SMAD3, PDGFD, IL6R, BMP1, CCDC97/TGFB1 and LMOD1. We validate our findings in expression quantitative trait loci cohorts, which together reveal new links between CAD associations and regulatory function in the appropriate disease context.Coronary artery disease (CAD) is the leading cause of mortality and morbidity, driven by both genetic and environmental risk factors. Meta-analyses of genome-wide association studies have identified >150 loci associated with CAD and myocardial infarction susceptibility in humans. A majority of these variants reside in non-coding regions and are co-inherited with hundreds of candidate regulatory variants, presenting a challenge to elucidate their functions. Herein, we use integrative genomic, epigenomic and transcriptomic profiling of perturbed human coronary artery smooth muscle cells and tissues to begin to identify causal regulatory variation and mechanisms responsible for CAD associations. Using these genome-wide maps, we prioritize 64 candidate variants and perform allele-specific binding and expression analyses at seven top candidate loci: 9p21.3,  Coronary heart disease is the leading cause of death worldwide with multiple environmental and genetic risk factors. Here the authors integrate genomic, epigenomic and transcriptomic mapping to elucidate causal variation and mechanisms of known genetic associations. Coronary artery disease (CAD) remains the leading cause of mortality and morbidity in the world, despite advances in treatment and lifestyle modification. As a complex disease, both genetic and environmental factors contribute to cumulative disease risk across human populations136cis- or trans-regulatory changes in gene expression910151618192122The majority of variants identified through GWAS (including those in linkage disequilibrium (LD)) represent common single-nucleotide polymorphisms (SNPs) located outside protein-coding sequences, which are predicted to function via Human coronary artery smooth muscle cells (HCASMCs) constitute the majority of the vessel wall and via their contractile functions are largely responsible for distributing blood to the heart muscle. However, these cells also undergo phenotypic modulation or ‘epigenetic re-programming' to a highly proliferative and invasive state in response to vessel injury, changes in blood flow, and during lesion expansion with diseasecis-regulatory mechanisms , followed by TGF-β-related TFs . This resulted in 5,240 total candidate CAD regulatory SNPs present in the human genome (http://genetics.bwh.harvard.edu/pph2/) and SIFT (http://sift.jcvi.org), which identified eight and six non-synonymous missense/nonsense variants, respectively, predicted to be damaging  patientsTo prioritize all CAD variants, we first combined a candidate list from CARDIoGRAMplusC4D replicated loci and those associated at false discovery rate (FDR) of 5%, as well as novel loci identified from CARDIoGRAMplusC4D 1000G fine-mapping effortsn genome . To ruledamaging  and 6. Ocis- and trans-acting causal transcription mechanisms, we restricted our functional mapping analysis to those variants located in validated binding sites by incorporating HCASMC ChIP-seq peaks for relevant TFs including, JUN, JUND and TCF21  or other chronic inflammatory diseases (ulcerative colitis (UC) and inflammatory bowel disease (IBD)), which resulted in 1.4, 1.8 or 1.9 odds ratios of enrichment  and enriched (>10-fold) SNPs for diseases with cardiovascular and chronic inflammatory  origin and unexpected overlap with bone mineral density and Schizophrenia SNPs, which appears to be cell-type dependent  between ATAC-seq tag densities from serum-free HCASMC and normal artery tissue , with HCASMC plus PDGF-BB, PDGF-DD or serum, and athero tissue in top half, and HCASMC serum-free, plus TGF-β, and normal tissue in bottom half  compared with normal coronary tissue , which was reversed using randomized AP-1 matrices  coronary arteries from human heart transplant donors and recipients, respectively. We observed a correlation  to annotate candidate variants predicted to alter TFBS motifs  functional scores ≤4 and evidence of protein binding in vivo (≥1 trans-acting factor), we obtained 64 (out of 87) variants in HCASMCs and 26 (out of 30) variants ex vivo. Interestingly, one of the lead candidate variants, rs17293632, located within an intergenic region of the SMAD3 gene was linked to a new association for CAD, rs56062135  at 15q22.33, (additive model P=5.72E−09)SMAD3 was previously associated with CAD through an independent SNPex vivo, and preferentially in TGF-β- and PDGF-BB-treated HCASMCs . These studies demonstrated preferential TF binding to the risk C allele, with non-allelic enrichment of H3K27ac (CDKN2B/CDKN2B-AS1), which overlapped open chromatin, H3K27ac, AP-1 and TCF21 peaks  at 11q22.3 (ref. PDGFD). This variant resides in a modest open chromatin peak ex vivo and in stimulated open chromatin in HCASMCs, with the major risk T allele enriched in TF and H3K27ac peaks , rs2019090 (PDGFD), rs7549250 (IL6R) and rs34091558 (LMOD1)  . The grereporter . In contreporter . Importa HCASMCs .SMAD3 gene expression levels in HCASMC , we measured SMAD3 pre-mRNA levels using individuals heterozygous at rs17293632 itself. This approach is supported by genome-wide methods correlating intronic reads to changes in transcriptionP=8.72E−13) , and NR4A1/2  . Similarand IL6R . As a po7 levels , but ratcis-eQTL databases, including the Genotype-Tissue Expression (GTEx) project  and internal mammary artery tissues (n=528) collected during coronary artery bypass graft procedures from the STARNET (Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task) database23cis-eQTLs within a 2-Mb window surrounding each candidate SNP, and then performed a conditional analysis using the most significant SNP or the lead GWAS SNP in this region  and was the second most significant SNP in this entire region in aorta , which represented the most significant cis-eQTL in this region for PDGFD in aortic tissue  and rs7549250 (IL6R)  , which iex vivo  in aortic tissues  to the lead GWAS variant, rs974819, identified in individuals of European and South Asian ancestryPDGFD, resides in a moderately active enhancer region in HCASMCs, and alters binding and chromatin accessibility despite being outside consensus TF-binding motifs , does appear to have a SMC-restricted expression pattern as a serum-response factor/myocardin target gene involved in actin filament assemblyLMOD1, rs34091558, was a significant cis-eQTL in aorta but not mammary artery  were all pre-validated according to ChIP-seq guidelines and ENCODE best practicesPrimary HCASMCs derived from normal human donor hearts were purchased from three different manufacturers, Lonza, PromoCell and Cell Applications . HCASMCs were maintained in growth-supplemented smooth muscle basal media (Lonza) according to the manufacturer's instructions. All experiments were performed on HCASMCs between passages 4 and 7. Rat aortic smooth muscle cells (A7r5) were purchased from ATCC (CRL-1444) and maintained in Dulbecco's modified Eagle media supplemented with 10% fetal bovine serum according to the manufacturer's instructions. Both HCASMCs and A7r5 (not listed in database of misidentified cell lines maintained by ICLA) were validated by immunostaining for alpha-smooth muscle actin using a mouse monoclonal antibody purchased from Sigma (A2547). Human recombinant PDGF-BB, PDGF-DD and TGF-β1  were purchased from R&D Systems and were used at 20, 10 and 10 ng mlg and washed twice with cold 1 × PBS. Nuclei-enriched fractions were extracted with cold lysis buffer containing 10 mM Tris–HCl, pH7.4, 10 mM NaCl, 3 mM MgCl2 and 0.1% IGEPAL (octylphenoxypolyethoxyethanol), and the pellets were resuspended in transposition reaction buffer containing Tn5 transposases (Illumina Nextera). Transposition reactions were incubated at 37 °C for 30 min, followed by DNA purification using the DNA Clean-up and Concentration kit (Zymo). Libraries were initially PCR amplified using Nextera barcodes and High Fidelity polymerase (NEB). The number of cycles was empirically determined from an aliquot of the PCR mix, by calculating the Ct value at 25–30% maximum Rn for each library preparation. The final amplified library was again purified using the Zymo DNA Clean-up and Concentration kit, and the DNA was evaluated by TBE gel electrophoresis and quantified using Bioanalyzer, nanodrop and quantitative PCR (KAPA Biosystems). Libraries were multiplexed and paired-end 50-bp sequencing was performed using an Illumina HiSeq 2500. Raw FASTQ files were evaluated using a modification of the FastQC pipeline to generate per base and per sequence-level summary statistics. Libraries that achieved consistent high-quality scores from this tool were accepted and paired-end reads were aligned to the human genome (hg19) using Bowtie2 with the –X 2000 maximum insert size parameter. Mitochondrial reads were not excluded as these represented <5–10% total reads. After adjusting the read start positions to account for Tn5 insertion bias using the preShift.pl script, peaks were called using Model-based Analysis for ChIP-Seq (MACS) with –P value 1.0E−05 cutoff to reveal open chromatin peaks. Bigwig files were generated for University of California Santa Cruz (UCSC) visualization, and peaks were annotated using HOMER. To calculate the differential chromatin accessibility between treatments, normalized read counts generated from HOMER were combined into a matrix and a generalized linear model likelihood ratio test was used to compute P values with the edgeR R/bioconductor package (https://bioconductor.org/packages/release/bioc/html/edgeR.html) using default parameters. These results are summarized in t-test with Welch's correction for unequal variances.ATAC-seq was performed with slight modifications to the published protocol. Briefly, HCASMCs (passages 5–6) were cultured in normal media until ∼75% confluence. Approximately 5.0E4 fresh cells were collected by centrifugation at 500g for 3 min at 4 °C. The pellet was resuspended in 1 ml of a lysis buffer containing 50 mM HEPES, pH 7.5, 140 mM NaCl, 1 mM EDTA, 10% glycerol, 0.5% IGEPAL CA-630 and 0.25% Triton X-100, and supplemented with an EDTA-free complete protease inhibitor (Roche). This suspension was incubated with rocking at 4 °C for 10 min and then homogenized via 15 loose strokes in a glass dounce. The homogenate was centrifuged at 2,000g for 5 min at 4 °C, and the resultant pellet was resuspended in 1 ml 1 × PBS. Cellular debris was filtered out with a 40-μm cell strainer, and intact nuclei were quantified. A total of 50,000 nuclei were subjected to Tn5-mediated transposition for 1 h and tagmented DNA was amplified and purified as previously reported. Libraries were subjected to an additional electrophoresis step on an E-Gel EX 2% agarose gel (Invitrogen), and gel fragments correlating to 100–1,000 bp were excised and purified. The quality of the library preparation was determined by evaluation of the electropherogram traces from an Agilent 2100 Bioanalyzer DNA High Sensitivity, and libraries demonstrating appropriate nucleosomal enrichment were multiplexed and subjected to a lane of Illumina HiSeq 2500 sequencing. Sequences were analysed as described above.Coronary artery tissues (left and right main arteries) were dissected from freshly explanted hearts from consenting donors  at the time of heart transplantation. Normal arteries were obtained from rejected donor hearts, and atherosclerotic arteries were obtained from ischaemic diseased hearts from transplant recipients. Adventital layers were carefully removed from coronary artery segments before snap-freezing medial layers in liquid nitrogen and storage at −80 °C. Medial tissues from either normal or atherosclerotic arteries were pulverized using a liquid nitrogen chilled steel homogenizer. An amount of 10–20 mg of pulverized fresh-frozen tissue was suspended in 1 ml 1 × PBS and centrifuged at 2,000P value threshold of 1.0E−05.HCASMCs were cultured in normal media containing serum and fixed in 1% formaldehyde to crosslink chromatin, followed by quenching with glycine. 