Colletotrichum kahawae infecting green coffee berries. The pathogenicity was investigated by comparing genomic variation between C. kahawae and its non-pathogenic sibling species, while the aggressiveness was studied by a genome-wide association approach with groups of isolates with different phenotypic profiles. High genetic differentiation was observed between C. kahawae and the most closely related species with 5,560 diagnostic SNPs identified, in which a significant enrichment of non-synonymous mutations was detected. Functional annotation of these non-synonymous mutations revealed a significant enrichment mainly in two gene ontology categories, “oxidation–reduction process” and “integral component of membrane.” Finally, the annotation of several genes potentially under-selection revealed that C. kahawae’s pathogenicity may be a complex biological process, in which important biological functions, such as, detoxification and transport, regulation of host and pathogen gene expression, and signaling are involved. On the other hand, the genome-wide association analyses for aggressiveness were able to identify 10 SNPs and 15 SNPs of small effect in single and multi-association analysis, respectively, from which 7 were common, giving in total 18 SNPs potentially associated. The annotation of these genomic regions allowed the identification of four candidate genes encoding F-box domain-containing, nitrosoguanidine resistance, Fungal specific transcription factor domain-containing and C6 transcription factor that could be associated with aggressiveness. This study shed light, for the first time, on the genetic mechanisms of C. kahawae host specialization.Plants and their pathogens are engaged in continuous evolutionary battles, with pathogens evolving to circumvent plant defense mechanisms and plants responding through enhanced protection to prevent or mitigate damage induced by pathogen attack. Managed ecosystems are composed of genetically identical populations of crop plants with few changes from year to year. These environments are highly conducive to the emergence and dissemination of pathogens and they exert selective pressure for both qualitative virulence factors responsible for fungal pathogenicity, and quantitative traits linked to pathogen fitness, such as aggressiveness. In this study, we used a comparative genome-wide approach to investigate the genomic basis underlying the pathogenicity and aggressiveness of the fungal coffee pathogen  Plant diseases have become one of the most challenging threats to modern agriculture, not only for their huge economic impact caused by severe production losses, but also due to a global food security problem. Fungi are among the most devastating plant pathogens given their ability to overcome plant defenses and exploit the host’s resources for their own reproduction and dispersion . In factNowadays, thanks to the development of high-throughput sequencing (HTS) a new era in plant pathology has emerged, making possible to unveil the genetic mechanisms underlying the pathogenicity and aggressiveness of pathogens . Genome Colletotrichum kahawae Waller & Bridge is a highly aggressive and specialized fungal pathogen, causing Coffee Berry Disease (CBD) in Arabica coffee in Africa. This pathogen emerged within the C. gloeosporioides complex, as a specialist pathogen with the ability to infect green coffee berries, an ecological niche previously unoccupied by other fungi  . By providing consistent phenotypic data on aggressiveness, this study brought the opportunity to perform a GWAS for this trait in this pathogen.er fungi . CBD caner fungi , and, fol weapon . Consequsistance . C. kahaAfrican) , and twoAfrican) . FurtherAfrican) . RecentlColletotrichum species, little is still known about the adaptive genetic variation of C. kahawae and no reports have been made on candidate genes underlying its pathogenicity and/or aggressiveness. Therefore, the current work aims to: (i) understand the genomic basis underlying the pathogenic behavior of C. kahawae on green coffee berries using a genomic comparative analysis with closely related non-pathogenic fungi, and (ii) identify the genomic regions potentially associated with aggressiveness through a GWAS. These results will contribute to better understand the genomic basis underlying these two complex processes, which may allow the establishment of more evidence-based and effective control measures in the future.Up to now, and in contrast with other C. kahawae isolates (CIFC/ISA/ULisboa collection) representative of the three genetic groups described by G. cingulata “f.sp. camelliae,” C. aotearoa and C. kahawae subsp. ciggaro (Cc)]. However, in this study, the two C. kahawae subspecies sensuIn this work, 30 de novo assembly within and between individuals to identify loci was performed using the program PyRAD v3.0.5 . Concatenation and conversion of the alignment matrices to the appropriate formats was performed with TriFusion. A maximum likelihood analysis was conducted with RAxML v. 8. 2  were discarded as burn-in. Trees from different runs were combined using Logcombiner and summarized in a majority rule 50% consensus tree. All trees were visualized in FigTree2 and further edited in Inkscape3. Regardless of the dataset under study (datasets generated with different PyRAD parameters), a similar phylogenetic tree was reconstructed.To assess phylogenetic relationships among the isolates we used a single concatenated alignment that includes loci with SNPs represented in more than 80% of the isolates and a minor allele frequency (MAF) above 5%  and non-pathogenic fungi  to Arabica coffee were analyzed in order to better understand the pathogenicity of C. kahawae. The initial dataset named as total_dataset comprise all the genetic variation observed within the species  by calculating the distribution of SNPs Fst values using VCFTOOLS v0.1.14  as C. gloeosporioides Nara gc5  was functionally annotated. The categorization was made through a similarity BLASTx search using Blast2GO  terms were assigned to the 2nd level of the biological process, molecular protein and cellular component categories. A GO enrichment analysis was performed to determine if any GO term was over or under represented in the filtered_dataset and ns_filtered_dataset when compared to the total_dataset. Statistically significant enrichment was tested against a reference of all genes analyzed using the Fisher’s exact test and a significance of FDR < 0.05.In this study, pathogenic  and the orthologous C. gloeosporioides genes  against the pathogen–host interaction reference database (PHI-base) v.4.2 (The dN/dS ratio was measured for the genes identified in the _dataset . The anne) v.4.2 .gwa_dataset) was filtered in three steps to remove: (i) all non-pathogenic fungi to green coffee berries; (ii) all SNPs that contributed to the genetic structuring within C. kahawae, since the power of GWAS can be significantly reduced by the inclusion of related individuals and population substructure  fHigh, Moderate, and Low), and a continuous analysis with the Area Under the Disease Progress Curve (AUDPC) parameter recorded for each isolate by The Bayesian variable selection regression (BVSR) model proposed by The single-SNP association analysis allowed the detection of the associated SNPs even in the absence of interactions between them . In thisC. kahawae isolates, collected from almost all coffee regions where CBD occurs, and 10 isolates from several closely related species of the C. gloeosporioides complex, generated an average of 3.76 × 106 reads per sample, amounting to a total of 150.41 × 106 of 85 bp single-end reads after barcode trimming. The individual read number ranged between 1.46 × 106 and 6.14 × 106, after an initial quality filter to remove the low-quality reads, in which an average of 5.83 × 105 reads were discarded. Ten de novo assemblies were performed, and the results are summarized in de novo assembly, i.e., the one that minimizes the number of missing data and maximizes the number of phylogenetically informative sites, was obtained with the following parameters: minimum depth of coverage of 10, maximum number of low quality of 4, clustering threshold of 0.90, minimal taxon coverage of 5, and maximum shared heterozygosity of 3. Additional filtering steps, including the removal of SNPs with less than 80% of the taxa represented and a MAF lower than 5%, yielded a final matrix of 83,528 SNPs across 28726 loci and 40 isolates, referred as total_dataset.Illumina RAD-seq of 30 total_dataset produced a completely resolved evolutionary tree for the C. gloeosporioides complex species under study  are more differentiated from the remaining Cc isolates and may even belong to a different species. The most differentiated species of the C. gloeosporioides complex under study was C. aotearoa. Finally, a geographical structuring within C. kahawae, like the one previously described by The phylogenetic analysis of the er study . OverallC. kahawae isolates , while the non-pathogenic group has isolates from the three closely related species  with a total of 71,503 SNPs. The genetic variability between the two groups comprises 83,528 SNPs, which as previously referred, constitutes the total_dataset . Only 28%  of the total_dataset and 34%  of the filtered_dataset were successfully mapped. This analysis revealed that, in the total_dataset, 47%  are located in non-coding regions, 53%  are located in genes and 7 SNPs in pseudo genes, while in the filtered_dataset, 55%  are located in non-coding regions, 45% (847 SNPs) are located in genes and 3 SNPs are located in pseudo genes. Regarding the number of synonymous and non-synonymous mutations, a significant increase on the number of non-synonymous mutations was found in the filtered_dataset (45%) when compared to the total_dataset (18%) , from which 26 had more than 1 non-synonymous mutation.Both datasets, et (18%) . The typtotal_dataset, filtered_dataset, and ns_filtered_dataset) was performed, in which 34, 29, and 87% respectively, matched to known genes in the NCBI nr database. GO terms were assigned to 25% of the total_dataset, 16% of the filtered_dataset and 57% of the ns_filtered_dataset, and analyzed at the 2nd level of functional annotation for biological process, molecular function and cellular component categories. Only small differences were observed between these three datasets  blasting the RAD loci of C. kahawae obtained during this study. A total of 77 C. fruticola genes had homology in the PHI-base, from which 40 gave also a match for C. kahawae’s RAD loci (cmt1 and cytochrome p450) and “lethal”  , using two distinct approaches: (i) blasting the complete gene retrieved from RAD loci . From thRAD loci . The majsubunit) . Overall process .C. kahawae.After filtering the data for the GWAS, 173 SNPs located in 141 loci across 26 isolates were identified. This dataset was used to test for associations of the population genetic variation with the phenotypic trait of aggressiveness. The efficiency of the filtering correction for the effect of population genetic