CCCGAGGCGTACAGGGACAG-3′).We examined the gene expression level of Protein was extracted from the tail adipose tissue using the Total Protein Extraction Kit . The protein extract was mixed with an equal amount of sample buffer and then separated on 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) gels (60 μg per lane). The SDS-PAGE-separated proteins were electrophoretically transferred to a polyvinylidene fluoride (PDVF) membrane and then incubated for 3 hours at room temperature in blocking buffer (5% BSA in PBS-Tween 20). Immunodetection was carried out with the Rabbit Anti-beta Actin antibody , Anti-SCDGFB/PDGFD antibody  and Goat Anti-Rabbit IgG H&L . The blot signals were imaged using Tanon 6100 Chemiluminescent Imaging System and quantified using ImageJ (NIH) software. Photoshop CS6 were used to crop images from unprocessed images.http://www.eytest.com/ey31f1.html). The tails were categorized into short fat-tailed, long fat-tailed, thin-tailed, and fat-rumped types according to the shape of tails of the animals as well as the recorded information for the breeds. Reproductive traits included number of litter per year, litter size in each birth, and seasonal or non-seasonal estrus extracted from the breeding records.Individual phenotype for traits, such as coat color, classes of fiber fineness, ear size, numbers of nipples and horns, and tail configurations were recorded for all the breeds whenever possible during sampling. Five different coat colors or color patterns, including white, white body with black head, black, brown, and gray were recorded for the animals sampled. The number of nipples ranged from 2  to 5 (selected). The horn phenotypes varied from polled to horned animals with 2–5 horns. The wool was graded into three classes  according to the British Wool Grading System  approved by the Committee for Animal Experiments of the Institute of Zoology, Chinese Academy of Sciences (CAS), China. For domestic sheep, animal sampling was also approved by local authorities where the samples were taken. For Asiatic mouflon, we collected peripheral blood samples from 14 captive Asiatic mouflon after receiving authorization for research from the Department of Environmental Protection in Iran (no. 93/34089). For other two Asiatic mouflon samples from Shahr-e Kord, Iran, sampling procedure was also approved by the governorate of Chaharmahal and Bakhtiari of Iran (no. 97.32.43.33165).Further information on research design is available in the Supplementary informationPeer Review FileDescription of Additional Supplementary InformationSupplementary Data 1-49Reporting summary


To determine if requiring Dual Eligible Special Need Plans (D-SNPs) to receive approval from the National Committee of Quality Assurance and contract with state Medicaid agencies impacts healthcare utilization.N = 360,405). We use segmented regression models to estimate changes in the time-trend and slope of the outcomes associated with D-SNP regulations, during the post-implementation (2012–2015) period, relative to the pre-implementation (2010–2011) period. Models include a treatment-status indicator, a monthly time-trend, indicators and splines for the post-period and the interactions between these variables. We conduct the following sensitivity analyses: (1) Re-estimating models stratified by state (2) Estimating models including interactions of D-SNP implementation variables with comorbidity count to assess for differential D-SNP regulation effects across comorbidity level. (3) Re-estimating the models stratifying by race/ethnicity and (4) Including a transition period (2012–2013) in the model.We use a Multiple Interrupted Time Series to examine the association of D-SNP regulations with dichotomized measures of emergency room (ER) and hospital utilization. Our treatment group is elderly D-SNP enrollees. Our comparison group is near-elderly (ages 60–64) beneficiaries enrolled in Medicaid Managed Care plans (p = 0.02)/ and among individuals with higher, relative to lower levels of co-morbidity .We do not find any statistically significant changes in ER or hospital utilization associated with D-SNP regulation implementation in the broad D-SNP population or among specific racial/ethnic groups; however, we do find a reduction in hospitalizations associated with D-SNP regulations in New Jersey (DD level = − 3.37%; These findings suggest that the impact of D-SNP regulations varies by state. Additionally, D-SNP regulations may be particularly effective in reducing hospital utilization among beneficiaries with high levels of co-morbidity.The online version contains supplementary material available at 10.1186/s12913-021-06228-3. More than 10 million individuals referred to as dual-eligible beneficiaries receive both Medicare and Medicaid health insurance benefits as a consequence of low income and assets in conjunction with age ≥ 65 years or disability [While differences in need partially explain the higher health care utilization and costs among dual-eligible beneficiaries relative to Medicare-only beneficiaries, care fragmentation has also been shown to play a role . A high In 2003, the Medicare Modernization Act authorized Dual-Eligible Special Needs Plans (D-SNPs), managed care plans offered by private insurers and targeted specifically to dual-eligible beneficiaries, with the goal of aligning financial incentives across Medicare and Medicaid to promote care coordination and reduce costs. Insurers offering D-SNPs are paid a fixed monthly amount per person (capitated payment) by Medicare to provide the full range of Medicare services and coordinate Medicaid services among dual-eligible beneficiaries. D-SNPs are at financial risk for Medicare costs in excess of the capitated payment but they are permitted to retain a portion of the Medicare payment not spent on covered services for contractually mandated activities . When D-Since the original authorization of D-SNPs, Congress has passed additional legislation with the aim of increasing quality of care, improving health outcomes and reducing healthcare costs among dual-eligible beneficiaries enrolled in D-SNPs . The MedNo studies, to our knowledge, have evaluated the impact of the PP-ACA D-SNP regulations (hereinafter referenced as “D-SNP regulations”) on healthcare utilization among dual-eligible beneficiaries. The only multi-state study of the effects of D-SNPs covered the pre-regulation time period 2007–2011) and focused exclusively on healthcare expenditures. Zhang et al. (2018) examined the impact of D-SNP penetration on state Medicare, Medicaid and total healthcare spending  and focu.In addition to examining effects of D-SNP regulations among the overall dual-eligible population, we conduct state-stratified analysis to reflect the heterogeneity in D-SNP regulation implementation across states. It is also important to examine effects across various subpopulations of beneficiaries. It is possible that certain subpopulations may experience more benefits associated with D-SNP enrollment than others. For example, the evidence supports the benefits of enhanced care coordination practices among subpopulations with the highest levels of medical complexity . ConsequEvidence has also shown that racial/ethnic minority beneficiaries, which constitute a disproportionate share of dual-eligible beneficiaries, are often enrolled in lower-performing managed care plans relative to non-minority beneficiaries . ConsequThis study contributes to the literature by using a quasi-experimental study design and administrative claims data from three states , from one of the largest insurers in the United States, to examine the impact of PP-ACA D-SNP regulations on emergency room (ER) and hospital utilization among community-dwelling elderly D-SNP enrollees, across states, co-morbidity level and racial/ethnic minority status. We hypothesize the following: 1) D-SNP regulations will lead to reduced ER and hospital utilization among D-SNP enrollees; 2) The effect of D-SNP regulations will be larger among enrollees with more, relative to less co-morbidity and 3) The D-SNP regulation effects will be stronger among racial/ethnic minorities.The study examines the ER/hospital utilization effects PP-ACA D-SNP regulations, specifically Medicaid contracting and NCQA MOC requirements. D-SNP contracting requirements grant state Medicaid programs the authority to define the scope of Medicaid benefits that a D-SNP must cover or coordinate, potentially shifting the financial risk for Medicaid cost-sharing requirements and high-cost Medicaid services such as nursing home care to the D-SNPs or the insurers that offer them. For example, states can mandate that insurers offering D-SNPs have a companion Medicaid Managed Long Term Services and Supports (MLTSS) plan, an insurance plan that offers Medicaid covered services under a capitated payment arrangement. This increased financial exposure at the plan/insurer level may incentivize better care coordination among D-SNPs, leading to reduced ER and hospital utilization. During the time period covered by this