Identifying genomic annotations that differentiate causal from trait-associated variants is essential to fine mapping disease loci. Although many studies have identified non-coding functional annotations that overlap disease-associated variants, these annotations often colocalize, complicating the ability to use these annotations for fine mapping causal variation. We developed a statistical approach (Genomic Annotation Shifter [GoShifter]) to assess whether enriched annotations are able to prioritize causal variation. GoShifter defines the null distribution of an annotation overlapping an allele by locally shifting annotations; this approach is less sensitive to biases arising from local genomic structure than commonly used enrichment methods that depend on SNP matching. Local shifting also allows GoShifter to identify independent causal effects from colocalizing annotations. Using GoShifter, we confirmed that variants in expression quantitative trail loci drive gene-expression changes though DNase-I hypersensitive sites (DHSs) near transcription start sites and independently through 3′ UTR regulation. We also showed that (1) 15%–36% of trait-associated loci map to DHSs independently of other annotations; (2) loci associated with breast cancer and rheumatoid arthritis harbor potentially causal variants near the summits of histone marks rather than full peak bodies; (3) variants associated with height are highly enriched in embryonic stem cell DHSs; and (4) we can effectively prioritize causal variation at specific loci. Functional annotations provide valuable information for prioritizing potential causal variants within complex-trait loci identified through genome-wide association studies (GWASs).Identifying the most informative annotations requires a robust statistical strategy that controls for two important types of potentially confounding genomic features: (1) local structure of genetic variation near SNP associations and (2) colocalization of multiple functional genomic annotations. First, trait-associated SNPs often map to regions with greater gene density, genetic variation, and linkage disequilibrium (LD) than the rest of the genome. Second, functional annotations that colocalize are often enriched within trait-associated loci. For example, DNase-I hypersensitive sites (DHSs) colocalize with exons,We developed Genomic Annotation Shifter (GoShifter), an enrichment test that controls for local genomic structure. GoShifter employs an intuitive method that locally shifts sites of tested features within each locus to generate a null distribution of annotations overlapping associated variants by chance. Other methods, such as Genome Structure Correction (GSC), assess the relationships between genomic features by shifting them.We show that compared with commonly used SNP-matching-based methods, GoShifter is able to robustly identify informative annotations under a range of different scenarios. We show that matching-based approaches are prone to inflating observed enrichment values: we highlight that the lack of matching on SNPs in LD can lead to misleading results. Furthermore, we implemented a stratified test to distinguish contributions from two colocalized annotations. Using the local-shifting approach, GoShifter allows prioritization of loci by determining the most informative functional variants driving the observed enrichment.We used three methods to assess the significance of enrichment: (1) local annotation shifting, (2) stratified local shifting, where we accounted for colocalization of a secondary annotation, and (3) SNP matching. We implemented (1) and (2) in GoShifter.2 > 0.8 in 1000 Genomes Project European [EUR] samplesTo assess the statistical significance of an overlap between trait-associated SNPs and a genomic annotation X, we first identified all variants in LD with each index SNP , exons, 5′ UTRs, 3′ UTRs, introns, and promoter regions, on the basis of RefSeq gene coordinates from the UCSC Genome Browser. We retained genes with at least one exon and at least one PubMed referenceTo assess GoShifter’s sensitivity and specificity, we (1) defined sets of SNPs within functional regions, (2) generated sets of variants tagging these SNPs, as is common in GWASs, and (3) generated sets of SNPs with variable proportions of functional variants.Using a total of 6,830,225 common autosomal SNPs (MAF > 5% in Europeans in the 1000 Genomes Project), we grouped SNPs into seven functional categories: nonsynonymous, intronic, 3′ UTR, 5′ UTR, promoter (<500 bp from the TSS), intergenic (>5 kb from the TSS), and those residing within DHSs. To identify nonsynonymous variants, we used SIFT.2 ≥ 0.5) by 1,218,618 common (MAF ≥ 5%) variants on the Illumina Human Omni 2.5 array. If multiple SNPs were in LD with the functional variant, we selected the best tag with the greatest r2. Finally, we required SNPs in the final set to be more than 100 kb apart from each other to ensure independence. As an alternative to simulating a sequencing-based study, we also simulated 200 sets of 1,416 causal SNPs for each of the predefined functional categories by selecting tagging SNPs with strong LD (r2 ≥ 0.8) from the EUR subset of the 1000 Genomes Project data. If multiple SNPs were in LD with the functional variant, we selected the best tag with the greatest r2, and we selected the functional SNP itself when no tag SNP could be selected according to these criteria.We simulated 1,000 sets of 1,416 SNPs , selected to overlap each of the predefined seven functional categories. In each set, variants were randomly selected from a given functional category. In order to mimic a typical GWAS approach, we then identified a tagging SNP that was in LD and was available on a commercial genotyping array (Illumina Human Omni2.5) for each functional variant. In total, 5,569,657 of the available SNPs were tagged  association with any trait. We included only studies where Europeans contributed to the majority of the final samples to simplify LD calculations. We conducted LD calculations across the selected SNPs by using 379 EUR samples from the 1000 Genomes Project2 > 0.1 or if the distance between the SNPs was <100 kb. Finally, we excluded phenotypes with fewer than ten independent SNP associations after our filtering criteria. This resulted in 1,416 SNPs in our test set.We obtained trait-associated SNPs from the GWAS Catalog180300]) in Europeans alone or shared between Europeans and Japanese,114480).We used 689 SNPs associated with height,cis-eQTL SNPs associated with whole-blood gene expression at a FDR of 0.5.2 > 0.1) to ensure independence of the SNPs in our final SNP set. This resulted in a final dataset of 6,381 eQTL SNPs.We assembled a set of 923,022 To evaluate GoShifter’s performance in prioritizing annotations that identify causal variants, we would ideally use a set of trait-associated loci in which the causal variants and relevant driving genomic annotations are known. However, causal variants are known for only a handful of complex-trait loci. Therefore, to capture a wide range of possible models of causal variation, we simulated 1,000 sets of 1,416 SNPs by tagging functional SNPs selected from seven distinct functional genomic annotations: DHSs, promoters, 5′ UTRs, nonsynonymous SNPs in exons, 3′ UTRs, introns, and intergenic regions . We thenWe observed that GoShifter was well powered to detect significant enrichment in simulated SNP sets that tagged variants in DHSs, promoters, and 5′ UTRs: 100% of such SNP sets obtained p < 0.001 according to 1,000 shifting iterations . By chanThe analyses to benchmark GoShifter were based on simulated GWAS SNP sets ascertained from a commercial array. To assess whether ascertaining tag SNPs from commercial genotyping arrays would affect observed statistics, we also evaluated GoShifter’s performance by using sequencing SNPs (from the 1000 Genomes Project).We then compared the results of GoShifter to those of the more commonly used matching-based enrichment testsWe then investigated whether matching on other combinations of SNP features was more effective. As expected, we observed that the results were highly sensitive to the choice of specific matching parameters. We noted that the number of SNPs in LD was critical for appropriate statistical performance . AlthougAfter determining the best-performing SNP-matching strategy, we wanted to more precisely quantify differences in sensitivity between GoShifter and matching-based tests. For this purpose, we generated SNP sets where only a minority of variants tagged functional variants within DHSs. We incremented the percentage of loci tagging functional variants in the simulated SNP set by 3%. These simulations represented a test of the methods for detecting enrichment under a range of more-challenging circumstances. For a set in which 10% of SNPs tagged DHSs, GoShifter had 55% power to detect enrichment, but only 31% power with stringent SNP matching B. For a To test the ability of GoShifter to control for the effects of colocalized annotations, we examined two scenarios.First, we tested the enrichment of different annotations in 1,000 sets of 1,416 SNPs tagging exonic variants. Although we observed significant enrichment of exons across all SNP sets (p < 0.05), 60% also showed DHS enrichment A. This sSecond, we assessed 1,000 sets of 1,416 SNPs defined to tag variants within DHSs. DHSs tend to overlap regions mapped by H3K4me3 because they both colocalize with active promoter regions. When testing for enrichment, we would therefore expect to observe significant signal for both of these annotations. However, because we know that functional signals were drawn from DHSs, an adequate method should not detect significant enrichment in H3K4me3 when it is stratified on the lead signal from DHSs. Whereas all SNP sets were enriched in DHSs at p < 0.05, 98% of the SNP sets were also enriched in H3K4me3 sites . As expected, after we stratified on DHSs, only 1.3% of the SNP sets showed H3K4me3 enrichment at p < 0.05, confirming that H3K4me3 enrichment is entirely dependent on DHS colocalization. Conversely, when tested for DHS enrichment stratifying on H3K4me3, 100% of the SNP sets remained significant. This confirmed that signal in H3K4me3 was primarily explained by the DHS enrichment.The stratified analysis modestly reduced power to detect enrichment effects: for SNP sets where functional variants were selected from both DHSs and exons, stratifying on exons reduced power to detect DHS enrichment at p < 0.001 only when ≤20% of the functional variants were from DHSs C. HoweveWe also assessed whether SNP-matching approaches effectively account for colocalization of annotations. We again considered the sets of DHS-tagging SNPs and tested for H3K4me3 enrichment. To control for the DHS effect, we used the most appropriate matching strategy  and included an extra matching parameter (DHS). We observed that 100% of SNP sets still obtained H3K4me3 enrichment at p < 0.05. We concluded that SNP-matching tests cannot easily control for colocalization of annotations .As a proof of concept, we applied our approach to eQTL variants, given that eQTLs are known to localize close to TSSs and to be enriched in DHSs.−4; −3), became insignificant , at genes, at H3K4me3 and H3K4me1 marks, and also at 5- and 10-kb windows around TSSs (−4). In contrast, the enrichment of gene transcripts and TSSs was not significant after stratification on DHSs. Both H3K4me1 and H3K4me3 retained nominal enrichment independently of each other and DHSs . We first tested 88 SNPs associated with RA+ memory T cells given recent observations of cell-type-specific gene expression and eQTLs within these loci.