structure can be confirmed in High vs. Moderate, High vs. Low, Low vs. Moderate) and continuous analysis (AUDPC), identified a total of 10 SNPs with BF0.975 (>97.5 quantile Bayes factor) associated with aggressiveness, corresponding to 6% of the analyzed markers. When a stricter threshold was applied (99% quantile), 6 BF0.99 SNPs (3.5%) showed the strongest association with aggressiveness . The number of SNPs identified in High vs. Low class was always two regardless the threshold used, while for the remaining pairwise analyses (High vs. Moderate and Low vs. Moderate) and continuous analysis (AUDPC) the number of SNPs ranged from 5 to 2 when a more restricted threshold was applied  classes and the continuous analyses with the AUDPC values (PIP = 0.312)  and continuous analysis (AUDPC)  underlying the aggressiveness variation ranged from 26.5 to 52.2 SNPs . When co= 0.312) . In mult (AUDPC) . Estimat (AUDPC) . OverallGIT3, srbA) the mutant phenotypes showed reduced virulence in Candida albicans and Aspergillus fumigatus, respectively  was described as unable to affect pathogenicity in other host–pathogen interactions, while for other two genes  instead of Cg_432 as previously referred by C. aotearoa as the most distant species of the C. gloeosporioides complex under study.One of the first and most striking findings of this work was the high genetic differentiation, at the genomic level, between pathogenic and non-pathogenic fungi to green coffee berries, reinforcing the idea that C. kahawae to green coffee berries. Although it is not probable that all these SNPs are related to this specific trait, the probability of finding the genetic variation involved in C. kahawae‘s pathogenicity is quite high. In fact, the enrichment in non-synonymous mutations found in this dataset (filtered_dataset) when compared to the total_dataset is quite promising, especially because this pattern was not observed for the total number of SNPs located in non-coding regions and/or coding regions. Functional annotation of these non-synonymous SNPs (ns_filtered_dataset), as well as of the diagnostic SNPs (filtered_dataset) and those of the total_dataset, revealed, for the 2nd level of the “biological process,” “molecular function,” and “cellular component” categories, only small differences between the datasets. In the “biological process” category, genes involved in “cellular component organization or biogenesis” are only present in the total_dataset, while for the remaining datasets a small enrichment in genes involved in “response to stimulus” was observed. In the “molecular protein” category, the genes involved in “transporter activity” seems to be over represented in both filtered datasets (filtered_dataset and ns_filtered_dataset), which could suggest that transporters have an important role in the pathogenicity of C. kahawae. Additionally, a significant enrichment of specific GO terms was detected in the ns_filtered_dataset when compared to the total_dataset, particularly the “oxidation–reduction process” and “integral component of membrane.” It is noteworthy that most of the genes associated with the term “integral component of membrane” are in fact transporters, namely, MFS transporter, ABC-2 type transporter and ammonium/hexose transporters. MFS transporters are the most common category of secondary carrier proteins. Members of this group are involved in the uptake of essential minerals and nutrients, also functioning as nutrient sensors, while others are responsible for the transport of various drugs and toxins   . Within ination) . Therefofiltered_dataset showed that 258 genes could be under positive selection (ps_filtered_dataset), from which 26 have more than one non-synonymous mutation. The potential role of these genes in fungal pathogenicity and virulence was assessed by a BLAST search against the PHI-base. A total of 30% (77 genes) had homology, and 15% (40 genes) were described as important for fungal pathogenicity and virulence when a mutant phenotype was produced. Five of them were identified as genes required for pathogenicity in other fungi, inducing mutant phenotypes of “total loss of pathogenicity”  and the remaining 36 genes, including the ones responsible for a change in virulence, are mainly involved in oxidative responses  and transport . Once again, these two biological processes stand out in our study and their importance is well-documented in the literature (Colletotrichum species (C. sublineola and C. graminicola) in different hosts, showed an enrichment of proteins of these classes in the non-conserved proteins dataset, with transporters being the most represented PFAM category  and correspond to only 5% of the total genetic variation. No causal SNPs were identified in this dataset, but instead a group of SNPs of small effect was detected. In single-SNP association analyses, the 10 individual SNPs found to be associated (BF0.97) showed a low phenotypic effect in aggressiveness (β| = 0.059 to |β| = 0.087) for all the pairwise analyses , but in the continuous analysis (AUDPC) 5 SNPs presented a moderate phenotypic effect (β| = 1.24). Despite these differences, three of the SNPs associated with aggressiveness in the continuous analysis were also found in the pairwise analyses.In High vs. Moderate, High vs. Low, Low vs. Moderate)  and for the continuous analysis (AUDPC) . The phenotypic effect seems to be smaller in all pairwise analyses that included the High aggressiveness class, probably due to the low number of isolates that compose this class. Moreover, 7 SNPs were common to the SNPs identified in single SNP analyses, specifically 1 in 5 SNPs was common to both approaches in High vs. Low and High vs. Moderate pairwise analyses, 3 in 4 for Low vs. Moderate pairwise analysis, and 3 in 5 for the continuous analysis with AUDPC. These results provide additional support for the reliability of the Low vs. Moderate and AUDPC analyses.In the multi-SNP association test, 15 SNPs with a posterior inclusion probability of 97.5% (PIP0.975) were found, showing moderate effect for all pairwise analyses  family of transcription factors and is crucial for antifungal drug resistance, hypoxia adaptation, and virulence , and two candidate genes in the multi-SNP association analysis (nitrosoguanidine resistance and C6 transcription factor). The gene coding for a nitrosoguanidine resistance protein, which is an integral component of the membrane that is able to regulate the fungal-type cell wall organization and phospholipid translocation , was theirulence , while girulence . The remC. kahawae, both conditions are far from ideal, as a low number of SNPs not related with the population structure pattern was identified and the entire genome is inherited asexually as a single, non-recombining linkage group, which can increase the number of false positives. Therefore, as in any GWA study, a further detailed investigation of these candidate genes is required to confirm their involvement in C. kahawae’s aggressiveness and assess their causative effect at the phenotypic level.Finally, the success and power of an association study relies on the number of SNP markers and on the LD decay. In C. kahawae to infect green coffee berries and its aggressiveness. Our results suggest that C. kahawae’s pathogenicity involves several biological processes such as detoxification and transport, regulation of host and pathogen gene expression, and signaling. Fifteen percent of the genes under selection were described as having an important role in fungal pathogenicity and virulence, being some of them identified as genes proven to lead to the total loss of pathogenicity in other fungi when mutated. Finally, the high abundance of TFs may suggest that expression changes in gene expression patterns can be more important than the presence/absence of individual gene alleles. On the other hand, aggressiveness does not seem to be regulated by any causal mutation and even the associated SNPs are of small effect, which leads to three possible conclusions: (i) aggressiveness is regulated by a set of many small effect SNPs difficult to detect with a GWAS analysis; (ii) aggressiveness is a plastic trait regulated by differential gene expression and associated regulatory mechanisms, consequently, a transcriptome and epigenome analysis is needed to complement the current study; (iii) aggressiveness is not under selection and is governed by physiological conditions. Nevertheless, a repertoire of candidate genes is now provided that can be studied through gene expression and additional functional analyses  to ascertain their causative role in C. kahawae aggressiveness and pathogenicity. Finally, the collected information may be of use to the development of future evidence-based sustainable control measures.This work took the first step toward the understanding of the genetic mechanisms underlying the ability of AV, VV, OP, and DB designed the experiments. AV performed the experiments and wrote the manuscript. AV and DS analyzed the experimental data. VV, OP, and DB contributed reagents, materials, and analysis tools. All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Mapping expression quantitative trait loci (eQTLs) has provided insight into gene regulation. Compared to cis-eQTLs, the regulatory mechanisms of trans-eQTLs are less known. Previous studies suggest that trans-eQTLs may regulate expression of remote genes by altering the expression of nearby genes. Trans-association has been studied in the mediation analysis with a single mediator. However, prior applications with one mediator are prone to model misspecification due to correlations between genes. Motivated from the observation that trans-eQTLs are more likely to associate with more than one cis-gene than randomly selected SNPs in the GTEx dataset, we developed a computational method to identify trans-eQTLs that are mediated by multiple mediators.We proposed two hypothesis tests for testing the total mediation effect (TME) and the component-wise mediation effects (CME), respectively. We demonstrated in simulation studies that the type I error rates were controlled in both tests despite model misspecification. The TME test was more powerful than the CME test when the two mediation effects are in the same direction, while the CME test was more powerful than the TME test when the two mediation effects are in opposite direction. Multiple mediator analysis had increased power to detect mediated trans-eQTLs, especially in large samples. In the HapMap3 data, we identified 11 mediated trans-eQTLs that were not detected by the single mediator analysis in the combined samples of African populations. Moreover, the mediated trans-eQTLs in the HapMap3 samples are more likely to be trait-associated SNPs. In terms of computation, although there is no limit in the number of mediators in our model, analysis takes more time when adding additional mediators. In the analysis of the HapMap3 samples, we included at most 5 cis-gene mediators. Majority of the trios we considered have one or two mediators.Trans-eQTLs are more likely to associate with multiple