2.0E07 cells were collected, and nuclear lysates were prepared using dounce homogenization (20 strokes) in cold hypotonic buffer, followed by lysis in 1 × RIPA buffer (Millipore). Chromatin nuclear lysates were then sheared to fragments of 100–500 bp using a Bioruptor Pico sonicator (Diagenode) according to the manufacturer. An amount of 5 μg of anti-rabbit IgG, TCF21 , JUN , JUND  or H3K27ac antibody  was added to sheared chromatin to immunoprecipitate TF–DNA complexes overnight at 4 °C. Following capture of the antibody–TF–DNA complexes to Protein G beads, the complexes were washed with ice-cold 1 × RIPA and 1 × PBS and eluted twice in 1 × TE containing 1% SDS for 10 min each at 65 °C. Protein–DNA crosslinks were reversed overnight at 65 °C and ChIP DNA was recovered using Qiagen PCR Purification kits. To generate the ChIP library, Illumina TruSeq adapters were ligated to the ChIP DNA, followed by PCR amplification and gel electrophoresis on a 2% agarose gel. ChIP DNA library fragments ∼300 bp were selected for PCR amplification. PCR reactions were performed under the following conditions 98 °C 30 s;  × 14 cycles; 72 °C 5 min. The ChIP DNA library concentrations were quantitated by Qubit fluorometric and bioanalyzer analyses. Libraries were sequenced on an Illumina HiSeq 2500 (2 × 101) to obtain ∼45–50 million reads per barcoded sample. Resulting fastq files were aligned to human genome hg19 using bowtie2 to generate bam files and peaks were called using MACS 1.4 with treatment sample as TCF21, JUN, JUND or H3K27ac, and control sample as IgG using the regional and local lambda model (TFs) or regional lambda (H3K27ac), bandwidth of 300 and a http://homer.salk.edu/homer/ngs/) findMotifsGenome.pl script was employed to search for known TRANSFAC motifs and to generate de novo motifsde novo motifs were generated as follows. ATAC open chromatin or ChIP-binding summits were defined using MACS with default parameters for the collection of all identified binding regions. Motif distribution plots were generated using HOMER annotatePeaks.pl script centred on the respective motifs using the location of the summits within each peak file. TRANSFAC matrices for the top HOMER known-motif outputs were used to scan open chromatin and binding summit locations in the human GRCh37/hg19 genome. Scanning for motifs was performed using annotatePeaks.pl with the following parameters: hg19-size 2000-hist 20.To identify DNA-binding sites enriched in ATAC-seq open chromatin peaks, AP-1, TCF21 or H3K27ac ChIP-seq peaks, the HOMER  algorithm. Test genomic regions were uploaded to the GREAT webserver and the entire hg19 human genome was used as a background. Genes were associated with the test genomic regions using the ‘basal plus extension' parameters, which defines gene regulatory domains within a proximal region of 5-kb upstream, 1-kb, downstream and a distal region extending up to 1,000 kb from the TSS. Annotations from various biological, molecular and disease-based ontologies were used to associate genomic regions to functional annotations. Also, we used the aligned reads from HCASMC-stimulated ATAC-seq with the HOMER annotatePeaks.pl script to identify normalized read counts at TSS of nearest gene using the ‘tss' and -size -500,100 parameters. These regions were intersected with CAD loci to perform k-means hierarchical clustering using a matrix of log normalized read counts across treatments, and heatmaps were generated using the ggplot2 R package. The CAD-associated gene list was also used with the Genes2FANs  algorithm to identify potential interactions from known functional association databases . The maximum path length to detect intermediate nodes connected to the seed nodes and z-score significance cutoff were set at 2.0.http://ccg.vital-it.ch/chipseq/chip_cor.php)). Counts were normalized to the total number of reference counts. The P values for enrichment of GWAS SNPs in ATAC-seq open chromatin regions were calculated as described previouslyP values were computed using binomial cumulative distribution function b in R (dbinom function). We set the parameter n equal to the total number of GWAS SNPs in a particular GWAS phenotype. Parameter x was set to the number of GWAS SNPs for a given GWAS phenotype that overlap ATAC-seq regions and parameter p was set to the fraction of the uniquely mappable human hg19 genome  that is localized in the ATAC-seq open chromatin regions and contains assessed GWAS phenotype SNPs. Calculated binomial P value equals the probability of having x or more of the n test genomic regions in the open chromatin domain given that the probability of that occurring for a single GWAS genomic region is p.GWAS SNP positions were downloaded from the NHGRI-EBI GWAS catalogue. ATAC-seq open chromatin regions were centred and GWAS SNPs were counted in a window of 100 bp for ±1 kb surrounding centred ATAC-seq regions using the Feature correlation tool from the ChIP-Cor module . For each analysis, we determined overlaps with CAD SNPs, IBD SNPs, UC SNPs or a whole GWAS catalogue minus CAD SNPs, as a control. Overlaps between datasets, as well as sites present in one or the other tested data set, were determined using the ENCODE DHSs as a background. Finally, we determined DHSs devoid of each of the tested data set intervals. One-sided Fisher's exact test was used to determine whether the observed peak overlap between two tested data set intervals was statistically greater when compared against a background of all DHS peaks. Contingency matrices were made in R and Fisher's exact test was performed using fisher.test R command.Custom R script was generated to intersect data sets and calculate Fisher's exact Processed DNase-seq data for 125 cell types generated by the ENCODE Analysis Working Group with the uniform processing pipeline (Uniform DNaseI HS track for Human Genome Build 37 (hg19)) were collected from the UCSC Golden Path FTP server. Each of 125 bedgraph files was merged using the bedtools merge tool to contain only unique and non-overlapping intervals with a mean intensity for the overlapping intervals. Bedgraph files were combined to make a genome-wide matrix using bedtools unionbedg tool and were filtered for the regions smaller than 100 bp using a custom script, since these small regions mainly represent edges of the intervals that were not identically called in different DNase-seq files and would influence the downstream variance analysis. Furthermore, peaks on chrX and chrY were filtered out to eliminate the gender effect on the downstream PCA analysis of variance. To eliminate the influence of extreme outliers, we selected only data points in the range 100–2,000 of the absolute peak intensity. Data generated with the ATAC-seq method were processed using MACS (MACS v1.4.2) pipeline. ATAC-seq data were normalized to encompass the same range of data points as the DNase-seq data using the parameter equal to the ratio of the average peak intensity for each ATAC-seq data set and the average peak intensity for the ENCODE AoSMC cell type (as this represents the closest cell type to HCASMCs used for ATAC-seq). Each of the values from ATAC-seq data sets was divided with the calculated factor for that data set. Sex chromosomes were removed and intervals smaller than 100 bp were filtered out as described above. PCA was computed in R using the prcomp function, and PCA plots for principal components 2 and 3 were generated using the ggplot2 package and visualized with the wesanderson colour palette package for R available on github.http://jaspar.genereg.net). The randomized model was created by permuting the columns so that the base composition of the randomized matrix remains the same as the JUN::FOS matrix to eliminate potential base composition bias. Whole-genome matrix scan was performed with PWMScan—genome-wide PWM scanner . PWM scan threshold was set to 0.001 to get approximately same number of sites as in AP-1 . Density plots were calculated with the ChIP-Cor Analysis Module  for AP-1 and AP-1-randomized matrix using the global normalization parameter and matching motives in both DNA strands. Correlation was repeated with the randomly selected set of 5,265 SNPs from the set of SNPs in low LD with CAD SNPs (<0.5).CAD and SNPs in LD were defined using the LD threshold of 0.8. Full-length AP-1 (JUN::FOS) motif (MA0099.1) was randomized using JASPAR .We also counted the total number of PWM sites for regions ±100 bp from the SNPs  and for 900–1,000 bp away (background regions) for the CAD GWAS and control SNPs, and obtained a contingency matrix  that gave us Fisher's exact Heterozygous genotypes at the candidate loci were determined using TaqMan SNP genotyping qPCR assays . SNP genRenilla luciferase constructs were co-transfected, with or without pcDNA3-empty, pcDNA3-JUN, JUNB or JUND expression constructs into A7r5 or HCASMCs using Lipofectamine 3000 (Life Technologies) according to the manufacturer's instructions. For siRNA knockdown studies, 20 nM Silencer Select siRNA negative control or siRNA against JUN, JUNB or JUND (Life Technologies) was transfected in HCASMCs 12 h before reporter transfections using RNAiMAX (Life Technologies). Media was changed after 6 h, and dual-luciferase activity (Promega) was recorded after 24 h using a SpectraMax L luminometer (Molecular Devices). Relative luciferase activity (firefly/Renilla luciferase ratio) is expressed as the fold change of the empty vector control (pLuc-MCS).Oligonucleotides containing the regulatory elements overlapping candidate CAD loci (156 nt for 1 × construct or 468 nt for 3 × construct) were synthesized for each allele and cloned into a shuttle vector pUC57 (GeneWiz). These fragments were then isolated by double restriction digest and subcloned into the multiple cloning site of the minimal promoter containing pLuc-MCS luciferase reporter vector (Agilent). All constructs were validated by Sanger sequencing. Empty vector (pLuc-MCS), 1 × and 3 × CAD loci and http://browser.1000genomes.org/) in Europeans (depending on the LD data availability for each SNP). D′ values of LD>0.90 were considered as the cutoff for linked variants; however, moderately linked variants with D′>0.75 were used if necessary. In the case of SNP rs17293632, given the weak linkage between the proxy transcript SNP rs1065080, we used the intronic SNP rs17293632 itself to measure imbalance in the nascent SMAD3 pre-mRNA as a transcription surrogateP values were then determined using a Student's t-test for the observed allelic ratios versus the expected ratio of 1.0.Total RNA was isolated from >60 primary HCASMCs from unrelated donor tissues, purchased from three different vendors  using the miRNeasy Mini kit (Qiagen) according to the manufacturer's instructions. Total cDNA was prepared using the High Output cDNA Synthesis kit (Applied Biosystems) according to the manufacturer's instructions. Total gDNA was prepared using the Blood and Tissue DNA Isolation kit (Qiagen). To identify appropriate markers for detecting allelic imbalance or ASE, linked coding or untranslated region SNPs were identified in haplotype blocks of genomic regions surrounding candidate regulatory SNPs using 1,000 Genomes Phase 1 or 3 variants . Second pass mapping with STAR was then performed using a new index that is created with splice