+ memory T cells (p = 0.17) or in an aggregate of all 118 cell types from our datasets . The CD4+ memory T cell signal remained significant after stratification on the summit regions of the other 117 cell types (p = 2.7 × 10−3). In contrast, the other cell types did not retain significant enrichment after stratification on CD4+ memory T cells (p = 0.08). These results suggest that H3K4me3 summit regions in CD4+ memory T cells could help prioritize causal variants in RA-associated loci.We examined two phenotypes to test GoShifter’s ability to identify cell-type-specific functional variants. To ensure that GoShifter was powered to detect significant enrichments , we sele−3) in variant human mammary epithelial cells  after stratification on peak summits from each of the other three breast tissues. We found that none of the DHS samples in our dataset showed nominally significant enrichment.Similarly, we examined 69 SNPs vHMECs; B. We per−3, demonstrated some evidence of overlap (−4) in embryonic stem cells (H1-hESCs) and primary CD3+ cells from cord blood. The enrichment in H1-hESCs remained significant after stratification on cord-blood CD3+ cells (p = 9.6 × 10−3), but the converse enrichment (p = 0.08) did not. These results suggest that examining DHSs in embryonic stem cells or a related cell type might be informative for fine mapping height-associated loci for potential causal variants.We assessed 697 SNPs associated with adult human height, overlap C. We obsAfter the most significantly enriched annotations are identified, the loci contributing the most to such enrichment are those with the lowest overlap score . A locusMAP3K1 (MIM: 600982) and modifies a predicted binding site for estrogen receptor alpha (ER-α),For example, in breast cancer, the locus with the best (lowest) overlap score (0.097) was rs889312 . That SNHere, we have presented GoShifter, a method that enables the identification of annotations that are the most informative in distinguishing causal variants from associated ones. GoShifter stringently controls for local features by shifting annotations within trait-associated loci. Our method represents an important advance over current approaches in its ability to assess independent effects from colocalizing annotations.GoShifter is suited to investigate whether a specific annotation might be effective in fine mapping known genetic loci for a trait. Consequently, GoShifter does tend to favor high-resolution annotations . But in certain instances, large annotations  might be important indicators that separate trait-associated variation from non-trait-associated variation.2 > 0.8) with an index SNP for overlap with an annotation. This is a widely used approach, but it has limitations. In many instances, index SNPs within loci that have been detected by sparse genotyping or in relatively smaller studies have a lower degree of linkage than r2 > 0.8 to a causal variant. This is particularly true in complex genetic regions, where multiple haplotypes might be driving an association, such as within the major histocompatibility complex in autoimmune diseasesIRF5 (MIM: 607218) in systemic lupus erythematosus.In this study, we examined variants in LD  might be driving complex-disease associations. In many instances, different annotations colocalize because they are derived from different molecular assays that query the same genomic functional process. For example, H3K4me1 marks and bidirectional transcriptionApplying GoShifter, we observed that different annotations play dominant roles in different complex traits. The eQTL results have highlighted that in addition to gene-regulatory regions and protein-coding genes, other mechanisms might be mediating causal variation for complex phenotypes. The observed enrichment of 3′ UTRs in eQTLs suggests that post-transcriptional gene regulation is one such mechanism by which genomic variation affects gene-transcript levels. Other functional genomic features, such as non-coding RNAs and copy-number variation, could also be driving complex phenotypic and disease variation.Our results indicate that SNPs represented in the GWAS Catalog are globally enriched within DHSs. However, this is not representative of enrichment for individual phenotypes and in fact might be driven by a selection of phenotypes with a large number of associated variants. When we applied GoShifter to variants associated with height, we observed strong DHS enrichment specifically in embryonic stem cells, whereas breast cancer variants were enriched in H3K4me1 but not DHSs. These could be partly due to the fact that different trait-relevant tissues were mapped with different assays. Nevertheless, variants associated with different phenotypes are likely to act through various mechanisms, resulting in differential enrichment of annotations. These results also show that our stratified approach can be applied to narrow down the specific cell type in which disease-associated variants function, even from within a single disease tissue of interest. The strong enrichment of breast-cancer- and RA-associated loci within the summit regions of histone marks is consistent with the well-described functional significance of the summit regions.We expect that the applicability of GoShifter will expand as relevant functional annotations increase over time. Once our approach identifies independently informative functional annotations by analyzing a large set of associated variants, a powerful aspect of GoShifter is the ability to identify the specific loci that drive the enrichment statistics. Combining the identification of the functional annotations associated with a trait and the identification of the loci that drive that association should facilitate effective and focused experimental interrogation of these loci.Software is available as the GoShifter package, written in Python-2.7.


The large-scale 16591 SNP genotyping- and phenotyping-based GWAS (genome-wide association study) identified 16 genomic loci/genes associated (29% combined PVE) with seed-Fe and Zn concentrations. Of these, 11 trait-associated SNPs in the genes linked tightly with eight QTLs were validated by QTL mapping. The seed-specific expression, including pronounced differential-regulation of 16 trait-associated genes particularly in accessions/mapping individuals with contrasting level of seed-Fe and Zn contents was apparent. Collectively, the aforementioned rapid integrated genomic strategy led to delineate novel functional non-synonymous and regulatory SNP allelic-variants from 16 known/candidate genes, including three strong trait-associated genes  and eight major QTLs regulating seed-Fe and Zn concentrations in chickpea. These essential inputs thus have potential to be deployed in marker-assisted genetic enhancement for developing nutritionally-rich iron/zinc-biofortified chickpea cultivars.The SNP-based high-resolution QTL mapping mapped eight major genomic regions harbouring robust QTLs governing seed-Fe and Zn concentrations (39.4% combined phenotypic variation explained/PVE) on six chromosomes of an intra-specific high-density genetic linkage map (1.56 cM map-density). 24620 SNPs discovered from genome-wide GBS (genotyping-by-sequencing) and 13 known cloned Fe and Zn contents-related chickpea gene-orthologs were genotyped in a structured population of 92 sequenced  The global population strength is fast rising in an alarming rate and anticipated to cross the mark of nine billion by 20501347834Medicago, lentil, field pea and chickpea89131420212223With the advancement of cutting edge next generation sequencing (NGS)- and array-based genotyping strategies, the genomics-assisted breeding, which primarily involves identification and introgression of useful genes/QTLs (quantitative trait loci) regulating Fe and Zn contents, can be deployed as a powerful intervention tool for biofortification of food crops41113256789101112131415161718desi and kabuli) with contrasting agronomic traits representing diverse genepools, have been sequenced27285313233343536373839404142Chickpea is a self-pollinated, annual diploid and highly nutritious legume with a genome size of ~740 Mbp that is considered as a staple source of protein2525In this aspect, the current study has made an effort to integrate QTL mapping with genome-wide association study (GWAS), candidate gene-based association mapping and expression profiling using large-scale natural germplasm lines and mapping populations to delineate functionally relevant molecular tags  regulating seed-Fe and Zn concentrations, with an ultimate objective of genetic enhancement in chickpea.kabuli reference genome (16376)- and de novo (8029)-based GBS (genotyping-by-sequencing) assays were employed to mine 24405 high-quality SNPs from 92 desi and kabuli chickpea accessions  with a mean density of one SNP per 215.9 bp (kabuli reference genome- and de novo-based GBS (24405) assay as well as known gene-derived (215) SNP genotyping assay discovered 24620 SNPs /QTLs governing complex Fe and Zn-content traits in chickpea.The structural annotation of 16591 SNPs, including 16376 ectively . The annea genes . The GBSdesi accession ICC 4958 and kabuli accession ICC 8261) were genotyped in 277 individuals of a RIL (recombinant inbred line) mapping population (ICC 4958 x ICC 8261). The use of these SNP genotyping data in linkage analysis mapped 533 SNPs onto eight LGs (linkage groups) of an intra-specific genetic map of chickpea  exhibiting polymorphism between parental accessions  and Zn  concentrations in two parental accessions and 277 individuals of a RIL mapping population [ICC 4958 (Fe: 43.0 ± 2.9 and Zn: 30.2 ± 2.5 ppm) × ICC 8261 (Fe: 64.8 ± 3.3 and Zn: 42.0 ± 2.7 ppm)] across two years based on ANOVA was observed (A highly significant difference (P < 0.001) of seed-Fe  were considered as robust QTLs governing chickpea seed-Fe and Zn contents. Eight major genomic regions harbouring eight robust QTLs  were mapped on six chromosomes (except chromosomes 6 and 8)  significantly associated with seed-Fe and Zn concentrations in chickpea , Fig. 2.6 and 8) , Fig. 2.For seed-Fe concentration, three major genomic regions harbouring three robust QTLs (21.1–23.4% PVE with 8.0–8.8 LOD) were mapped on three chromosomes  , Fig. 2.CaqFe1.1 and CaqFe4.1) between previous and present study. Molecular mapping of QTLs associated with any of such traits pertaining to seed-Fe and Zn concentrations has not been reported so far in chickpea. In this context, the informative SNPs especially derived from the major known/candidate genes identified and mapped at robust seed-Fe and Zn concentrations-associated QTL regions in our study seem quite relevant. The gene-based SNPs tightly linked with major QTLs identified, can be utilized in marker-assisted genetic improvement for developing cultivars with high level of Fe and Zn contents in the seeds of chickpea.The 11 SNP loci linked to the robust eight major QTLs governing seed-Fe and Zn concentrations were compared/correlated with that of an only previous association mapping study on similar traits available till date in chickpeadesi and kabuli chickpea accessions) mined through genome-wide GBS (16376 SNPs)- and known/candidate gene (215 SNPs)-based SNP genotyping assays were utilized (kabuli chromosomes) in 92 desi and kabuli chickpea accessions exhibited 200–250 kb LD decay across chromosomes. The normal frequency distribution along with a wider phenotypic variation for seed-Fe  and Zn  concentrations among 92 desi and kabuli chickpea accessions was observed  with seed-Fe and Zn concentrations in chickpea  of kabuli genome (−8 to 1.0 × 10−9) among 92 chickpea accessions (2 with P: 3.2 × 10−8 to 1.3 × 10−9) with seed-Fe concentration in chickpea (2 with P: 1.0 × 10−7 to 1.0 × 10−9) with target trait were detected. In addition, four gene-derived SNPs (including one non-synonymous and two URR-SNPs) associated (6–12% R2 with P: 1.2 × 10−7 to 1.0 × 10−9) with both seed-Fe and Zn concentrations were identified (The CMLM and P3D/EMMAX model-based association mapping approach (FDR cut-off ≤0.05) identified 16 genomic loci (gene-based SNPs) exhibiting significant association , vacuolar protein sorting-associated protein (14% R2 with P: 1.0 × 10−9) and yellow stripe-like 1 (YSL1) protein (12% R2 with P: 1.0 × 10−9)] localized at three major target robust QTLs (17.7–23.4% R2 at 6.5–8.8 LOD) governing seed-Fe, Zn and both Fe-Zn concentrations, respectively was evident  in chickpea  and 3. O evident  and 3. HA set of 16 trait-associated known/candidate genes with SNPs, including 11 validated by both QTL and association mapping were analysed to determine the differential expression profile and functional regulatory pattern of these genes governing seed-Fe and Zn concentrations in chickpea. The differential expression analysis (by quantitative RT-PCR assay) of these trait-associated genes in the leaves and seed maturation developmental stages of five chickpea accessions/parents and four homozygous mapping individuals with contrasting level of low (ICC 4958 and ICC 8933 with 42–43 ppm Fe and 27–30 ppm Zn) and high  seed-Fe and Zn concentrations was performed. All 16 trait-associated known/candidate genes  exhibited seed-specific expression (>4-fold) as compared to leaf tissues of parental accessions and homozygous mapping individuals with low and high level of Fe and Zn contents . Of thesYSL1 and vacuolar protein sorting-associated proteins] have ascertained significant involvement of these genes in storing high level of Fe and Zn contents in the maturing seeds of multiple crop (legumes) accessions4647485859HMA2/4  (ATPase/P-type/H+ transporting proton pump) in regulation of seed Fe and Zn-contents suggests the critical involvement of these genes in translocation, remobilization and storage of Fe and Zn-contents in crop plantsYSL gene representing oligopeptide transporter OPT superfamily and ferritin (FER) gene regulating Fe translocation and storage, respectively have been isolated and characterized in higher plants4647ZIP1) gene encodes zinc/iron regulated transporter-related protein 1 belonging