analysis, all three states had implemented contract requirements mandating closer relationships between D-SNPs and the entity offering MLTSS services. Specifically, Arizona, only contracted with D-SNPs that had a companion MLTSS plan, New Jersey restricted D-SNP enrollment to beneficiaries covered by an aligned Managed Care Organization (MCO) and Tennessee required D-SNPs formed after 2014 to have a companion MLTSS plan .The NCQA MOC requirements may also have the effect of improving care coordination among D-SNP beneficiaries. MOC requirements such as maintaining network-level medical provider access, the use of interdisciplinary care teams, performance of routine patient assessment, care management and performance measurement are aimed at developing a comprehensive assessment of beneficiary needs and facilitating the timely match between patient care needs and services. For example, a comprehensive patient assessment, conducted by a multi-disciplinary team , may reveal the need for home health services that may prevent a patient from going to the ER or requiring hospitalization . During An individual-level, Multiple-group Interrupted Time Series (MITS) study design was used to estimate changes in the monthly time trend of ER and hospital utilization associated with the D-SNP regulations, between the pre-implementation period 2010–2011) and the post-implementation period (2012–2015) , the largest health insurance provider in the United States. As of 2018, UnitedHealthcare had the largest market share for D-SNPs at 31% [Our outcomes included dichotomous indicators for whether an enrollee had any hospital admissions and any ER visits in the given month.Figure Utilizationit is the utilization  of person i in month t. “T” is a monthly linear time trend of utilization. “Regulation” is an indicator coded as “1” if the data is from a year ≥2012 and coded as “0” otherwise. “Time After Regulation” is a spline variable counting months from January 2012 forward . “D-SNP” is an indicator coded as “1” if the data is from a D-SNP beneficiary and coded as “0” if the data is from a MMC beneficiary. βit0 is the baseline healthcare utilization for the month in the near-elderly MMC beneficiary group, during the pre-regulation period. β1 is the slope of utilization among the MMC beneficiary group during the pre-regulation period. β2 is the immediate change in the level of utilization among the MMC beneficiary group immediately after implementation of D-SNP regulations. β3 is the gradual change in the slope of utilization among the MMC beneficiary group after regulation implementation. β4 is the difference in the pre-regulation utilization level across the MMC and D-SNP beneficiary groups. β5 is the pre-regulation difference in the utilization slopes across the MMC and D-SNP beneficiary groups. β6 is the difference in the size of the pre-post regulation change in the level of utilization across the MMC and D-SNP beneficiary groups. β7 is the difference in the size of the pre-post regulation change in the slope of utilization across the MMC and D-SNP beneficiary groups. β6 and β7 are the coefficients of interest and reflect the impact of D-SNP regulations net of secular time trends, provided the assumptions of the MITS are met. Coefficients for β6 and β7 that are statistically significant and ≤ 0 are supportive of our first hypothesis.Where Our other covariates (not shown in the equation above) include demographic factors , behavioral health conditions , medical co-morbidities , SSI receipt and state fixed-effects.For each outcome, we model the impact of D-SNP regulations using a Linear Probability Model (Ordinary Least Squares Regression applied to dichotomous outcomes). Use of the LPM model allows for direct interpretation of the regression coefficients and if the sample size is large, as is the case for this study, the point estimates will be statistically consistent . Standard errors were adjusted for within-person clustering of months using generalized estimating equations . To invep-value for all above-mentioned differences was < 0.01.The person-month demographic and clinical characteristics of the D-SNP and MMC-enrolled beneficiaries can be found in Table For completeness, we compare secular time trends for ER/hospital utilization across the D-SNP and the MMC groups during the pre-regulation period and do not detect significant differences 2010–2011) /; ; Table . Among t); Table . This es); Table . LikewisWe conducted the first study analyzing the impact of PP-ACA D-SNP regulations on ER and hospital utilization using administrative data from one of the largest health insurers in the United States, for three states  with a MITS study design. We did not find any statistically significant effects of D-SNP regulations among the total population of D-SNP enrollees, which is inconsistent with our hypothesis. However, we did find reductions in hospitalizations associated with D-SNP regulation implementation in New Jersey which supports our hypotheses. Additionally, we found evidence that the impact of D-SNP regulations on hospitalization grows as the number of co-morbidities increases, which supports our hypothesis that D-SNP regulations would have stronger effects among the subpopulation with the highest levels of co-morbidity. We also do not find evidence of differential impacts of D-SNP regulations across racial/ethnic subgroups, which does not support our hypothesis of stronger D-SNP regulation utilization effects among racial/ethnic minorities.The inability of this study to detect utilization effects of D-SNP regulations among the total population of D-SNP enrollees may be attributable to the states included in the study sample. Specifically, Arizona, which contributes the bulk of the data for D-SNP enrollees, developed the Arizona Long Term Care System (ALTCS), a managed care plan for dual-eligible beneficiaries (in the community or long term care settings), requiring nursing home level care  in 1989 . D-SNP eOur finding of stronger D-SNP regulation effects on hospital utilization with increasing co-morbidity is particularly important because studies suggest that individuals with higher levels of co-morbidity are less likely to enroll in managed care plans and are more likely to leave these plans even in states with seamless conversion . ConsequLastly, we do not find differential effects of D-SNP regulations among racial/ethnic minority groups. This finding is surprising given the higher baseline levels of ER/hospital utilization among racial/ethnic minorities, relative to non-minorities, in this sample Table . One potThis study has important limitations. First, we cannot guarantee that our treatment and comparison groups would have followed constant outcome trends in the absence of the policy change; which would represent a violation of a key assumption of our study design. However, data limitations preclude us from using an alternative comparison group for this analysis. Additionally, our study does not adjust for selection bias. Since the treatment group reflects individuals who decided to enroll in D-SNPs, the findings of this study may not be attributable to the broader population of dual-eligible beneficiaries. However, we try to minimize this bias by controlling for a number of demographic, socioeconomic and health factors that have been associated with plan selection and ER/hospital utilization. Additionally, our study design does not control for interventions that are temporally proximate to the implementation of D-SNP regulations if they did not impact the ER/hospital utilization of both D-SNP and MMC enrollees in the same way. Data limitations also prevent us from evaluating the effects of D-SNP regulations on outcomes outside of ER/hospital utilization such as nursing home days which would be valuable for constructing a more comprehensive assessment of the impact of D-SNP regulations. Our study sample also comes from a single insurer and includes states with longstanding experience in implementing integrated care plans and that have been at the forefront of the managed care movement among dual-eligible beneficiaries. All D-SNPs in our analysis effectively operate as Highly Integrated D-SNPs (HIDE-SNPs) in that they offer Medicare and MLTSS or behavioral health benefits under a single plan, through a companion plan or through a subsidiary offered by the parent organization . ConsequWhile our study did not find an effect of D-SNP regulations on ER/hospital utilization among the broader population of dual-eligible beneficiaries, our findings suggest positive impacts of D-SNP regulations on hospitalizations in states with low baseline levels of integration that mandate integration across all D-SNPs. Additionally, the reduction in hospitalizations appears proportional to the co-morbidity level of the patient population. Consequently, the impact of D-SNPs on healthcare utilization and costs may be enhanced by increasing enrollment and retention among the beneficiaries with the highest level of co-morbidity in the most integrated plans.Additional file 1.


