The fidelity of DNA replication serves as the nidus for both genetic evolution and genomic instability fostering disease. Single nucleotide polymorphisms (SNPs) constitute greater than 80% of the genetic variation between individuals. A new theory regarding DNA replication fidelity has emerged in which selectivity is governed by base-pair geometry through interactions between the selected nucleotide, the complementary strand, and the polymerase active site. We hypothesize that specific nucleotide combinations in the flanking regions of SNP fragments are associated with mutation.p ≤ 0.05. Significant models (p << 0.001) were detected for each SNP type examined in the larger NCBI dataset. Importantly, the flanking region models were elongated or truncated depending on the nucleotide change. Additionally, nucleotide distributions differed significantly at motif sites relative to the type of variation observed. The MDR approach effectively discerned specific sites within the flanking regions of observed SNPs and their respective identities, supporting the collective contribution of these sites to SNP genesis.We modeled the relationship between DNA sequence and observed polymorphisms using the novel multifactor dimensionality reduction (MDR) approach. MDR was originally developed to detect synergistic interactions between multiple SNPs that are predictive of disease susceptibility. We initially assembled data from the Broad Institute as a pilot test for the hypothesis that flanking region patterns associate with mutagenesis (n = 2194). We then confirmed and expanded our inquiry with human SNPs within coding regions and their flanking sequences collected from the National Center for Biotechnology Information (NCBI) database (n = 29967) and a control set of sequences (coding region) not associated with SNP sites randomly selected from the NCBI database (n = 29967). We discovered seven flanking region pattern associations in the Broad dataset which reached a minimum significance level of The present study represents the first use of this computational methodology for modeling nonlinear patterns in molecular genetics. MDR was able to identify distinct nucleotide patterning around sites of mutations dependent upon the observed nucleotide change. We discovered one flanking region set that included five nucleotides clustered around a specific type of SNP site. Based on the strongly associated patterns identified in this study, it may become possible to scan genomic databases for such clustering of nucleotides in order to predict likely sites of future SNPs, and even the type of polymorphism most likely to occur. The fidelity of DNA replication serves as the nidus for both genetic evolution and genomic instability fostering disease. Our knowledge of these processes requires an understanding of polymerase fidelity and the means by which genes are faithfully copied, proofread, and maintained in the face of environmental factors. Single nucleotide polymorphisms (SNPs) constitute greater than 80% of the genetic variation between individuals with such alterations observed every 1000 to 2000 nucleotides when comparing two human gene sequences within the genome . A new tAccurate DNA replication is therefore governed by correct nucleotide insertion, and in the case of polymerases with proofreading ability, by the favored extension of correctly paired complementary strands. Kunkel The overall structure of DNA polymerases is comparable to a right hand with palm, finger, and thumb domains . StructuOther investigators have studied the association of flanking regions on polymerase fidelity. Zhao and Boerwinkle examined neighboring-nucleotide effects on SNP genesis in the human genome and found a bias in regard to nucleotide identity in the flanking regions Their woTo account for the possibility of nonadditive interactions among sequences, we utilize MDR methodology developed specifically for detecting nonlinear patterns of discrete attributes predictive of a discrete endpoint. The MDR method, and associated software, was originally developed to detect interactions among genetic variations in population-based studies of disease susceptibility -23. The We hypothesize that certain sequence combinations in the flanking regions of SNPs predispose toward mutation due to effects on primer strand geometry within the polymerase active site and interactions with side-chains essential for proper catalytic function, possibly altering solvation dynamics within the active site. The goal of the present study is to identify nucleotide patterns in SNP flanking regions that predispose to mutation. To accomplish this goal, we employed a novel machine learning method, multifactor dimensionality reduction (MDR), capable of identifying nonlinear patterns among discrete attributes (nucleotides) and discrete endpoints (mutation type). We found both common and unique nucleotide patterns in the flanking regions of various polymorphism types and delineated detailed associations indicative of neighboring-nucleotide effects.T, a combination of levels from two or more attributes, for example, is considered 'associated' with the class of interest if the ratio of class A to class B exceeds T; otherwise it is considered "not associated'. Once multifactor level combinations are labeled 'associated' and 'not associated' a new binary attribute is created with those two levels. Here, the classes are SNP(+) and SNP(-), with each attribute representing the nucleotide at a specific position in the flanking sequences.The goal of this approach is to identify combinations of nucleotides predictive of mutation type. Defining a new attribute as a function of two or more other attributes is referred to as constructive induction, or attribute construction, and was first described by Michalski et al. ConstruP value derived from the permutation test was ≤ 0.05. The combination of cross-validation and permutation testing helps control the type I error rate in the presence of multiple testing. This general analytical approach has been successfully used to apply MDR in dozens of population-based association studies.Once an MDR attribute is constructed it can be statistically evaluated using any classification method such as naïve Bayes, decision trees, or logistic regression. It is aX2) analysis to each of the datasets. In general, chi square testing demonstrated a robust level of significance  far greater than that found using the 1000-fold permutation testing approach. This is not surprising when one considers that 1000-fold permutation testing examines the predictive power of the complete flanking region model rather than each nucleotide position within that model separately.To further test our findings we employed chi square (Sequences were limited to single nucleotide polymorphisms (SNPs) from the human genome and were downloaded from The Human SNP Database . SNPs sequences were collected for each of the 46 human chromosomes and classified in terms of their transition or transposition error, i.e. A/C, A/G, A/T, and so forth. Each sequence was trimmed to include 10 nucleotides in both the 3' direction (positions -10 through -1) and the 5' direction (positions +1 through +10) relative to the observed polymorphism site  and SNP(-) ratio met or exceeded a threshold of T = 1, and labeled "low association" if this threshold was not met. A model for each SNP(+) and SNP(-) was formed by pooling high association cells into their prospective groups. This reduced the n-dimension model to a one dimensional model. This was performed both on balanced and unbalanced datasets where the control group far outnumbered the study set. The MDR computational suite 6.1 allows for the use of unbalanced sets allowing greater discrimination against type II errors.First, the SNP sequences of a given type  were culled from the Broad Institute data. The 20 attributes, corresponding to 10 nucleotides in the 3' and 5' direction of the mutation site, were further classified by nucleotide type   sets to the value of consistencies of the SNP(-) sets (the null groups) derived from 1,000 permutations. The null hypothesis was rejected when the upper value of the Monte Carlo P value derived from the permutation test was = 0.05. MDR computation methods have been used previously with good success in analyzing epistatic models of disease where multiple genes interact with one another in the disease model.Among all combinations of the two classes, a single model for the high risk group is constructed with the best SNP+/SNP- ratio. Single best multifactoral models are selected for each of the 2T) and applying balanced accuracy corrects for the decreased ratio of cases to controls AND "true" [Genotype]) AND (("coding nonsynonymous" [Function class] OR "intron" [Function class]) OR "coding synonymous" [Function class])) AND "sequence" [METHODCLASS]) AND "snp" [SnpClass]), was performed on November 2, 2006 using dbSNP build 126. The resulting 920,181 records were collected in FASTA format for post-query parsing using a series of in-house developed Perl scripts. The initial records were later pruned to 29,967 due to inconsistencies in the original dataset. The first 20 nucleic acids of each sequence became an unmatched control sequence, with the requirement that control strands contain no characters other than A/a, C/c, T/t, or G/g. The 10 nucleic acids immediately flanking the identified SNP site were extracted as a case sequence. Additionally, flanking regions including 20 nucleic acids in each direction were extracted, but demonstrated no pattern association differences from