junction information contained in the file SJ.out.tab from the first pass STAR mapping. Reads that have been mapped with STAR second pass mapping algorithm were subsequently counted using the htseq-count script distributed with the HTSeq Python package (https://pypi.python.org/pypi/HTSeq). Differential expression of exons, genes and transcripts were assayed using the DESeq2 R package from Bioconductor (http://bioconductor.org/packages/release/bioc/html/DESeq2.html), which uses negative binomial distribution to estimate dispersion and model differential expression such as to permit biological variability to be different among tested genes (transcripts).HCASMCs were cultured as described above and stimulated for 1 or 6 h using recombinant human TGF-β1 and PDGF-BB, and total RNA was purified from 5.0E5 cells using the Qiagen miRNeasy kit. RNA libraries were prepared using the Illumina TruSeq library kit as described by the manufacturer. RNA molecules were sequenced using Illumina HiSeq 2500 (2 × 101). Reads contained in raw fastq files were mapped to hg19 using the RNA-seq aligner STAR (v2.4.0i), which processes the data with short run times and yields high numbers of uniquely mapped reads (Gene expression and genotyping data were obtained from the STARNET databasehttps://cran.r-project.org/web/packages/MatrixEQTL/index.html) running a linear model and returning all calls with a maximum P value of 0.05. A follow-up conditional analysis was done to test the P(max SNP | SNP) as well as P(SNP | max SNP) for each gene. This was calculated by regressing the genotype of SNP_1 onto the residuals of SNP_2 regressed onto the gene expression, where SNP_2 is the SNP being conditioned on ). Results were imported into LocusZoom (http://locuszoom.sph.umich.edu/locuszoom/) to generate regional association plots.eQTLs were called using the MatrixEQTL R package , seeQTL (http://www.bios.unc.edu/research/genomic_software/seeQTL/), the University of Chicago eQTL Browser (http://eqtl.uchicago.edu/cgi-bin/gbrowse/eqtl/) or the Blood eQTL browser (http://genenetwork.nl/bloodeqtlbrowser/). Queries were performed by rsID or genomic coordinate for candidate SNP, and eQTLs were ranked by P value for all genes. Alternatively, proxy SNPs (r2=1.0) were used to report any known eQTLs for the candidate SNPs. Results were imported into LocusZoom (http://locuszoom.sph.umich.edu/locuszoom/) to generate regional association plots.In addition to calling eQTLs in STARNET, candidate SNPs were queried in public eQTL databases, including GTEx , an unpaired two-tailed Student's https://github.com/milospjanic/IntegrativeFunctionalGenomics. Additional modified scripts can be accessed upon request. All sequencing data that support the findings of this study have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) and are accessible through the GEO Series accession number GSE72696. All other relevant data are available from the corresponding author on request.All custom scripts have been made available at How to cite this article: Miller, C. L. et al. Integrative functional genomics identifies regulatory mechanisms at coronary artery disease loci. Nat. Commun. 7:12092 doi: 10.1038/ncomms12092 (2016).Supplementary Figures 1-20GREAT functional enrichment analysis of integrative overlapping CAD-associated regions in HCASMC (Top 50).Functional association network (FAN) of highly connected intermediate genes using protein-protein interaction databases.Complete list of candidate CAD-associated regulatory SNPs including LD SNPs.PolyPhen-2 functional predictions of non-synonymous CAD-associated SNPs.SIFT functional predictions for non-synonymous CAD associated SNPs.Gene annotation of CAD variants overlapping enhancer and TF binding regions of open chromatin in HCASMC and coronary arteries ex vivo.RegulomeDb annotation of prioritized CAD variants overlapping regulatory features from ENCODE and Roadmap Epigenomics datasets.HaploReg annotation of prioritized CAD variants overlapping regulatory features from ENCODE and Roadmap Epigenomics datasets.DEseq2 analysis of control vs. PDGF-BB stimulated HCASMC.Cis-eQTL analysis for prioritized CAD variants and genes.Summary of top candidate causal CAD variants and predicted functional annotationsSummary of published eQTL results for candidate causal CAD variantsSummary of candidate causal CAD variant chromatin and expression based validation resultsIngenuity Upstream Regulator Analysis of CARDIoGRAMplusC4D lociOligonucleotide sequences and probesCandidate CAD SNP and proxy transcribed SNP used for ASE analysis.Summary of edgeR analysis for CAD SNPs in differentially accessible chromatin between HCASMC perturbations.


Mangalicas are fatty type local/rare pig breeds with an increasing presence in the niche pork market in Hungary and in other countries. To explore their genetic resources, we have analysed data from next-generation sequencing of an individual male from each of three Mangalica breeds along with a local male Duroc pig. Structural variations, such as SNPs, INDELs and CNVs, were identified and particular genes with SNP variations were analysed with special emphasis on functions related to fat metabolism in pigs.ACACA, ANKRD23, GM2A, KIT, MOGAT2, MTTP, FASN, SGMS1, SLC27A6 and RETSAT, have not previously been associated with fat-related phenotypes.More than 60 Gb of sequence data were generated for each of the sequenced individuals, resulting in 11× to 19× autosomal median coverage. After stringent filtering, around six million SNPs, of which approximately 10% are novel compared to the dbSNP138 database, were identified in each animal. Several hundred thousands of INDELs and about 1,000 CNV gains were also identified. The functional annotation of genes with exonic, non-synonymous SNPs, which are common in all three Mangalicas but are absent in either the reference genome or the sequenced Duroc of this study, highlighted 52 genes in lipid metabolism processes. Further analysis revealed that 41 of these genes are associated with lipid metabolic or regulatory pathways, 49 are in fat-metabolism and fatness-phenotype QTLs and, with the exception of Genome analysis of Mangalica breeds revealed that local/rare breeds could be a rich source of sequence variations not present in cosmopolitan/industrial breeds. The identified Mangalica variations may, therefore, be a very useful resource for future studies of agronomically important traits in pigs.The online version of this article (doi:10.1186/1471-2164-15-761) contains supplementary material, which is available to authorized users. Due to the economic value of farm animals, their genomics, in general, and whole genome sequencing, in particular, are important issues. Results of such research have already had an impact and will continue to do so in the future in terms of production of meat, milk, fibre and other products, environmental effects of animal husbandry, breeding, animal health, feeding, and even human medical issues such as xenotransplantation and disease modelling , 2. RegaPig is one of the most important farm animals, providing about 103,000 thousand tonnes of pork for meat consumption worldwide in 2012 , 14. MosIn the last decade, enormous efforts have been made to exploit the genetic and genomic resources of pigs. Genome sequencing of swine goes back to the early 2000’s, when the Sino-Danish Pig Genome Project was initiated and subsequently a 0.66× coverage genome survey, based on shotgun sequencing, was published . Deeper Sus genus have also been included [th century [Despite the large number of local pig breeds, only a few of them , were included in genome sequencing projects. In addition to the major industrial and the few local breeds, Asian and European wild boars, several Asian pig breeds and several other species of the included , 27–29. included , 30. Man century . Mangali century , with hi century . Mangali century . As the  century , the Blo century –38, but  century . Previou century  and very century .In order to explore how the genomes of Mangalicas differ from the reference pig genome, we have sequenced a male individual of each of the three Mangalica breeds along with a male Duroc individual of Hungarian origin. The genome sequence of Mangalicas can serve as a basis for future conservation of the breeds and for an extended Mangalica pork industry.Three Mangalica male pigs with a Mangalica-specific mitochondrial D-loop haplotype were selected  for geno8, 4.15 × 108, 4.06 × 108 and 3.32 × 108 reads for the genomes of the Blond, Red and Swallow-belly Mangalica and the Duroc animals, respectively  resulted in 6.2 × 105, 6.2 × 105, 6.1 × 105 and 4.5 × 105 such INDELs were identified, respectively. Of these, 99.9% were novel compared to the dbSNP138 database. With respect to the size distribution, of the INDELs among the four genomes, single base-pair INDELs were the most abundant  provided the same result (data not shown) suggesting that our analysis with the pig genome is reliable.The detection of large INDELs was not the scope of the current study, and so only INDELs shorter than 52 bp were identified. For the genomes of the Blond, Red, Swallow-belly Mangalicas and the Duroc pig, approximately 6.9 × 10−7). One of the 16 genes, HOXB8, encoding a homeobox protein, is neither present in the literature [Copy number variants (CNVs) were identified that were common amongst the three sequenced Mangalicas. Only CNV gains were analysed further due to the effect of sequence coverage depth on CNV losses . One thoterature  nor in tPNLIP and PNLIPRP2 genes, which were not associated to fatness phenotypes in pigs before, are the target of Orlistat (tetrahydrolipstatin), a drug used for treating obesity in humans (data not shown). The possible effect of exonic SNPs on protein function is discussed below using FASN as an example.Due to the importance of the Mangalica × Duroc hybrids to the Hungarian pork industry, the 2,328 exonic, non-synonymous SNPs common to all three Mangalica breeds but absent from sequenced Duroc animal Figure  and the −3 and 2.95 × 10−19, respectively, indicating that the enrichment of the 14 genes in these QTLs deviate significantly from random.To study the possible relationship between the 52 genes in the lipid metabolic process GO category and QTLs, the chromosomal position of each genes was compared to the positions of the “Fatness” and “Fat composition” QTLs downloaded from the QTLdb, Release 19, . Forty-nNKX2-3 and EPHX2, and OMA1 and FAM135B, respectively  of the mapped genes were associated with lipid metabolic pathways Figure , while oty acids . The otht of pig . The enct of pig .Figure 6The 90 SNPs in the above described 52 genes were present in all three sequenced Mangalicas, but absent from the sequenced Duroc and the reference genome. To learn about their wider occurrence, we have “e-genotyped” 55 animals whose genome was sequenced [MOGAT2 (ENSSSCG00000014861), which encodes a monoacylglycerol O-acyltransferase 2 enzyme, and is in several back- and belly-fat QTLs and in the “Fat digestion and absorption” (KEGG: 04975) pathway  occurs at a much higher frequency than the B alleles in Mangalica, whereas in contrast the B alleles  are more prevalent in Duroc , and a Duroc animal from a Hungarian herd was sequenced and analysed. More than 100 million reads were obtained from the genome of each animal. On average for the four genomes sequenced, 81% of the reads were mapped to the reference genome, resulting in 14.5× median autosomal coverage. Millions of SNP and hundred-thousands of INDEL variations were identified in the three Mangalicas and the one Duroc genome, respectively, when compared to the reference pig genome assembly Sscrofa 10.2. By filtering the SNPs, about