In the traditional single marker regression approach for genome-wide association studies, if the number of involved individuals is small and the number of single nucleotide polymorphisms (SNPs) to be tested is very large, the probability of getting a significant association simply due to chance becomes enormous. Other techniques, such as the Bayesian methods, require several a priori assumptions, as an a priori posterior inclusion probability threshold, that can limit their effectiveness. In the present study, a multivariate algorithm able to partially overcome this problem was proposed. On simulated data, with 3000 individuals, only 13 and 3 quantitative trait loci (QTLs) were obtained with the single marker regression and the Bayesian approaches, respectively. On the other hand, the multivariate algorithm detected 65 QTLs in the same scenario. The gap between the single marker regression and the multivariate methods slowly decreased as the number of animals increased. This figure was also confirmed on real data.Genome-wide association studies (GWAS) are traditionally carried out by using the single marker regression model that, if a small number of individuals is involved, often lead to very few associations. The Bayesian methods, such as BayesR, have obtained encouraging results when they are applied to the GWAS. However, these approaches, require that an a priori posterior inclusion probability threshold be fixed, thus arbitrarily affecting the obtained associations. To partially overcome these problems, a multivariate statistical algorithm was proposed. The basic idea was that animals with different phenotypic values of a specific trait share different allelic combinations for genes involved in its determinism. Three multivariate techniques were used to highlight the differences between the individuals assembled in high and low phenotype groups: the canonical discriminant analysis, the discriminant analysis and the stepwise discriminant analysis. The multivariate method was tested both on simulated and on real data. The results from the simulation study highlighted that the multivariate GWAS detected a greater number of true associated single nucleotide polymorphisms (SNPs) and Quantitative trait loci (QTLs) than the single marker model and the Bayesian approach. For example, with 3000 animals, the traditional GWAS highlighted only 29 significantly associated markers and 13 QTLs, whereas the multivariate method found 127 associated SNPs and 65 QTLs. The gap between the two approaches slowly decreased as the number of animals increased. The Bayesian method gave worse results than the other two. On average, with the real data, the multivariate GWAS found 108 associated markers for each trait under study and among them, around 63% SNPs were also found in the single marker approach. Among the top 118 associated markers, 76 SNPs harbored putative candidate genes. Genome-wide association studies (GWAS) are mainly aimed at understanding the genetic background of complex traits by relating a large number of single nucleotide polymorphism (SNP) genotypes to observed phenotypes. Traditionally, GWAS is carried out by using the single marker regression model that includes both fixed and random effects. We will refer to this approach as traditional GWAS (T-GWAS). When the number of SNPs to be tested is very large, the probability of getting a significant association simply due to chance becomes enormous. This multiple effect is often controlled by using the Bonferroni’s correction that, however, requires that all tests are independent of each other . In genoEvidently, a T-GWAS has a high probability of obtaining reliable results if the number of involved individuals is large. Frequently, this number is small due to the high genotyping costs or to difficulties in phenotype recording . Moreover, some species or breeds, as local breeds, have an intrinsically small population size, thus conditioning a priori any association study. The combination of the great number of markers and the small number of genotyped individuals leads to obtain very few associations.A significant SNP usually explains only a small fraction of the genetic variance of quantitative traits ,5. Genetp-value) to declare a SNP associated to a particular trait and are able to analyze hundreds of thousands of SNPs simultaneously.The aim of the present research was to propose a new statistical approach to perform a GWAS, able to overcome most of difficulties encountered with T and B-GWAS, especially when few genotyped animals are involved. The method, called multivariate GWAS (M-GWAS), exploits three multivariate discriminant techniques that are successively applied to the data: the canonical discriminant analysis (CDA), the discriminant analysis (DA) and the stepwise discriminant analysis (SDA). The above listed techniques are not considerably affected by the number of involved individuals, do not require any statistical test  are highlighted by using the three multivariate discriminant procedures listed above.The proposed M-GWAS algorithm was tested both on a simulated cattle population and on the real data from a beef cattle breed previously analyzed with a T-GWAS, whose results were published by Sorbolini et al. .−5 per locus per generation [A cattle population was created using QMSim . Data weneration . The hisThe real data used in the present research was previously analyzed by Sorbolini et al.  who carrPhenotypes analyzed in this study were pre-adjusted both for fixed and random effects. For the simulated phenotype, the following mixed linear model was used:The model used for the real data was :(2)Y=D+bAnimals both in the 20 simulated datasets and in the real data were ranked according to their corrected phenotypes and divided into low phenotype (LP) and high phenotype (HP). The two groups contained an equal number of individuals.p-values of each SNP were corrected using the FDR procedure [For the simulated data, the T-GWAS was developed using PLINK software according to Marees et al. . Since trocedure ,3.https://cnsgenomics.com/content/software). Briefly, in the Bayesian R approach, the SNP effects are drown from one of four normal distributions with N , N , N  and N, where σ2g is the additive genetic variance. The procedure classifies SNPs, according to their posterior inclusion probability, into one of the four distributions. Two different strategies were adopted to the select associated markers. In the first, one SNP was declared associated to the trait under study if its posterior inclusion probability threshold was greater than 0.30 [Among the various Bayesian methods commonly used to identify marker-traits associations, the BayesR approach was preferred to the others for its ability in simultaneously accommodating SNPs with large, small, and no effect . Simulathan 0.30 . In the For real data, the T-GWAS results were those reported by Sorbolini et al. .i are the original variables (SNP genotypes in the present study), and di are the canonical