to a novel metal transporter ZIP gene family, is known to regulate transport of a wide-range of cations, including Fe and Zn for retaining their cellular homeostasis in plants4748The seed-specific expression, including a pronounced upregulation and enhanced transcript accumulation of three strong seed-Fe and Zn contents-associated known/candidate genes  and the contents left overnight for cold digestion in a digestion chamber. The samples were digested initially at 120 °C for one h, followed by digestion at 230 °C for about 2 h to get clear and colourless digests. Following cooling of the digests, the contents were dissolved in distilled water and volume made up to 75 ml, and shaken well. Aliquots were taken from the digests and analyzed for Fe and Zn concentrations ] of these two target traits was measured individually using the optimum criteria such as σ2g (genetic), σ2ge (G × E) and σ2e (error) variance with n  =2 and r (number of replicates) =2.The diverse statistical parameters, including mean, standard deviation, coefficient of variation (CV), analysis of variance (ANOVA), frequency distribution and Pearson’s correlation coefficient of seed-Fe and Zn concentrations among accessions and RIL mapping individuals were measured using SPSSv17.0 as per Bajaj et al.et al.et al.et al.The multi-location/years replicated field phenotyping data for seed-Fe and Zn contents (ppm) of parental accessions and 277 individuals derived from a RIL mapping population (ICC 4958 × ICC 8261) was obtained. A selected set of parental polymorphic GBS-based (obtained from Kujur 10(P)-value relative distribution for each trait-associated genomic loci based on quantile-quantile plot and correction of their adjusted P-value threshold of significance for multiple comparison by false discovery rate  were performed following Kujur et al.et al.2 using a model with the SNP and adjusted P-value following a FDR-controlling method. The SNP loci in the genomic (gene) regions revealing significant association with seed-Fe and Zn concentrations at the lowest FDR adjusted P-values (threshold P < 10−7) and highest R2 (degree of SNP marker-trait association) were identified in chickpea.For GWAS, the information regarding the genetic diversity  and genome-wide/population-specific LD patterns (LD estimates and LD decay) among 92 chickpea accessions were obtained from the previous study38EF1α) for normalization of expression value and gene-specific primers for determining the differential gene expression profile across tissues of accessions and mapping parents/individuals were used .To determine the regulatory pattern of candidate genes associated  with seed-Fe and Zn concentrations, the differential expression profiling of these genes was performed using the quantitative RT-PCR assay. The leaves and late seed maturation developmental stages [21–30 days after podding (DAP)How to cite this article: Upadhyaya, H. D. et al. Genetic dissection of seed-iron and zinc concentrations in chickpea. Sci. Rep. 6, 24050; doi: 10.1038/srep24050 (2016).


Meiotic recombination hotspots play important roles in various aspects of genomics, but the underlying mechanisms for regulating the locations and strengths of recombination hotspots are not yet fully revealed. Most existing algorithms for estimating recombination rates from sequence polymorphism data can only output average recombination rates of a population, although there is evidence for the heterogeneity in recombination rates among individuals. For genome-wide association studies (GWAS) of recombination hotspots, an efficient algorithm that estimates the individualized strengths of recombination hotspots is highly desirable.cis-regulatory signals have been detected, which is corroborated by the enrichment of the well-known 13-mer motif CCNCCNTNNCCNC of PRDM9 protein. Moreover, two new DNA motifs have been identified in the flanking regions of the significantly associated SNPs (single nucleotide polymorphisms), which are likely to be new cis-regulatory elements of meiotic recombination hotspots of the human genome.In this work, we propose a novel graph mining algorithm named ARG-walker, based on random walks on ancestral recombination graphs (ARG), to estimate individual-specific recombination hotspot strengths. Extensive simulations demonstrate that ARG-walker is able to distinguish the hot allele of a recombination hotspot from the cold allele. Integrated with output of ARG-walker, we performed GWAS on the phased haplotype data of the 22 autosome chromosomes of the HapMap Asian population samples of Chinese and Japanese (JPT+CHB). Significant Our results on both simulated and real data suggest that ARG-walker is a promising new method for estimating the individual recombination variations. In the future, it could be used to uncover the mechanisms of recombination regulation and human diseases related with recombination hotspots. PRDM9 protein was found to be an important trans-regulator controlling the activity of recombination hotspots through binding to a 13-mer DNA motif (CCNCCNTNNCCNC) in the human genome [PRDM9 does not cover all the human hotspots [trans- and cis- regulators that can also regulate recombination hotspots is highly desired.Meiotic recombination is a crucial step in the reproduction of many species. The reciprocal exchange of genetic material between homologous chromosomes during the meiosis (i.e. meiotic recombination) is an important evolutionary force for increasing genetic diversity and also essential for proper chromosome segregation. Knowledge about recombination is important for understanding the linkage disequilibrium (LD) structure of the genome , phenotyn genome -8. Howevhotspots , and theMost existing methods for the statistical analysis of recombination mainly rely on the estimation of average recombination rate from a population, such as coalescent based analysis of linkage disequilibrium (LD) ,9. SoftwAncestral recombination graph (ARG) is a topological structure that captures the genealogical history of individuals, including historical mutations, recombination events and merging, back to a common ancestor. Thus ARG is indispensable for study of the recombination events in various evolutionary scenarios. Despite the computational complexity of the ARG inference, in recent years, there emerged several methods that have largely solved this problem. For example the algorithm of IRiS  can detecis-regulation, corroborated by the enrichment of the aforementioned 13-mer PRDM9 binding motif CCNCCNTNNCCNC in proximal regions of SNPs identified by our GWAS. Through further analysis of the flanking DNA sequences of associated SNPs, we found two new motifs, AAAATANA and CNGCCTCC, which could be potential cis-regulators of the meiosis recombination hotspots in the human genome. Moreover, by screening the GWAS results on MHC (major histocompatibility complex) region in human chromosome 6, we detected two significantly associated SNPs, rs576205 and rs2061915. The two SNPs are located in the coding regions of KSR2 and ZNF708 protein respectively, both of which have been reported to have regulatory roles in T-cell activation. It demonstrates the potential of ARG-walker for detecting trans-factors of meiotic recombination hotspots, and for study of recombination hotspots important in the human immune system.In this paper, we propose a graph mining method, namely ARG-walker, to infer the different strengths of a recombination hotspot among individuals in a sample. Given a set of extant haplotypes, we first adapt the IRiS algorithm to detect the recombination events and integrate them to construct the ARG; then a random walk method is applied on the ARG to estimate the individual-specific recombination propensities; as such, ARG-walker can translate SNP sequences to a recombination profile, i.e. a vector of floating numbers each representing the corresponding individual strength of a recombination hotspot. This method can be used to exploit the power of the variation in individual recombination frequency, to shed light on the regulatory system of recombination hotspots. Our extensive simulation tests demonstrated the statistical power of ARG-walker in detecting phenotypic variations of recombination rate. Then, applying ARG-walker on the HapMap phased SNP data for GWAS of recombination hotspots, we detected strong association signals of r2 was set to 0.9. As such we obtained 179,671 tag SNPs. The Python script for generating the simulation data and Perl scripts for extracting and pre-processing of these SNP data are available in Additional File The input data of ARG-walker consist of SNP data generated by simulations or collected by the HapMap project. Our simulation data was generated by a Python script which we developed using Python 2.6 and based on simuPOP version 1.0.3)  which is.0.3  whiThe main idea of ARG-walker is to estimate, through analysis of ARG topology, the frequencies of historical recombinations, which are then used to approximate the recombination propensities of extant individual chromosomes. First, to reconstruct the ARG, we use the IRiS algorithm which is able to infer historical recombination events from a set of extant haplotypes and integrate these events into an ARG . Then, wG. Readers interested in details of the algorithm behind IRiS are referred to [ν ∈ V, three types of degrees are calculated, i.e. indegree deg-(ν), outdegree deg+-(ν) and degree deg(ν) = deg-(ν) + deg+(ν). With the information of degrees, the nodes are classified into three types: (1) the root nodes in set Rv with deg-(ν) = 0 and deg+(ν) > 0, (2) the recombination nodes in set Recomv with deg-(ν) ≥ 2 and deg+(ν) > 0, and (3) the leaf nodes with deg-(ν) > 0 and deg+(ν) = 0. Here each node is represented by an integer, which is an index in the vertex set V. Note that the ARG constructed by IRiS could contain more than one root nodes because the reconstructed graph is only partial ARG (or subARG), which may contain only a subset of nodes and edges of the true ARG. For conciseness, however, we will hereafter still refer to the output of IRiS as ARG, rather than subARG.The input of our method consists of a sample of extant human haplotypes. The software of IRiS is employed to reconstruct the ARG from the input haplotype sample. IRiS first cuts the haplotypes into segments, from which phylogenetic forests can be inferred to explain the segmentation. From the segments and forests, compatible sub-networks are constructed using an algorithm called DSR , which is a greedy algorithm that attempts to minimize the number of recombinations needed to explain the given data. These sub-networks are merged to construct the ARG represented as a directed graph erred to ,21. For Wn×n, where n is the number of nodes in the ARG. Initially, each entry of the matrix W is set to 0. Then, the matrix entries are iteratively updated according to the topological structure of the ARG, following the rule that j to node i in the ARG. In other words, the weight of the edge from node j to node i is set to be the number of all descendants of node j plus one. If there is no edge from node j to node i, the corresponding entry in the matrix W will be 0. Due to its Y-shape structure in the ARG, a recombination node will be double-counted as two descendants, and as a result, edges with more recombination nodes as descendants can collect larger weights through the backward walk. Later the edge weights will be passed downward all the way to the leave nodes to reflect the inherited propensity of recombination. To apply the forward random walk on the ARG, we transform the edge-weight matrix Wn×n into the transition probability matrix Tn×n by normalization, i.e. n entries corresponding to the ARG nodes. The first vector, denoted by I, contains the amounts of signals flowed to ARG nodes at the end of a step, which will be passed to their children in the next step. The second vector, denoted by LV, records the amounts of signals that the leaf nodes will gather at the end of the random walk. At the beginning, vector I is initialized by setting each entry corresponding to a root note equal to the sum of weights of its outgoing edges, and setting other entries to 0, i.e. i = 1,2,...,n. Vector L Vconsists of all 0s initially. For each iteration of the forward random walk, the following two operations are carried out to update the two vectors: (1) I = I × T, and (2) L V= L V+ I. Note that an entry of the transition probability matrix, say uv T, contains a positive value if there is a directed edge from node u to node v, and is equal to 0 otherwise. Thus, by the first operation of I = I × T, the signal in a node i will all be passed to its child nodes in proportion to the transition probabilities. This iterative procedure is repeated until every entry of vector I is equal to 0, which means every node has no more information to pass