the 10 nucleic acid strands. Case and control sequences were collated into tab-delimited MDR input files, with sequences labeled as case (1) or control (0), according to MDR system specifications.Escherichia coli pol IV[TGG primer sequence in the binding pocket[It has been previously determined that certain replication errors are influenced by flanking regions adjacent to the mutation site. Small frameshifts of one-base deletions are made on undamaged DNA by DNA pol μ, pol λ, pol β, and li pol IV. One sucli pol IV. This meng pocket. These sWe tested the hypothesis that specific types of replication errors  would be associated within distinct flanking region patterns. Sequences were refined from both the Broad Institute database followed by the NCBI dBSNP as described in methods. The Broad dataset, although relatively small, provides directionality of nucleotide change and would serve as an ideal pilot set to test the power of MDR. The larger NCBI set could then used to confirm, expand, and refine the identified models.p = 0.02). This was consistent with results for the same dataset when positions were identified as A, G, C, or T. The Y model (C or T) showed position +2 to be significantly associated with SNP genesis of this type at the p < 0.01 level. And the K model (G or T) demonstrated positions -2 and +2 to be significantly associated with occurrence of the T/G polymorphism, p = 0.02.The Broad Institute dataset represented a small collection of sequences n = 2194) compared to the larger NCBI dataset  and was chosen as a pilot study to evaluate the application of MDR methodology to flanking region pattern associations with single nucleotide polymorphisms. Each position in the flanking region was identified by its specific nucleotide, generating four distinct models with positions identified as A, G, C, or T. When analyzing data sets with the nucleotide identity at each position, we discovered four SNP models that reached significance . Nucleotide position +1 was also found to have significant association with the S (C or G) and K (G or T) polymorphism-type models in the Broad Institute dataset. The Y (C or T) model favors nucleotide position +2 in its motif. In the larger NCBI dataset, position +2 is included in the motif only for the Y (C or T) model, but not for the W (A or T) and K (G or T) models. Also in the Broad dataset, the K (G or T) model includes nucleotide positions -2 and +2 (p < 0.02), whereas in the NCBI dataset, only the Y (C or T) and R (A or G) flanking region sets include positions -2 and +2.When we examined the Broad dataset identifying the positions in the flanking regions only by their purine or pyrimidine identity we also discovered an overlapping of flanking region sets seen previously in the NCBI dataset. In this instance, the R (A or G) model again indicated significance at position +1 . The data set was then further refined to exonic sequences   models. Importantly, this pattern grows to include additional nucleotide positions depending on the type of polymorphism observed. We have also found that for a given SNP type, the distribution of nucleotides within the flanking region shows specificity in association with certain SNP types. Comparison of the Broad Institute dataset with the NCBI dataset demonstrated an overlap in flanking region sets and provided some directionality with regard to SNP genesis. Such studies will allow for the development of more powerful and predictive algorithms offering the possibility of predicting both occurrence and direction of SNP genesis The authors declare that they have no competing interests.EA and SG devised the experimental approach and drafted the manuscript. SG collected the genomic sequences, calculated nucleotide distributions at each site, and parsed the polymorphic sequences into MDR input files. EA and BW carried out the MDR calculations, permutation testing, and Chi-square analysis. JH and JM participated in the design, coordination, and interpretation of the study. All authors read and approved the final manuscript.


For genes linked to genetic diseases, most identified causal mutations alter an encoded protein sequence. Technological advances for measuring RNA abundance suggest that a significant number of undiscovered causal mutations may alter the regulation of gene transcription. However, it remains a challenge to separate causal genetic variations from linked neutral variations. Here we present an in silico driven approach to identify possible genetic variation in regulatory sequences. The approach combines phylogenetic footprinting and transcription factor binding site prediction to identify variation in candidate cis-regulatory elements. The bioinformatics approach has been tested on a set of SNPs that are reported to have a regulatory function, as well as background SNPs. In the absence of additional information about an analyzed gene, the poor specificity of binding site prediction is prohibitive to its application. However, when additional data is available that can give guidance on which transcription factor is involved in the regulation of the gene, the in silico binding site prediction improves the selection of candidate regulatory polymorphisms for further analyses. The bioinformatics software generated for the analysis has been implemented as a Web-based application system entitled RAVEN . The RAVEN system is available at  DNA sequence variations (polymorphisms) that affect the expression levels of genes play important roles in the pathogenesis of many complex diseases. Compared with genetic variations that alter the amino acid sequences of encoded proteins, which are relatively easy to identify, sequence variants that affect the regulation of genes are difficult to pinpoint among the large amount of nonfunctional polymorphisms located in the vicinity of genes. Computational methods to distinguish functional from neutral variations could therefore prove useful to direct limited laboratory resources to sites most likely to exhibit a phenotypic effect. In this paper we present a Web-based tool for the identification of genetic variation in potential transcription factor binding sites. This tool can be used by any scientist interested in the characterization of regulatory polymorphisms. Using experimentally verified regulatory polymorphisms and background data collected from the literature, we evaluate the method's capacity to identify regulatory genetic variation, and we discuss the limitations of its application. Medical genetics research has produced a continuous stream of successes in which functional defects in genes have been linked to disease phenotypes. Such advances can lead to improved diagnostics, therapies, and therapeutics. While most discovered genetic defects create missense or nonsense substitutions in protein-coding sequences, there remain disease-associated genes for which there is no difference in protein-coding information between individuals of different phenotypes. Examples of the latter include CD36 type II deficiency , phenotyUGT1A1 gene causes Gilbert syndrome in homozygous humans [α-globin gene cluster, which has been shown to generate a new transcriptional promoter by creating a novel functional binding site for GATA-1 [α-globin genes and anemia. Other examples of regulatory genetic variations include two mutations in HNF-1 binding sites in the promoter of the alpha fetoprotein, causing hereditary persistence of the protein which is otherwise only expressed in the fetus [There are few confirmed examples of regulatory genetic variants with a pronounced impact on disease. A common TA-repeat polymorphism in the cis-regulatory TATA box of the s humans . The varr GATA-1 . The muthe fetus . Usuallyhe fetus –11. Sevehe fetus –14.Large-scale genomics studies have demonstrated that allele-specific differences in gene expression are common (reviewed in ), and thComputational methods to distinguish functional variations from neutral variations could prove useful to direct limited laboratory resources to sites most likely to exhibit a phenotypic effect. The identification of potential transcription factor binding sites (TFBSs) in DNA is fundamental to regulatory analysis. A traditional bioinformatics approach to predict TFBSs is through the application of binding site profile models known as position-specific weight matrices (PWMs) . Such maHere we present an in silico driven approach to selecting genetic variation likely to influence gene regulation. We combine phylogenetic footprinting with detection of effects of genetic variation on putative TFBSs to identify variation with the potential to alter gene regulation. We test our in silico approach on genes with documented regulatory genetic variation and compare the results to a large set of background SNPs, to evaluate the enrichment of regulatory SNPs by our selection method. We introduce RAVEN , a Web interface to our application, that enables the scientific community to apply our approach to their genes of interest.Stormo and colleagues have shown that PWM representations of TFBSs gives scores that are proportional to the binding energy between the DNA sequence and the protein   of genes are generally more conserved between species than genomic regions far away from genes. Since research on regulatory genetic variation traditionally has been focused on the proximal promoter regions, and since the background SNPs were relatively evenly distributed in the region from 10 kb upstream of the TSS to the TSS, there was an overrepresentation of SNPs within the proximal promoter in the set of rSNPs compared