five to six million variations were obtained, and about one-tenth of these were novel SNPs compared to the dbSNP138 database  percentage QTL in a Guadyerbas × Landrace cross, although none of the identified SNPs had any putative effect on the protein structure [FASN gene and inhibition of the FASN protein in mice resulted in reduced total body fat [1088 is in close vicinity to the active site of the dehydratase domain containing an open-ended hydrophobic tunnel [1088 is strongly hydrophobic, while T1088 is hydrophilic (data not shown). It is possible, therefore, that in the FASNT1088I protein the substrate-binding nature of the active site is altered, which may influence the dehydration step of the fatty acid cyclic elongation. This might be particularly important in Mangalicas, where no BB homozygotes were found. Thus the active site in the catalytic domain of their FASN protein is expected to be hydrophobic, although allele-specific expression of the FASN gene in heterozygotes might influence this.Some of the 52 genes, for example  in pigs –54. Of ttructure . The FAStructure  and catabody fat  and bodybody fat , respectbody fat . The amibody fat . T1088 ic tunnel . PredictMOGAT2 (ENSSSCG00000014861), with seven SNPs, of which four are present in Mangalicas, but not in other 56 sequenced pig individuals (see Results). The MOGAT2 protein catalyses the conversion of 1-acylglycerol obtained from dietary fat into diacylglycerol in the smooth endoplasmatic reticulum of the small intestinal epithelial cells, and thus participates in the production of chylomicron . Chylomicron affects the PPAR signalling pathway, which in turn regulates a number of lipid metabolic processes . DNA concentration was measured using the Quant-iT™ PicoGreen dsDNA® Assay . Preparation of 500 bp fragment libraries and 2 × 100 bp Illumina paired-end genome sequencing was performed by Aros Applied Biotechnology  as a custom service, using Illumina’s HiSeq2000 platform.Sus scrofa reference genome sequence 10.2 was indexed using the “bwtsw” algorithm option of BWA 0.5.9rc1 [The 0.5.9rc1  followedTo detect small genetic variants (SNPs and INDELs), the SAMtools  and GATKCopy number variations (CNVs) were detected as described by Paudel and coworkers  using thTo determine novel variants in our sequence data, we compared the identified SNPs and INDELs with the dbSNP138 data using BEDTools  and annoGene ontology analysis was performed by the web-based software PANTHER . For oveData from Ensembl were retrieved using BioMart ; Venn diFASN gene, High Resolution Melting (HRM) analysis was performed with a Rotor-Gene Q 5plex HRM Platform using a saturating dye (EvaGreen) technology . PCR reactions were performed in 25 μl reaction volumes using 60 ng total DNA as template and the Type-it HRM PCR kit , according to the instruction of the manufacturer. The primers for FASN SNP1 and SNP2 were FASN1_F: 5′ CGCGATCTCGTTGAGCAT 3′, FASN1_R: 5′ GTGCAGACCCTGCTGGAG 3′ and FASN2_F: 5′ GGATAGGCTTGAGATGCTCTT 3′, FASN2_R 5′ GTGGTGGTGGACAGGAATCT 3′, respectively. Reactions were carried out with an initial denaturation step at 95°C for 5 min, followed by 35 cycles of 95°C for 15 sec, 60°C for 30 sec and 72°C for 10 sec and then HRM curves were generated by acquiring florescence data between 80 and 91°C. Individuals with homozygous and heterozygous genotypes were assigned according to their HRM curve determined by the Rotor-Gene software and visual inspection.To genotype the two Mangalica-specific SNPs in the The data sets supporting the results of this article are included within the article and its additional files. Sequence data are deposited to the NCBI Sequence Read Archive under identifier SRP039012.Additional file 1: Figure S1: Distribution of insert length in paired-end sequencing. Figure showing the distribution of insert length and number of reads in four sequenced pig individuals. (PDF 22 KB)Additional file 2: Figure S2: Comparison of SNPs and INDELs detected by SAMtools and GATK. The numbers in the overlapping areas represent the absolute number of concordant variants, while coloured numbers represent the percentage of unique variants. BM, Blond Mangalica; RM, Red Mangalica; SM, Swallow-belly Mangalica; D, Duroc. (PDF 4 KB)Table S1. The number of filtered SNPs in four sequenced pig individuals. Table S2. The number of filtered SNPs in the four animals that are present in the dbSNP 138 database. BM, Blond Mangalica; RM, Red Mangalica; SM, Swallow-belly Mangalica; D, Duroc. (PDF 34 KB)Additional file 3: Table S1 and S2: Number of filtered SNPs. Additional file 4: Table S3: Annotation of SNPs. Table showing the annotation of SNPs into categories and the ratio of heterozygous-homozygous SNPs in each category. (XLS 46 KB)Additional file 5: Figure S3: Distribution of the size of INDELs. Figure showing the number of INDELs with respect to their size in four sequenced pig individuals. (PDF 23 KB)Additional file 6: Table S4: Annotation of INDELs. Table showing the number and percentage of INDELs annotated into categories. (XLS 54 KB)Additional file 7: Figure S4: Distribution of the size of exonic frame-shift INDELs. Figure showing the number of exonic frame-shift INDELs with respect to their size in four sequenced pig individuals. (PDF 23 KB)Additional file 8: Table S5: Copy number variations in Mangalicas. Table showing CNVs found in Mangalica pigs, their overlap with a Duroc animal and published data , and theAdditional file 9: Table S6: SNP variations in Mangalicas. Table showing SNPs identified in Mangalicas, which are not present in the dbSNP138 and the sequenced Duroc animal used in this study. Multiple SNPs of the same gene are shown in separate rows with gene ID in grey. Transcript and alternative transcript annotation of the SNP are shown in the “SNP Annotation” column. (XLS 464 KB)Additional file 10: Table S7: Overrepresentation analysis of genes with exonic non-synonymous SNPs in Mangalicas. Represented GO categories of 1,389 genes, which carry SNPs identified in the three sequenced Mangalica individuals, and of two same size control pig gene sets. Overrepresentation can be considered statistically significant where the corrected P value is smaller than 5E-2. (PDF 5 KB)Additional file 11: Table S8: Selected genes with Mangalica-specific exonic non-synonymous SNPs. Table showing the genes with Mangalica-specific SNPs that belong to the GO:0006629 lipid metabolic process category, their annotation to pathways and the QTL regions that overlap them. (XLS 95 KB)Additional file 12: Table S9: Genes with Mangalica-specific SNPs in “Fatty acid percentage” QTLs. Table showing the name, Ensembl Gene ID, description and associated fatty acid percentage QTLs of genes with Mangalica-specific SNPs and belonging to the GO:0006629 lipid metabolic process category. (XLS 25 KB)Additional file 13: Table S10: Occurrence of SNPs in 59 genome sequenced pig individuals. Table showing the presence (1) or absence (0) of those exonic, non-synonymous SNPs, which were identified in all three genome-sequenced Mangalicas, but not in the sequenced Duroc and the reference genome, and which are lipid metabolic genes. The genome sequenced individuals [Additional file 14: Figure S5: Heat-map of the frequency of 82 SNPs in genome sequenced pigs. Figure showing the clustering of 82 SNPs by their frequency in pig breeds/species. Four distinct clusters can be observed consisting of Mangalicas, European pigs/wild boars, Duroc of different origin and non-European pigs/wild boars/related species. The order of SPNs from top to bottom corresponds to those in Table S10 (Additional file FASN gene. Figure showing the frequencies of two SNPs in 59 genome sequenced pigs, including the four in this study and 55 published by Groenen et al. [Sus, three species, Sus cebifrons, Sus celebensis and Sus verrucosus from the genus; Other, Bearded pig and warthog; WE, Western European; Nature total, overall frequency of the published [Additional file 15: Figure S6: SNP frequencies in the


Further, the SNP information generated may not be readily used for downstream processes such as genotyping. Hence, a comprehensive pipeline has been developed by integrating several open source next generation sequencing (NGS) tools along with a graphical user interface called Integrated SNP Mining and Utilization (ISMU) for SNP discovery and their utilization by developing genotyping assays. The pipeline features functionalities such as pre-processing of raw data, integration of open source alignment tools , SNP prediction (SAMtools/SOAPsnp/CNS2snp and CbCC) methods and interfaces for developing genotyping assays. The pipeline outputs a list of high quality SNPs between all pairwise combinations of genotypes analyzed, in addition to the reference genome/sequence. Visualization tools (Tablet and Flapjack) integrated into the pipeline enable inspection of the alignment and errors, if any. The pipeline also provides a confidence score or polymorphism information content value with flanking sequences for identified SNPs in standard format required for developing marker genotyping (KASP and Golden Gate) assays. The pipeline enables users to process a range of NGS datasets such as whole genome re-sequencing, restriction site associated DNA sequencing and transcriptome sequencing data at a fast speed. The pipeline is very useful for plant genetics and breeding community with no computational expertise in order to discover SNPs and utilize in genomics, genetics and breeding studies. The pipeline has been parallelized to process huge datasets of next generation sequencing. It has been developed in Java language and is available at  Next Generation Sequencing (NGS) technology has changed the research landscape of genomics especially crop genomics since the last few years Identified SNPs are highly effective only when they are distributed throughout the genome and genotyped for the population of interest or for a set of germplasm. In general, SNP discovery using NGS is limited to the number of lines assayed or sequenced. Genotyping large number of SNPs facilitates various genetic analyses  and important applications . The best approach towards the SNP marker discovery for genotyping is to compare fully sequenced genomes from individuals of a given species. However, in case full reference sequence is not available, reads could be assembled to derive a mimic reference sequence and genome-wide SNPs can be identified In general, SNPs could be utilized to enrich the genomic resources for a species in mainly two steps, first, is to identify DNA sequence polymorphisms (marker discovery) and then, second is to assay the identified SNP markers (genotyping) across the segregating population(s) or germplasm set. In this context, several data analysis pipelines such as ngs-backbone ISMU is an easy to use GUI based pipeline for SNP discovery useful for experimental biologists, geneticists and plant molecular breeding community. It allows researchers to analyse NGS data generated by Illumina, Life Technologies (SOLiD) or reanalyze already deposited data from SRA (Sequence Read Archive) of NCBI. It is highly customizable and integrates popular command line based tools for alignment and SNP prediction with support for both SE and PE data in a standard FASTQ format Identification of SNPs is a multi-step process involving various tools for pre-processing the data, alignment against the reference and identifies variations subject to filters. The work-flow  could beThe pipeline requires input files in the FASTQ format The pipeline processes the input FASTQ files to assess and produce an overview of quality distribution of the dataset. Pre-processing enhances the quality of the input data by filtering