cis-genes than randomly selected SNPs. Mediation analysis with multiple mediators improves power of identification of mediated trans-eQTLs, especially in large samples.The online version of this article (10.1186/s12859-019-2651-6) contains supplementary material, which is available to authorized users. Expression quantitative trait loci (eQTLs) are genetic variants that influence expression levels of mRNA transcripts. Cis-eQTLs commonly refer to genetic variations that act on local genes Fig. a, and trMoreover, the biological mechanisms underlying trans-eQTLs are less understood. Previous studies have shown that trans-eQTLs are more likely to be cis-eQTLs than randomly selected SNPs in the human genome , 7, suggIn this paper, we showed that the assumptions in the multivariate extension of mediation analysis are more likely to be satisfied than that in the single-mediator model , MKK , YRI , CEU (Utah residents with Northern and Western European ancestry from the CEPH collection), CHB , and JPT  . There ap-value <10-5) were removed. We then converted the SNP coordinates according to the human reference genome hg38. In addition, we found that some SNPs were in complete Linkage Disequilibrium (LD) with each other or mapped to identical genome positions. For such cases, we randomly selected one SNP to be included in the analysis. The number of individuals and SNPs before and after quality control were listed in Table In the quality control step, a series of filters were applied to remove samples and SNPs with poor quality in each population. We removed samples with a call rate less than 0.97; next we retained autosomal SNPs with a missing rate less than 0.08 and minor allele frequency (MAF) greater than 0.10; finally, SNPs that failed the Hardy-Weinberg test , we adopted the strategy of  to correWe conducted genome-wide eQTL analysis using the R package, Matrix eQTL . SNPs anp-value was obtained by resampling 1000 times. The same test procedure was applied to the trans-eQTLs identified in the HapMap3 dataset.The motivation of our work was based on the observation that many trans-eQTLs are also identified as cis-eQTLs and they are often associated with more than one cis-gene in the GTEx database. In order to test whether the association with multiple cis-genes is over-represented in trans-eQTLs, we compared the proportion of trans-eQTLs that are associated with more than one cis-gene with that in the human genome. We considered the trans-eQTLs reported in the GTEx V6p dataset and those identified in the HapMap3 dataset. Permutation tests were used to assess significance. To elaborate, for the trans-eQTLs reported in the GTEx V6p dataset, we randomly sampled the same number of SNPs with matched MAF from the 1000 Genomes Project  and calcTo understand the role of mediated trans-eQTLs in disease association, we performed Fisher’s exact test to assess the enrichment of trait-associated SNPs in the trans-eQTLs identified by our method. The trait-associated SNPs were obtained from the NHGRI GWAS catalog .p-value is less than 10-6. The p-value cutoff was chosen to reduce the multiple-testing burden [To identify trans-eQTLs that are mediated by one or more cis-genes, we first selected candidate trios, composed of SNP, one or multiple cis-genes, and trans-gene. The trios were selected based on the following criteria. First, trans SNP-gene pairs were selected if their p-value was used to assess significance when testing the single mediation effect (SME). In mediation analysis with multiple mediators, we considered the following model. For the ith subject, let Yi be the expression level of a trans-gene, Xi be the SNP genotype coded by the number of minor alleles, Mi = Tbe the expression levels of the p cis-genes, Ci = Tbe the q covariates. The mediation model is stated below:In all the analyses described below, we assume gene expression data have been normalized and transformed so that the expression values approximately follow a normal distribution. In mediation analysis with a single mediator, we followed the test procedure in . The boop cis-genes on the trans-gene adjusting for the SNP and covariates, αX = T is the effect of the SNP on the p cis-genes adjusting for covariates. Σ can be non-zero [where non-zero .δ = T, where Denote the total mediation effect (TME) as j=αXjβMj . In the δj ’s are nonzero in different directions and sum to 0, the TME is zero while the CME is not. Thus, the CME test is of particular interest in the presence of the cancellation effect, which is evident in the HapMap3 dataset. That is, if a SNP has a positive mediation effect through one cis-gene and a negative mediation effect through another cis-gene, the CME test can be more powerful than the TME test, as demonstrated in the simulation studies.where (2) consists of a broader class of null than (3). For example, when Conventional multivariate tests for CME, such as likelihood ratio test, have limited power when there are a large number of mediators . In our We conducted simulation studies to evaluate the impact of model misspecification on type I error and statistical power. In detail, we considered three types of model misspecification: Scenario I, the true model has only one mediator while the analysis includes the true mediator and another irrelevant variable as the mediators; Scenario II, the true model has two mediators and the mediation effects are in the same direction; Scenario III, the true model has two mediators and the mediation effects are in opposite direction. In all three scenarios, the performance of the TME, CME, and SME tests are evaluated and compared. We considered sample size of 100 and 300 to mimic the sample size in the HapMap3 single population analysis and combined analysis.αX1 varies from 0.2 to 1, αX2 is fixed at 0.6, andεM with the variance being 1 and the correlation coefficient being 0.2, and εY follows the standard normal distribution. We set Scenario I: The MAF of the SNP is set to 0.3. For the cis-regulatory effect in model (1), Scenario II: We setScenario III: We setPrevious studies showed that trans-eQTLs are more likely to associate with cis-genes , 7, whicIn simulations, we studied the effect of model misspecification in three scenarios of trans-eQTL identification (see Simulation setup in Methods) from two perspectives, the type I error and the statistical power, and we compared three tests, TME, CME, and SME.In Scenario I, the type I error rates did not differ significantly from the nominal level of 0.05 even though a second mediator was falsely included in the analysis Fig. a, and thαX1 reached 0.6 when the two mediation effects were cancelled. After that, the power of the TME test rose with the value of αX1, but was still inferior to that of CME and SME .We applied the mediation tests to LWK, MKK, YRI, CEU, and the combined samples of African populations and Asian populations in the HapMap3 dataset. In each population, mediated trans-eQTLs with We demonstrated the replication of trans-eQTLs from LWK, MKK, YRI, and the combined samples of African populations. When the FDR was controlled at 0.1, the trans-eQTLs identified in LWK, MKK, YRI, and the combined samples have a large overlap , suggesting a mediating pathway of the previously reported trans-regulation of RPL34 Additional file 2:Mediated trans-eQTLs in LWK. (XLS 64 kb)Additional file 3:Mediated trans-eQTLs in MKK. (XLS 73 kb)Additional file 4:Mediated trans-eQTLs in YRI. (XLS 71 kb)Additional file 5:Mediated trans-eQTLs in the combined samples of African populations. (XLS 106 kb)Additional file 6:Mediated trans-eQTLs in CEU. (XLS 94 kb)Additional file 7:Mediated trans-eQTLs in the combined samples of Asian populations. (XLS 91 kb)Additional file 8:Venn diagram of trans-eQTLs in African populations. (PNG 81 kb)Additional file 9:Overlap of trans-eQTLs with a previous study .


Gossypium hirsutum) is the world's largest source of natural fibre and dominates the global textile industry. Hybrid cotton varieties exhibit strong heterosis that confers high fibre yields, yet the genome‐wide effects of artificial selection that have influenced Upland cotton during its breeding history are poorly understood. Here, we resequenced Upland cotton genomes and constructed a variation map of an intact breeding pedigree comprising seven elite and 19 backbone parents. Compared to wild accessions, the 26 pedigree accessions underwent strong artificial selection during domestication that has resulted in reduced genetic diversity but stronger linkage disequilibrium and higher extents of selective sweeps. In contrast to the backbone parents, the elite parents have acquired significantly improved agronomic traits, with an especially pronounced increase in the lint percentage. Notably, identify by descent (IBD) tracking revealed that the elite parents inherited abundant beneficial trait segments and loci from the backbone parents and our combined analyses led to the identification of a core genomic segment which was inherited in the elite lines from the parents Zhong 7263 and Ejing 1 and that was strongly associated with lint percentage. Additionally, SNP correlation analysis of this core segment showed that a non‐synonymous SNP (A‐to‐G) site in a gene encoding the cell wall‐associated receptor‐like kinase 3 (GhWAKL3) protein was highly correlated with increased lint percentage. Our results substantially increase the valuable genomics resources available for future genetic and functional genomics studies of cotton and reveal insights that will facilitate yield increases in the molecular breeding of cotton.Upland cotton ( Gossypium genus, which originated from a paleo‐hexaploid eudicot, has evolved into various species during its evolutionary history, including approximately 45 diploid and five tetraploid species , the largest natural source of industrial fibre, plays a predominant role in the supply of cotton fibre, which is estimated to comprise more than 90% of the fibre produced by cultivated cotton worldwide  have been published, which provided insights into how both genome evolution and domestication contribute to cotton fibre development  and an average depth of 34.65× Table . An averet al., SNP detection was performed for each sample based on this high‐quality resequencing data. The number of SNPs detected per sample varied from 1 699 580 to 2 685 167, with an average of 2 121 628 Table , indicatSNP density analysis was performed to clearly investigate whole genome SNP distribution across these accessions Fig. a. These et al., et al., −3, which was significantly lower than wild cotton (1.32 × 10−3)   Figure . 