on. After the repeated updates, the signals are collected in vector L Vby the second operation above. But we will set to 0 all the entries in L Vthat do not correspond to the leaf nodes, so that L Vonly contains signals of recombination of the extant haplotypes. Moreover, the recombination probability of each individual leaf node is estimated by normalization of LV, i.e. L is the set of indices of leaf nodes in the vertex set of ARG, and i ∈ L. Thus the final result of ARG-walker is a vector L Vwhich contains the amount of information flow gathered by each individual haplotype corresponding to a leaf node in the ARG. This information flow is used to represent the strength of recombination of each individual chromosome. In addition, vector p Vis also output to represent the recombination probability of each individual chromosome. ARG-walker was developed in Perl (v5.16.3) and the source code is available in Additional File After the nodes are classified, a backward random walk from the leaf nodes to the root nodes is conducted to assign weights on the edges which are stored into an edge-weight matrix 10(p) value, we can filter out 88% samples with non-variant recombination strengths, while keeping 87.8% samples of two-allele recombination strengths. Applying this strategy in our GWAS analysis, we selected 5,200 recombination hotspots  as phenotypes.In the simulation, we adapted the Hartigan's dip test  to test x - mean)/SD, and then we combined the standardized hotspot phenotypes by chromosomes, i.e. the mean value of all the standardized hotspot phenotypes on the same chromosome was calculated to represent the chromosomal recombination strength of each individual. Finally we got 22 combined chromosomal recombination phenotypes. Each of this newly generated phenotype was then mapped to the genotypes for GWAS analysis.Besides individual hotspot phenotype analysis, we also merged recombination strengths of hotspots on the same chromosome into one collective chromosome-wise phenotype. First, we did standardization for each hotspot using formula . To slightly simplify our simulation , we focused on the common scenario of two-allele variation of recombination, i.e. hot versus cold alleles in a population. Currently large amounts of evidence from both sperm typing and genetic analyses suggest that the general patterns of recombination in the human genomes are highly unstable throughout the genome ,9,29. HeIn addition to these tests of parameters, we also investigated two special cases, i.e. all-hot and all-cold. In these two cases, there is no difference in recombination strengths among individuals. Hartigan's dip test was used to test if our ARG-walker can identify these two special cases (see details in Methods section). In contrast to two-allele cases, here the test shows a significant difference on dip p-values, as shown in the boxplot in Figure In summary, with relatively higher differences in recombination rates, an MAF of 0.3 or higher at the causal SNP, and BGC rate less than 0.3, our ARG-walker has a reasonably good performance of prediction, with the average accuracy about 0.64 Table . In additrans- and cis-regulators for meiotic recombination hotspots. Encouraged by the results of our simulation study, we performed a GWAS analysis on the real HapMap data. For each pair of phenotype (i.e. a recombination hotspot) and genotype (i.e. a SNP), we used unpaired t-test to get the p-value of association between the recombination strengths and the SNP. To view the association, we did log transformation for the p-values and plotted the result in Figure cis-regulation shown as the diagonal red line on the plot, compared with trans-regulation. It could be that SNPs located inside or proximal to a recombination hotspot may change the DNA sequence thereby affecting the binding affinities of trans-factors. With a threshold of 1e-7.3 for the p-value, we identified 15,920 significantly associated SNPs. Then we extracted the flanking DNA sequences of these significant SNPs, each with a length of 1 kb. Using the software of FIMO [PRDM9) to these sequences. 45,293 motif occurrences were found in these 15,920 sequences. The strong cis-regulatory signal provides new evidence to confirm the relation of this 13-mer motif with recombination hotspots previously reported [A major motivation of estimating the individual-specific strengths of recombination hotspots is to perform genome-wide association study (GWAS) to identify  of FIMO , we matcreported ,9. Howevreported  for discrs1874165, rs16874441, and rs3805547, located inside Prdm9 gene, we only find one significant association between the three SNPs and one hotspot on Chromosome 5 as shown in Figure trans-regulator like PRDM9, it is expected to show association with many hotspots, but it seems not the case according to our analysis. A reason might be that our GWAS method does not have sufficient statistical power to capture the trans-regulatory signals, e.g. the association might have been eliminated due to low MAF values in our pre-processing. It might also be the case that the assumption of two-allele recombination strengths (i.e. hot vs. cold) does not always apply for PRDM9. In addition, as demonstrated in [cis- and trans-effects.From the GWAS, we have not found any single SNP that is significantly associated with hotspots from different chromosomes. The number of hotspots affected by each single SNP was quite small compared with the sample size of 5,200 hotspots. For the three SNPs, rated in , the hotTo see whether the hotspot-SNP association is robust across different resolutions of recombination rate, we combined the recombination strengths of hotspots in the same chromosomes into 22 chromosome-wise recombination phenotypes. With the genotype of all tag SNPs, we did a GWAS analysis for these 22 phenotypes. The Manhattan plot in rs576205 on chromosome 12, and rs2061915 on chromosome 19. Interestingly, the rs2061915 SNP is located inside the gene of ZNF708 which is one of the zinc finger genes reported to be expressed in human T cells [rs576205 is located in the gene of KSR2 which is a marker of immortalization, and was predicted to have a role in cell proliferation [KSR2 also has association with the IL-2 expression through mir-31 thereby affecting the T cell differentiation [ZNF708 and KSR2 proteins may participate in the regulation of MHC activation or expression through the regulation of recombination in that region, which however needs to be verified through wet-lab experiments. Overall, our GWAS on MHC suggests that using ARG-walker is promising to help search for trans-regulators through GWAS.Recombination in the MHC (major histocompatibility complex) region on human chromosome 6 is of particular interest due to the roles of recombination in the generation of genetic diversity at HLA (Human Leukocyte Antigen) loci and the functions of MHC genes in the human immune system. Many studies have shown some associations between susceptibility to autoimmune diseases and particular alleles of MHC genes . However T cells ,36. And feration . KSR2 alntiation . These fcis-regulatory signals that can corroborate the function of the 13-mer PRDM9 binding motif CCNCCNTNNCCNC, which is known to be critical for the regulation of meiotic recombination hotspots of human. In GWAS at both levels of hotspots and chromosomes, we identified two new motifs in the flanking regions of the significantly associated SNPs, i.e. AAAATANA and CNGCCTCC, which could be cis-regulators for the meiotic recombination hotspots of the human genome. Moreover, the significantly associated SNPs we detected in the combined chromosomal association study are mostly located near the ends of the chromosomes. It would be interesting to investigate the functional and evolutionary implication of this observation. By screening the GWAS results on the MHC region, two trans-regulatory SNPs were detected, and the genes flanking these two SNPs are both functionally related with the T-cell activation, demonstrating the potential of ARG-walker for the detection of trans-regulators of recombination hotspots. Nonetheless, ARG-walker and GWAS of recombination hotspots could be further improved by incorporating more advanced algorithmic and statistical techniques and new types of data (e.g. single-cell whole-genome sequencing data). Moreover, it would be interesting to also apply ARG-walker to understanding the relation of recombination hotspots with human autoimmune diseases.In this paper, we proposed a method named ARG-walker which is a graph mining algorithm for estimating individual-specific recombination strengths by random walks on ancestral recombination graphs. Most existing LD-based algorithms can only estimate the average recombination rate of a population. To the best of our knowledge, ARG-walker is the first computational method for estimating individual-specific strengths of recombination hotspots using only sequence polymorphism data. In most testing cases, ARG-walker performed well. In our simulation, ARG-walker can not only differentiate the recombination alleles of individuals, but also detect the cases of no variance in recombination frequency among individuals. Applying ARG-walker to the haplotype data of JPT+CHB population of HapMap Phase 3, we detected strong ARG - ancestral recombination graph; LD - linkage disequilibrium; GWAS - genome-wide association study; BGC - biased gene conversion; MHC - major histocompatibility complex; MFA - minor allele frequency.The authors declare that they have no competing interests.JZ, TMP and PY envisioned the idea behind the ARG-walker method. CH and PY implemented the ARG-walker in Perl script and conducted simulation analysis and GWAS. HC and JZ did the analysis of the GWAS results. HC and JG did screening analysis on the MHC region. HC drafted the manuscript with input from JZ, PY, CKK and TMP. All authors read and approved the final manuscript.Python and Perl scripts for generating simulation data. Python scripts for generating simulated SNP data, and Perl scripts for pre-processing the SNP data as input of ARG-walker.Click here for fileARG-walker source code. Perl scripts for the implementation of ARG-walker and a sample input file.Click here for filePerl script for GWAS and supplementary MHC screening result. A Perl script for the implementation of GWAS, and SNPs significantly associated with hotspots in the human MHC region.Click here for file


ADG) of growing animals. The objective of this study was to identify commercial single nucleotide polymorphism (SNP) panels that could improve the predictability of days on feed (DOF) to reach a target United States Department of Agriculture (USDA) grade given animal, diet, and environmental information under feedyard conditions. The data for this study was comprised of crossbred heifers (n = 681) and steers (n = 836) from commercial feedyards. Eleven molecular breeding value (MBV) scores derived from SNP panels of candidate gene polymorphisms and two-leptin gene SNP (UASMS2 and E2FB) were evaluated. The empty body fat (EBF) and the shrunk body weight (SBW) at 28% EBF (AFSBW) were computed by the Cattle Value Discovery System (CVDS) model using hip height (EBFHH and AFSBWHH) or carcass traits (EBFCT and AFSBWCT) of the animals. The DOFHH was calculated when AFSBWHH and ADGHH were used and DOFCT was calculated when AFSBWCT and ADGCT were used. The CVDS estimates dry matter required (DMR) by individuals fed in groups when observed ADG and AFSBW are provided. The AFSBWCT was assumed more accurate than the AFSBWHH because it was computed using carcass traits. The difference between AFSBWCT and AFSBWHH, DOFCT and DOFHH, and DMR and dry matter intake (DMI) were regressed on the MBV scores and leptin gene SNP to explain the variation. Our results indicate quite a large range of correlations among MBV scores and model input and output variables, but MBV ribeye area was the most strongly correlated with the differences in DOF, AFSBW, and DMI by explaining 8, 13.2 and 6.5%, respectively, of the variation. This suggests that specific MBV scores might explain additional variation of input and output variables used by nutritional models in predicting individual animal performance.Cattle body composition is difficult to model because several factors affect the composition of the average daily gain ( USDA) Choice or Canadian AAA graded meat versus lower quality grade meat  CT was asAn analysis  indicateIn conclusion, our analysis indicates that it is possible (and desirable) to incorporate genomic information into mathematical nutrition models to enhance their predictability by accounting for individual makeup  differences. This incorporation, however, may require profound modifications of nutrition models to accommodate new concepts, such as genome wide associated studies , 55, to S1 File(ZIP)Click here for additional data file.