and trimming reads. It filters off low quality reads followed by trimming low quality regions at the end of the residual reads. This pre-processing step is executed with a Perl script which reads the input data and considers the bases of reads as low quality, if the phred score The pre-processed high quality reads are mapped onto the reference sequence. Alignment tools namely BWA Good alignment with high read depth is an essential prerequisite with NGS data for efficient SNP discovery irrespective of sequencing platforms. The pipeline provides two different SNP calling methods namely: (i) SAMtools majorF threshold of 0.66, it is considered as a major/consensus base In the CbCC method, the pipeline reads the SAM files of samples and extracts the pileup information at all positions. This information was used to calculate and compare the consensus base or consensus allele for each sample at every corresponding position of reference. If the consensus base differs between the samples at that position then it is reported as an SNP. The pipeline computes the allele frequency at every position of the aligned sequence. If this allele frequency is above a Finally, SNPs between the samples (pairwise) as well as against the reference sequence are reported. Additionally, a comprehensive non-redundant set of SNPs across all the samples as well as the reference is also reported as SNP genotyping matrix. All the reported SNPs are described with their position on reference sequence, major bases and the corresponding confidence ratio in each of the samples provided. These statistics further provide a confidence measure in selecting the SNPs for the design of genotyping assays.i.e., SNPs between any two samples) from the drop down list on the results window to view in Tablet. Flapjack is another Java based graphical genotyping software that could be used to visualize the SNPs or the allele distribution on each chromosome amongst the samples. A haplotype can be viewed and the user could also group, cluster or sort the genotypes/lines of similarity with other lines.Although this step is optional, visualization of alignment and distribution of SNPs identified in samples would help highlight any possible sequencing errors or artefacts. The pipeline is integrated with two visualization tools namely, Tablet This step of the pipeline generates an input file for the design of genotyping assays. The pipeline checks for various criteria for selecting SNPs suitable for genotyping. First, the SNP should have complete flanking sequences on either side. Second, the flanking sequences must not contain any ambiguous bases like “N”, “K”, “R”. Third, the flanking sequences should not contain any other SNP. An SNP satisfying these criteria are preferred for genotyping assay . All such potential SNPs are filtered to prepare a preliminary input file for genotyping assay development. One can exclude less informative SNPs either by visual inspection or on the basis of PIC value, so that highly informative SNPs could be used further in genotyping experiments.The output generated by the pipeline include a summary report, SNP information for a pair wise combination of samples, amongst the samples, input files for KASP genotyping and preliminary input file for ADT scoring for Illumina genotyping. All the files are in a tab separated text file and a spread sheet with the file name “called_allele_data.xls” is also produced. Other than the log and vcf files, many other intermediate files like SAM and BAM files for each sample and filtered high quality FASTQ datasets are generated, which could be accessed in standalone ISMU. Input files could also be visualized.The pipeline was evaluated for its performance, accuracy and speed using two genomic and one transcriptomic datasets.The genomic dataset included whole genome re-sequencing (WGRS) data from four genotypes of chickpea  and the RAD sequencing data from ten genotypes of chickpea . The transcriptomic dataset included RNAseq data from two peanut genotypes (HuaU12 and HuaU606). After pre-processing, the reads passing filter criteria in the WGRS, RAD and RNAseq datasets were 97.11% (125.07 million), 95.39% (74.22 million) and 98.18% (6.73 million) respectively. WGRS and RAD datasets were aligned to chickpea genome while RNAseq dataset was mapped against unigenes of peanut. On an average, 92.68% , 90.5%  and 80.6A total of 579,813 SNPs were identified from WGRS data  against Allele calling is the most memory intensive part of the pipeline. A computational profile of the pipeline with the three datasets was recorded in order to observe the peak memory consumption, disk-space allocated and the time taken for the analysis to complete. In each case it was tested with 18 processors on a Linux based desktop machine with 48 Gb RAM . The RNAISMU is a novel pipeline for SNP mining packed with unique features that are superior in comparison to other pipelines . In geneIn fact, this pipeline was initially developed for transcriptome data and later extended for genomic data. The pipeline has been designed to run on resource constrained environments  enabling NGS data to be processed even with very low amount of RAM. Installation of software and its dependency tools for processing NGS data and the downstream analysis are a daunting task for many researchers especially for a molecular breeder who may have little computational expertise. Therefore ISMU is also provided as a ready to use Virtual machine image facilitating the user to quickly get started on Desktop/Workstation and comfortably add more analytic power if required.Open source NGS tools have been selected carefully and integrated in the ISMU. The pipeline presents the user with numerous options for alignment and SNP calling that makes it versatile and unique in comparison with similar pipelines. Multiple options help the user to reanalyze with different tools to find the best or concordant results. However each tool has its pros and cons. In case, one has high quality data available, the results with different tools would appear similar, but with low quality  data the differences in results are evident Sequence alignment is one of the major steps that greatly affect the marker discovery. In this step the mapping tools use heuristics to align short reads to the reference sequence as exhaustive and accurate algorithms would not be computationally feasible. Initially, small regions (seed) in reads would be identified against the reference sequence where the location of the best match is most likely to be found. After identification of a smaller subset of possible mapping locations, slower but more accurate alignment algorithms such as Smith-Waterman are run on the limited subset The ISMU pipeline integrates four SNP calling programs. SAMtools (v0.1.19) Alternately, another SNP calling method, Coverage based consensus calling (CbCC) Several open source pipelines for NGS data analysis are available for the research community. But not all of them support the analysis from raw NGS data. Very few pipelines like ISMU start with preprocessing of raw NGS data while pipelines such as GATK, GAMES, Atlas2  are desiThe pipeline is designed to handle genomic (WGRS or RAD) and transcriptomic datasets which include both SE (single end) and PE (paired-end) reads. To facilitate downstream analysis, SNP matrix or a matrix of called alleles for all genotypes is presented. The SNPs reported as a result were to be considered of high confidence. In WGRS dataset, the number of SNPs was observed to be very high with an average SNP density of 1.1/kb, while in RAD dataset SNP density was 0.05/kb owing to the nature of complexity reduction technologies which does not capture the complete variation. In RAD dataset, the extent of missing genotype information at the called position is huge and requires a better depth of sequencing or imputation of data. For alignment of RNAseq dataset, SOAP2 was used which neither allows gap nor clipping at the ends of reads at alignment. Because of this strict alignment mode, SOAP2 aligned lesser proportion of reads to distant reference sequences (unigene), evident in the low number of SNPs detected in comparison with gapped alignment based methods ISMU is a validated SNP mining pipeline for NGS data from single or multiple samples. The prominent features of the pipeline include GUI, parallelization, multiple analysis tools, and multi-user support to facilitate analysis of huge NGS data for genotyping experiments. Although it has been developed for use with crop plants  it could also be used with animal and microbe data. Hence, the data could be processed independent of the organism. The easy to use GUI encourages non-bioinformatics scientists/researchers to analyse the data in their laboratories on the desktop machines. ISMU integrates proven NGS analysis tools and offers multiple options for alignment and SNP calling. It includes a complete workflow from pre-processing of raw data to deciphering SNPs and reporting markers towards genotyping experiments. The pipeline outputs variation information of the samples analyzed in a simple format for downstream analyses such as genotyping studies. Parallelization of ISMU enables efficient use of modern desktops with multiple cores. The complete application is rigorously tested and is also distributed on CDs as well as ready to use virtual box image.ISMU targets plant molecular breeding community with little computational expertise. Plant breeders/researchers could potentially use it to quickly process the data and thereby obtain biological insights into genetic events. The SNPs detected by the pipeline could be used in applications such as linkage mapping, trait mapping, TILLING, GWAS and QTLSeq .http://bio-bwa.sourceforge.net/bwa.shtml) http://maq.sourceforge.net/maq-man.shtml) http://bowtie-bio.sourceforge.net/bowtie2/index.shtml) http://www.novocraft.com/main/index.php) http://soap.genomics.org.cn) http://samtools.sourceforge.net) and CbCC v2.0 http://ics.hutton.ac.uk/tablet) http://ics.hutton.ac.uk/flapjack) In order to develop an ISMU pipeline, open source tools such as BWA v0.6 (http://hpc.icrisat.cgiar.org/ISMU/datasets. The third dataset includes transcriptome data (RNAseq) from two peanut genotypes HuaU12 (SRR647081) and HuaU606 (SRR647076) downloaded from sequence read archive (SRA) database of NCBI. The WGRS and RNAseq datasets were paired end (PE) data while the RAD dataset was single end (SE). The WGRS and RAD datasets use the chickpea draft genome sequence ftp://ftp.ncbi.nih.gov/repository/UniGene/Arachis_hypogea/Ahy.seq.uniq.gz).In order to evaluate the performance of newly developed ISMU pipeline, two genomic datasets from chickpea and one transcriptomic plant dataset from peanut were collected. The first dataset comprises of the whole genome re-sequencing (WGRS) data from four genotypes of chickpea namely Pistol, Slasher, Hat Trick and Genesis 90, as reported in Varshney et al. Tools for alignment , SNP calling (Samtools and CbCC), visualization of SNPs  and analysing allele distribution (Flapjack) were decompressed and compiled on Redhat Linux. These tools were then integrated into a pipeline using Perl language. In this process, several in-house perl/bash scripts were written and embedded into the pipeline in order to bring automation, functionality and visualization. These scripts were used to provide required features such as pre-processing of data, create input files for Flapjack, processing SNP for calculating allele frequency, PIC value and generate files for developing Illumina and KASPar assays. Most of the scripts were written with multi-threading feature for speedy execution of the analysis for handling large datasets. In order to develop a GUI pipeline, interactive interface was developed related to data upload, selection of tools, customizing parameters for analysis, location of analysed data and output format. The input given by user through GUI is passed to backbone script of the pipeline which automatically generates a shell script to execute the different steps of pipeline. During the execution, shell scripted calls other softwares and embedded scripts in step wise manner while a GUI window shows the status of a run. In addition, it also provide an option to terminate the execution of pipeline at any stage. At the end of execution of pipeline, another page pops up showing options to download and display different outputs. The pipeline save all the output files in the user provided output folder which can be retrieved by the user at later stage.Selected datasets were subjected to quality filtering in order to discard low quality reads i.e., reads having more than 30% of low quality bases (Q<20). These filtered reads were trimmed from both the ends which resulted in shorter read lengths. The resultant paired end reads would not be uniform in length. The reads passing these quality filters in the WGRS, RAD and RNAseq datasets were then aligned to a reference using different alignment tools in ISMU. WGRS dataset and RAD datasets were aligned to chickpea genome using BWA and Bowtie2 respectively. RNAseq dataset was mapped against unigenes of peanut using SOAP2. Samtools was used to call variants for WGRS and RAD datasets while CbCC was used with RNAseq dataset. All datasets were analysed on a 24 core Linux machine with 48 GB RAM.