1885 prEjing 1, one of the parents of Ekangmian 9, is highly productive in the Yangtze River Basin and produces big bolls, large seeds and has a high lint percentage. Using identity by decent (IBD) detection and tracking, we found that 10.5% of the chromosomal sequences in Ejing 1 were derived from the parents Fig. . These tZhong 7263, which is resistant to Fusarium wilt and tolerant to verticillium, was bred from the progeny of a combination of CCRI4 and Shan 5012 Fig. . These IEkangmian 9, an important breeding variety, is a common parent of seven elite parents Fig. a. Genetiet al., Compared with their backbone parents, seven elite parents and Ekangmian 9 each displayed higher lint percentage Fig. b, suggesGhWAKL3 was highly up‐regulated during secondary cell wall biosynthesis, indicating that GhWAKL3 may play an important role during cotton fibre development. These results were further verified by showing that expression levels of GhWAKL3 were significantly higher in elite parents relative to backbone parents  located in one of these fragments, was found to be responsible for fibre development and is highly expressed during cotton fibre elongation. A non‐synonymous SNP in GhWAKL3, which results in an amino acid substitution from leucine to proline in the protein kinase domain of GhWAKL3, is significantly correlated with increased lint percentage . Next, the DNA fragments were treated according to the following manufacturer's specifications (Illumina): fragments were end repaired, polyA‐tails were added and they were ligated to paired‐end adaptors and amplified by PCR. Re‐sequencing was performed using the Illumina HiSeq 4000 platform in the Novogene Bioinformatics Institute . In order to improve the reliability of the clean data, the raw sequence reads were trimmed using the following filtering procedure: removing reads with ≥10% unidentified nucleotides (N) and >10 nt aligned to the adapter; removing reads with >50% bases having phred quality <5; and removing putative PCR duplicates amplification in the library.A paired‐end sequencing library with an insert size of 350 bp was constructed for each cultivar. First, 350 bp DNA fragments were randomly generated for each variety using a hydrodynamic shearing platform  software platform and the following parameters: ‘mem ‐t 10 ‐k 32’   values were further confirmed.To validate our population genetics analyses, previous SNP detection using 31 wild cotton cultivars was used in this study  data and drew its figure. Zhenyu Wang and Wei Li wrote the manuscript, which was revised by Guanghui Xiao, Yuzhou Zhang and Daigang Yang.The authors declare no competing financial interests.Figure S1 Analysis of lint percentage, lint index and seed index in 26 accessions cultivated in four locations in China. (a) Lint percentage, (b) lint index and (c) seed index analyses across 26 accessions grown in Jingzhou, Jiujiang, Alaer and Anyang.Figure S2 The distribution of single‐nucleotide polymorphisms (SNPs) in the pedigree. (a) Analysis of SNP number, (b) and identification of synonymous and non‐synonymous SNPs and (c) stop gain and stop loss SNPs present in the pedigree.Figure S3 Comparative analysis of selective improvement sweeps in wild type and pedigree cotton accessions along the genome. A genome‐wide threshold of 1.38 was defined by the top 5% of the log10(πwilds/πpedigrees).Figure S4 Distribution of IBD fragments in the pedigree. (a) Length of IBD fragments in Ejing 1 inherited from multiple parents. (b) Number of genes located in IBD fragments of Ejing 1. (c) Length of IBD fragments in Zhong 7263 inherited from multiple parents. (d) Number of genes located in IBD fragments of Zhong 7263. The x axis indicates the length of IBD fragments  or the number of genes  affected in the pedigree. The y axis indicates the accessions used in this study.Figure S5 Analysis of Ekangmian 9 and identification of IBD regions in seven elite parents. (a) The genomic proportion of Ekangmian 9 inherited from Ejing 1, Zhong 7263 and MO‐3. The blue, green, purple and red bars represent the genetic makeup of Zhong 7263, MO‐3, Ejing 1 and unknown genetic components, respectively. (b) The genetic contribution of backbone parents to Ekangmian 9. (c) The genetic contribution of Ekangmian 9 to 7 elite parents. The blue bar represents the genetic makeup of Ekangmian 9 and the red bar represents unknown genetic components. (d) The genetic constitution of seven elite parents from Ekangmian 9, Ejing 1, Zhong 7263 and MO‐3.Figure S6 Correlation between two different genotypes of GhWAKL3 and yield traits across nine environments. (a) Lint percentage, (b) lint index and (c) seed index analyses of accessions with AA and GG genotypes. Centre line, median; box limits, upper and lower quartiles; whiskers, 1.5 × the interquartile range. All accessions were grown in three locations in China for 3 years. an, ku and nan represent Anyang, Kuche and Nanjing, respectively. *P < 0.05, **P < 0.01, two‐sided t‐test.Click here for additional data file.Table S1 106 phenotype sets measured in the pedigree accessions.Table S2 Phenotypic differences between elite parents and backbone parents.Table S3 Results of sequencing data and alignments.Table S4 Results of SNP annotation.Table S5 Large effect SNPs and affected genes.Table S6 KEGG pathway enrichment analyses for effected SNP genes.Table S7 Gene ontology term enrichment analyses for effected SNP genes.Table S8 Distribution of SNP density on genome.Table S9 Zero diversity segments and genes contained within them.Table S10 KEGG pathway enrichment analyses for genes contained in zero diversity regions.Table S11 Gene ontology term enrichment analyses for genes contained in zero diversity regions.Table S12 Statistical analysis of potential selective sweeps and genes contained within them.Table S13 Cotton QTLs overlapping with putative selective sweeps.Table S14 KEGG pathway enrichment analyses for genes contained in potential selective sweeps.Table S15 Gene ontology term enrichment analyses for genes contained in potential selective sweeps.Table S16 Genetic constitution of Ejing 1.Table S17 KEGG pathway enrichment analyses for genes overlapped with traceable IBD fragments of Ejing 1.Table S18 Gene ontology term enrichment analyses for genes overlapped with traceable IBD fragments of Ejing 1.Table S19 Cotton QTLs overlapping with IBD segments of Ejing 1.Table S20 The length of IBD segments passed between materials during Ejing 1 improvement.Table S21 Genetic constitution of Zhong 7263.Table S22 KEGG pathway enrichment analyses for genes overlapped with traceable IBD fragments of Zhong 7263.Table S23 Gene ontology term enrichment analyses for genes overlapped with traceable IBD fragments of Zhong 7263.Table S24 Cotton QTLs overlapping with IBD segments of Zhong 7263.Table S25 Length of IBD segments passed between materials during improvement of Zhong 7263.Table S26 Genetic constitution of Ekangmian 9.Table S27 The ratio of genome contribution of parents to Ekangmian 9.Table S28 KEGG pathway enrichment analyses for genes overlapped with traceable IBD fragments of Ekangmian 9.Table S29 Gene ontology term enrichment analyses for genes overlapped with traceable IBD fragments of Ekangmian 9.Table S30 Cotton QTLs overlapping with IBD segments of Ekangmian 9.Table S31 Common IBD fragments inherited from Ekangmian 9 to seven elite parents.Table S32 KEGG pathway enrichment analyses for genes overlapped with traceable common IBD fragments of seven elite parents.Table S33 Gene ontology term enrichment analyses for genes overlapped with traceable common IBD fragments of seven elite parents.Table S34 Cotton QTLs overlapping with common IBD fragments of seven elite parents.Click here for additional data file.


VRTN) gene known to increase number of thoracic vertebrae (ribs) reside on this haplotype. In Landrace, the linkage disequilibrium (LD) extends over a region of more than 3 Mb also containing both VRTN mutations. Here, other modifying loci are expected to cause the breed-specific effect. Additional variants found on the wildtype haplotype surrounding the VRTN region in all sequenced Landrace animals point toward breed specific differences which are expected to be present also across the whole genome. This Landrace specific haplotype contains two missense mutations in the ABCD4 gene, one of which is expected to have a negative effect on the protein function. Together, the integration of largescale genotype, phenotype and sequence data shows exemplarily how population parameters are influenced by underlying variation at the molecular level.Modern breeding schemes for livestock species accumulate a large amount of genotype and phenotype data which can be used for genome-wide association studies (GWAS). Many chromosomal regions harboring effects on quantitative traits have been reported from these studies, but the underlying causative mutations remain mostly undetected. In this study, we combine large genotype and phenotype data available from a commercial pig breeding scheme for three different breeds  to pinpoint functional variation for a region on porcine chromosome 7 affecting number of teats (NTE). Our results show that refining trait definition by counting number of vertebrae (NVE) and ribs (RIB) helps to reduce noise from other genetic variation and increases heritability from 0.28 up to 0.62 NVE and 0.78 RIB in Duroc. However, in Landrace, the effect of the same QTL on NTE mainly affects NVE and not RIB, which is reflected in reduced heritability for RIB (0.24) compared to NVE (0.59). Further, differences in allele frequencies and accuracy of rib counting influence genetic parameters. Correction for the top SNP does not detect any other QTL effect on NTE, NVE, or RIB in Landrace or Duroc. At the molecular level, haplotypes derived from 660K SNP data detects a core haplotype of seven SNPs in Duroc. Sequence analysis of 16 Duroc animals shows that two functional mutations of the Vertnin ( Sus scrofa chromosome 7 (SSC7) has been identified showing a large effect on NTE in several commercial breeding lines and crosses  as being the most promising candidate gene. Several mutations have been detected in the region including a SNP in the promotor region (g.19034A > C) and an insertion of a PRE1-SINE element of 291-basepairs (g.20311_20312ins291) in the first intron of the VRTN gene (VRTN has been characterized as a novel transcription factor affecting vertebra development (VRTN expression in an early embryonic stage in an additive way. Together these two mutations increase the number of thoracic vertebrae by 1 in the homozygous state (QQ). The effect of the PRE1-insertion on RIB has been validated in different commercial crossbred (VRTN and not in high linkage disequilibrium (LD) with the PRE1 insertion in crossbreds (LTBP2 (latent transforming growth factor binding protein 2) has been pinpointed by VRTN insertion was not segregating but only the promoter SNP g.19034A > C. Finally, LTBP2 at c.4481A > C associated with thoracic vertebrae number in 1,105 F2 animals of a Landrace cross with Korean native pigs where also the VRTN insertion is increasing thoracic vertebrae number independently.Number of teats (NTE) in pigs is a highly heritable trait and shows considerable variation across breeds (8–21)  and alsoes e.g., . In the es e.g., . After fRTN gene . Just reelopment . Duan etrossbred  and purerossbred . Howeverossbreds . Yang etIn this study, we analyzed an extended data set of three commercial pig breeds linking genotype data of 20,366 Large White (LW), 23,398 Landrace (L), and 10,044 Duroc (D) animals with phenotypic data on NTE. In addition, NVE, and RIB data were scored from computer tomography (CT) images on 2,756 L and 2,961 D animals. We show that scoring phenotypes closer to the molecular basis of the observed variation (NVE or RIB vs. NTE) increases population genetic parameters such as heritability and explained genetic variance considerably. Furthermore, a detailed analysis combining medium and high density SNP data with whole-genome sequence (WGS) data with functional parameters of mutations has been performed. Our results show that the two functional mutations analyzed by In this study, data from the three pig populations LW (Large White-based), L (Landrace) and D (Duroc) were evaluated. The LW population was located in Dutch nucleus farms and were born between 2006 and 2017 . The Norwegian L and D populations were located in Norwegian nucleus farms and at a boar testing station  and were born between 2010 and 2017 .