with the background SNPs. To evaluate if this bias affected the enrichment of rSNPs in conserved regions relative the background, we compared the distributions of phastCons scores in the two datasets for SNPs located at different distances from the TSS. We binned all SNPs located from 10 kb to 2 kb upstream of the TSS, all SNPs located from 2 kb to 500 bases upstream of the TSS, all SNPs located from 500 bases upstream of the TSS to the TSS, and all SNPs from 100 bases upstream of the TSS to the TSS. The four bins contained 8, 20, 76, and 23 rSNPs, respectively. Due to the relatively low number of available rSNPs, separation of SNPs into smaller intervals was unpractical.p-values 0.001 and 0.0002, respectively). Also in the interval closest to the TSS the phastCons score, values were higher for the regulatory than for the background SNPs, but the difference was not statistically significant. In the intervals closest to the TSS, the bias in location closer to the TSS for rSNPs is small or eliminated . This suggests that the higher fraction of rSNPs in conserved regions relative to the background is not simply an effect of the rSNPs being located closer to the TSS than the background SNPs.Although the TFBS analysis alone did not provide enrichment of rSNPs relative to the background, we tested if the intersection of TFBS analysis and phylogenetic footprinting could increase the enrichment given by phylogenetic footprinting alone. We counted the number of regulatory and background SNPs that both affected predicted TFBSs and were located in conserved regions (predicted rSNP ), for phastCons score thresholds 0.1 to 0.9 and for TFBS score delta thresholds between one and nine. We also counted the number of predicted rSNPs based on phylogenetic footprinting alone. Sometimes additional knowledge is available for the gene in which regulatory polymorphisms are searched, for example a researcher may know a set of transcription factors likely to be involved in the regulation of the gene in the context of the studied disease. When this type of information is available, it is possible to reduce the number of false positive TFBS predictions, since predictions of SNPs affecting the “wrong” transcription factor can be eliminated.We analyzed a dataset of genes in which known TFBSs have been disrupted either by naturally occurring SNPs or by mutagenesis experiments. In this set, the transcription factors involved in the regulation of the corresponding genes had been determined experimentally. We searched a region from −3,000 bp to +500 bp relative to the TSS of every gene in the dataset for potential rSNPs affecting a binding site for the transcription factor associated with the gene. The analyzed regions  contained between four and 43 SNPs in addition to the analyzed rSNP. When the regions were analyzed with all PWMs in JASPAR with information content higher than 10 bits, between two and 32  SNPs were predicted to affect a TFBS, and between zero and 11 of these were also located in conserved regions . When the regions were analyzed with the PWM corresponding to the verified TFBS, the number of SNPs affecting predicted TFBS (in addition to the rSNP) was between zero and eight, with mean value one. Between zero and two of these were also located in conserved regions . This analysis indicates that when information regarding candidate transcription factors involved in the regulation of a gene is available, specificity is increased.We also compared the score deltas obtained from analyzing the regulatory polymorphisms with known affected TFBS to the score deltas obtained when analyzing the larger data set used in http://www.cisreg.ca). The organizational schema of the Web application is shown in To facilitate efficient analyses of the type presented in this paper, computational methods and newly implemented algorithms were developed as an integrated framework for rSNP analysis. The framework includes all the components for the location and extraction of data from genome and SNP databases, pattern detection, phylogenetic footprinting, and SNP effect estimation. To facilitate user access, we developed a flexible Web application that enables researchers to easily detect potential regulatory gene variation in their gene of interest  Graphical mode, in which all the submitted data and features detected by subsequent analysis are displayed as tracks in a genome browser-like view D. GenomiTo search for potential TFBSs affected by SNPs, the user can choose a subset of eukaryotic profiles from the JASPAR database  Figure F. The alUsers can upload private sequence variation data to be analyzed for potential impact on TFBSs G. The upIdentification of functional genetic variation associated with increased susceptibility to complex diseases is crucial for elucidating the biochemical mechanisms of disease and for pinpointing therapeutic targets . Here weTranscription factor binding profiles are appropriate for the study of genetic variation in binding sites. It was shown previously that scores obtained from PWM representations of TFBSs are proportional to the binding energy for the protein–DNA interaction . This biDue to the availability of data, a focus in the study of regulatory variation has been placed on the study of SNPs. The analysis of synthetic TFBSs with inserted mutations defines the expected ranges of score deltas between alleles for various mutation types. Interestingly, the median value of the score deltas for insertion and deletion mutations falls between the median value for one-base substitutions and that for two-base substitutions. This simulation indicates that, at the median, insertions and deletions are only slightly more disruptive of TFBSs than are one base substitutions, but perhaps importantly they have the capacity to generate more severe effects—as shown by the outliers in Phylogenetic footprinting is useful for the selection of functional regulatory elements. The application of phylogenetic footprinting alone gives an enrichment of the experimentally verified regulatory variations versus the background SNPs, as shown in A priori information about candidate transcription factors improves the identification of regulatory variations. The high number of SNPs results in a significant number of false predictions from our in silico method, even when TFBS analysis is combined with phylogenetic footprinting. To generate more meaningful predictions of TFBSs affected by polymorphisms, additional information relevant for the studied gene and in the clinical context must be incorporated. We have shown that when the transcription factor involved in the regulation of a particular gene is known, the number of SNPs that affect a potential TFBS in the region from 3,000 bases upstream to 500 bases downstream of the TSS is decreased from 13 to one. When also applying a conservation threshold (phastCons score above 0.4), the numbers are three and 0.3 . This isIn silico analysis of regulatory variation has been the focus of several recent efforts. Stepanova et al. compiled a collection of potential rSNPs in the upstream region of human genes . The autWe have presented a computational method and a flexible, user-oriented Internet based tool for prediction of genetic variation with a potential to affect the regulation of the corresponding genes. In the absence of information about which transcription factor regulates a gene, our sequenced-based in silico analysis does not provide enough selectivity between polymorphisms with a documented impact on transcriptional regulation and background SNPs. However, if such prior information is available, we have demonstrated that our Web-based tool can aid experimental researchers in prioritizing which SNPs are most likely to affect the biological function of genomic regulatory elements.Synthetic SNP set. Panels of simulated binding sites for transcription factors were generated from the distributions of bases at each position of all the TFBS frequency matrices available in the JASPAR database (123 matrices) [n+2 bp long, where n is the length of the TFBS model. The insertion of extra bases before and after the motif enables the TFBS to shift one base in the mutated sequence if that would give a better match between the TFBS model and the sequence. Mutations of different types were introduced into the synthesized sequences: 1 bp substitutions, 2 bp substitutions at adjacent positions, two randomly placed 1 bp substitutions, 3 bp substitutions both in adjacent and at random positions, four randomly placed base pair substitutions, five randomly placed substitutions, one randomly placed 1 bp insertion, and one randomly placed 1 bp deletion. In case of insertion mutations, the random base after the site was removed so that the alleles are of same length and also of the same length as the other sequences of the corresponding site, i.e., n+2 bp. In case of deletion mutation, an extra random base was introduced in the end of the deleted allele so that both alleles were of length n+2 bp. Ten synthetic TFBS sequences were generated from each TFBS model (123 models), and ten mutants of each type were created for each generated wild-type sequence. This makes a total of 123 × 10 × 9 × 10 sequences = 98,400 sequences.atrices) . A randoRegulatory SNP set. We searched the literature for variations with a documented regulatory role. All single base variations will be referred to as SNPs, although the frequency of the variations may be rare or unknown. The criteria for selecting SNPs to this set was that they should show allele-specific binding to nuclear extracts or