Although several interesting MoS2 and WS2-based optoelectronic devices have been reported, their processability and reproducibility are limited since their electrical properties are strongly dependent of the number of layers, strain and sample sizes. It is highly desirable to have a robust direct bandgap TMD, which is insensitive to those factors. In this work, using density functional theory, we explore the effects of layer number, strain and ribbon width on the electronic properties of ReS2, a new member in the TMD family. The calculation results reveal that for monolayer ReS2, the nature (direct versus indirect) and magnitude of its bandgap are insensitive to strain. Importantly, the predicted bandgap and also charge carrier mobilities are nearly independent of the number of layers. In addition, the direct bandgap of ReS2 nanoribbons is only weakly dependent on their width. These robust characteristics strongly suggest that ReS2 has great potential for applications in optoelectronic nanodevices.Two-dimensional (2D) transition-metal dichalcogenides (TMDs), most notably, MoS It is well-known that these TMDs exhibit a semiconducting characteristic with sizable and direct bandgap in their monolayer (ML) form. However, this direct bandgap characteristic changes to an indirect one in their multilayered or bulk form. Besides, with increasing the number of layer, there is a drastic reduction in bandgap. It is believed that such direct-to-indirect bandgap transition and drastic reduction in bandgap are caused by the quantum confinement along the thickness direction and the strong interlayer coupling, which can significantly change the electronic properties of these TMDs8910Recently, two-dimensional 2D) transition-metal dichalcogenides (TMDs), a new class of 2D materials with a chemical formula of MXD transit2), a new member in the TMD family, is a promising candidate as it exhibits a weak band renormalization, absence of interlayer registry and weak interlayer coupling arising from Peierls distortion of the 1T structure of ReS22 in optoelectronic device applications, an in-depth understanding on the fundamental properties of ML, NR and multilayer ReS2 is indispensable. Similar research has been intensely focused on MoS2 and WS2 to explore their electrical properties for device applications814152, with the aim of demonstrating their robustness against those factors.2D rhenium disulphide et al. 2. Hence, ReS2 has a unique crystal structure, which is distinctively different from other TMDs, in which their graphene-like hexagonal crystal structure is composed of layers of metal atoms sandwiched between layers of chalcogen atoms. From 2 is a direct gap semiconductor with a bandgap of 1.30 eV, which is close to the experimental value of 1.32 eV2, we increase the interlayer spacing c to 20 Å to build a ML ReS2 model. It is found that the optimized lattice constants of ML ReS2 are a = 6.51 Å and b = 6.41 Å, which are exactly the same as the bulk. The same optimized lattice constants of bulk and ML ReS2 indicate that the interlayer coupling is negligible. This issue will be discussed in details later. The calculated band structure of ML ReS2 is shown in 2 also shows a direct gap semiconductor characteristic with a bandgap of 1.43 eV, which is exactly the same as recently reported value2 is independent of the number of layer, which is in strong contrast to other TMDs, such as MoS2 and WS2, in which the nature of their band structures is strongly dependent on the number of layersThe optimized ReS2, ΔEf, using following equation:In order to confirm the weak interlayer coupling, we further calculate the formation enthalpies of bulk and ML ReS2, μRe,(S) denotes the corresponding atomic chemical potential calculated from metallic Re or solid S unitcell, respectively. In the calculation, we neglect the entropy contribution at 0 K. It should be noted that the calculated formation enthalpies of bulk and ML ReS2 are nearly identical (1.69 eV per ReS2 unit). Our DFT calculations without considering van der Waals (vdW) correction indeed confirm the weak interlayer coupling with the coupling energy of only 7 meV per unitcell, which is slightly smaller than the reported value of 18 meVEf which is defined as S is the surface area of ML ReS2 unitcell and EML,bulk is the energy of ML and bulk ReS2 unitcell, respectively. NL is the number of layers in each bulk ReS2 unitcell, in which, there are two layers in ReS2 unitcell. The calculated energy is 3.46 × 10−4 or 0.098 eV/Å2 without or with vdW correction, respectively. The low coupling energy and surface energy for the exfoliation of the monolayer indicate that single layer ReS2 can be easily exfoliated from the bulkwhere It is well-known that in inorganic semiconductor, the coherent wavelength of thermally activated electrons or holes at room temperature is much larger than lattice constants and is nearly equal to that of acoustic phonon modes in the center of first Brillouin zone (FBZ). Since the electron-acoustic phonon coupling dominates the scattering at the low energy regime2023m* is the effective mass and T is the temperature, E1 is the DP constant which is defined as the shift of the band edges induced by strain, and C is the elastic modulus of uniformly deformed crystal under strain. For a 2D material, the in-plane stiffness C is defined as E is the total energy of layered supercell, δ is the applied biaxial strain and S0 is the area of the optimized supercell. The effective mass m* for charge transport is calculated by 2 varying with strain are shown in where 2 NRs can be directly produced by cutting ML ReS2 along x or y direction  are employed. Calculations on NRs with and without spin polarization are carried out to investigate the magnetic properties and the structure stability. According to the calculated density of states (DOS) see  online. Eedge) is an indication of the NR’s stability, and can be calculated based on:The edge energy  is the number of extra S or Re atoms at the edges, respectively. μS,(Re) is the chemical potentials of S or Re, respectively. It should be noted that the chemical potentials of Re and S are decided by the thermodynamics condition. For ReS2, at equilibrium, we have 2where 2, the electronic properties of ReS2 NRs remain largely unexplored, which are crucial for their applications in nanodevices. Here, we focus on the size effect on their electronic properties. It is well-known that NRs (1D) have different electronic properties compared to mono- or multi-layer (2D) form. The edge atoms of NRs introduce new flat energy levels at both valence and conduction band edges, narrowing the band gap accordingly. Here, we further investigate the quantum size effect on the bandgap of ReS2 NRs (RY1-series). Our calculation results show that RY1-NRs are semiconductors with a director bandgap, and the bandgap increases with increasing ribbon width (N), and converges to 0.92 eV as shown in k-space conduction band minimum and valence band maximum shift away from Γ point. But the NR still retains the nature of direct bandgap, which may be caused by the narrow width and symmetry-breaking in the FBZ.Beyond ML ReS2 obtained from the DFT calculation results of strain-dependent band structure  as shown in the inset of d56s2) may result in the elongated Jahn-Teller distortions when degeneracy is broken. The d7 electronic configuration of Re provides one electron in the two degenerate eg orbitals , which shows that the bandgap of ReS2 NR is less strongly dependent on the width than that of other layered TMDs. Compared with other TMD-based NRs, such as MoS2 NRs, the bandgap finally converges to 0.56 eV2 NRs with armchair edges reduces to 0.38 eV from their monolayer value 1.55 eV112 NRs, the reduction in the bandgap compared to the monolayer value is much smaller than other TMDs NRs. It is known that the reduced bandgap may limit the applications of TMDs in electronic nanodevices, especially in optoelectronics, in which the nature and the magnitude of bandgap play critical roles in devices. Hence, we consider that ReS2 NRs have more stable structure and more robust direct bandgap characteristic, thus ideal for applications in optoelectronic nanodevices.Quantum size effects on band structure of ReS2. In contrast to other TMDs, ML ReS2 has similar behavior as its bulk due to interlayer electronic and vibrational decoupling. We also calculate the mobilities of electron and hole in ML ReS2 and find that the mobilities of ML and bulk ReS2 are comparable, which is in strong contrast to other TMDs in which a drastic reduction in mobilities from bulk to ML form is often observed. Moreover, we also investigate the quantum size effect on the bandgap of ReS2 NRs and find the bandgap is weakly dependent on the NR width, and converges to 0.92 eV for wide NRs. Our work suggests that ReS2, with robust sizable and direct bandgap semiconducting characteristics, is a promising material suitable for TMD-based optoelectronic nanodevices.In conclusion, we have studied the electronic properties of ML, multilayer and NR forms of ReSd5 and 6s2 valence for Re; for S, the n = 3 shell is included as valence (3s2 and 3p4). A 10 × 10 × 1 Monkhorst-Pack k-point grid was used for monolayered unitcell geometry optimization calculations and a plane-wave basis set with an energy cutoff of 500 eV. A 8 × 1 × 1 grid for k-point sampling in geometry optimization was consistently used for NRs in our calculations. Good convergence was obtained with these parameters and the total energy was converged to 1.0 × 10−5 eV per atom, as well as edge energy was converged to 1.0 × 10−3 eV/Å. For the interlayer coupling energy calculation, we carried out comparative calculations both with and without the van deer Waals correction by employing optB88-vdW functionalAll calculations were carried out using the density functional theory (DFT) with the generalized gradient approximation (PBE-GGA)How to cite this article: Yu, Z. G. et al. Robust Direct Bandgap Characteristics of One- and Two-Dimensional ReS2. Sci. Rep.5, 13783; doi: 10.1038/srep13783 (2015).