S1-, αS2-, β-, and k-CN and the two whey proteins, β-LG and α-LA. It has been shown that both the amount of specific CN and their isoforms including post-translational modifications (PTM) influence technological properties of milk. Therefore, the aim of this study was to 1) estimate genetic parameters for individual proteins in Danish Holstein (DH) (n = 371) and Danish Jersey (DJ) (n = 321) milk, and 2) detect genomic regions associated with specific milk protein and their different PTM forms using a genome-wide association study (GWAS) approach.In the Western world bovine milk products are an important protein source in human diet. The major proteins in bovine milk are the four caseins (CN), αS1-CN was lower in DH compared to DJ, whereas the PD for αS2-CN was higher in DH compared to DJ. The GWAS results for the main milk proteins were in line what has been earlier published. However, we showed that there were SNPs specifically regulating G-k-CN in DH. Some of these SNPs were assigned to casein protein kinase genes .For DH, high heritability estimates were found for protein percentage (0.47), casein percentage (0.43), k-CN (0.77), β-LG (0.58), and α-LA (0.40). For DJ, high heritability estimates were found for protein percentage (0.70), casein percentage (0.52), and α-LA (0.44). The heritability for G-k-CN, U-k-CN and GD was higher in the DH compared to the DJ, whereas the heritability for the PD of αPEAP), and BTA14 (DGAT1) as important regions influencing protein composition in milk. The results from this study provide confidence that it is possible to breed for specific milk protein including the different PTM forms.The genetic analysis of the major milk proteins and their PTM forms revealed that these were heritable in both DH and DJ. In DH, genomic regions specific for glycosylation of k-CN were detected. Furthermore, genomic regions for the major milk proteins confirmed the regions on BTA6 (casein cluster), BTA11 (The online version of this article (doi:10.1186/s12863-016-0421-2) contains supplementary material, which is available to authorized users. S1-, αS2-, β-, and k-CN which occur in the approximate ratio of 4:1:4:1 (w/w) in milk, and the two whey proteins, β-lactoglobulin (β-LG) and α-lactalbumin (α-LA), which occur in the mutual ratio of 3:1 in milk (w/w) )Within the Danish-Swedish Milk Genomics Initiative the milk protein profile of the Danish Holsteins and Danish Jerseys has been studied in detail , 9, 24. The objective of this study was to estimate the heritability of the major milk proteins and their isoforms representing post-translational modifications and to perform a genome-wide association study (GWAS) for the detailed milk protein profile in Danish Holstein (DH) and Danish Jersey (DJ) dairy cattle.All samples were taken within the Danish-Swedish Milk Genomics Initiative. “The overall experimental strategy underlying this study was to minimize potential sources of environmental variation and maximize the level of genetic variation in the sample population. As a result, the pedigree of the selected animals was designed to include as unrelated animals as possible“ . In totag at 4 °C. A detailed protein profile  of the milk was determined in duplicates using liquid chromatography/electrospray ionization-mass spectrometry (LC/ESI-MS) as described in detail by Jensen et al.  [In total 371 DH and 321 DJ cows were genotyped using the bovine HD SNP array   was usedtau_4.0) . In shori(1-pi)] .Variance components were estimated using the REML approach in DMU . Within ijkl is the phenotype of individual l in herd i and lactation j, μ is the fixed mean effect, herdi is a fixed effect , parityj is a fixed effect , b1 is the regression coefficient for DIMk, DIMk is a covariate of days in milk , and animal is the random additive genetic effect based on G of animal l [Where Y2a was the additive genetic variation, and σ2e was the residual variation.Univariate analyses were performed to estimate the heritability, which was defined as:2 is the allele substitution effect, SNPm is a covariate indicating if a SNP is homozygote  or heterozygote (1). The effect of the SNP was tested by a Wald test with a null hypothesis of H0: b = 0. The analyses were performed using REML in the R interface of DMU [http://dmu.agrsci.dk). Significance thresholds were determined using a false discovery rate (FDR) correction using the R package “qvalue” version 1.34.0  [P < 0.10 was considered significant.The association analysis was performed using model 1 extended with an extra covariate for the SNP:be of DMU   . A FDR or2) between SNP markers on BTA14 was calculated using haploview [r2 as a measure based on the software plink v1.07 [Local pairwise LD .The overall P-value is then calculated as: http://github.com/jdstorey/qvalue) [P < 0.10 was considered significant.Significance thresholds were determined using a false discovery rate (FDR) correction using the R package “qvalue” version 1.34.0  . A FDR oThe SNPs on the bovineHD chip were mapped to the Btau4.0 assembly. The data from this download contained 26,352 genes with an Entrez Gene ID. For each gene the location on the bovine genome was determined as 5 Kb before the start position of the first exon to 5 Kb after the end position of the last exon. Hence, the defined gene region includes all introns and parts of the upstream and downstream regions of the gene. When a SNP was located in this region it was assigned to the corresponding gene.S1-CN, αS2-CN, k-CN, β-LG, and α-LA were more similar between the two breeds. Differences between breeds in relation to degree of PTMs were observed, both in terms of phosphorylation and glycosylation. There is a difference in the PD of αS1-CN and αS2-CN, with the less phosphorylated forms being lower in DH compared with DJ, resulting in lower relative amounts of the 8 and 11 P forms of αS1- and αS2-CN in DH, respectively. On the other hand, fraction of G-k-CN% was higher in DH compared with DJ, with GD for k-CN in DH was 24 % versus 20 % in DJ.The descriptive statistics for the protein traits in both DH and DJ are reported in Table S2-CN, G-k-CN and U-k-CN compared to DJ, whereas the heritability for 8P-αS1-CN is much lower in the DH compared to the DJ. This is also reflected in the PD for αS1-CN and GD of k-CN , β-CN% (2), k-CN% , β-LG% (166), G-k-CN% , U-k-CN% , 11P-αS2-CN% (244), and 12P-αS2-CN% (2). No significant SNP markers were detected for αS1-CN%, 8P-αS1-CN%, 9P-αS1-CN% and α-LA% as well as for PD of αS1-CN% and αS2-CN% and GD for k-CN.In total 11,052 SNP markers have been detected at the FDR <10 % level for protein percentage (200), CN percentage (193), αFor protein percentage SNP markers were detected on chromosomes 2, 3, 4, 5, 6, 8, 9, 10, 12, 13, 14, 15, 16, 20, 28 and 29, whereas for CN percentage SNP markers were detected on chromosomes 3, 5, 8, 9, 10, 12, 13, 14, 15, 16, 20, 21 and 23. The SNPs located on BTA6 for protein percentage were located around 44.4 Mb, which is more than 40 Mb apart from the casein cluster on BTA6. No significant markers were detected for CN percentage on BTA6. A comparison between the SNP markers detected for protein percentage and CN percentage revealed that 120 markers were overlapping between these traits with the majority of the overlapping markers located on BTA14 in the DGAT region. The most significant markers for both protein percentage and CN percentage on BTA14 were BOVINEHD1400000275 (rs133271979), and BOVINEHD1400000281 (rs137203218). These two markers are located in the same haplotype block, but are located in a different haplotype block than DGAT  and BOVINEHD0600023927 (rs136864341) with each a –log10 = 49.21 explaining 2.7 % of the total variation.Most significant SNPs were detected for k-CN% . Of these 4,742 SNP markers 2,609 SNP markers were located on BTA6. The most significant quantitative trait locus (QTL) was detected on BTA6 in a region from 87,385,233 bp to 87,421,141 bp spanning the 10 = 7.74 explaining 2.1 % of the total variation. These markers are in complete LD (r2 = 1).The most significant specific SNP markers for U-k-CN% was BOVINEHD1000005681 (rs110977200) on BTA10 with a –log10 = 5.99 explaining 16.9 % of the total variation. The location of the peak for G-k-CN% and U-k-CN% on BTA10 were approximately 55 Mb apart. Even though the significant SNP markers for both G-k-CN% and U-k-CN% were located across all autosomes, trait specific peaks were detected on BTA7, BTA11, BTA13, BTA18, BTA22 and BTA29 for G-k-CN% and on BTA1, BTA2, BTA4 