®, Janssen-Cilag Ltd., Buckinghamshire, United Kingdom), which was injected intramuscularly. The whole skeleton was segmented out by applying a threshold value at 200 Hounsfield units to the full CT volumes. Individual vertebras and ribs were segmented and identified in accordance with The traits evaluated were NTE, recorded at birth on both males and females from all three populations, and NVE and RIB retrieved from CT images only on males from the L and D populations at 120 Kg of live weight. The traits NVE and RIB were recorded using the CT scanner GE Healthcare LightSpeed 32 VCT, and the settings used were 120 kV, slice thickness 1.25 mm and dynamic mA (400–500 mA) adjusting for object thickness. Prior to scanning, the boars were sedated using Azaperone , N.where NVE/RIBThe dataset GENOTYPED was a subset of ALL consisting of all animals that had both phenotypes and genotypes . This dataset was used to perform the GWAS.All animals from the GENOTYPED dataset were genotyped using a medium density SNP chip. Genotyping was performed at CIGENE  and at GeneSeek , mainly using the (Illumina) GeneSeek custom 80K SNP chip . However, a small part of the animals from the three populations were genotyped using the (Illumina) GeneSeek custom 50K SNP chip  and the Illumina Porcine SNP60 Beadchip .2> 600), SNPs located on sex chromosomes and unmapped SNPs. The positions of the SNPs were based on the Sscrofa11.1 assembly of the reference genome. Animals with frequency of missing genotypes ≥ 0.05 would be removed from the dataset. However, all genotyped animals had a frequency of missing genotypes < 0.05 and were therefore kept for further analyses.Quality control consisted of excluding SNPs with GenCall < 0.15 , call rate < 0.95, minor allele frequency < 0.01, strong deviation from Hardy-Weinberg equilibrium  GeneSeek custom 80K SNP chip were imputed within population using Fimpute v2.2 . At the Genotyping of high density genotypes was also performed at CIGENE  and GeneSeek . In total, 290 LW, 415 L, and 140 D animals from the GENOTYPED dataset were in addition genotyped using the Axiom porcine 660K array from Affymetrix . These animals were the most influential sires from each population (sires with the largest number of offspring in the GENOTYPED dataset). Quality control of 660K array was as described above for the medium density genotypes. The imputation from 80K genotypes toward 660K genotypes was performed within population using Fimpute v2.2 . After qBeagle v.4.1  was usedSus scrofa build 11.1, duplicated marked and indexed using the speedseq align module available in SpeedSeq  in L and D. WGS data from 24 L and 23 D boars were available for this purpose. The boars were previously frequently used AI boars and all of them were part of the GENOTYPED dataset. DNA extraction and the sequencing procedure for whole genome re-sequencing is described in detail in SpeedSeq . FreebaySpeedSeq  was usedSpeedSeq  and SAMtSpeedSeq . The filThe WGS data SNPs from the 24 L and 23 D boars in the SSC7 QTL region were phased within breed using Beagle v.4.1 . Prior tA single-SNP GWAS was performed with the GENOTYPED dataset within each population using the following linear animal model in GCTA , 2014:∗k was the pre-corrected phenotype of the k animal ; μ the average of the pre-corrected phenotype; X was the genotype  of the k animal for the evaluated SNP; k was the random additive genetic effect, being that the vector of additive genetic effects was assumed to be distributed as ∼N, k was the random residual effect which was assumed to be distributed as ∼N was estimated based on the allele frequencies (p and q) and the estimated allele substitution effect (α). The proportion of phenotypic variance explained by the SNP was defined as p-value < 1.0 × 10-8.The genetic variance explained by a SNP  in GCTA , 2014. Llink 1.9 .VRTN insertion g.20311_20312ins291. Three groups were created: Homozygous Wild Type  (wt/wt), Heterozygous Insertion  (wt/ins), and Homozygous Insertion  (ins/ins). The filtered SNPs generated with sequence data was then used to search for variants that were overrepresented in the different groups.The variants identified by WGS data analysis were used to find potentially functional mutations. All L and D animals with WGS and NVE data  were grouped based on their genotype for the Sus scrofa genome build 11.1. Known regulatory regions of the human LTBP2 gene , was genotyped using primers and PCR conditions reported by VRTN insertion, out of which 82 had phenotypes and the rest of the animals were parents and grandparents used to confirm observed genotypes.The 291-basepair insertion VRTN insertion was done in the WGS animals by the Integrative Genomics Viewer (IGV) software     and the 7751432) .A schematic overview of the approach linking large-scale phenotypic, genetic and genomic data is given in 2), defined as the proportion of the total phenotypic variance explained by additive genetic variance, for NTE was 0.41 (LW), 0.39 (L), and slightly lower for the D breed (0.28)  in the D breed, however, it was considerably lower (0.24) in the L breed (The heritability (hd (0.28) . For NVE L breed . Data onAX-116329721) to 97.53 Mb for NVE and RIB (AX-116329717). However, 10 of the p-value increases from 50 to 84 in the L breed and from 59 to 184 in D for NTE and NVE, respectively. Furthermore, scoring RIB instead of NVE increases significance levels considerably further but only in D and decreases extremely in the L breed . This is in line with the explained variance increasing from 5 and 6% for NTE up to 34% (L) and 52% (D) for NVE. However, for RIB, we observe a further increase of explained variance up to 69% in D whereas a severe reduction is seen again in the L breed down to 20%. The size of the effect for NTE is comparable across breeds between 0.33 (L) and 0.38 (LW and D). For NVE the effect is much higher in D than in L (0.68 vs. 0.49) and for RIB it only increases in D up to 0.83 but remains the same in the L breed (0.49).The GWAS results for NTE show a strong QTL on SSC7 in all three breeds in the same region . In addiVRTN insertion (g.20311_20312ins291)  (n = 96). Pigs with the insertion allele and the C allele of the promoter SNP were associated with increasing NVE in both L and D breeds, and the two variants were completely linked in the examined animals. The PCR genotyping of the insertion confirmed the findings by IGV.The previously identified 2ins291)  and prom9317845)  were comVRTN genotypes and analyzing the WGS data within the SSC7 QTL region for protein coding variants, no other compelling candidates for functional coding variants were found (ABCD4) that are far more common in the wt/wt and wt/ins samples, with ABCD4 having the most unique mutations of any gene in this locus. Several non-coding mutations in ABCD4 occur in 7/7 of the wt/wt samples and 18/18 wt/ins samples, and 0/9 of the ins/ins samples , are putative causal candidates within this wildtype haplotype observed only in L. The SNPs located on the 660K chip that are segregating with this haplotype are presented in Haplotype analysis also identified a haplotype that is specific for the L breed animals not carrying the n allele . The hapVRTN and pinpoint LTBP2 as a candidate gene. Here, known human LTBP2 regulatory regions   and it is outside the core region identified in haplotype analyses   was invegenotype , X7. Moranalyses . This maIn a previous study, we identified a QTL for NTE on SSC7 in a population of 936 LW animals . Expandi2 for RIB compared to NVE was only observed in D, whereas a strong reduction of h2 was estimated in L. The size of the effect on RIB remained the same (0.49) for NVE in L and in the expected range as reported for thoracic vertebrae (ribs) by 2 and total phenotypic variance for RIB in L compared to D. Furthermore, the size of the allelic effect in D is much larger (0.68 NVE and 0.83 RIB) generating 1.3 vertebrae more in the homozygous state compared to homozygous wildtype animals and even developing 1.6 more ribs phenotypically. The sum of these effects together  is mirrored by the phenotypic variance explained by the top SNP which accumulates to 69% in D for RIB and is the main indicator for the expected breeding progress in this trait.In this study, we show that the size of the effect is comparable in all three breeds, but the frequency of the allele related to increased NTE at the top SNP is more than twice as high in L as in LW and D breeds . AssuminVRTN had an effect on NTE, NVE, or RIB, as previously reported by 2 for these traits in these breeds. Therefore, there is still genetic variance that is not captured by this SNP. Trying to get closer to the causal mutation, we also performed a GWAS using imputed WGS data. However, no additional information was obtained from increasing the resolution to imputed WGS variants as the same top SNP was identified using both SNP chip and imputed WGS data. Although the added benefit of WGS data is including the functional mutation, the WGS dataset mostly contains haplotypes that are in complete LD with the 660K SNPs. The analysis of extremely large data set from a granddaughter design in dairy cattle shows clearly that with statistical evaluation of SNP chip and WGS information only, the identification of the causative quantitative trait nucleotide just based on concordance is not achieved  and is therefore involved in the regulation of the synchronized oscillation of the segmentation clock (HES7 (hes family bHLH transcription factor 7) gene (HOXB6 (homeobox B6) and HOXB9 (homeobox B9) expression by one or two somites in mice mutants with an accelerated expression of HES7. A partial transformation of lumbar vertebrae into a thoracic vertebra was also observed by knockout of HOXC8 (homeobox C8) gene in mice  gene . Their r in mice . Even en in mice . So bothOther QTL for NTE are detected in the GWAS that are most likely due to variation further downstream in the developmental cascade for the formation of the mammary gland. Different genetic factors have been described regulating pairs of mammary glands at different locations and even between the left and right counterpart . All paiABCD4 gene