The use of whole-genome sequence data can lead to higher accuracy in genome-wide association studies and genomic predictions. However, to benefit from whole-genome sequence data, a large dataset of sequenced individuals is needed. Imputation from SNP panels, such as the Illumina BovineSNP50 BeadChip and Illumina BovineHD BeadChip, to whole-genome sequence data is an attractive and less expensive approach to obtain whole-genome sequence genotypes for a large number of individuals than sequencing all individuals. Our objective was to investigate accuracy of imputation from lower density SNP panels to whole-genome sequence data in a typical dataset for cattle.Whole-genome sequence data of chromosome 1 (1737 471 SNPs) for 114 Holstein Friesian bulls were used. Beagle software was used for imputation from the BovineSNP50 (3132 SNPs) and BovineHD (40 492 SNPs) beadchips. Accuracy was calculated as the correlation between observed and imputed genotypes and assessed by five-fold cross-validation. Three scenarios S40, S60 and S80 with respectively 40%, 60%, and 80% of the individuals as reference individuals were investigated.Mean accuracies of imputation per SNP from the BovineHD panel to sequence data and from the BovineSNP50 panel to sequence data for scenarios S40 and S80 ranged from 0.77 to 0.83 and from 0.37 to 0.46, respectively. Stepwise imputation from the BovineSNP50 to BovineHD panel and then to sequence data for scenario S40 improved accuracy per SNP to 0.65 but it varied considerably between SNPs.Accuracy of imputation to whole-genome sequence data was generally high for imputation from the BovineHD beadchip, but was low from the BovineSNP50 beadchip. Stepwise imputation from the BovineSNP50 to the BovineHD beadchip and then to sequence data substantially improved accuracy of imputation. SNPs with a low minor allele frequency were more difficult to impute correctly and the reliability of imputation varied more. Linkage disequilibrium between an imputed SNP and the SNP on the lower density panel, minor allele frequency of the imputed SNP and size of the reference group affected imputation reliability. One advantage of using whole-genome sequence data over genotypes from SNP (single nucleotide polymorphisms) panels for genome-wide association studies (GWAS) and genomic prediction is that polymorphisms causing genetic differences can be included in whole-genome sequence data. Because the causative mutation is included, decay in linkage disequilibrium (LD) between a SNP and the causative mutation by recombination events is not an issue. Accordingly, testing variants directly associated with a given trait is possible and may lead to higher accuracy in GWAS and genomic predictions. Moreover, since there is no decay in LD when using sequence data compared to traditional smaller-sized marker panels, genomic selection across generations and across breeds may be improved e.g. -3.Costs to generate whole-genome sequence data are decreasing rapidly. It is expected that, in the next few years, whole-genome sequence data will be widely available for crops and livestock, as is already the case for human studies . DespiteHowever, using sequence data may not lead to higher accuracy in genomic predictions and GWAS if the accuracy of imputation to sequence data is too low. Accuracy of imputation was studied in barley with 3200 SNPs , in maizet al.[In contrast to crops and livestock, human sequence data are available and accuracy of imputation to sequence data has been investigated e.g. -16, whicCrop and livestock populations differ from human populations, in extent of LD and population structure -19. In cThe objective of this study was to investigate the accuracy of imputation of genotypes from SNP panels to whole-genome sequence data in a typical dataset of domestic animals and to gain insights on the factors that affect accuracy of imputation, such as number of sequenced individuals, number of SNPs on the lower density marker panel, location and MAF of the imputed SNPs. Because in practice true genotypes are unknown, it is important to understand the underlying factors that influence imputation accuracy. Holstein Friesian cattle data provided by the 1000 bull genomes project ,23 was uet al.[Bos taurus autosome 1 (BTA1) were used. Similar results were expected for other chromosomes.Whole-genome sequence data of 114 Holstein Friesian bulls were provided by the 1000 bull genomes project (Run 2.0) ,23. Bullet al.. Varianthttp://www.1000bullgenomes.com[A set of sequence variants and genotypes that can be used to test imputation programs is available at request via nomes.com.r) was calculated as the correlation between observed and imputed genotypes. Imputed genotypes were assessed by estimated B-allele dosage, which had a value between 0 and 2 and was calculated using posterior genotype probabilities as estimated by Beagle: 0 * P(AA) + 1 * P(AB) + 2 * P(BB). SNPs with fixed observed genotypes or estimated B-allele dosages for one or more validation groups were removed. Accuracy of imputation ranged between -1 (opposite genotype imputed) and +1 (correct genotype imputed). An imputation accuracy with a value around 0 meant random imputation.Beagle 3.3.2 software  with defTo assess imputation accuracy, five-fold cross validation was performed. Individuals were randomly divided in five groups, group 1 to 5, and each group was used as validation set once. For validation individuals, SNP genotypes for SNPs corresponding to the Illumina BovineSNP50 BeadChip  or Illumina BovineHD BeadChip  were retained, while the remaining SNPs on the sequence panel were masked.To study the effect of number of sequenced individuals on imputation accuracy, three scenarios were considered: S80, S60, and S40. Reference group in scenarios S80, S60 and S40 contained 80% , 60% and 40% of the individuals, respectively. In scenarios S40 and S60, the two or three following groups were designated as reference group. For example for scenario S60, if individuals in group 1 were designated as validation individuals, then individuals in group 2, 3, and 4 were designated as reference individuals.et al.[According to VanRaden et al., accuracr2).Factors that can influence imputation accuracy per SNP are number of sequenced individuals, distance (in base pairs) and MAF difference between an imputed SNP and its nearest SNP on the lower density marker panel, and MAF of imputed SNPs. MAF was calculated for each SNP based on all 114 individuals. For graphical representation and to illustrate the average behavior of SNPs, SNPs were binned in groups of 1000 based on distance or MAF (difference), and these binned SNPs were used to study imputation reliability  are used for imputation of missing SNPs, our first assumption was that imputation reliability is based on LD between known and unknown SNPs, and our second assumption was that MAF together with number of sequenced individuals will affect imputation reliability.rdist2) based on effective population size (Ne) and distance of an imputed SNP to its nearest SNP on the lower density marker panel :Two functions were used to model LD between two SNPs: one was based on distance  and one Ne was assumed to be equal to 100 or 1000 and for distances, it was assumed that 106 base-pairs (1 Mb) are equal to 1 centiMorgan (cM) [gan (cM) ,27. The l (dMAF) :If two SNPs differ in MAF, LD between those SNPs is expected to be low ,29.These two functions do not account for the MAF of imputed SNPs or number of reference individuals. With a low number of reference individuals, the probability that individuals carry the rare allele of a SNP with a low MAF is lower, thus increasing the number of reference individuals may increase imputation reliability of this SNP. To our knowledge, there is no theoretical function that describes the relationship between imputation reliability or LD and MAF of imputed SNPs or number of reference individuals. Therefore, an empirical function was derived by fitting a Michaelis-Menten function  on the dVmax is the estimate of the upper limit of Km is the deflection point, i.e. the estimated MAF when Vmax. The Michaelis-Menten function is often used in studies on enzyme kinetics that describe the rate of enzymatic reactions based on substrate concentration [where ntration . This fuThe three functions mentioned each explain a part of the imputation reliability. For overall imputation reliability Vmax and Km were estimated by fitting r2 from the sequence data.In the functions for 6 bp. In the current 1000 bull genomes dataset, 1737 471 SNPs (of which 5.5% were INDELs) were called on BTA1 based on a multi-breed population. Of these SNPs, 76.8% showed variation within the 114 Holstein Friesians. The BovineSNP50 and BovineHD panels contained respectively 3514 and 46 499 SNPs on BTA1, however, not all these SNPs were found in the sequence data. For the BovineSNP50 panel, 3132 SNPs (0.18% of the SNPs in the sequence data) and the BovineHD panel, 40 492 SNPs (2.33% of the SNPs in the sequence data) were found in the sequence data. Figure BTA1 is the largest bovine chromosome and contains approximately 160.10Mean accuracy of imputation per SNP was assessed by cross-validation. For imputation from the BovineSNP50 beadchip to sequence data, it ranged between 0.37 for scenario S40 and 0.46 for S80, and for imputation from the BovineHD beadchip to sequence data, it ranged between 0.77 for scenario S40 to 0.83 for S80 Table . StandarMean accuracy of imputation per individual was higher than mean accuracy per SNP. For imputation from the BovineSNP50 panel and from the BovineHD panel to sequence data, mean accuracies ranged from 0.78 for scenario S40 to 0.95 for S80, and from 0.93 for scenario S40 to 0.95 for S80, respectively Table . Reasons3 Mb there is a region in which the distance between imputed SNPs and SNPs on the BovineHD panel is large and for which imputation was difficult  was 1.01 (SE = 0.007) for scenario S40, 0.98 for S60 (SE = 0.005), and 0.95 (SE = 0.004) for S80. The maximum value of Vmax, or at the deflection point Km was equal to 0.073 (SE = 0.002) for scenario S40, 0.049 (SE = 0.001) for S60 and 0.036 (SE = 0.001) for S80.The effect of MAF of imputed SNPs on imputation reliability is shown in Figure Ne = 1000) against observed imputation reliability for the three scenarios . The estimated Figure B-allele dosages. Mean accuracy of imputation per SNP to whole-genome sequence data was equal to 0.46 with 0.18% of SNPs known (BovineSNP50), and 0.83 with 2.33% of SNPs known (BovineHD). We chose to use the correlation between observed and imputed genotypes to measure accuracy of imputation, whereas most studies used percentage of correctly imputed SNPs. Compared to correlation between observed and imputed genotypes, percentage of correctly imputed SNPs does not account for the (low) MAF of imputed SNPs. A necessary condition for correlation between two random variables is that both variables