transcription factors in vitro using electrophoretic mobility shift assays, and that the SNPs should show evidence of allele-specific transcription rates in reporter gene assays. We also used SNPs from previous collections of regulatory polymorphisms in human: 22 SNPs were taken from a collection created by Rockman and Wray [and Wray , 17 wereand Wray , and 39 and Wray . We did and Wray ). In totRegulatory SNPs with known affected transcription factor binding site. To generate a collection of regulatory polymorphisms for which the involved transcription factor has been experimentally determined, we mined the literature for examples of human TFBSs containing regulatory variation, either naturally occurring or generated in mutagenesis experiments. We applied the same inclusion criteria as for the rSNP set above, but only polymorphisms affecting binding sites for transcription factors for which there are PWMs available in JASPAR were included. The sequences for the regulatory variations with known affected TFBSs are provided in Background SNP sets. For background SNPs we downloaded from dbSNP those SNPs located in the upstream regions of human genes with a known mouse ortholog. We included SNPs in the regions stretching from −10 kb to the TSS (ortholog mappings and definitions of TSSs were determined as in [ed as in ), unlessSaturation mutagenesis data used in  Data was obtained from a saturation mutagenesis experiment of the heat shock element of the human HSP70.1 gene promoter [promoter . The autpromoter . The autAnalysis of synthetic mutated TFBS sequences. The mutated and wild-type versions of the synthetic TFBS sequences were scored with the PWM used to generate a particular wild-type sequence, using perl scripts based on the TFBS framework [ramework . The besramework .TFBS analysis of documented regulatory SNPs and the background SNPs. PWMs corresponding to all TFBSs in the JASPAR data base were used to score both alleles of the SNPs in the background and rSNP datasets. Scoring was performed as above, but with two modifications. (1) We required a predicted binding site to be located at the same position in the sequence for both alleles of a SNP. This is the same methodology as is used in the Web interface to our application. (2) If several matches were obtained between a SNP and a particular TFBS model , we collected the hit that gave the largest score delta between the sequence variants since we wanted to calculate the fraction of retained SNPs for different TFBS score deltas. Sometimes a SNP overlapped potential binding sites for more than one TFBS model. To calculate the fraction of retained SNPs for various TFBS score delta thresholds we collected, for each SNP, the maximum score delta over all TFBS models . We measured the fraction of retained SNPs in respective datasets for the absolute TFBS score delta thresholds in one-unit increments from one to nine.Phylogenetic footprinting analysis of regulatory SNPs and the background SNPs. We used the phastCons scores from multiple alignments of eight vertebrate genomes available in the UCSC genome browser (the May 2004 release) to perform phylogenetic footprinting. SNPs were assigned the mean phastCons score in a window of 21 bases (ten bases upstream and downstream of the SNP). Several other window sizes were tested, from one to 41 bp, but the 21 bp window gave the best enrichment of rSNPs.Analysis of regulatory SNPs with known affected TFBS. The examples of regulatory variation from the literature for which the affected TFBS are known were analyzed using the Web interface to our application . The following parameters were used for the analysis: the phastCons threshold for sequence conservation between species was 0.4. The threshold for a match between the top-scoring allele of a variation and a TFBS model was 80%. The absolute score difference between the two alleles of a variation was required to be greater than 1.5 [than 1.5 . The TFBCorrelation analysis of relative free energy of mutated binding sites versus TFBS score delta for  The mutated and wild-type sequences in the saturation mutagenesis experiments were scored with the PWMs corresponding to position frequency matrices for the respective transcription factors [ factors . A scoreThe position frequency matrix for the heat shock element was taken from Transfac . The maty matrix . The matThe frequency matrices were transformed into weight matrixes using the TFBS bioinformatics programming framework . FunctioThe application is implemented in object-oriented Perl  with extensive use of Bioperl  and TFBSData. The dbSNP [ftp://ftp.ncbi.nlm.nih.gov/). Genomic sequence data with the positions of repeat sequences, mappings of cDNAs and dbSNP SNPs to the genome, as well as phastCons scores, were obtained from the UCSC Genome Informatics repository (http://genome.ucsc.edu/) [http://www.hgvbase.org/) [he dbSNP  sequencesc.edu/) . HGVbasese.org/)  data wasFigure S1HSP70.1 gene [2 = 0.73, p-value = 2.2e−16 for HSE, and r2 = 0.61, p-value = 7.571e−07 for Mnt).Two published saturation mutagenesis experiments demonstrate, as previously shown by Stormo and colleagues , how PWM0.1 gene  and in t0.1 gene  were sys(296 KB PDF).Click here for additional data file.Dataset S1(18 KB TDS).Click here for additional data file.Dataset S2(30 KB DOC).Click here for additional data file.Dataset S3(3.8 MB TDS).Click here for additional data file.Dataset S4(5.8 MB TDS).Click here for additional data file.Dataset S5(5.95 KB TDS).Click here for additional data file.


Single nucleotide polymorphisms (SNPs) are ideal genetic markers due to their high abundance and the highly automated way in which SNPs are detected and SNP assays are performed. The number of SNPs identified in the pig thus far is still limited.A total of 4.8 million whole genome shotgun sequences obtained from the NCBI trace-repository with center name "SDJVP", and project name "Sino-Danish Pig Genome Project" were analysed for the presence of SNPs. Available BAC and BAC-end sequences and their naming and mapping information, all obtained from SangerInstitute FTP site, served as a rough assembly of a reference genome. In 1.2 Gb of pig genome sequence, we identified 98,151 SNPs in which one of the sequences in the alignment represented the polymorphism and 6,374 SNPs in which two sequences represent an identical polymorphism. To benchmark the SNP identification method, 163 SNPs, in which the polymorphism was represented twice in the sequence alignment, were selected and tested on a panel of three purebred boar lines and wild boar. Of these 163 in silico identified SNPs, 134 were shown to be polymorphic in our animal panel.This SNP identification method, which mines for SNPs in publicly available porcine shotgun sequences repositories, provides thousands of high quality SNPs. Benchmarking in an animal panel showed that more than 80% of the predicted SNPs represented true genetic variation. Single nucleotide polymorphisms (SNPs), one of the most abundant types of sequence polymorphisms in the genome, are the most suitable markers for genetic linkage mapping, fine-mapping and haplotype reconstruction. Over the past decade, SNPs have been the marker of choice due to their high stability, density and the highly automated way in which SNPs are detected and SNP assays are performed. However only a limited number of SNPs have been identified in the pig, a species of considerable economical and medical importance. A few thousand SNPs in the pig are currently available, and these were mainly identified in expressed genes by either in vitro techniques  or by miHere we describe a high throughput genome sequence mining pipeline from data of the ongoing pig genome sequencing project. With this approach, we performed a SNP mining analysis on the whole genome shotgun dataset generated by the Danish-Chinese Pig Genome Sequencing Initiative  that is The encouraging outcome of this study is a good starting point for the development of a rapidly growing genome-wide set of SNP markers in the pig.At completion of this analysis, the number of finished and contigs of unfinished porcine BAC sequences was 318 and 84,017, resulting in 50,225,986 and 1,164,409,065 total nucleotides, respectively. The NCBI Trace repository contained 4,774,371 whole genome shotgun sequences for center SDJVP, with a total of 3,478,199,073 nucleotides.Because the analysis of the complete data set for the whole genome was computationally too demanding, the identification of SNPs was performed by a 2-step process. First, the shotgun sequences were assigned to a fingerprint contig by clustering based on their sequence similarity to BAC and BAC-end sequences. The results of the clustering by alignment were stored in a relational database. The BAC and the BAC-end naming as well as the mapping data provided the necessary information to assign the obtained sequence clusters to a specific fingerprint contig on the porcine physical BAC map. Clustering of the shotgun reads with BAC or BAC-end sequences is outlined in Figure In the second step, the actual identification of SNPs was performed per fingerprint contig. In this respect, a fingerprint contig can be considered a 'genomic region of interest', which is the starting point in traditional SNP mining in species for which a genome draft is available. Per fingerprint contig, the relational database was queried for shotgun reads, in which repetitive sequences were tagged, and were aligned using PhredPhrap -23. FinaAt completion of this analysis Dec 2007), the sequencing of the pig genome was ongoing and most assembled BAC contig