and BTA5 for U-k-CN% , BTA6 , BTA7 (107), BTA10 (125), BTA11 (143), BTA13 (78), and BTA29 (116), whereas for the U-k-CN% a total of 2,544 significant SNP markers were detected. Most significant SNP markers were detected on BTA1 (161), BTA2 (136), BTA6 , BTA11 (94). Just like k-CN%, both G-k-CN% and U-k-CN% have the majority of the significant SNP on BTA6 close to the k-CN gene. Figure s2-CN% SNP markers were detected on chromosomes 1, 3, 5, 6, 8, 9, 11, 14, 18, 19, 22. The majority of the SNP markers was detected on BTA6 (1 70) spanning the CN cluster. The most significant SNP markers were BOVINEHD0600022660 (rs109331183), BOVINEHD0600022661 (rs136565233), BOVINEHD0600022662 (rs110000224) and BOVINEHD0600022663 (rs137452713) with –log10 = 8.51. However 244 significant SNP makers were detected for 11P-αs2-CN %. These SNPs were divided over eight chromosomes . The majority of the significant SNP markers (222) were detected on BTA6. In total 20 significant SNP markers with a –log10 = 9.56 were detected in the range of 87,194,287 bp to 87,296,185 of which 4 SNP markers (BOVINEHD4100005312 (rs110808655), BOVINEHD4100005314 (rs109565340), BOVINEHD4100005315 (rs110122319), BOVINEHD4100005316 (rs109185641)) were assigned to CSN1S2.For α10 = 18.84 located in the range of 103,302,351 bp to 103,308,330 bp. BOVINEHD1100030066 and BOVINEHD1100030069 were assigned to the PAEP gene (LGB).For β-LG%, SNP markers were detected on chromosomes 1, 2, 3, 7, 9, 11, 12, 17, and 20. The majority of the significant SNP markers were detected on BTA11 (130). The most significant markers were BOVINEHD1100030066 (rs110186753), BOVINEHD1100030069 (rs110143060) and BOVINEHD1100030070 (rs109087963) with –logS2-CN% (21), k-CN% (21), and β-LG (102). 25 SNP markers were detected for 11P- αS2-CN%. Furthermore, PD for αs1-CN  and GD for k-CN  revealed 11 and 1 SNP markers, respectively. No significant SNP markers were detected for β-CN%, αS1-CN %, U-k-CN, G-k-CN, 8P-αS1-CN %, 9P-αS1-CN%, 12P- αS2-CN and α-LA% as well as for PD of αS2-CN%.In total 287 SNP markers have been detected at the FDR <10 % level for protein percentage (46), CN percentage (60), αr2 between the markers was between 0.026 between ARS-BFGL-NGS-21411 and ARS-BFGL-NGS-112329 to 0.40 between BOVINEHD0400021104 and ARS-BFGL-NGS-112329. The overlapping markers detected on BTA14 were located in the DGAT region. Additional file For protein percentage SNP markers were detected on chromosomes 2, 4, 12, 14 and 20. For CN percentage SNP markers were detected on chromosomes 4, 6, 14, 16 and 20. A comparison between the significant SNP markers for protein percentage and casein percentage revealed that there were overlapping markers on BTA4 (4), BTA14 (26) and BTA20 (2). The four SNP markers on BTA4 were ARS-BFGL-NGS-21411 (rs110554452), BOVINEHD0400020836 (rs134218776), BOVINEHD0400021104 (rs136496474), and ARS-BFGL-NGS-112329 (rs109846161) spanning a distance of 1.2 Mb. The 10 = 7.04.For k-CN% significant SNP markers were detected on BTA6 in the range of 86,627,280 bp to 87,714,272 bp. The most significant markers were BOVINEHD0600023975 (rs109708618) BOVINEHD0600023978 (rs135203089), BOVINEHD0600023979 (rs135983032), BOVINEHD0600023981 (rs137370056), BOVINEHD0600023982 (rs133024540), and BOVINEHD0600023985 (rs110108928) with a –logS2-CN% significant SNP markers were detected on chromosomes 2, 6, and 9. The most significant maker on BTA2 was BOVINEHD0200026786 (rs109649678) with a –log10 = 6.16. The most significant maker on BTA6 was B OVINEHD0600023975 (rs109708618) with a –log10 = 5.79, which was also detected for k-CN. The most significant marker on BTA9 was BOVINEHD0900030427 (rs134789789) at 103.7 Mb with a –log10 = 5.79. Furthermore 25 SNP markers were detected for the PTM of 11P-αS2-CN%. These markers were detected on BTA2 (6), BTA6 (13), and BTA9 (6). The most significant SNP marker on BTA6 (BOVINEHD0600023003 rs109168832) had a –log10 = 6.04.For αS1-CN/total αS1-CN), the most significant SNP markers were detected on BTA12 (9). The most significant SNP markers were BOVINEHD1200019300 (rs41668561), BOVINEHD1200019842 (rs134086955), and BOVINEHD1200019848 (rs135231437) with a –log10 = 6.64. The markers are located in the range of 70 to 78 Mb. BOVINEHD1200019842 and BOVINEHD1200019848 are located in an unknown gene  = 32.16. BOVINEHD1100030069 was assigned to the PAEP gene (BLG).For β-LG SNP markers were detected on chromosomes 7, 11, and 16. The majority of the significant SNP markers were detected on BTA11 (93). The most significant markers were BOVINEHD1100030069 (rs110143060) and BOVINEHD1100030070 (rs109087963) with a –logr2 = 0.75, min mean r2 = 0.09) compared to the DH   for the DJ was near 23,000 bp, while the decay in DH reaching half of the maximum value (r2/2 = 0.37) was near 19,000 bp.The LD analysis of the DH and DJ data-sets showed that there is a difference in the bin-wise LD between these two breeds. The DJ showed higher bin-wise LD across the genome , BTA4 (DJ), BTA12 (DJ) and BTA20 (DJ) , BTA6 (DJ), BTA10 (DH) and BTA20 (DH) , BTA8 (DH), BTA9 (DJ), BTA23 (DJ)  . Relatively low overlap has been found between the individual protein traits. Most overlap was detected for protein percentage and casein percentage on BTA14 (82 SNP markers) followed by BTA20 (15) and BTA25 (14), whereas for αS2-CN and k-CN most overlap was detected on BTA6 (147). There was only one SNP marker in common between k-CN and β-LG on BTA11 (BOVINEHD4100009262 (rs109608280)).Figure S1-CN, αS2-CN, β-CN, k-CN, β-LG and α-LA), as well as the PTM isoforms; 8P-αS1-CN, 9P-αS1-CN, 11P- αS2-CN, 12P- αS2-CN, G-k-CN, U-k-CN, protein and casein contents. Further, GD of k-CN and PD of αS1-CN and αS2-CN were also explored.This study reports the heritability and GWAS results of the major milk proteins  compared to the 9P-αS1-CN form (0.76) for the Dutch Holstein population. In DH the heritability for 8P-αS1-CN is close to 0 most likely for the reason mentioned above. Interestingly, the heritability for 9P-αS1-CN is 0.25, which could indicate that other genes than the αS1-CN gene can be involved in the formation of 9P-αS1-CN. In a recent study the heritability of the glycosylation of k-CN heritability was estimated in Simmental cattle (0.46) [So far not many studies have focused the genetic analysis of the PTM forms of the milk proteins. Bijl et al.  showed de (0.46) . This isIn this study the majority of the QTL for PTM were detected in the DH comparing the results of the G-k-CN versus the U-k-CN Fig. . There wCSNK1G3) on BTA7. This gene is a ubiquitous serine/threonine-specific protein kinase that phosphorylates caseins and other acidic proteins [PRKCQ) on BTA13. Protein kinase C (PKC) is a family of serine- and threonine-specific protein kinases that can be activated by calcium and the second messenger diacylglycerol (http://www.genecards.org/cgi-bin/carddisp.pl?gene=prkcq). However it remains unclear if these genes have a specific role in the glycosylation of k-CN.At this stage the information regarding the genetic regulation of the glycosylation of the k-CN is poorly understood. In this study we detected different SNPs which were found to be specific for G-k-CN  covering the PEAP gene. This is in line with the findings of Schopen et al. [If the genome-wide Bonferroni correction for multiple testing was applied only three major regions for the protein composition would be detected both in the DH and DJ: BTA6 (k-CN) covering the CN gene complex, BTA14 (protein percentage and casein percentage) covering the n et al. . Interesn et al. . This isn et al.  who deten et al. .PEAP), and BTA14 (DGAT1) as important regions influencing protein composition in the milk. The genetic analysis k-CN and the U-k-CN and G-k-CN showed specific genomic regions regulating the glycosylation of k-CN in the DH. The results from this study provide confidence that it is possible to breed for specific milk protein composition and its molecular forms in the future.The genetic analysis of the major milk proteins and their PTM forms revealed that these were heritable in both the DH and the DJ. Furthermore genomic regions for the major milk proteins confirmed the regions on BTA6 (CN cluster), BTA11 (α-LA, α-lactoalbumin; β-LG, β-lactoglobulin; CN, casein; DH, Danish Holstein; DJ, Danish Jersey; FDR, False discovery rate; GD, glycosylation degree; GWAS, genome-wide association study; LD, linkage disequilibrium; PD, phosphorylation degree; PTM, post-translational modifications; QTL, quantitative trait loci.