coefficients (CNC), i.e., the loadings of original variables in composing the CAN. The greater a CNC , the larger the contribution of the corresponding variable to the CAN.Three multivariate discriminant techniques were successively applied to the data: the canonical discriminant analysis (CDA), the discriminant analysis (DA) and the stepwise discriminant analysis (SDA). CDA is a multivariate dimension-reduction technique whose main objective is the determination of relationships among a categorical variable and a list of independent variables. In particular, CDA tests whether the independent variables are able to identify the groups listed in the categorical variable. In our research, the categories were HP and LP, whereas the independent variables were the SNP genotypes. CDA derives a set of new variables, called canonical functions (CAN) that are linear combinations of the original characters. The structure of the generic CAN is:In general, if k-groups are involved, the CDA extracts k-1 CANs. In this research, having two groups (HP and LP), only one CAN for each phenotype was obtained. The separation between the groups can be assessed by the means of the Mahalanobis distance and the corresponding Hotelling’s T-squared test . This teThe DA was used to classify animals into groups (LP or HP in this research). In DA, the CAN (extracted in the CDA) is applied to each individual, thus producing a discriminant score. An animal is assigned to a particular group if its discriminant score is lower than the cutoff value obtained by calculating the weighted mean distance among the group centroids .The CDA was applied by chromosome and the mean and the standard deviation of the absolute value of the CNCs in the CAN were calculated. Only markers whose CNC’s absolute value is greater than the mean plus one standard deviation are retained in each chromosome;Markers selected in the 29 autosomes were joined and the SDA was applied to obtain the maximum number of linearly independent markers. The resulting SNPs are, in consequence, not in complete linkage disequilibrium;Markers selected in the previous step were first used, in a new run of CDA, to test the effective separation of LP from HP, and then in a run of DA to ascertain if animals were correctly assigned to the group of origin (HP and LP);p-value < 0.001) and at the same time, all the animals were correctly assigned to LP and HP. The obtained SNPs were considered associated to the phenotype.Finally, the minimum number of markers able to significantly discriminate LP from HP and, in the same time, correctly assign animals to the true group of origin was selected. This objective is achieved applying, in sequence, the CDA and the DA and deleting, in a recursive procedure, SNPs with the lower CNC absolute values. The procedure stopped when Hotelling’s test was still highly significant , after the LP and HP groups were obtained, the M-GWAS method can be summarized as follows:Statistical analyses in the M-GWAS were developed by using the MIXED, CANDISC, DISCRIM and STEPDISC procedures of SAS, release 9.4 . The SAS code and data to develop the M-GWAS are available in For the simulated data, the ability of the GWAS approaches in detecting associations was assessed comparing the obtained number of the true associated SNPs, i.e., the markers close to one of the 203 (known) simulated QTLs (250 Kb upstream and downstream of the QTL position). Since a QTL could be associated with more than one SNP, the effective number of detected QTLs was considered the most important index for comparing the GWAS methods.http://genome.ucsc.edu./) and National Centre for Biotechnology Information (NCBI) (www.ncbi.nlm.nih.gov) databases. Intervals of 250 Kb upstream and downstream of each SNP were considered. Gene-specific functional analyses were performed by GeneCards (www.genecards.org) and NCBI databases consultation. The biological function of each annotated gene (and the related proteins) contained in the significant genomic regions was studied by means of an accurate literature search. Gene names and symbols were derived from the HUGO Gene nomenclature database (www.genenames.org).With real data, being QTLs and their position across the genome, unknown, the ability of M-GWAS in detecting SNPs strongly associated to some putative gene was tested through a gene discovery study. With this aim, for each of the 10 traits under study, a restricted pool of highly associated SNPs was selected. These markers were obtained by ranking the associated SNPs and fixing an arbitrary CNC threshold such as the number of remaining markers, which was, at the end, around 12 for each trait. Annotated genes were identified from the UCSC Genome Browser Gateway , M-GWAS always detected a greater number of QTLs than the T-GWAS did. When the animal sample size was small, from 250 to 750, T-GWAS was not able to highlight any association. Only 1 QTL was found with 1000 individuals. In any case, M-GWAS detected a larger number of QTLs than T-GWAS when the involved animals ranged from 250 to 3000. The gap in the QTL detection between M-GWAS and T-GWAS slowly decreased as the number of animals increased. When 8000 individuals were used, this difference was only of two QTLs. The proportion of detected QTLs with respect to the true associated markers a was almResults from B-GWAS2  were quiResults found by Sorbolini et al.  with theT-test p-value < 0.001) and the DA correctly assigned all the animals to the correct group.On average, around 190 linearly independent SNPs  for eachConsidering the markers associated in the T-GWAS , around The minimum number of SNPs able to significantly discriminate LP from HP and correctly assign animals to groups is also reported in The M-GWAS was then applied to the three traits  that in the T-GWAS did not have any associated SNP. The procedure selected 141, 148 and 176 linearly independent markers for CC, SW and pH24h, respectively, whereas the minimum number of SNPs able to significantly discriminate groups was slightly lower .Finally, a restricted group of top discriminant SNPs (118) was selected by using a CNC arbitrary threshold equal to 0.25 . Only thAmong the top discriminant markers, only 76 over 118 SNPs harbored genes in their boundaries .Twenty-six significant highly discriminant markers were found to be associated with SC. For example, in BTA 2, close to ARS-BFGL-NGS-71755 and to BTB-02054371, the gene Titin (TTN) that encodes a large protein of striated muscle and the protein activator of interferon-induced protein kinase (EIF2AK2) can be found, respectively.Eighteen highly discriminant SNPs were found to be associated with CW but only one of them (UA-IFASA-6018 on BTA22) was close to two interesting genes: the transketolase (TKT) and protein kinase C delta (PRKCD).For ADG, seventeen highly discriminant SNPs, distributed across ten chromosomes, were selected. For example, on BTA 3, four candidate genes close to marker ARS-BFGL-NGS-119955 were considered as interesting: PAS domain-containing