sequences were available for chromosomes 1, 4, 7, and 14 The number of SNPs as a percentage of total number of identified SNPs per analysed chromosome is provided in Table 007, the To evaluate if the SNPs distribute equally throughout the pig chromosomes the exact locations of unique SNPs predicted on chromosome 1,4,7,14 were determined by alignment. A total of 1783 SNPs that mapped uniquely were plotted along these chromosomes as shown in Figure The SNPs in the subsets of candidate SNPs in which identical nucleotide substitution is represented in one, two, three or four sequences in the alignment were categorized according to nucleotide substitutions: C/T or G/A (transitions) and C/G, A/G, C/A, T/G (transversions). For each category, we calculated the relative nucleotide substitution frequencies for our SNP dataset and for the genomic porcine SNPs recorded in dbSNP  Table 33. For thTo estimate whether SNPs predicted by our method are already present in the public database of dbSNP , we compTo balance the sequence context and the number of times a polymorphism is represented in the sequence alignment, SNPs in which a nucleotide substitution was represented at least twice in the sequence alignment were chosen for experimental validation. A total of 163 selected candidate SNPs were validated by genotyping in a panel of three purebred boar lines (+ wild boar). A total of 61,777 genotype analyses were performed providing, in addition to SNP prediction validation, insights into allele frequencies that will be valuable information for association mapping and QTL studies. To measure the performance of our analyses, validated SNPs were included that previously had been used within the European Union (EU) pig biodiversity project II (Pig-BioDiv II)  as well For all 331 SNPs, the allelic variation was determined in our animal panel. In 29 cases, the predicted candidate SNPs turned out to be monomorphic. Smaller fractions of SNPs are observed to be monomorphic in the PigBiodiv and Various Literature SNP sets. The SNPs described by Rohrer et al  and the For each predicted candidate SNP that appeared to be polymorphic in our panel, minor allele frequencies per boar line and overall average minor allele frequencies were calculated [see Additional file Because of their highly automated high-throughput assays, SNPs are the marker of choice for molecular genetic analysis. SNPs can be obtained cost effectively by analysing public sequence data sets -28. WhenAlthough sequence quality scores and a redundancy-based approach were used to filter sequencing errors from true nucleotide polymorphisms, a non-random distribution of polymorphisms might occur in a particular dataset. These artefacts become visible when SNP statistics are compared to other SNP collections in the same species and are comparable to those found in related species. When compared to porcine SNPs deposited in dbSNP , our preA comparison of our collection of predicted candidate SNPs to the porcine SNPs in dbSNP  revealedNot all predicted candidate SNPs turned out to be polymorphic in the animal panel. This doesn't implicitly mean that this 0.18 fraction Table  includesWithin our breed panel, we observed very low (<5%) Minor Allele Frequencies (MAF) in predicted candidate SNPs [see Additional file The overall performance of the SNPs identified by our genome shotgun sequence mining approach is comparable to those available in existing SNP repositories. In perspective of the ongoing sequencing of the pig genome, the SNP data generated by this approach will provide a growing number of available markers that can be applied for genotyping and will increase the SNP marker density on the pig genome..The entire genome shotgun sequences used in this study were downloaded from the NCBI Trace repository . For all sequences, breed and mate pair information was obtained and stored in a relational database. Finished and unfinished BAC sequences obtained within the porcine genome sequencing project were retrieved from the SangerInstitute FTP site at .BAC-end sequences were downloaded from the Ensembl  FTP siteBAC naming and mapping data were obtained from , respectively. Naming and mapping data were stored in a local relational database.and Whole genome shotgun sequences were masked for mammalian-specific repeats and low complexity regions using RepeatMasker version open-3.1.7  with optClustering of data was performed by aligning the whole genome shotgun reads to the BAC sequences and BAC-end sequences using MegaBlast 2.2.16 .Shotgun reads were aligned to BAC sequences using the alignment parameters -U T -s 122 -p95 -F m. Results were filtered for alignments with more than 90% of the shotgun sequence length. To reduce the amount of ambiguous results in the clustering, only alignment results with a bitscore >90% of the best scoring alignment for that shotgun sequence were stored in a relational database.Clustering of shotgun sequences by alignment to BAC-end sequences was followed by assembling each cluster using CAP3 . MegaBlaUsing the BAC and BAC-end naming and mapping information, we were able to query our clustering results by fingerprint contig name as used in the porcine physical map provided by the Sanger Institute. Per fingerprint contig the shotgun sequences that clustered to this region were selected and mate pairs were added using the mate pair information in the NCBI Trace repository.Multiple sequence alignments of the selected shotgun sequences and their mate pairs were generated by the sequence assembly script PhredPhrap -23. ShotFor identification of SNPs in the multiple sequence alignments of the shotgun sequences, we used PolyPhred  version Polyphred results were parsed into tables, information from which breed a sequence was derived and whether the SNP is located within a suspected repetitive sequence was added.To remove any redundancy in our SNP predictions, the results (SNP position flanked by 50 bp genomic sequence) were first stored in FASTA format. The actual clustering was performed using blastclust  with parUnique SNPs flanked by 50 bp genomic sequence predicted on chromosome 1,4,7,14 were mapped on the corresponding chromosomal sequence as provided by pre-ensemble . The ali® BeadStation with veraCode(TM) technology. Oligonucleotides were designed, synthesized and assembled into oligo pooled assays (OPA) by Illumina Inc. Typing was carried out in a multiplex reaction, which included 384 loci.For SNP validation, 163 SNPs were selected, with regions covered by at least 4 reads and with a minimum SNP redundancy score of 2. These SNPs were subsequently genotyped in an animal panel consisting of three purebred boar lines that originated from (1) Duroc and Belgian Landrace, (2) Large White, (3) German Pietrain and (4) Wild Boar. The four lines included 129, 120, 109 and 21 individuals, respectively. Genotyping was performed using the Illumina GoldenGate(R) Genotyping assay on an IlluminaThe SNPs identified in this study, in which the polymorphism was represented twice in the sequence alignment, have been deposited in the National Center of Biotechnology (NCBI) SNP database (dbSNP) under submitter handle WU_ABGC. NCBI_ss 106817370–106823609 represent predicted SNPs that were not tested on in our animal panel. Predicted SNPs that were confirmed are listed in [see Additional file HHDK designed and developed the SNP prediction method and wrote the manuscript. AK and MdR designed and implemented the relational database. BD, SMK, RPC and SK collected and prepared the samples and performed the genotyping analysis. SK summarized the genotyping results. MAMG coordinated and supervised the experiment implementation, and assisted in the manuscript preparation. All authors read and approved the final manuscript.Supplementary information. References to published SNPs used in this study.Click here for fileValidation of candidate SNPs by genotyping in a panel of three purebred boarlines and wildboar. Listed are the 134 out of 163 predicted SNPs that show to be polymorphic in our animal panel. For all SNPs minor allele frequencies (MAF) were calculated in each purebred boarline and wildbore. From the obtained MAFs the average minor allele frequency was calculated.Click here for file