Marek’s disease (MD) is a lymphoproliferative disease of poultry induced by Marek’s disease virus (MDV), a highly oncogenic alphaherpesvirus. Identifying the underlying genes conferring MD genetic resistance is desired for more efficacious control measures including genomic selection, which requires accurately identified genetic markers throughout the chicken genome.Hypothesizing that variants located in transcriptional regulatory regions are the main mechanism underlying this complex trait, a genome-wide association study was conducted by genotyping a ~1,000 bird MD resource population derived from experimental inbred layers with SNPs containing 1,824 previously identified allele-specific expression (ASE) SNPs in response to MDV infection as well as 3,097 random SNPs equally spaced throughout the chicken genome. Based on the calculated associations, genomic predictions were determined for 200 roosters and selected sires had their progeny tested for Marek’s disease incidence.Our analyses indicate that these ASE SNPs account for more than 83 % of the genetic variance and exhibit nearly all the highest associations. To validate these findings, 200 roosters had their genetic merit predicted from the ASE SNPs only, and the top 30 and bottom 30 ranked roosters were reciprocally mated to random hens. The resulting progeny showed that after only one generation of bidirectional selection, there was a 22 % difference in MD incidence and this approach gave a 125 % increase in accuracy compared to current pedigree-based estimates.We conclude that variation in transcriptional regulation is the major driving cause for genetic resistance to MD, and ASE SNPs identify the underlying genes and are sufficiently linked to the causative polymorphisms that they can be used for accurate genomic prediction as well as help define the underlying molecular basis. Furthermore, this approach should be applicable to other complex traits.The online version of this article (doi:10.1186/s12864-015-2016-0) contains supplementary material, which is available to authorized users. Several major issues confront the poultry industry today. With high-density chicken rearing, reduced genetic diversity from industry consolidation , and limThe main control strategy for MD is vaccination. However, while these vaccines effectively prevent MD and tumor formation, they do not prevent infection or shedding of pathogenic MDV . And becAs a sustainable alternative to vaccination, we have been pursuing a strategy of identifying chickens with enhanced genetic resistance to MD based on genomic selection (GS) for breeding naturally MD resistant flocks . By idenIdentifying underlying causative genes or even tightly linked markers is difficult for genetic resistance to MD, as is the situation for other complex traits. Even with modern tools and efforts , the significantly-associated genetic markers, typically SNPs, often define linkage disequilibrium (LD) blocks with either no single candidate gene or multiple genes . And whiwithin an RNA sample derived from an individual test subject but across biological replicates. If allelic imbalance is observed, then a polymorphic cis-acting element must be present for that gene since allelic variation is by definition reflective of a cis-acting genetic influence. This is in contrast to eQTL analyses where RNA samples among individuals, genes with “cis” regulation are defined as being proximal to the gene, but may not be allele specific, which has resulted in many identifications that are actually trans and, thus, incorrectly identify specific genes [There is growing awareness that variation in gene expression is a major factor accounting for phenotypic variations such as those involved in human disease . One tecic genes .1 progeny from experimental inbred White Leghorn layer lines that differ greatly in MD genetic resistance [The study had two aims. First, we sought to increase our understanding of the complex trait of genetic resistance to MD and use this information to improve commercial poultry breeding via GS. The second goal was to demonstrate the power and utility of using ASE to identify specific genes impacting disease and genetic resistance. Previously, we identified 4528 SNPs in 3718 genes that exhibit ASE in response to MDV infection using Fsistance , 14; lin6) MD resource population was generated and genotyped with a custom SNP array. To accommodate our 5 K SNP capacity as well as provide genome-wide coverage, the chicken genome was divided into 1 cM blocks based on physical distances determined by the chicken genome assembly [To address whether genes with SNPs exhibiting ASE in response to MDV infection are associated with MD genetic resistance, an advanced intercross  estimated breeding values (EBVs) based on both SNPs and pedigree calculated, followed by bidirectional selection of roosters based on the SNP EBVs only. The top 30 and bottom 30 ranked roosters were each reciprocally mated to 6 random F7 hens, and the resulting ~30 progeny per sire tested for MD incidence; therefore, ~1800 total progeny challenged with MDV and evaluated for MD. As a result, after only one generation of selection, there was a 22 % difference in MD incidence after bidirectional selection based on the ASE SNPs  based on the ASE SNPs only. Accuracy was calculated as the correlation between the true and predicted estimated breeding values (EBV). For BLUP, the accuracy was 0.325 while the GS EBVs (GEBV) was 0.736  that explain the majority of genetic variation effects for a trait.It is relevant to highlight the fact that due to the simplicity of this approach, many fewer individuals are required to achieve high power compared to eQTL screens as the latter suffers from the same mapping problems as do other QTL mapping approaches. Another advantage of the ASE approach is to detect candidate genes for further evaluation as the only requirement is, given the proper tissue and timing, that the transcripts be differentially expressed between groups of interest , which is not limited by the effect size. This differs from classic QTL analysis where the effect of the QTL on the trait is directly proportional to the ability to detect it. Furthermore, as ASE is initially based on analysis of RNA within individuals, it is possible that random micro-environmental influences that reduce the heritability of a trait may have less negative influence on the ASE approach because the within individual analysis removes impacts of between individual biological variability and further increases power.P values of markers, and phenotype predictions, were estimated simultaneously. This means that our estimates were free from collinearity due to LD among markers. Also it means that we have been able to partition the variance among random SNPs and ASE SNPs, something that cannot be easily done with those GWAS-like approaches that work one marker at a time.It is also relevant to point out that all analyses in this study were whole-genome based. Effects and Using this process, we identified genes that account for the majority of genetic resistance to MD, and our predictions were validated by bidirectional selection. To our knowledge, this is the first attempt to quantify the contribution of transcriptional regulation on a whole genome scale. Having said this, recently Gusev et al.  reportedOnce identified, our results clearly demonstrate that is possible to accurately select for improved genetic resistance to MD using less than 2 K SNPs. We hypothesize that predictions based on ASE SNPs should be more accurate and persist longer over multiple generations of selection, than even whole genome sequencing containing the ASE SNPs as a subset. This is because effects of SNPs that have not recombined, either closely linked or further away, are confounded. Use of distantly related SNPs in the predictions will eventually fail due to recombination in advanced generations. As previously seen with QTL mapping, adding more genotypes does not necessarily increase resolution, only adding biological replicates with different combinations of genotypes is capable of doing that. With an increase in the number of genotyped loci, a corresponding increase in biological replicates is necessary to properly train the model. For example, given a simply inherited trait based on a single QTN and two data sets, one with 3 phenotypes associated with genotypes at the causative QTN, and another data set with the complete genome sequence of each individual. Both data sets include the true QTN while the latter also includes noise. The noise will decrease the accuracy of prediction in the first generation while in the latter generations become even worse because distantly linked loci will recombine and become less predictive. The parallel to complex traits and high density genotyping is similar.Conceptually a SNP chip with only the causative QTN would be the most accurate with predictions that would not decay over generations due to recombination and/or imperfect training, but would still loose predictive ability due to fixation of the favorable alleles . An ASE P (non-null) values greater than 0.25 of which 7 were ASE. And for 2 of the top 6 random SNPs, there were ASE SNPs that tagged the same gene. For example, both a random SNP at chr. 2, position 40,419,282 (ranked as the second highest) and an ASE SNP at chr. 40, position 442,578 (sixth highest) identified CKLF-like marvel transmembrane domain-containing 7 (CMTM7). Most of the candidate genes for these top hits were associated with cell cycle regulation, metabolism, and tumor biology, which is consistent with our prior QTL results that suggested the underlying genes were either involved in restriction of viral replication or, more often, control of neoplastic transformation [Examination of the top SNPs was indicative of the power of this approach. There were 13 SNPs with ormation .Although satisfying, the ultimate goal is to identify the causative polymorphisms to connect phenotype with genotype, which theoretically should allow for near perfect predictions. ASE is most likely the result of different allelic rates of transcription, transcript processing, or transcript stability. As ASE SNPs are found only in coding regions as they rely on screening mRNAs, identifying causative variants requires knowledge of transcriptional regulatory regions for each gene. With regard to investigating polymorphisms in promoters of candidate genes, we have had some success in profiling for binding sites of the MDV Meq, the viral oncogene and a bZIP transcription factor, that result in ASE in response to viral infection , 24. SimWe conclude that ASE based SNPs are functionally linked to causative polymorphisms that alter transcriptional levels in genes manifesting changes due to disease incidence. Our results also clearly show that variation in cis-regulatory elements is the major mechanism that accounts for the majority of variation in MD genetic resistance between these two experimental lines, which supports the hypothesis that phenotypic variation in traits is mainly due to changes in regulation of gene expression rather than protein composition. Our results also suggest that complex traits are controlled by many genes, most of which have small effects. And in theory, this method should be generally applicable to other infectious diseases and complex traits at the whole genome level or to fine map existing QTL or associations.6 and F7 birds. To test disease susceptibility, at 2 days of age, each bird was injected intra-abdominally with 2000 pfu MDV (JM strain) and housed for up to 8 weeks in Horsfall-Bauer isolators. Moribund birds, or those that survived up to eight weeks post challenge, were terminated and examined via necropsy. Birds were scored as having MD if they displayed visceral tumors or had enlarged nerves. All experiments were approved by the USDA, ADOL Animal Care and Use Committee (ACUC). The ACUC guidelines established and approved by the ADOL ACUC (April 2005) and the Guide for the Care and Use of Laboratory Animals by the Institute for Laboratory Animal Research (2011) were followed throughout the experiments.The highly inbred Avian Disease and Oncology Laboratory (ADOL) line 6 (MD resistant) and line 7 (MD susceptible) experimental White Leghorn chickens were intermated to produced FTo achieve genome-wide coverage within a limit of 5 K SNPs, the ~ 1 billion bases of the chicken genome was partitioned into ~3000 1 cM bins based on the derived physical distances per chromosome from the chicken genome assembly . Priorit6 MD resource population were produced, challenged with MDV as described above, and scored as either disease present or absent based on necropsy. DNA was isolated from 10 μl of blood from each bird using 96-Well Plate Blood Genomic DNA Mini-Preps kits  then genotyped by DNA Landmarks  using the custom 5 K SNP array described above. The data were subjected to mixed model analysis using the binary option of the GS3 set of programs [Markers were fit simultaneously using a BayesCPi model, which produced estimates of marker effects and posterior probabilities of markers having an effect different from zero , 28. To programs  with SNP7 generation were genotyped, BLUP EBVs based on SNPs and pedigree were calculated, and were bidirectionally selected based on the SNP EBVs. The top 30 and bottom 30 ranked roosters were each mated to 6 random F7 hens, and ~30 progeny per sire tested for MD resistance over a total of 3 hatches. The accuracy of selection was determined from the correlation of EBVs estimated based on either SNPs or pedigree with progeny test performance.Roosters in the FMention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture.