located just upstream of VRTN. One missense variant has a SIFT code of 0.03 and is therefore expected to be deleterious for function of the protein. These two variants together with a large list of non-coding variants are present in all L wt/wt animals. Obviously, these mutations accumulated in the L breed and are not present in the D breed. These mutations could be altering ABCD4, possibly changing its expression or function. Intriguingly there is seemingly a link between ABCD4 and the development of the spine. ABCD4 is believed to play a role in the intracellular processing of vitamin B12, and mutations affecting the ATPase domain of this protein have been shown to alter intracellular vitamin B12 trafficking (12 is required as a cofactor in methionine synthase, and low levels of vitamin B12 during development are associated with higher levels of neural tube defects in humans (Analysis of the sequence variation in the SSC7 QTL region suggested that protein coding variants are unlikely to be having a large impact on the observed phenotypes. However, we identified two missense mutations in the fficking . Vitaminn humans . With thVRTN are only 1.2 kb apart. VRTN variants to be in complete LD in three experimental populations of Western and Chinese origin. The insertion allele segregates in some Chinese breeds but Chinese wild boar and most Chinese indigenous breeds are wt/wt. Also in our data set, both VRTN mutations are in strong LD, but VRTN expression caused by the insertion alone is sufficient to generate an additional vertebra. LTBP2 gene further distal on SSC7.The insertion allele characterized by the PRE1 insertion element and the SNP in the promoter of VRTN variants on thoracic vertebrae was found to be dependent on the genetic background. Allele frequencies at the QTL, size of the effect and accuracy of the phenotype differ between breeds and thereby influence genetic and phenotypic variance. Also, the overall population mean for RIB differs between L and D breeds. At the molecular lever, the large number of non-coding and coding variants observed to be present only in L on the wildtype haplotype show that the genetic background in the 3 Mb region encompassing VRTN differs between the two breeds. Moreover, other more subtle differences, such as variants affecting ABCD4, can also be expected in the remainder of the genome although they were not detectable in the GWAS results. The relationship between quantitative genetic parameters and the underlying factors of the developmental cascade of skeletal and mammary gland development gives a good example how biological factors influence our population parameters used for practical breeding value estimation. Identification and proof of causative mutations for oligogenic or polygenic traits without functional laboratory studies remains nearly impossible, especially for non-coding variants.In this study, a clear relationship between formation of the vertebral column and development of teats is observed in two populations of commercial pigs, L and D, differing largely in NTE. In both breeds, this difference in NTE is partly due to genetic variation in a region on SSC7, which has earlier been reported in several studies. By refining phenotype and examining NVE, noise from other loci is omitted, increasing overall heritability and significance of the SSC7 QTL region. However, the effect of two previously reported The datasets analyzed for this study are included in the manuscript and/or the The data used for this study were obtained as part of routine data recording in a commercial breeding program. Samples collected for DNA extraction were only used for routine diagnostic purposes of the breeding program. Data recording and sample collection were conducted strictly in line with the rules given by Dutch and Norwegian Animal Research Authorities.MS performed SNP detection in WGS data, was involved in imputation and haplotype work, and contributed to writing the paper. ML calculated genetic parameters, performed GWAS analyses, and was involved in writing the paper. HM performed functional SNP analyses, performed analyses of the LTBP2, and contributed to writing the paper. MD performed haplotype analyses and contributed to writing the paper. LG and JK performed phenotyping using CT images and was involved in writing the paper. MW and EG was involved in planning the project and contributed to writing the paper. BH was involved in planning the project, coordinated the studies, and drafted the paper. All authors have read and approved the final manuscript.MS, JK, and EG were employed by the company Norsvin SA. ML and BH were employed by the company Topigs Norsvin. LG was employed by the company Animalia AS. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Regions of homozygosity (ROH) are continuous homozygous segments commonly seen in the human genome. The integration of single nucleotide polymorphism (SNP) probes into current array comparative genomic hybridization (aCGH) analysis has enabled the detection of the ROH. However, for detecting and reporting biologically relevant ROH in a clinical setting, it is necessary to assess the analytical validity of SNP calling and the chromosomal distribution of ROH in normal populations.The analytical validity was evaluated by correlating the consistency of SNP calling with the quality parameters of aCGH and by accessing the accuracy of SNP calling using PCR based restriction enzyme digestion and Sanger sequencing. The distribution of ROH was evaluated by the numbers, sizes, locations, and frequencies of ROH from the collection of data from parental, postnatal, and prenatal case series that had normal aCGH and chromosome results.The SNP calling failure rate was 20–30% with a derivative Log2 ratio (DLR) below 0.2 and increased significantly to 30–40% with DLR of 0.2–0.4. The accuracy of SNP calling is 93%. Of the 958 cases tested, 34% had no ROH, 64% had one to four ROH, and less than 1% had more than five ROH. Of the 1196 ROH detected, 95% were less than 10 Mb. The distribution of numbers and sizes of ROH showed no differences among the parental, pediatric and prenatal case series and test tissues. The chromosomal distribution of ROH was non-random with ROH seen most frequently in chromosome 8, less frequently in chromosomes 2, 6, 10, 12, 11 and 18, and most rarely seen on chromosomes 15, 19, 21 and 22. Recurrent ROH occurring with a frequency greater than 1% were detected in 17 chromosomal loci which locates either in the pericentric or interstitial regions.With a quality control parameter of DLR set at below 0.2, the consistency of SNP calling would be 75%, the accuracy of SNP call could be 93%, and the observed chromosomal distribution of ROH could be used as a reference. This aCGH analysis could be a reliable screening tool to document biologically relevant ROH and recommend further molecular analysis.The online version of this article (10.1186/s13039-019-0424-6) contains supplementary material, which is available to authorized users. Copy number neutral segments showing continuous homozygosity with no intervening heterozygosity, termed ‘regions of homozygosity’ (ROH) or sometimes ‘runs of homozygosity’, were first identified using the denser genome-wide microsatellite scans by Broman in 1999 . ROH ariOver the past decade, the validation and utilization of genomic microarray platforms in clinical cytogenetics has significantly increased the analytical resolution and diagnostic yield in prenatal and postnatal genetic evaluations , 9. SubsThe ROH data were collected from aCGH studies performed on prenatal and postnatal case series at the Yale Clinical Cytogenetics Laboratory from January 2014 to December 2017 . All cas< 0.2, 0.2–0.3, > 0.3) were included. The average SNP probes per megabase (Mb) genome is 19 probes, the arbitrary size cutoff of ROH is 1 Mb. Therefore, any ROH less than 1 Mb was excluded. This study also excluded cases with multiple ROH with a sum size greater than 3% of the genome size that could indicate a family relationship.Additional criteria for data selection included the Derivative Log2 Ratio (DLR) spread and ROH size limitations. ROH data was collected only from cases that had a DLR equal to or less than 0.2, except for cases used for the analysis of SNP calling failure rate, in which the cases with the different range of DLR (http://genome.ucsc.edu/).DNA was extracted from peripheral blood specimens, CVS and cultured amniocytes using the Gentra Puregene Kit  following standardized procedures adopted from the manufacturer’s instructions. The oligonucleotide aCGH analyses using Agilent’s SurePrint G3 Human Genome CGH + SNP microarray 180 K kit or the 400 K kit  were performed as previously described [The readout of SNP alleles by Agilent Cytogenomics Software includes successful calling of homozygous and heterozygous alleles and failure calling of ‘NN’. The SNP calling failure rate was calculated by the number of ‘NN’ alleles divided by the total number of SNPs. It is thought that the SNP calling can be affected by the quality of fluorescent labeling of test and control DNAs and co-hybridization them onto the microarray. DLR from the test and control hybridization signals has been used as an important quality control parameter for aCGH. To assess the consistency of SNP calling, 30 cases each analyzed by 180 K and 400 K with DLR ranging from 0.14 to 0.4 were collected to analyze the correlation between SNP calling failure rate and DLR.The SNP alleles in the Agilent CGH + SNP arrays locate in the recognition site AGCT by restriction enzyme Alu I and GTAC by RsaI. Polymerase chain reaction (PCR)-based restriction enzyme digestion and Sanger sequencing were used to assess the accuracy of SNP calling by Agilent CGH + SNP arrays. Five SNPs  were chosen and PCR primer pairs for each SNP were designed using Primer Express 3.0 . Chi-square analysis was performed to compare the difference among the case series for the number and size distribution of ROH by using the webtool: Chi-square Test Calculator (http://www.socscistatistics.com/tests/chisquare2/Default2.aspx).One-way ANOVA analysis was used to compare the difference of SNP calling failure rate among samples with different ranges of DLR (p < 0.01), but no difference between the DLR of 0.2–0.3 and DLR > 0.3 on both platforms. This result indicated that the DLR below 0.2 should be used as a quality control parameter to ensure consistency of SNP calling.The density of SNP probes (defined by the number of probes per Mb) varied from 3 to 59 of detected ROH. The average number of SNP probes per Mb is 18 for both platforms which set analytical resolution arbitrarily as 1 Mb. The data of SNP calling on each SNP probe run by Agilent array 180 K and 400 K were exported from 30 cases with a range of DLR from 0.14 to 0.4. A total of 59,645 SNP probes were exported for each case run by the Agilent 180 K array, and 64,366 SNP probes for the 400 K array. The 30 cases were divided into three groups by DLR range of below 0.2, 0.2–0.3, and above 0.3. Of the 30 cases ran by 180 K, the mean SNP calling failure rate  for 10 cases with DLR below 0.2 was 28.2%, for 15 cases with DLR range 0.2 to 0.3 was 36.5%, and for five cases with DLR above 0.3 was 38.5%