show variation. Therefore, SNPs with fixed observed genotypes or estimated B-allele dosages for one or more validation groups were removed. This might have caused a positive bias in the results, because of removal of monomorphic loci with poor imputation. In other studies e.g. [Our objective was to investigate accuracy of imputation from the lower density SNP panels to whole-genome sequence data in Holstein Friesian cattle. Accuracy of imputation was defined as the correlation between observed genotypes and the imputed ies e.g. ,13,31, cPrevious studies showed that increasing the number of close relatives between imputed and reference individuals increased imputation accuracy -11,32. TSNPs used in this study were called in a larger multi-breed population than the 114 Holstein individuals included here. Ideally, to better mimic the reality and answer the question on how many individuals need to be sequenced, the number of reference individuals used in the three scenarios should also be used for variant calling. This is important since the set of individuals used for variant calling influences the called genotypes and therefore a bias might be introduced in this study. However, we expect that the effect on the results is small, because we disregarded SNPs that did not show variation in either the reference or validation set. These are also SNPs that will not be called if only the Holstein individuals are used for variant calling. Another deviation from a real situation is that, for imputation, we assumed that the called genotypes from the sequence data were true genotypes, while it would have been more correct to use the probabilities of inferred genotypes from the sequence data as starting point for imputation. Therefore, imputation accuracies estimated in this study may differ slightly from accuracies obtained from “true genotypes”.et al.[Mean imputation accuracy per SNP from the BovineSNP50 panel to whole-genome sequence data was below 0.46. Our results showed that an alternative approach, i.e. using stepwise imputation from the BovineSNP50 to the BovineHD panel and then to sequence data, also yielded high accuracies of imputation. For example, in scenario S40, accuracy of the stepwise imputation was higher (0.65) than that of direct imputation from the BovineSNP50 beadchip to sequence data (0.37) or even than that of direct imputation from the BovineSNP50 beadchip in scenario S80 (0.46). Such a high accuracy with the stepwise approach was unexpected, because less information was available in the reference set. In the two-step approach, 20% of the individuals had genotypes similar to those of the BovineSNP50 panel , 40% had genotypes similar to those of the BovineHD panel , and 40% had sequenced genotypes . Whereas, in scenario S80, with direct imputation from the BovineSNP50 panel to sequence data, all reference individuals  had sequenced genotypes. VanRaden et al. found anIn cattle, many individuals with BovineHD genotypes are available. Using those individuals to impute BovineSNP50 genotypes to BovineHD genotypes may increase the accuracy gained in the first step, which would result in even higher accuracies when using the two-step approach than those obtained here. In some species, this is not a realistic scenario because no high-density marker panel is available yet, i.e. for pig. Developing these high-density panels and re-genotyping individuals can be expensive, especially if the end goal is to impute to sequence genotypes. In a scenario in which no high-density panel is available, it might be more cost effective to sequence additional animals and use the two-step approach by masking part of the SNPs of the individuals used for the first imputation step. This will mimic a high-density marker panel, and according to the results reported here, the overall imputation accuracy would be higher than that obtained by direct imputation from the lower density SNP chip. An improvement of this step-wise approach could be to use information of all individuals in the reference population in both steps instead of using disjoint reference sets as was done in this study, to mimic dairy cattle breeding practice. In the former case, the expected advantage is that all the genotype information will be available in the last step, while with disjoint datasets, the masked genotype information of individuals in the first step is not used in the second step. Moreover, it would be interesting to investigate the use of more than two steps because there may be an optimum number of steps to reach the highest accuracy.et al.[In genomic selection, it is important to know the imputation accuracy per individual, because there is a direct relation with the accuracy of genomic prediction  and theret al.. They aret al..B-allele dosages for one or more validation groups were removed. The remaining numbers of SNPs per scenario and per SNP chip are in Table et al.[Using whole-genome sequence data for genomic prediction and GWAS is interesting because the actual polymorphisms that cause genetic differences are potentially included in the data e.g. -3. The dle et al. showed tStill, in species with a small number of sequenced individuals, imputation of SNPs with a low MAF may remain an issue. In such a situation, it might be beneficial to use another algorithm for imputation, such as IMPUTE  or MaCH 2 measure, which predicts accuracy of imputation per SNP. Allelic R2 is the squared correlation between allele dosage of the most likely imputed genotype and allele dosage of the true imputed genotype [2 measure from Beagle and true imputation reliability that we calculated was equal to 0.79 for imputation from the BovineHD beadchip to sequence data in scenario S80 (results not shown). Of the 622 862 SNPs with estimates for both measures, 67,2% showed a difference between the allelic R2 measure from Beagle and true imputation reliability of less than 0.1, although the maximum difference between both measures was 0.78. This indicates that the allelic R2 measure provided by Beagle gives a good indication of imputation reliability in general, although in specific cases it may severely underestimate imputation reliability.Although the assumption that the polymorphisms responsible for genetic differences are included in the dataset may be true for sequence data, for imputed sequence data it is important to know if polymorphisms are imputed correctly. Beagle calculates an allelic Rgenotype  and the In human studies, imputed genotypes did not result in a high increase in power in GWAS compared to lower density marker panels ,39,40. TNe of 1000 seemed more appropriate for our data than a Ne of 100. Hayes et al. [Ne, rather than the current value of 100. Another reason for imputation reliability to decay more quickly than that expected from the decay in LD based on a Ne of 100 is that other factors also affected imputation reliability, or that the factors interacted with respect to their effect on accuracy. For example, when the SNP selected on the high-density panel and the SNP in the sequence are close, their MAF may be comparable, while as the distance between them increases the difference in MAF may also increase. Since these factors, distance and MAF, have a multiplicative effect, the decay in imputation reliability is larger than that expected from the decay in LD based on a Ne of 100. This expectation is confirmed by the resemblance between the combined functions for Ne of 100 (results not shown) and the combined functions for Ne of 1000  showed that, with more than 500 reference individuals, the increase in imputation reliability was expected to be small (results not shown). This agrees with other cattle studies that used lower density marker data and showed that, with more than 1000 reference individuals, the increase in imputation accuracy is expected to be small [Imputation reliability was also affected by the MAF of the imputed SNPs and by the number of sequenced individuals. Our results indicate that, if causal mutations have a low MAF, a large-sized reference group is required to impute those mutations correctly and to benefit from using sequence data, which confirms previous reports ,42. Extrbe small ,32.Vmax and Km based on BTA1 (results not shown). Hence within this population and dataset, the predictions hold across chromosomes, at least on average since bins of 1000 SNPs were used. However, these functions could be further improved. For example, currently the functions are based on the use of an individual SNP (the closest SNP or the SNP in highest LD of the five closest SNPs) to estimate imputation reliability, whereas a program like Beagle uses haplotypes for imputation. Moreover, instead of choosing the closest SNP, a more distant SNP might be in higher LD with the imputed SNP. Therefore, using all SNPs or haplotypes is likely to estimate imputation reliability better than the functions used here. However, taking all SNPs into account or using haplotypes will make estimation more time-consuming and less generic applicable. Further research using simulation is necessary to investigate the generality of the estimations and the obtained imputation reliability. However, our study shows that the functions described above provide a good indication of the factors that affect imputation reliability per SNP.The goal of imputation is to assemble a large group of individuals with phenotypic information and sequence genotypes for genomic prediction or GWAS. For power calculations in GWAS, imputation reliability  should be taken into account when imputed genotypes are used . Our resObviously, imputation reliability does not rely only on LD, MAF, and reference group size. Other factors, such as genotyping errors , or degrAccuracy of imputation to whole-genome sequence data was generally high for imputation from the BovineHD beadchip, but was low for imputation from the BovineSNP50 beadchip. Stepwise imputation from the BovineSNP50 to the BovineHD beadchip and to sequence data substantially improved accuracy of imputation. SNPs with a lower MAF were more difficult to impute correctly and led to more variation in reliability of imputation. Functions that estimate LD based on distance only or on the difference in MAF between the imputed SNP and the closest SNP on the lower density marker panel did not provide a good indication of imputation reliability. However, when these functions were combined with an empirical derived function that corrects for MAF of the imputed SNPs and size of the reference group, estimation of imputation reliability was greatly improved.The authors declare that they have no competing interests.RvB participated in the design of the study, performed the statistical analyses, and drafted the manuscript. MCAMB, MPLC, FAvE, and RFV participated in the design of the study and helped to draft the manuscript. BJH and IH contributed the genotype data. All authors read and approved the final manuscript.