The success of genome-wide association (GWA) studies for the detection of sequence variation affecting complex traits in human has spurred interest in the use of large-scale high-density single nucleotide polymorphism (SNP) genotyping for the identification of quantitative trait loci (QTL) and for marker-assisted selection in model and agricultural species. A cost-effective and efficient approach for the development of a custom genotyping assay interrogating 54,001 SNP loci to support GWA applications in cattle is described. A novel algorithm for achieving a compressed inter-marker interval distribution proved remarkably successful, with median interval of 37 kb and maximum predicted gap of <350 kb. The assay was tested on a panel of 576 animals from 21 cattle breeds and six outgroup species and revealed that from 39,765 to 46,492 SNP are polymorphic within individual breeds  ranging from 0.24 to 0.27). The assay also identified 79 putative copy number variants in cattle. Utility for GWA was demonstrated by localizing known variation for coat color and the presence/absence of horns to their correct genomic locations. The combination of SNP selection and the novel spacing algorithm allows an efficient approach for the development of high-density genotyping platforms in species having full or even moderate quality draft sequence. Aspects of the approach can be exploited in species which lack an available genome sequence. The BovineSNP50 assay described here is commercially available from Illumina and provides a robust platform for mapping disease genes and QTL in cattle. Moreover, large phenotypic datasets are routinely collected to assist beef and dairy production management and animal breeding decisions. These data represent a vast and relatively untapped resource to assist the investigation of the complex interplay of genetics and the environment, and the consequences of long-term selection for specific traits. Cattle have been a valuable model for studying mammalian physiology and comparative genomics, and represent the most thoroughly studied ruminant. Recently, a complete draft genome sequence, based on the DNA of a partially inbred individual from the Hereford breed and her sire, has been published (ftp://ftp.hgsc.bcm.tmc.edu/pub/data/Btaurus/). This draft sequence is an important tool for comparative and evolutionary studies. However, the full potential of genomic research in cattle cannot be realized without the ability to efficiently scan genome-wide for variation affecting the wide range of phenotypes recorded for this species.Cattle play a unique role in biology, serving as models for basic studies of metabolism, reproduction, and disease, as well as providing critical sources of human dietary protein and economic security  studies performed in human and has resulted in the identification of disease risk genes for complex polygenic traits 2, which is the squared correlation between alleles present at two loci, typically on the same chromosome. Thus, what varies among species and certain populations within a species is the average length of genomic segments for which the r2 between terminal loci achieves a predetermined value, say r2 = 0.6. Within such blocks, one or a few markers  may act as proxies for predicting all, or most, of the haplotypes which are present within the entire block. It has been estimated that the average length of LD blocks is longer for cattle than for humans, due to significant population bottlenecks that occurred during domestication and the establishment of modern cattle breeds 2 = 0.25, and this block size is approximately three times greater than is found among human populations at the same value of r2. Thus, a target marker density for a cattle genotyping array of greater than one marker per 100 kb would position all quantitative trait loci, on average, within 50 kb of a marker for which the expected r2 = 0.3 Bos taurus, and at this density, a set of approximately 30,000 variable markers would be sufficient to represent the 3 billion bases of sequence in the bovine genome 2≥0.3. Since not all SNP appearing in an assay will be variable in all populations, this target number should be increased to achieve 30,000 variable SNP in each cattle population in which GWA studies are proposed. For comparison, only 80% of the tagSNP on the Infinium® HumanHap550 BeadChip  are polymorphic in the African Yoruba population In cattle, GWA can be used to localize the genomic regions that contribute to natural genetic variation in any phenotypic trait. The identified target regions can then be fine-mapped at higher marker density to allow the efficient identification of candidate genes. The density of markers required for efficient GWA scans in a species is dependent on the average length of chromosome “blocks” possessing a high level of linkage disequilibrium (LD). The extent of LD between two loci is usually measured with statistics such as rThe aim of this study was to develop and characterize a high-density, genome-wide SNP assay for cattle with the power to detect genomic segments harboring inter-individual DNA sequence variation affecting phenotypic traits and for application to GWS, in which an animal's genetic merit is estimated solely from its multilocus genotype L1 Dominette 01449, the cow used to produce the sequence assembly. Because these 2.1 million SNP were derived from the assembly of sequence data primarily from this animal, she was expected to be heterozygous if the SNP was real. However, we found a low (<50%) conversion rate to polymorphic assays. Through the application of additional quality filters , and a set of highly selected, and curated SNP designed to be polymorphic and informative across a broad array of cattle breeds that have been proposed as parentage verification and traceablility SNP Utility of the SNP set was examined by characterizing SNP allele frequencies among the multi-breed panel, revealing that 51,383 or nearly 95% of the 54,001 called SNP were polymorphic among the 558 cattle , with anThe average MAF when determined across all breeds for the parentage markers was 0.31, considerably higher than the rest of the markers in the assay. The RRL SNP produced a higher conversion rate than did any of the other SNP sources which lacked MAF estimates, although with a substantially lower average MAF than when these markers were validated by genotyping the SNP discovery animals Average MAF was homogeneous among the taurine breeds , and with MAF condensed into 0.1 intervals , illustrFrom the experimental validation of samples of SNP from various sources by sequencing and genotyping see , we demoA – waves 1, 4, 5 and 6 – High MAFB – waves 2 and 7 – Low MAFC – waves 3, 8, 9 and 10 - No MAF availableD – waves 11, 12, 13 and 14 - Draft SNPThere were 121 parentage SNP available for design, of which, 118 SNP passed the design criteria and 116 were among the 54,001 markers that performed sufficiently reliably for genotyping on the BovineSNP50. Two of the 116 assays were incorrectly targeted to adjacent non-parentage, thus leaving 114 true parentage SNPs on the chip. No genotype inconsistencies were observed among the parent-child pairs and parent-parent-child trios for these markers and all had consistently high call rates. The concordance of genotype calls for the 116 parentage markers genotyped using the BovineSNP50 assay and also obtained from Sanger sequencing reads was evaluated in 8 animals. In 912 possible comparisons, the call rates were 99.6% and 97.1%, respectively, for the BovineSNP50 and Sanger sequencing genotype calls. Discordance between the two assays was identified in 8 of 882 possible comparisons, and thus the error rate was less than 1% (99.1% concordance). The MAF for these markers in taurine breeds ranged from 0.31–0.39. However, these markers had an average MAF of 0.20 within the indicine and African breeds. Plotting MAF within breed type, with MAF condensed into intervals of 0.1 , illustrBovinae, including two from different genera, to determine the ancestral allele at each locus. Despite the divergence (1–5 Mya) between these species and Bos taurus, over 96% of the SNP produced genotype calls for at least five of the species, including the four species within the genus Bos and the Lowland Anoa  Syncerus caffer), which is estimated to be diverged from Bos taurus by 9 Mya, had lower overall genotyping success, but still provided scored genotypes for over 86% of the SNP. Surprisingly, between 1 and 5 percent of the SNP that produced genotype calls were detected as polymorphic in these outgroup species, despite the fact that only 2 or 4 individuals were genotyped within each outgroup species. A small proportion of these variable loci were also common between outgroup species. This result suggests that the origin of some of the SNP chosen for the array predates the divergence of these species. The ancestral allele determined from the analysis of the outgroup samples is provided in The BovineSNP50 assay was applied to a set of DNA samples from six other species within The Illumina genotyping software module for detecting copy number variations (CNV) (cnvPartition v1.0.2) was used to analyze the genotyped samples. The resulting CNV were compared to arrayCGH  results produced from the DNA samples of eight genotyped animals . The BeadStudio algorithm performed well for the detection of homozygous deletions which were validated by multiple observations within and across breeds . FinallyWe discovered 79 CNV loci from the application of the BovineSNP50 assay to these diverse cattle breeds. Of these, 10 CNV loci overlapped with loci detected from arrayCGH data and these were readily verifiable since the same animals were analyzed using both platforms. The arrayCGH results in turn have been experimentally validated by Q-PCR (unpublished results). However, the two datasets were not completely concordant and had several non-overlapping CNV loci, probably because the arrayCGH experiment has higher resolution due to the availability of more probes  but was performed in fewer breeds. On the other hand, the BovineSNP50 assay was applied to a more diverse set of breeds. More CNV loci were predicted in the African, composite, and indicine breeds than in the taurine breeds, which is consistent with the subspecies divergence and the bioinformatic analyses performed to filter .loci associated with segmental duplications within taurine breeds in the SNP discovery process. However, it is interesting that several of the predicted CNV loci fall within gene boundaries and these loci may play an important role in determining the subspecies divergence. For example, on chromosome 5 (BTA5), CNV loci were detected within olfactory receptor (OR) genes, on BTA12 within two immune response genes UL16 binding protein 2 and ATP-binding cassette, sub-family C (CFTR/MRP), member 4 and also within the two major histocompatibility (MHC) gene regions on BTA23.MC1RMC1R and no other