serine/threonine kinase (PASK), the high density lipoprotein-binding protein (HDLBP), the inhibitor of growth family member (ING5) and the deoxythymidylate kinase (DTYMK). On BTA10 instead, two putative genes were found close to marker ARS-BFGL-NGS-116295: lactase-like (LCT) and a member of small nuclear RNA-activating complex polypeptide 5 (SNAPC5).Seventeen highly discriminant SNPs were found associated with DP. In particular, on BTAs 18 and 22 two loci were identified as candidate genes, the syntrophin beta 2 (SNTB2) and the solute carrier family 6 member 6 (SLC6A6), respectively.In addition, for HW, several (12) highly discriminant markers were found. Among the others, on BTA 7 at 44.8 Mb three putative candidate genes involved in the brain biology were annotated, the Basigin (OK blood group) (BSG), the hyperpolarization-activated cyclin nucleotide gated potassium channel 2 (HCN2) and the follistatin-like 3 (FSTL3).A total of nine highly discriminant SNPs distributed across six autosomes were found associated with BW. On BTA 4, the annotated sequence nearest to the marker BTB-00182742 was the phosphoinositide 3-kinase gamma (PIK3GC). On BTA 22 the marker Hapmap41774-BTA-121358 was already reported as significant for CW.Only five highly discriminant markers were found to be associated with pH. For exanple, on BTA18, the phosphorylase kinase regulatory subunit beta (PHKB) was associated with the marker ARS-BFGL-NGS-24006, whereas on BTA 23 at 9.1 Mb, close to the marker Hapmap38418-BTA-146026, the gene peroxisome proliferator-activated receptor delta (PPARD) was detected.Moreover, for CC, five highly discriminant associated markers were found. In their neighbor fourteen genes were detected. Among them, for example, on BTA 1 the TBC1 domain family member 5 (TBC1D5) gene was retrieved.For SW, four highly discriminant-associated markers were found with a total of 15 genes in the nearest of them. Among these genes, two, the BAF chromatin remodeling complex subunit BCL11A (BCL11A) and PTEN-induced kinase 1 (PINK1) were mapped on BTA11 and BTA2.Finally, for pH24h, five highly discriminant SNPs were found with 31 genes in their boundaries. The chromosome with the highest number of genes was BTA29 (11), followed by BTA19 (10). Other genes were found on BTA9 and 10 (three genes each one) and BTA27 (four genes). Most of these genes were reported in literature to be associated with meat traits.The M-GWAS approach was tested and compared with T-GWAS and B-GWAS using genotypes from a simulated bovine population of 12,000 individuals. All the statistical techniques were applied to 20 random sub-samples extracted from the population with an increasing sample size . In M-GWAmong the four statistical approaches, the B-GWAS1 gave the poorest results, with a maximum number of detected QTLs equal to seven with 9000 animals. If these data were compared with that obtained by B-GWAS2, it appears obvious that the reason of the great difference between the number of detected QTLs by the two methods was due to the a priori posterior inclusion probability threshold that was imposed on B-GWAS1 . The neeApart from the initial scenarios where M-GWAS clearly prevailed on T-GWAS, the proportion of detected QTLs with respect to true associated markers a was neaWith a reduced number of animals, the proportion of false positive markers was greater for M-GWAS than for T-GWAS. These values slowly met as the number of involved individuals increased. However, considering that a lot of false associations can be discarded when the gene discovery is developed, the high number of false positive SNPs does not invalidate the multivariate procedure. In any case, the overall results indicate that especially when the number of genotyped animals is low, the M-GWAS could be preferred to T-GWAS.Several QTLs were simultaneously detected by M-GWAS and the other methods . ResultsThe M-GWAS applied by chromosomes selected 1031 associated SNPs for the first seven traits  under study. Their distribution across the genome is displayed in Considering one of the seven traits, BW for example, the 191 selected SNPs perfectly captured the differences between the animals belonging to the LP and HP. However, not all SNPs equally contribute in separating groups. Markers with greater CNC absolute values have a more important role in discriminating groups than those with lower CNCs. According to this suggestion, the minimum number of markers, for each phenotype, able to discriminate groups was obtained  by deletIn the T-GWAS developed by Sorbolini et al. , SW, CC Finally, for the 10 traits, a pool of markers with a CNC absolute value greater than 0.25 was selected. This threshold allowed to obtain a restricted number of highly discriminant markers (108) to submit to gene discovery analysis. Their distribution is reported in The number of highly discriminant SNPs obtained for SW, CC and pH24h  was loweAPLNR and TKT loci are involved in the glucose metabolism, whereas SLC6A6 gene was related to the taurine transmembrane transport activity.The gene discovery analysis conducted on the 108 most discriminant SNPs highlighted several interesting candidate genes, only in the nearby of 76 SNPs. TBC1D5 was reported to be associated with the sum of omega-3 and the relative growth rate [RIPK1, associated with cellular response to stress and apoptosis; TUBB2B, associated with gap junction; SLC22A23, associated with ion transport. Finally, the AANAT gene was flagged for pH24h: this gene was associated with the muscle fatty acid profile [Among the genes highlighted for CC, wth rate  and withwth rate . For thewth rate  highligh profile , collage profile .In the present research, a multivariate approach to develop a GWAS was proposed. The results of the simulation study highlighted the ability of M-GWAS in detecting the true associated markers and QTLs, especially when a small number of animals are genotyped and phenotyped. The proposed approach was more efficient than T-GWAS and B-GWAS1, also when the number of available individuals increases. Performances of M-GWAS and T-GWAS converge when more than 8000 animals are used. The B-GWAS2 approach gave results sometimes better than the M-GWAS, however, B-GWAS2 requires the number of associated SNP to be previously fixed by M-GWAS. The proposed methods detected, at least in part, QTLs in different regions of the genome. Therefore, their combined use, especially when the number of involved animals is high (more than 5000) could enhance the number of discoveries. The ability of M-GWAS in detecting associations was also confirmed when real data was used. For the first seven traits, the multivariate approach found around 60% of the associated markers obtained with the T-GWAS. Moreover, associations were detected by the M-GWAS also for the last three traits that, in the traditional approach, did not have any significant SNPs. The reliability of those associations was verified by the gene discovery analysis that was carried out on most discriminant markers.