TOX, FAM184B, PPARGC1A, PRKDC, LCORL, and COL1A2 were noteworthy. BayesR analysis found that most SNPs (>93%) had very small effects and the 4.02–6.92% that had larger effects  information. Here, imputed sequence level SNP data  of 2109 Hanwoo steers (Korean native cattle) were partitioned according to functional annotation, chromosome, and minor allele frequency (MAF). Genomic relationship matrices (GRMs) were constructed for each classified region and fitted in the model both separately and together for carcass weight (CWT), eye muscle area (EMA), backfat thickness (BFT), and marbling score (MS) traits. A genome-wide association study (GWAS) was performed to identify significantly associated variants in genic and exon regions using a linear mixed model, and the genetic contribution of each exonic SNP was determined using a Bayesian mixture model. Considering all SNPs together, the heritability estimates for CWT, EMA, BFT, and MS were 0.57 ± 0.05, 0.46 ± 0.05, 0.45 ± 0.05, and 0.49 ± 0.05, respectively, which reflected substantial genomic contributions. Joint analysis revealed that the variance explained by each chromosome was proportional to its physical length with weak linear relationships for all traits. Moreover, genomic variances explained by functional category and MAF class differed greatly among the traits studied in joint analysis. For example, exon regions had larger contributions for BFT (0.13 ± 0.08) and MS (0.22 ± 0.08), whereas intron and intergenic regions explained most of the total genomic variances for CWT and EMA (0.22 ± 0.09–0.32 ± 0.11). Considering different functional classes of exon regions and the per SNP contribution revealed the largest proportion of genetic variance was attributable to synonymous variants. GWAS detected 206 and 27 SNPs in genic and exon regions, respectively, on BTA4, BTA6, and BTA14 that were significantly associated with CWT and EMA. These SNPs were harbored by 31 candidate genes, among which  The genetic architecture of complex traits like carcass and meat quality in cattle includes a large number of loci with small individual effects on each trait. Variations in those traits are due to interactions among the loci dispersed across the genome as well as influenced by environmental factors. It is important to know how additive genetic variances are distributed across different genomic regions for better understanding of the genetic composition of complex traits. Several genome-wide association studies (GWAS) using dense single nucleotide polymorphism (SNP) marker panels have shown the differential contribution of genic and non-genic (intergenic) regions of genomes to additive genetic variance in human , dairy aThe Encyclopedia of DNA Elements (ENCODE) project found that about 80% of the human genome was engaged in relevant biochemical activities, even though only about 1% of the genome encodes a defined product such as a protein or reproducible biochemical signature . HindorfIn GWAS, stringent statistical thresholds are considered in most cases to control false positive results using multiple hypothesis testing and, therefore, many variants with small effects fail to reach significance levels despite some of them being causal variants. The proportion of phenotypic variance explained by all SNPs is relatively lower than the estimates of pedigree data because the former includes only the contributions of causal variants that are in linkage disequilibrium (LD) with genotyped SNPs . This isBos taurus coreanae), an indigenous cattle breed of South Korea, has been bred intensively over the last four decades for the improvement of carcass and meat quality traits. Hanwoo beef is regarded as a cultural icon and is very popular for its extensive marbling and eating quality attributes like tenderness, juiciness, and characteristic flavor (Hanwoo (c flavor . Presentc flavor . Previouc flavor . NotablyLongissimus dorsi muscle samples (approximately 1.5 kg) were collected from the junction between the 12th and 13th rib for the EMA, MS, and BFT measurements. MS was assessed on a 1–9 point scale according to the Korean Beef Marbling Standard , Rural Development Administration, South Korea, were used in this study. All the steers were progeny of 251 sires and unrelated dams (1–3 progenies per dam). Animal health and welfare issues were followed according to approved guidelines of the Animal Care and Use Committee (NIAS) and the ethics committee approval number was 2015-150. Feeding and management practices were uniform under feedlot condition with a concentrate mixture and rice straw-based ration. In the total feed, the proportions of concentrate and roughage were approximately 1.5:1, 2.5:1, and 4.5:1 in the grower (4–12 months), fattening I (13–18 months), and fattening II (19–23 months) rations, respectively. Crude protein and total digestible nutrients contents in the concentrate mixtures of these three rations were 14–16 and 68–70%, 11–13 and 71–73, and 11–12 and 72–73%, respectively. All animals were slaughtered at about 24 months of age. The carcass and meat quality traits investigated in this study were CWT, EMA, BFT, and MS. Feeding, management, and trait measurements were according to Standard . Descrip2R > 0.60 according to a previous In total, 2605 individuals were genotyped initially using two different SNP platforms, Illumina Bovine SNP50 BeadChip  and Bovine HD BeadChip . The unphased genotypes were converted into phased data using Eagle v. 2.3.2 based on long-range phasing approach . The genTable 1) except regulatory regions. Then, all splice variants and start and stop sites were excluded because they contained a very low proportion of the total SNPs or because, in exon regions, SNPs might already be represented by coding sequences and untranslated regions (UTRs). Finally, six major functional classes of genomic regions were considered: synonymous, non-synonymous (missense), 5′- and 3′-UTRs, intron, regulatory, and intergenic regions. Regulatory regions were defined as regions located 5-kb upstream and 5-kb downstream of genes, and intergenic regions were defined as regions more than 5-kb distant from genes. Besides, the variants were categorized into six classes based on their MAF as 0.01–0.05, 0.05–0.1, 0.1–0.2, 0.2–0.3, 0.3–0.4, and 0.4–0.5.The physical positions of the imputed SNPs were determined using the UMD 3.1  bovine gTo decipher the genomic architecture of traits and predictive ability of particular genomic regions, the total genomic variance was partitioned based on MAF category (six classes), chromosome (29 autosome), and functional annotations (six classes). To do this, genomic relationship matrices (GRMs) were estimated based on the SNPs in the respective categories  following the method of y is the vector of phenotypes, β is a vector of fixed effects (year and season) and covariate (age) with its incidence matrix X, n is the number of subsets for non-overlapping SNPs partitioning , gG is a vector of random additive genetic effects attributed from aggregated SNP information, and e is a random residual error. The variance component of phenotypic values from the joint analysis is g is the genetic relationship matrix calculated from SNP data, where Two different approaches were used for the single-marker association analysis using SNPs in genic (exon or intronic SNPs) and exon regions, as well as to know the contribution of exonic SNPs to phenotypes. Phenotypic data were adjusted using a linear mixed model for fixed effects (year and season) and covariate . The adjusted phenotypes and constructed GRMs were subsequently used for GWAS under a mixed linear model including all candidate SNPs implemented in GCTA v.1.26. In GCTA, the mixed linear model assumes that all markers are to be in LD with quantitative trait loci (QTL) in close proximity and additive effects are derived based on the SNP mediated overall covariance. Thus, single trait association analysis was performed using the following model:y is the adjusted phenotypic value, a is the mean, b is the additive effect (fixed effect) of the candidate SNP to be tested for association, X is the SNP genotype indicator variable coded as 0, 1, or 2 depending on the number of copies of a specified allele, g is the accumulated effect of all SNPs, and e is the random residual effect. The Bonferroni adjusted P-value threshold was determined to correct multiple hypotheses testing at the genome-wide suggestive (1.0/number of SNPs tested) and significant (0.05/number of SNPs tested) levels. Manhattan plots were drawn from genome-wide associated P-values  using the “gap” package  accounted for by each SNP was calculated using the formula:where  package  in R pro burn-in . The perp and q are the allele frequencies for a given trait, β is the additive effects of the SNPs, and where, h2is the heritability, n is the total SNPs in the respective annotated class, results were multiplied by 100 to get percent (%) of the genetic variance explained and results were divided by 10-4 for visualization of the data. The derived variance components  tools supported by Ensembl  However, because of the low SNP proportion in some classes, only six major classes  were included in our analysis. As expected, intergenic variants were the most common, followed by intron, upstream and downstream, and exon variants, representing 70.30, 28.79, 8.24, and 0.88% of the total SNPs, respectively. The proportions of SNPs in the other functional categories were very low . In a previous study using bovine next-generation sequencing data, Genome sequence level SNP data were annotated into 14 different functional classes (Table 2), suggesting that a substantial genomic contribution explained the phenotypic variation in the studied population. To determine what proportions of the variance were explained by individual chromosomes, we performed a joint analysis by fitting 29 GRMs (from 29 autosomes) simultaneously. The chromosomes contributed to the total genomic variance in various degrees; namely, from 0.000 to 0.089 for CWT, from 0.000 to 0.064 for EMA, from 0.000 to 0.044 for BFT, and from 0.000 to 0.047 for MS. Moreover, the sum of variances attributed to individual chromosomes was slightly lower than the estimated total genomic variance for all four traits . However, the magnitudes of linear relationships (2R) were comparatively low and varied from 0.06 to 0.15 among the four traits studied.The proportions of genomic variance attributed to all SNPs were found to be 0.57, 0.44, 0.45, and 0.49 for CWT, EMA, BFT, and MS, respectively (Tables 3). For the separate analyses, the LD between SNPs in the different functional classes might have led to overestimation of the genomic variance for each class. For the joint analysis, the genomic variances explained by genic  and upstream and downstream regulatory variants were negligible and close to zero (data not shown) for the four traits studied. Therefore, the variants in those functional