Genome-wide comparisons revealed abundant genetic variations in Four-row Wax, which may be associated with environmental adaptation. The sequence and SNP variations described here enrich genetic resources of maize, and provide guidance into study of seed numbers for crop yield improvement.In southwest China, some maize landraces have long been isolated geographically, and have phenotypes that differ from those of widely grown cultivars. These landraces may harbor rich genetic variation responsible for those phenotypes. Four-row Wax is one such landrace, with four rows of kernels on the cob. We resequenced the genome of Four-row Wax, obtaining 50.46 Gb sequence at 21.87× coverage, then identified and characterized 3,252,194 SNPs, 213,181 short InDels (1–5 bp) and 39,631 structural variations (greater than 5 bp). Of those, 312,511 (9.6%) SNPs were novel compared to the most detailed haplotype map (HapMap) SNP database of maize. Characterization of variations in reported kernel row number (KRN) related genes and KRN QTL regions revealed potential causal mutations in  Palomero was the second maize genome sequence published, and its comparison to B73 revealed variations, as well as monomorphic gene such as heavy metal detoxification genes that may have been selected during domestication . It has a long history of production in Menghai County, Yunnan Province in southwest China as food by local Dai minority people . This region has a tropical and subtropical monsoon climate, an annual average temperature of 18.7°C and total rainfall of 1341 mm. Four-row Wax is not known to have been subjected to scientific breeding -based method, then randomly sheared the DNA. Fragments of ~500 bp were selected from a gel and used for a library preparation according to Illumina’s instructions. The libraries were sequenced at paired ends using an Illumina Hiseq 2000 in four sequencing lanes. The average length of sequencing reads was 90 bp.Four-row Wax, a Chinese waxy maize landrace, is not known to have been subjected to scientific breeding . We randThe maize B73 reference genome sequence and gene set were downloaded from MaizeGDB  with more than 5% Ns or As; (2) with 20 bp or more having quality lower than 7; (3) contaminated by the adaptor ; (4) with the first and second ends overlapping ; (5) with both ends identical to other reads, suggesting PCR duplication. The filtered reads were then aligned to the B73 reference genome sequence using SOAPaligner , for InDel (more than 5 bp in length), replication, reversion, transposition, and other variations. The minimum read depth was set to 3.We first calculated the distances between adjacent SNPs, and the distribution of interval distance was depicted. Genes located between two adjacent SNPs were extracted as candidate genes without SNPs. Then, to further verify that there were no SNPs and other variations within those genes, we filtered the genes located in, or overlapping with, repeated elements (according to the annotation information), and genes that were not sequenced (no reads mapped to the gene region).We obtained the maize HapMap SNPs (in B73 RefGen_v1 coordinates) from the Panzea website . SNPs prWe focused on four reported genes , believed to be mostly the low copy part of the maize genome. The contig N50 was 1022 bp, with the longest contig being 24,347 bp. The scaffold N50 was 2,410 bp, with the longest scaffold being 49,840 bp.In order to detect possible novel genes in Four-row Wax, we conducted a Table 1).Sequencing depth affects accuracy of DNA variation detection . TherefoTable 1). The average densities of detected SNPs, InDels, and SVs were 1578, 103, and 19 per Mb, respectively. The density of SNPs or InDels was uneven among chromosomes . For SNPs, 10 and 9 regions with high (>3500/Mb) and low density (<500/Mb) were found, respectively. High SNP regions were on chromosomes 1, 2, 4, 7, and 8, while low SNP regions were on chromosomes 1, 2, 3, 4, 6, and 9. Interestingly, four of ten high SNP regions were on chromosome 2, the highest being 3962/Mb. Three of nine low SNP regions were on chromosome 4, the lowest being 181/Mb. There were eleven regions with high InDel density (>250/Mb) on chromosomes 1, 3, 4, 7, and 9, with the highest (285/Mb) on chromosome 9. Five regions with low InDel density (<10/Mb) were identified on chromosome 3, 4, and 6, the lowest (2/Mb) located on chromosome 4. The majority of InDels (63.87%) were mononucleotide variations, including 61,433 insertions and 76,778 deletions . The abundance of InDels decreased with increasing length, while different InDels of similar length had similar abundance.We characterized the distributions of SNPs, InDels, and SVs in the Four-row Wax genome (Table 1). Of the 39,631 SVs, 30,046, 6,879, and 1,887 were classified as deletion type, insertion type and tandem duplication, respectively. The types, numbers and length distributions of SVs are shown in Figure 4.Four regions had high SV density (>80/Mb) on chromosomes 1, 4, and 8. The highest SV density (145/Mb) region was on chromosome 1 and the lowest (1/Mb) was on chromosome 4. The average SV density on each chromosome was similar, although SV variations were not uniformly distributed within a chromosome . Of all non-synonymous SNPs, 2,617 located in 2,259 transcripts induced premature stop codons; 681 abolished stop codons, resulting in longer open reading frames; 298 altered initiation methionine residues. A total of 2,769 SNPs were located within splice sites (1–3 bp into exons or 2 bp into introns), 718 of which affected essential splice donor or acceptor sites (first 2 bp or last 2 bp of an intron), resulting in 704 transcripts with open reading frame changes. In addition, 48,523 SNPs, located in 21,663 transcripts, encoded amino acids with significant property difference from those of B73, based on four types of hydrophobic, negative, positive, and polar without charge properties.We further classified the non-synonymous SNPs for inferred effects on gene function (Table 3). GO analysis . Four regions with high novel SNP density (>500/Mb) were located on chromosomes 4, 7, 8, and 10, respectively. Seven regions with low novel SNP density (<20/Mb) were located on chromosomes 3, 4, 6, 9, and 10, respectively. Comparing the distributions of SNPs and InDels, we found some overlapping regions, such as a region (located about 216M of chromosome 1) with high SNPs and InDels; two regions (~90M of chr 3 and ~138M of chr 4) with low SNPs and InDels; and two regions (~27M of chr 6 and ~7M of chr 0) with low densities of all three kinds of variations . Most novel SNPs, 248,518 (79.52%), were located in intergenic regions and introns; with 37,198 (11.9% of) novel SNPs in coding sequences. The distribution of novel SNPs was similar to that of total detected SNPs described above. Novel SNPs were distributed in 24,285 genes, mainly in introns and UTRs, with only a few in CDSs. Among the ten genes containing the highest number of novel SNPs  of the 3,252,194 SNPs differentiating it from B73 can also be found in the most comprehensive genome-wide maize SNP dataset, maize HapMap , while tTable 3). Among all SNPs, the number that were non-synonymous was far more than the number of novel SNPs, so the large effect SNP number for each type was more than that of novel SNPs. However, for total SNPs and novel SNPs, the proportions that were non-synonymous were similar. Two types of mutations  occurred at higher proportions in novel SNPs than total SNPs. GO term analysis revealed that of genes enriched for these large effect novel SNPs were in four categories based on molecular function  belonged to the large effect SNP class. The types and numbers of large effect SNPs are provided . Four novel maize genes were identified based on less than 20% similarity to other genes in the maize protein database , resulted in the fourth position deletion of five consecutive glutamic acids, which may alter gene function and contribute to the low KRN phenotype in Four-row Wax.Kernel row number is an important trait for maize yield. Previous research showed that it was a quantitative trait controlled by multiple genes, each with small phenotypic effects . SeveralFigure 5 and Supplementary Figure SAUR39 in rice  SNPs were unique to Four-row Wax, excluding 774,231 (23.81%) SNPs in repeated regions. The new SNPs detected in Four-row wax enrich the current HapMap database. Novel SNPs in Four-row Wax may have resulted from the following scenarios: (1) Four-row Wax was a landrace, derived from southwestern China, with a different genetic improvement history from modern maize lines; (2) maize is a cross-pollinated plant harboring rich genetic diversity, especially among different landraces.Arabidopsis (We found that protein kinases (such as serine/threonine kinase and tyrosine kinase) were enriched for large-effect SNPs. This tendency was also found in previous resequencing in maize  and othebidopsis . It was In the Palomero landrace , genes ide novo assembled the genome sequence using 21.87× coverage reads, though sequence from only one insert size library was not enough to fully assemble the genome. Only four novel maize genes were predicted in the limited assembled sequence, which did not exist in the reference genome and were not homologous to annotated genes. One gene is homologous to a rice gene, and the remaining three are homologous to sorghum genes. The four genes showed the same overall depth of sequence coverage as other annotated genes, making it unlikely that these reads were due to contaminant DNA. The four novel genes may be really present only in the genome of Four-row Wax but not in other maize lines, because the genome of maize is complex and diversifying, with PAVs well known among genomes , we could not find the gene, thus could not analyze variation in it.Besides the above four genes, ids1 , indeterMoreover, we compared DNA polymorphisms between Four-row Wax and six inbred lines in five QTLs related to KRN. SNPs, InDels, and SVs in these regions may be responsible for low KRN. Because the reported QTL regions delimited by linked molecular markers were large, with many differences among compared varieties, the causal DNA polymorphisms contributing to low KRN have not been identified. Some genes located in these regions are enriched in novel SNPs and identified as potential candidate genes for the low KRN of Four-row Wax, such as the auxin responsive gene of SAUR. It is of high priority to investigate the relationship between these genes and the KRN phenotype.In summary, we identified 3.3 M SNPs, 213.2 K InDels, and 39.6 K SVs distinguishing Four-row Wax, a waxy maize landrace with low KRN from southwestern China, from the B73 reference genome. This variety harbored novel SNPs (absent from the present maize Hapmap), and novel genes were also identified by comparing to known maize variation or gene datasets. By analyzing the Four-row Wax variations in reported genes and QTLs related to KRN, candidate genes and mutations related to its low KRN phenotype were inferred. These data and results, combined with other variation and population studies of maize, may facilitate the genetic analysis and improvement of important agronomic traits of maize.YH and HL conceived and designed the experiments. YL, JL, and ZX contributed materials. BW, HL, JZ, and GY contributed to data analysis. YW and YH performed the experiments. HL and XW wrote and revised the manuscript. All authors discussed the results and commented on the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.