Vigna radiata (L.) R. Wilczek, or green gram) is important tropical and sub-tropical legume and a rich source of dietary protein and micronutrients. In this study we employ GWAS to examine the genetic basis of variation in several important traits in mungbean, using the mini-core collection established by the World Vegetable Center, which includes 296 accessions that represent the major market classes. This collection has been grown in a common field plot in southern European part of Russia in 2018.Mungbean  are located. Phenotyping of WorldVeg collection for maturation traits in temperate climatic locations is important as phenology remains a critical breeding target for mungbean. As demand rises for mungbean, production in temperate regions with shorter growing seasons becomes crucial to keep up with needs. Uncovering SNPs for phenology traits will speed breeding efforts. Vigna radiata (L.) R. Wilczek, or green gram) is an important but underutilized tropical and sub-tropical legume that is a rich source of dietary protein and micronutrients and plays an vital role in restoring soil fertility in a variety of crop rotational systems. Mungbean has been of the greatest agronomic and cultural significance in India, which accounts for most of the 6 million hectares of it that are produced globally, as well as other tropical countries such as China, Indonesia, Thailand, Myanmar, and the Philippines [Mungbean , assessing the genetic basis of flowering time and related agronomic traits is critical for finding useful genetic variation for ongoing breeding efforts. Genome-wide association studies (GWAS) have emerged as powerful approach for finding genetic variation in germplasm. This approach has been used in a range of legumes, from soybean  and the uncatula –11, to ouncatula , 13, cowuncatula  and pigeuncatula .Here we employ GWAS to examine the genetic basis of variation in several important traits in mungbean, using the mini-core collection of mungbean established by the WorldVeg, which includes 296 accessions that represent the major market classes of mungbean and all the microsatellite alleles present in a collection of 1400 accessions . This coGenotyping by Sequencing (GBS) was used to survey polymorphism within the genomes of 293 accessions. SNPs were filtered to retain polymorphisms present in at least 90% of genotypes with a minor allele frequency of at least 3%. Four thousand two hundred eighty-nine polymorphisms are distributed among all chromosomes, but with variable density (from 269 SNPs on chromosome 4 to 632 SNPs on chromosome 8) and 752 SNPs are on scaffolds Fig. a, b.Fig2 maximum value (r2 = 0.29) was taken as a critical value. The smothering spline of intra-chromosomal r2 values intersects this threshold at approximately 105 kb physical distance . Figure ST) among the four sub-populations was calculated using VCFtools [ST = 0.08) and populations 2, 3 and 4 show considerable degree of differentiation  . The analysis revealed significant associations with two phenotypic traits (possibility of maturation and hypocotyl colour), measured in Kuban  based on linkage disequilibrium , such as A20/AN1 zinc-finger domain-containing proteins play important roles in abiotic stress signaling in plants. SAPs are important candidates for preventing the loss of yield caused by exposure to environmental stresses .Mungbean, long an orphan legume crop of the tropical world, is emerging as a multi-faceted crop suited to a range of agroecologies. As a rich source of protein and micronutrients that can be consumed in many ways , and with seeds that can be rapidly cooked, it has a place in a number of traditional cuisines across Asia, and in increasingly diverse global palettes. It is a key ingredient in the new vegetarian egg (Just eggs), showing its adaptability as a protein source. Furthermore, with a short duration lifecycle, it fits into a range of crop rotations. Аdapting varieties to a range of conditions from tropical to temperate regions is necessary to meet rising global demand.ST among the four sub-populations, two populations (populations 1 and 4 representing Indian subcontinent) were closely related, whereas others showed considerable degree of differentiation.The core collection of mungbean established by the WorldVeg is a valuable resource for mungbean breeding. Two hundred ninety-three accessions that passed all filters and remained for our analysis were collected from 17 countries all over the world. 70% of accessions were attributed to one of the four populations identified in structure analysis. Populations 1 and 4 consist of accessions from Indian subcontinent (India and Pakistan), they are clustered together as seen in PCA plot Fig. b. Popula2 values at approximately 105 kb physical distance. The extent of LD reflect the effective size of population and the extent of population admixture. It is typically smaller in natural populations, strongly extended in bread cultivars, and intermediate among landraces [LD was estimated between 5041 SNPs and fell to half of the maximal randraces .Phenology will remain a critical breeding target for mungbean. As demand rises for mungbean, production in temperate regions with shorter growing seasons can help demand. Furthermore, with climate change, seasons in semi-arid tropical regions, such as much of South Asia and East Africa, may also become shorter. In the Kuban region in 2018 abnormally hot weather persisted through the flowering and beginning of maturation phases of some accessions and the development of the accessions was extremely non-uniform. This non-uniformity was elicited by plant exposure to different photoperiod in conjunction with drought, and represents plant response to these incidental stresses.By performing GWAS on mungbean mini-core collection planted in Southern Russia, we have found several loci significantly associated with maturation and colour of hypocotyl. We found that SNPs associated with possibility of maturation in Kuban localize to a region on chromosome 2 with strong linkage in which genes encoding zinc finger A20 and AN1 domain SAP are located. SAPs are known to prevent the loss of yield caused by environmental stresses . UncoverHere we perform GWAS in accessions of mungbean, using the mini-core collection established by the WorldVeg. The collection has been grown in southern European part of Russia in 2018 under incidental stresses caused by abnormally hot weather and different photoperiod. We have found several loci significantly associated with maturation and colour of hypocotyl. We found that SNPs associated with possibility of maturation in Kuban localize to a region on chromosome 2 with strong linkage, in which genes encoding zinc finger A20 and AN1 domain SAP are located. SAPs are known to prevent the loss of yield caused by environmental stresses. Phenotyping of this collection for maturation traits in temperate climatic locations is important for future breeding applications.Vigna radiata) minicore collection was assembled from accessions held in trust under the multilateral system by the World Vegetable Center genebank as described in [http://seed.worldveg.org/) . The list of phenotypes and units of measurement are in Additional file Shapiro-Wilk test for normality  was implTotal genomic DNA was extracted from bulked young leaves of three single plants. GBS was performed at Diversity Array Technology P/L (DArT). The sequence data generated were then aligned to the mungbean reference genome sequence, Vradi_ver6 [SNPs were further filtered using VCFtools  to requiPrincipal component analysis (PCA) was conducted using the “SNPRelate” R library . Custom 2) between genotypes. VCFtools [2 values. Only r2 values for SNPs with the physical distance between markers less than 500 kb were used to plot LD decay graph. LD decay was assessed by fitting a smooth spline of intra-chromosomal r2 values against the physical distance (bp) between markers in R [2 maximum value was taken as a critical value. The threshold beyond which the LD was accepted as real physical linkage was estimated to be r2 = 0.29. The intersection of the smothering spline of intra-chromosomal r2 values with this threshold was considered to be an estimate of the range of LD.LD was estimated using the squared correlation coefficient  among the four sub-populations was calculated using VCFtools [Structure-like Population Genetic Analysis in R package LEA  was usedVCFtools .GC). We used a false discovery rate (FDR) [GWAS analysis was performed using a single-locus linear mixed model, implemented in FaST-LMM toolset  . Principte (FDR)  of 0.05 te (FDR)  in R. Ante (FDR)  databaseTo divide the genome into haplotype blocks (haploblocks) based on linkage disequilibrium, Haploview tools  were appAdditional file 1: Figure S1. Cross-entropy plot for 293 mungbean accessions. X-axis indicates the number of ancestral populations, Y-axis represents the minimal cross-entropy. Ten independent runs were performed for each simulated value of K, ranging from 1 to 10. K-value for which the cross-entropy curve exhibits a plateau was chosen (K = 4).Additional file 2: Table S1. List of accessions, collection sites and accession’s groups. Four populations were identified using Structure-like Population Genetic Analysis in R package LEA. An individual accession with more than 55% identity from a single sub-population was classified as representative of that sub-population. The remaining accessions were considered as admixture.Additional file 3: Table S2. Number of mungbean accessions in populations among the 17 countries of origin.Additional file 4: Figure S2. Scatter plots of the first five principal components of PCA analysis based on 5041 SNPs. Each dot represents an accession. Color-coded is according to membership (based on > 55% of identity) to populations identified from structure analysis.Additional file 5: Table S3. Fixation index (FST) among the four sub-populations.Additional file 6: Table S4. List of phenotypes measured at Kuban in 2018.Additional file 7: Table S5. The proportion of variance in a phenotype explained by all GWAS SNPs (i.e. the SNP-based heritability) for traits measured in Kuban.Additional file 8: Table S6. The correlation coefficients between phenotypic traits.Additional file 9: Figure S3. Summary of GWAS analyses for phenotypic trait Possibility of maturation measured in Kuban. (a) SNP QQ-plot. (b) SNP Manhattan plot (different colors correspond to different chromosomes). SNPs with q-value < 0.05 are shown for each chromosome, marked as triangles.Additional file 10: Figure S4. Summary of GWAS analyses for phenotypic trait Hypocotyl color measured in Kuban. (a) SNP QQ-plot. (b) SNP Manhattan plot (different colors correspond to different chromosomes). SNPs with q-value < 0.05 are shown for each chromosome, marked as triangles.Additional file 11: Table S7. Significant SNPs for phenotypic traits measured in Kuban. Results of GWAS analysis for phenotypic traits measured in Kuban, 2018. Annotation of significant associated markers was performed using Legume information system (LIS) database.Additional file 12: Table S8. Haploblocks inferred by Haploview tools.