classes were merged with the exon and intergenic classes, respectively. The sum of variances for both the functional classes and the MAF categories were similar to the estimates for the separate and joint analyses using all the SNPs (Tables 4) for all four traits and justified the well-fitted genome partitioning analysis.To determine the genomic variation that was explained by the six major functional classes, at first, similarity matrices of each category were used separately and then all the matrices were fitted simultaneously in a joint analysis. The separate analyses showed that the six classes explained substantial amounts of the genomic variations for all traits, and their contributions were larger than those from the joint analysis . Relatively lower genetic variance was explained per SNP in the UTRs for the CWT, EMA, and MS traits, and by SNPs in the non-synonymous class for the BFT and MS traits. In the intron class, the genetic variance explained per SNP was low, but higher than that for the upstream and downstream and intergenic classes for all four traits.In the joint analysis, the genomic variances that accounted for the six functional classes varied among the carcass and meat quality traits. For example, the genomic heritability explained by exons was 0.13 and 0.22 for the BFT and MS traits, respectively, but close to zero for the CWT and EMA traits, whereas the genomic heritability explained by intron and intergenic regions ranged from 0.22 to 0.32 for the CWT and EMA traits, and from 0.09 to 0.19 for the BFT and MS traits. These results suggest distinct genetic architectures underlie the processes involved in muscle development and fat biosynthesis in the studied population. In particular, when the different functional classes in the exon regions  were considered in the joint analysis, the genomic variances attributable to the synonymous class were significantly more than those attributable to the 5′- and 3′-UTRs and non-synonymous classes for all four traits. In the joint analysis, the genetic variance explained by each SNP was estimated to determine the contribution of the SNPs in each class. Regardless of the trait studied, the per SNP analysis also revealed that the variants in coding and UTR regions contributed more to the variance than variants in the intron and intergenic regions. Specifically, the largest proportion of the genetic variance was explained per SNP in the synonymous class, particularly for the CWT, BFT, and MS traits (Table 4). Similar to the results for the functional annotations, the variance explained by the six different MAF bins from a joint analysis varied greatly among the traits and MAF categories. In general, two common alleles groups (0.10–0.20 and 0.30–0.40) contributed more to the variance for all traits than the other allele groups. Specifically, the highest genomic variance was explained by SNPs in MAF category 0.10–0.20 for the CWT (0.26) and EMA (0.14) traits, and by SNPs in MAF category 0.30–0.40 for the MS (0.23) and BFT (0.14) traits. Remarkably, the low frequent alleles (MAF < 0.05) accounted for the highest variance only for the BFT (0.15) trait. The other three MAF bins explained comparatively lower proportions of the genetic variance (from close to zero to 0.10) for all four traits investigated. This finding supports the idea that different genomic architectures exist between carcass and meat quality traits in Hanwoo cattle.The distribution of SNPs in the six different MAF classes was 27.90, 14.70, 18.40, 14.20, 12.60, and 12.10% of the total SNPs (P < 1.49 × 10-8) and six SNPs significantly associated with EMA. These significant SNPs were located on BTA6 and 14, and were harbored by 24 candidate genes  and had the second highest association with CWT. Significant SNPs for the CWT and EMA traits were located at 3.32 Mb on BTA6 and were in LAP3, FAM184B, NCAPG, LCORL, and SLIT2. Besides, significantly associated SNPs for CWT spanned a 13.69 Mb region on BTA14 that harbored 19 genes, among which PRKDC, XKR4, IMPAD1, SDCBP, TOX, DNAJC5B, PREX2, C8orf46, and C8orf34 were notable (Table 5 and Supplementary Table Genome-wide association study was performed using SNPs in both genic (exon and intron together) and exon regions to identify their intra-genetic association with the four traits studied. Considering all the SNPs in the genic region , the mixed linear model-based GWAS revealed 206 SNPs significantly associated with CWT . The significant exonic SNPs were harbored by 14 candidate genes, seven of which had already been detected when the SNPs in genic (exon and intron together) regions were used in the mixed linear model-based GWAS. Among the candidate genes, TOX, COL1A2, PPARGC1A, PRKDC, IMPAD1, DNAJC5B, and CRH were noteworthy (Table 6). Importantly, the coding variants on COL1A2, PPARGC1A, and CRH were significantly associated only with the exonic SNPs. The most significant SNP (rs110132121) was located in the 3′-UTR of TOX (P < 5.31 × 10-15) on BTA14 for CWT, followed by two synonymous SNPs (rs461493029 and rs449968016) in PRKDC (P < 6.22 × 10-14), also for CWT.In GWAS, only a few markers with the largest effects cross the significant threshold level through multiple hypothesis testing, and most variants fail to reach statistical significance, even though some of them are causal. To overcome the limitations of stringent criteria, we selected only the exonic SNPs  for further association study. The mixed linear model-based GWAS identified a total of 27 significant SNPs on BTA4, 6, and 14 . In particular, the proportion of SNPs that had the largest effects  for chromosomal variance on chromosomal lengths for complex traits in Holstein cattle. They also stated that aggregated chromosomal variance accounted for 96–97% of the total genomic variance, which is similar to our findings (Supplementary Table 2R) varied from 0.03 to 0.77 for milk production and milk composition traits in Holstein cattle, which is in partial agreement with the present study. Similar results were also found by 2R = 0.03–0.80) linear relationships between genetic variance explained by each chromosome with its length for four complex traits and a complex genetic disorder, schizophrenia in human. Remarkably, we observed notable differences in genetic contribution among chromosomes of similar lengths, which is supported by the findings of 2-Rvalues between chromosomal lengths and their contributing genomic variances reflected only a weak relationship, which may be because genes that had large effects contributed a greater proportion of genomic variance for the harboring chromosome. The results of the present study also indicate that major genes or QTLs are not evenly segregated across the Hanwoo genome. For instance, DGAT1 and PLAG1 on BTA14 are known to make large contributions to genomic variance for carcass and milk traits in cattle, and accordingly we found the highest variance was attributed to BTA14, which is a small sized autosome. However, SNP density in the marker panel, statistical model used, types of traits investigated, and species of interest are major contributing factors to differences between our results and previous results. Overall, we found variable genomic contribution attributed across all chromosomes, which support a polygenic model for carcass and meat quality traits, and is similar to the findings of We found a linear but weak relationship between the variance explained by each chromosome and its length, which is consistent with the study of In agreement with our results, PLAG1 and a nearby major QTL on BTA14 for their associations with bovine stature  was located in TOX, which is 1.61 Mb distant from PLAG1, and also confirmed the previous findings of PLAG1, CHCHD7, FAM110B, CYP7A1, SDCBP, and TOX were positional and functional candidate genes for a CWT QTL in Hanwoo cattle, which supports our findings. In addition, they reported that the variants located near PLAG1 and CHCHD7 had non-significant associations with CWT, which is similar to the present findings. TOX acts as a transcription factor in the hypothalamus and plays a key role in the development of puberty in Brahman cattle  on BTA14 in the Hanwoo population.Previous GWAS using both 50K and 777K data have revealed major QTL(s) on BTA14 associated with CWT and bovine stature in different cattle breeds including Hanwoo . Here, a stature , CWT  was located in NCAPG. Recently, LAP3, FAM184B, LCORL, and NCAPG on BTA6, which have been regarded as positional candidate regions for carcass and growth traits in cattle  of this gene had significant association with CWT in our study. A missense mutation of CRH (G1084A) was significantly associated with the EMA trait in Hanwoo  for carcass traits only on BTA14 in Hanwoo populations. Using sequence level SNP data, we detected two additional genomic regions (a 0.58 Mb region on BTA4 and a 1.61 Mb region on BTA6) in this study that may be new candidate loci for carcass traits in the investigated population. This information can be used to detect causal variants as well as in genomic selection programs in Hanwoo cattle.In addition, similar to our study, a number of coding variants on f cattle . The synf cattle , as wellf cattle . Besidesn Hanwoo , which i-4 × -3 × -2 × Our results on the effect sizes of SNPs are in agreement with the infinitesimal theory as well as with the findings of Imputed genome sequence level data revealed the contributions of both genic and non-genic SNPs to phenotypic variations for four carcass and meat quality traits. Intragenic SNPs explained more genomic variance than intergenic variants, and the highest variance was attributed to synonymous SNPs. Genomic regions partitioned based on functional annotations, chromosome, and MAF category showed distinct differences in the variance explained for carcass and meat quality traits, and thus depicted different genetic architectures between the two types of traits. A wide range of significant SNPs and their contributions were established through this study. Some of these variants or genes that harbor them, first reported in this study, could be included in the genomic evaluation of quantitative traits in Hanwoo. Only 4–7% of the genic variants potentially contributed to the total explained genetic variance, while the remaining thousands had close to zero contribution and largely point toward the polygenic composition of these traits.The high density SNP genotypic data and full genome sequence data of Korean Hanwoo cattle used in this study are deposited and available at digital repository of NIAS, South Korea (website) and would be available to the interested researcher upon the request.SeL and MB conceived and designed the study. MB drafted the manuscript. DL and CG were responsible for imputation of 50K and 777K genotype data to sequence level. DL and SoL were responsible for phenotypic data collection. SoL and YK contributed in quality control of genotype data, partitioning of genome and SNP annotation. MB and YK performed GWAS. All authors read and agreed on the contents of manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.