SNP had a p-value<1e-12. We next analyzed the genotype distribution at ARS-BFGL-NGS-31386 between the two breed groups. The 61 Angus were homozygous AA and the Red Angus had 8 AG and 7 GG animals. Although the black allele is dominant, black-red heterozygote animals often have identifiable red in their coat color, thus the SNP data support the history of strong selection within Angus cattle against red coat color which has driven the black allele to near fixation within the breed. The high frequency of the AG heterozygote within Red Angus indicates recombination between the SNP and alleles at the MC1R locus, suggesting that the red allele is ancestral.The BovineSNP50 assay was applied to determine its utility in mapping two Mendelian phenotypes in cattle, black/red coat color and the presence or absence of horns, to identify the chromosomal locations of the loci underlying these traits. The black/red color coat gene POLL locus is a negative regulator of the horned phenotype. Animals that are homozygous for the ancestral allele are horned while those that carry at least one copy of the derived allele do not have horns and are defined as being polled. Despite considerable research, the mutation responsible for the polled phenotype has yet to be identified. Horns segregate within both the Limousin and Hereford breeds and we utilized phenotypes on genotyped animals, their parents, mates and progeny provided by the North American Limousin Foundation and the American Hereford Association to establish the genotypes at the POLL locus for 21 genotyped Limousin and 21 genotyped Hereford animals. Sufficient progeny were produced in matings to horned animals in all cases to differentiate between heterozygous and homozygous polled animals. Among the Limousin, 3 animals were horned, 12 were established as being heterozygotes and 6 as homozygous polled. Among the Herefords, 11 were horned, 8 were heterozygous and 2 were homozygous polled. To avoid breed stratification effects, we independently analyzed each breed by estimating the squared correlation (r2) between alleles at each SNP and the POLL locus using maximum likelihood. A combined likelihood ratio statistic was constructed for each SNP by summing the log-likelihoods for each breed, and 10(p) values computed for the across-breed likelihood ratio test of Ho: r2 = 0. POLL locus In cattle, the Bovinae and has desirable properties of whole genome coverage and minimal variation in SNP spacing. All of the markers were obtained from public sources, so application of the assay should not be constrained by intellectual property considerations. The panel and platform provides robust, reproducible and sequence validated genotype calls across a wide range of breeds. The high average MAF suggests that the assay will be useful for implementing GWA and GWS to identify QTL regions and improve production traits in both beef and dairy cattle. By including parentage markers in the assay and validating their utility across several breeds of cattle we can expect to see their widespread use, replacing the current suite of microsatellite markers used for correcting pedigree errors.The BovineSNP50 assay represents a valuable resource for genetic research in The design and construction of a high quality multiplexed SNP assay requires large numbers of confirmed SNP segregating with high MAF in the target populations. The publicly available SNP data primarily came from the bovine genome sequencing efforts at the Baylor College of Medicine (BCM) and is comprised of two sets: the first set included 114,958 SNP discovered by comparing random shotgun reads from individuals from six diverse breeds to the Hereford genome assembly. These SNP represent only 56,634 unique DNA sequence reads. Slightly more than half  of these reads contain a single SNP, while the remaining 24,953 reads contain as many as 17 putative SNP, with a putative SNP every 94 bp, on average, for reads containing multiple SNP. The conversion rate for these Interbreed SNP was estimated to be about 80% from the public Bovine HapMap project efforts and from our validations using MassARRAY®  assays. The second source of putative SNP included ∼2.1 million markers derived from the observed heterozygous positions within the cattle genome sequence assembly (Draft SNP). Because the sequenced animal had an inbreeding coefficient of approximately 30%, much of the genome was identical by descent leading to large tracts on many chromosomes where few SNP were identified. Further, from an experimental validation of a random sample of 151 SNP derived from this source we observed that the conversion rate was only 41%  from Holstein BAC  and BAC-ends to the Hereford genome assembly.Applying high stringency filters to the ∼2.1 million Draft SNP to improve their conversion rate.Using a novel reduced representation library (RRL) next-generation sequencing strategy to discover large numbers of genome-wide SNP with high MAF We retrieved all sequence traces for the bovine BAC-end and BAC sequences available at the NCBI trace archive as of September 2006 from the RPCI-42 library, which was derived from a juvenile Holstein bull Removed very low quality sequences based on Phred scores.Masked vector sequences.http://www.repeatmasker.org).Masked repeat sequences using RepeatMasker (ftp://ftp.hgsc.bcm.tmc.edu/pub/data/Btaurus/) after requiring unique mapping with high significance, i.e., a minimum alignment length of 500 bases with no gaps of more than 2 bp.Aligned the sequence reads to the Btau2.0 assembly .A total of 89,832 unique putative SNP were identified from this pipeline and a sample of 48 SNP were randomly selected and tested by designing primers and sequencing a panel of 96 individuals on an ABI 3730xl sequencer. The conversion rate for these SNP was 82%. All BAC-derived Based on the analysis of the results from the Draft SNP validation .Application of these filters reduced the total number of available SNP from 2.1 million to 278,429. A random sample from this filtered SNP set was again tested by designing 48 primer sets and sequencing 16 animals on the ABI3730 platform. The conversion rate among these filtered SNP increased to 85%, meeting our requirement for their inclusion in the design of the assay. Using these simple filtering strategies, this SNP source, while greatly reduced in number, was transformed from very low potential to the source with the second highest conversion rate.HaeIII, isolated fragments in the 70–130 bp size range, and sequenced these using an Illumina Genome Analyzer to a depth of about 22-fold coverage across the libraries. From the sequence alignments we detected more than 62,000 SNP ftp://ftp.hgsc.bcm.tmc.edu/pub/data/Btaurus/).To obtain highly informative SNP with genome-wide coverage, we constructed three reduced representation libraries http://cgemm.louisville.edu/USDA/index.html.A panel of 121 SNP had previously been identified to accomplish both DNA based traceback and parentage testing in U.S. and Canadian beef and dairy populations . These markers were developed by screening >1000 candidate SNP for high MAF, wide genome spacing, and flanking sequence features that were amenable to assay design. Features that were avoided included large blocks of repetitive elements, high-melting temperature (>75°C) stem-loops near the target SNP, high-frequency indel polymorphisms and adjacent SNP near the target SNP. To identify these flanking sequence features and eliminate SNP likely to be problematic for genotyping, the region surrounding the target SNP was sequenced in a diverse beef and dairy cattle breed panel of 192 individuals. These high-quality SNP were designated for inclusion in the assay design for use in parentage validation. Their details are available at http://www.illumina.com/downloads/Illumina_Assay_Design_Tool.pdf). Potential adjacent SNP within 11 bases were identified by their Btau3.1 assembly coordinates and were indicated by IUPAC codes in the sequence submitted for design. We curated potential designs for final submission to manufacturing to maximize the number of SNP present in the design. In addition, SNP submitted to synthesis were screened with an emphasis on design scores. Final SNP selected for the BeadChip design included approximately 4% Infinium I assays with an average design score of 0.92 while the average design score for the Infinium II assays was 0.95.Sequences flanking putative SNP were evaluated for design using Illumina's Assay Design Tool , which sequentially considers the selection of SNP for each genomic interval. While greedy algorithm approaches may not always converge to global optima, they tend to be highly efficient and our empirical data suggest that its application has yielded satisfactory results  plots were generated using R.To determine the ability of the BovineSNP50 assay to map traits, we performed two different analyses that are best suited for the trait segregation patterns. To map the black/red color coat gene, we performed a case-control analysis between the Black and Red Angus breeds using the ASSOC option within PLINK 2 between each SNP and the POLL locus within each breed and finally constructed a statistic that allowed the pooling of breeds without allowing any stratification issues.For mapping the horn/poll loci we applied more sophisticated statistical analysis using custom developed software. First we determined genotypes at the horn/poll locus for each of the genotyped animals using mating information and mate/progeny phenotypes and then we performed a pseudo-linkage analysis in which we estimated rPOLL genotypes