Fenugreek as a self-pollinated plant is ideal for genome-wide association mapping where traits can be marked by their association with natural mutations. However, fenugreek is poorly investigated at the genomic level due to the lack of information regarding its genome. To fill this gap, we genotyped a collection of 112 genotypes with 153,881 SNPs using double digest restriction site-associated DNA sequencing. We used 38,142 polymorphic SNPs to prove the suitability of the population for association mapping. One significant SNP was associated with both seed length and seed width, and another SNP was associated with seed color. Due to the lack of a comprehensive genetic map, it is neither possible to align the newly developed markers to chromosomes nor to predict the underlying genes. Therefore, systematic targeting of those markers to homologous genomes of other legumes can overcome those problems. A BLAST search using the genomic fenugreek sequence flanking the identified SNPs showed high homology with several members of the Trifolieae tribe indicating the potential of translational approaches to improving our understanding of the fenugreek genome. Using such a comprehensively-genotyped fenugreek population is the first step towards identifying genes underlying complex traits and to underpin fenugreek marker-assisted breeding programs. Trigonella foenum-graecum L.) is a small-seeded annual dicotyledonous legume that belongs to the family Leguminosae (Fabaceae). The genus Trigonella belongs to the Trifolieae tribe and the Trigonellinae subtribe that includes other several genera such as Trifolium, Melilotus, and Medicago [Fenugreek  is a small-seeded annual dicotyledonous legume that belongs to the family Leguminosae (Fabaceae). The genus Trigonella belongs to the Trifolieae tribe and the Trigonellinae subtribe that includes other several genera such as Trifolium, Melilotus, and Medicago [Fenugreek  or genes is by using the progeny of selected crosses such as recombinant inbred lines, backcrosses, or double haploid populations. However, this approach suffers from the limited variation existing in the parents and the time required to create such populations. Therefore, the use of the alternative approach known as association mapping (AM), relying on linkage disequilibrium (LD) between polymorphic molecular markers and the causal variants in a large number of individuals, has become commonplace. The advantage of AM over traditional QTL mapping is the use of already-existing naturally evolved and adapted genotypes with wider genetic variation, eliminating the need to generate new mapping populations. This criterion makes neat use of the historical recombination accumulated over hundreds or thousands of generations in a large number of diverse genotypes. To detect such crossing-overs, those genotypes need to be densely genotyped to obtain high statistical mapping resolution and to identify single nucleotide polymorphisms (SNP) associated with the examined trait ,16. To aMedicago truncatula (barrel clover) and Trifolium repens L. (white clover) [Trifolium pretense and Trifolium medium (zigzag clover) [Very little information is available about the fenugreek genome. Most available studies focused on identifying genes involved in the biosynthesis of diosgenin using de novo transcriptome sequencing  and next clover) , and bet clover) . To our knowledge, no previous study has examined the population structure and association mapping or homology analysis using fenugreek germplasm genotyped with a large number of SNPs. Therefore, we used a local collection of 112 genotypes collected from different locations in Egypt and genotyped at high density using the ddRAD-seq technique. To our knowledge, there are no certified accessions collected in the Egyptian gene bank. Therefore, we have collected seeds of 112 fenugreek genotypes directly from local farmers in all governorates that produce fenugreek . The larwww.zymoresearch.com). DNA concentration was measured using NanoDrop . One hundred micrograms of DNA per sample in 96 well plates were digested in 1× NEB Cut Smart Buffer with EcoRI and MboI (NEB) at 37 °C for 4 h. Following a 20 min 80 °C enzyme inactivation, samples were held at 12 °C until ligation with T4 DNA Ligase (NEB) and adapters containing 1 of 48 unique barcodes and Illumina-compatible P5 sequences coupled to an EcoRI overhang and Illumina-compatible P7 sequences coupled to the MboI overhang. Plates were incubated 8 h at 16 °C and heat inactivated at 80 °C for 20 min. Samples were then pooled in three pools of 40, 38 and 34 samples respectively and mixed with EDTA and ethanol precipitated. Pellets were re-suspended in EB, purified with PCR Purification columns  and further cleaned up with one volume of AMPure XP beads. One to three µg DNA was subjected to Pippin Prep size selection on a 2% dye-free agarose gel with internal size markers aiming for 350–500 bp inserts. Recovered samples were cleaned with AMPure XP beads and subjected to a pre-selection PCR (PreCR) in which a biotinylated forward primer and unique indexed reverse primers were used to amplify and tag desired DNA fragments. PCR products were cleaned up with Qiagen PCR purification columns and 1X AMPure XP beads as before. DNA fragments, with biotin at the 5′ ends, only were selected using Dynabeads M-270 Streptavidin coupled magnetic beads (ThermoFisher). Briefly, 50 µL of beads were mixed with up to 2000 ng of each pool and incubated for 20 min at RT. Bead/DNA complexes were captured and washed several times, then resuspended in 50 µL 1× SSC and heated at 98 °C for 5 min then placed on a magnet and supernatant removed as soon as possible. This elution was repeated, and the final supernatants were cleaned up with Qiagen PCR columns. The eluted ssDNA was quantified and diluted to 1 ng µL−1 with EB. A final PCR was performed on 10 ng of input DNA using P5 and P7 primers with only 8 cycles. Final PCR products were purified with 1× AMPure XP beads, quantified and assessed for quality on a Fragment Analyzer System . The samples were sequenced at the Texas A&M AgriLife Genomics and Bioinformatics Services on one lane of Illumina NovaSeq 6000 using a S4 XP sequencing kit. The raw sequencing reads are available through the NCBI BioProject number PRJNA648770, and the NCBI BioSample SAMN15647967. Genomic DNA was extracted from the seeds of 112 Fenugreek genotypes using Quick-DNA plant/seed Miniprep kit . Seed length and width were measured for every single seed and the mean value of the 15 seeds was used for association mapping. ImageJ generated three values of red (R), green (G), and blue (B) colors for each genotype that were used in the following equation [Seeds were cleaned and placed on a white opaque sheet with a rigid sparse to guarantee to have single separated seeds. For each genotype, 15 homogeneous, in shape and color, and healthy seeds were selected and separated then represented in one picture with a resolution of 4632*2608 (width*height). A digital camera was fixed at 25 cm height and JPEG images with a resolution of 96 dpi were taken and analyzed using ImageJ software  to search for sequences showing high homology to the contig sequences on which the significant SNPs associated with these traits were located. The identified top hit sequences were subsequently subjected to six-frame translation followed by a search for existing, conceptual, Open Reading Frames (ORFs). To predict the putative function of the genes represented by these ORFs, we scanned the corresponding protein sequences for conserved domains using the Simple Modular Architecture Research Tool (SMART) (http://smart.embl-heidelberg.de/). To investigate whether the identified genes, in which the SNPs are located, are transcribed, we collected public, raw, fenugreek RNAseq data from the Sequence Read Archive (SRA) at NCBI (https://www.ncbi.nlm.nih.gov/sra) and used it to construct either a genotype-specific (from one SRR file representing a single fenugreek accession) or a consensus (from multiple SRR files representing multiple fenugreek accessions) de novo transcriptome assembly. For that, we made use of the software package Trinity in combination with Trimmomatic for filtering and trimming the reads and then used local BLAST  to search for mRNA transcripts matching our contigs.To map possible candidate genes associated with seed length, width, and color, we used the online NCBI BLASTn tool and the non-redundant (NR) database . A total of 83,532 unique contigs were assembled and further used as reference for mapping the reads. Among these contigs, FreeBayes reported >2M raw variations that were further filtered to minimize the calling of false SNPs due to sequencing error, paralogs, or artifacts from library preparation. The raw variant call file (VCF) was filtered using vcftools v0.1.15 [The 38,142 generated SNPs of the 112 tested fenugreek genotypes were used as an input for the fastSTRUCTURE software , to builA high level of variation was observed among genotypes for seed length, with a heritability of 0.53, and seed width, with a heritability of 0.51 . MinimumP) association detection threshold was set to 6.5 and 5.9 based on Bonferroni correction, and FDR of α = 0.05, respectively. As shown by the Manhattan plots (P) values of 6.71 and 8.36, respectively (SNP-based association mapping was performed using a mixed linear model (MLM) excluding rare alleles with minor allele frequency (MAF) < 5%. The MLM included population structure (Q), and kinship matrix (K) to avoid spurious associations. The −log 10 = 6.88, FDR of α = 0.002, and explained 38% of the seed width variation. In total, the two SNPs explained 84% of the seed width variation. For seed color, the SNP dDocent_Contig_84790_24 was associated at −log10(P) = 6.32, FDR of α = 0.016, and explained 28% of the seed color. The sequences of these three ddRAD markers are provided in This SNP explained 39 and 46% of the seed length and width variation, respectively. The second significant SNP, dDocent_Contig_39741_151, was associated only with seed width with −log10 values of 6.7 and 8.4, and FDR of α = 0.007 and 0.000, respectively. However, using this very strict threshold would result in no significant SNPs associated with seed color. Earlier studies [P-value. Therefore, we checked the SNPs with −log10(P) values less than 6.5 and with FDR < 0.05. One SNP marker, dDocent_Contig_84790_24, was found to be associated with seed color at −log10(P) = 6.32. Considering the low FDR of this SNP, α = 0.019, and the high explained variance, 28%, of the observed variation, altogether, we believe this association can be considered as a true association. However, so far, no data is available which allows the alignment of the newly discovered markers in fenugreek to a comprehensive consensus map that covers its eight chromosomes [As large seeds are expected to emerge more rapidly, to have greater seedling survival rate, and stress tolerance, we have chosen to study seed length and width. The high level of variation together with the relatively high heritability recorded for both traits suggested the suitability of our collection for association mapping studies and to effectively map the associated SNPs that can be further used in the fenugreek marker-assisted breeding programs. We employed MLM including population structure (Q), and kinship (K) matrix to avoid spurious associations. The two commonly used multiple comparison methods to select for the significant threshold level in association mapping studies are Bonferroni correction  and fals studies ,48,49 haomosomes ,51. TherTrifolium pretense and Trifolium medium genomes were reported to have more than 30 and 40%, respectively, of retrotransposable elements in their genome [A more in-depth analysis of the identified trait-associating SNPs shows that two SNPs are located in contigs containing sequences that are conserved among several species from the Trifolieae tribe. The close taxonomic relationship implied by this sequences conservation raises the prospect that the same genomic regions harboring the SNPs might be controlling the same traits among the species that belong to this tribe, permitting a translational approach to gene function discovery. However, further studies are required to confirm this hypothesis. Our finding that two SNPs associated with seed size (length and width) and seed color are both localized in TE genes could be explained by the fact that other species from the Trifolieae tribe, in contrast to other legumes, have genomes that are rich in TEs. For example, r genome ,52, and r genome ,53,54 anM. truncatula and which was found to affect seed size [Trifolium pretense (PNX92211.1) which, similar to our SNP-related ORF, contains additional TE-related domains downstream of the Znf-C2HC domain. This strengthens the notion that this SNP could be in a gene that has a direct effect on seed size. For the other identified SNPs, associated with only seed width or seed color, it is however difficult to conclude whether the corresponding ORFs are affecting the respective trait. These results highlight the possible role of M. truncatula as well as the Trifolieae tribe as a proxy for gene content and order in the fenugreek genome. This is similar to previous studies that reported synteny between M. truncatula and legumes such as white clover [Due to the existence of linkage disequilibrium, we are aware that it is not likely that the identified SNPs are indicating the exact genes underlying a certain trait and that they could rather be merely an association with the trait. However, it is still possible that once a SNP is located in an ORF of a gene, investigating the putative function of this gene and its functional domains can help accepting or rejecting the hypothesis regarding the direct involvement of this gene in the studied trait. In the current scenario, gene modeling analysis and the RNAseq expression data indicate that all the associating SNPs were localized in transcribed genes. In the case of the SNP associated with seed length and width, dDocent_Contig_466_145, it is possible that the identified Znf-C2HC domain, downstream of the GAG domain of the TE is playing a role in gene transcription and thus affecting seed size. This would be similar to what has been reported for the ZnF-C2H2 domain encoding gene that was identified in eed size . Our fure clover , red cloe clover , birdsfoe clover ,58, comme clover , and fabe clover . Syntenye clover .M. truncatula and the Trifolieae tribe to improve our understanding to the fenugreek genome. Using such a well-genotyped collection to investigate more complex traits is the first step towards identifying the underlying genes. Further evaluating this population under diverse environmental conditions can help to dissect genotype by environment interactions and to improve fenugreek marker-assisted breeding programs.Genotyping an Egyptian collection of 112 fenugreek genotypes with 38,142 SNPs using the ddRAD sequencing enabled us to investigate the genetic diversity and the population structure of our collection. Our results revealed that the population is divided into two main and 5 sub-populations. We used seed length, width and color to prove the suitability of this population for association mapping studies and we found three trait-associated SNPs. Our results indicated the possible role of