of 10 of the 14 polled Hereford and all 18 of the polled Limousin animals. Polled animals that had been produced from a mating of horned and polled parents were obligate carriers, as were animals that produced horned progeny from matings to horned animals. Two Hereford and six Limousin animals had produced no horned progeny in at least 12 matings to horned animals and were classified as homozygous for the polled allele with a level of confidence of at least 99.98%.We obtained phenotype data for the animals, their parents, all progeny and mates that produced these progeny for the 25 Hereford and 21 Limousin animals that were registered with the American Hereford Association or North American Limousin Foundation, respectively. From this information we were able to infer the 2 was sequentially estimated between genotypes for each SNP and genotypes at the POLL locus by maximum likelihood and the null hypothesis Ho: r2 = 0 versus Ha: r2>0 was tested using a likelihood ratio test. This analysis does not require that the chromosomes be phased to estimate r2, since the haplotype frequencies are estimated as one of the parameters within the model POLL locus using the usual estimators that are unaffected by the extent of linkage disequilibrium between the loci and then estimated the haplotype frequencies by performing a grid search on the remaining unknown parameter . Finally, because the data for the Hereford and Limousin animals defined independent samples, we pooled the likelihood ratio statistics for each tested SNP to produce a whole genome scan for the POLL locus in which the SNP and POLL locus allele and haplotype frequencies  were allowed to vary between the breeds, but a single statistic was computed for evidence of LD between the SNP and POLL locus across breeds.We performed case-control association analyses in which SNP allele and genotype frequencies were contrasted between the horned and polled animals within each breed (data not shown) and also a more direct linkage disequilibrium mapping approach in which rTable S1Definitions of waves for SNP used to design the BovineSNP50 assay. <br>(0.04 MB DOC)Click here for additional data file.Table S2Detailed SNP Selection and Performance Results. This table shows the total numbers of SNP available for the assay and the performance of each individual groups of SNPs within the various major cattle sub-species Bos indicus, Bos taurus and outgroup animals(0.10 MB XLS)Click here for additional data file.Table S3Numbers of species, breeds, animals and trios genotyped to characterize the BovineSNP50 assay.(0.08 MB DOC)Click here for additional data file.Table S4Numbers markers producing discordant genotypes with Parent-Child pairs and Parent-Parent-Child trios.(0.04 MB DOC)Click here for additional data file.Table S5The derived ancestral allele along with minor allele frequencies of individual markers within each of the cattle breeds and outgroups.(19.24 MB XLS)Click here for additional data file.Table S6Locations and nature of copy number variation loci detected with the BovineSNP50 assay.(0.06 MB XLS)Click here for additional data file.Figure S1Comparison of minor allele frequency by SNP source for Holstein (blue) and Brahman (red). Panel A) RRL SNNP, B) HapMap SNP, C) BAC-derived SNP, and D) Draft assembly derived SNP.(0.04 MB DOC)Click here for additional data file.Figure S2Distribution of BovineSNP50 SNP by MAF for taurine, indicine, composite and African breeds of cattle by SNP source. Panels A–C) SNP sources see  with pri(0.08 MB DOC)Click here for additional data file.Figure S3The minor allele frequency distribution for parentage SNP within representative classes of cattle composite (Beefmaster), beef (Angus), dairy (Holstein) and indicine (Gir) shows that the markers are highly informative among the common taurine beef and dairy breeds and in composites but are least informative within the indicine breed.(0.02 MB DOC)Click here for additional data file.Methods S1Supplementary Methods(0.03 MB DOC)Click here for additional data file.


In genomic selection, a model for prediction of genome-wide breeding value (GBV) is constructed by estimating a large number of SNP effects that are included in a model. Two Bayesian methods based on MCMC algorithm, Bayesian shrinkage regression (BSR) method and stochastic search variable selection (SSVS) method, , have been so far proposed for the estimation of SNP effects. However, much computational burden is imposed on the MCMC-based Bayesian methods. A method with both high computing efficiency and prediction accuracy is desired to be developed for practical use of genomic selection.EM algorithm applicable for BSR is described. Subsequently, we propose a new EM-based Bayesian method, called wBSR (weighted BSR), which is a modification of BSR incorporating a weight for each SNP according to the strength of its association to a trait. Simulation experiments show that the computational time is much reduced with wBSR based on EM algorithm and the accuracy in predicting GBV is improved by wBSR in comparison with BSR based on MCMC algorithm. However, the accuracy of predicted GBV with wBSR is inferior to that with SSVS based on MCMC algorithm which is currently considered to be a method of choice for genomic selection.EM-based wBSR method proposed in this study is much advantageous over MCMC-based Bayesian methods in computational time and can predict GBV more accurately than MCMC-based BSR. Therefore, wBSR is considered a practical method for genomic selection with a large number of SNP markers. Genome-wide polymorphisms are increasingly elucidated in livestock and crops with the recent development of the sequencing technologies. Accordingly, high-throughput genotyping systems, such as high-density SNP chips containing several tens of thousands of genome-wide SNP markers, have become available to efficiently identify genotypes of individuals for a large number of SNPs with low cost. As a new breeding technology utilizing the information of genome-wide dense SNP markers, genomic selection was proposed by Meuwissen et al. 2001) [ [1]. In BayesA method can be classified into a method of Bayesian shrinkage regression (BSR)  from a vUsually, Markov chain Monte Carlo (MCMC) algorithm has been applied to the model construction with BSR and SSVS in genomic selection. However, MCMC-based Bayesian methods are much time-consuming and therefore might be prohibited for application as the sample size and/or the number of SNPs become much larger. Accordingly, a fast non-MCMC algorithm for SSVS utilizing the analytical form of posterior means of SNP effects was devised , where cXu 2003) [003 [2] pIn this study, we apply the EM algorithm described in  for the p, of SNP inclusion in the model. It is shown that the accuracy of wBSR can be improved in comparison with MCMC-based BSR although the accuracy of wBSR is inferior to SSVS and is influenced by the values of p and the hyperparameters of the prior inverted chi-square distribution assumed for the variances of SNP effects. Moreover, the computational cost of wBSR is much less than the MCMC-based Bayesian methods. Therefore, wBSR is considered a practical and useful method for genomic selection with a large number of SNP markers.Using the simulation experiments, we compare the accuracies of EM-based wBSR with BSR and SSVS using MCMC algorithm in the prediction of GBV for several values of the prior probability, In this section, we will describe the methods of BSR (BayesA) and SSVS (BayesB) for genomic selection and EM algorithm for BSR to obtain the estimates of parameters included in the model that maximize the posterior distribution function. Subsequently, we will modify BSR method (wBSR) by assigning the weight for each SNP according to the strength of its association to a trait for improvement of the prediction accuracy. The weight of SNP can be obtained from a prior probability of each SNP to be included in a model, which is also considered in SSVS procedure, using EM algorithm as well as the estimate of SNP effect. For the evaluation of the accuracies of the predicted GBVs, we apply wBSR with variable prior probabilities of SNP inclusion for simulated data sets as well as MCMC-based BSR and SSVS.N and a training data set including n individuals with the records of phenotypes and SNP genotypes is available for the estimation of parameters in the model. We also assume that selection candidates consists of individuals with only SNP genotypes, for each of which GBV is predicted based on the model with SNP effects estimated with training data sets. We denote two alleles at each SNP by 0 and 1 and three genotypes by '0_0', '0_1', and '1_1'.In the statistical model described below, we consider not haplotype effect but the effect of each single SNP. We assume that the number of SNPs genotyped is In BSR (BayesA) method ,2, the fy = ' is a vector of phenotypic values of a trait for n individuals of a training data set, ul = ' is a vector of genotypes of n individuals at the lth SNP with uli taking a value of -1, 0, or 1 corresponding to the genotypes '0_0', '0_1', or '1_1', respectively, gl is the effect of the lth SNP, b = ' is a vector of fixed non-genetic effects with dimension f including a general mean, X = (xij)  is a design matrix relating b to y and e = ' is a vector of random deviates with ei ~N. It is assumed that the prior distribution of the SNP effect, gl, is a normal distribution with a mean 0 and a variance σgl2, which differs for every SNP. Moreover, the prior distribution of σgl2 is considered. In this study, we assume that it is a scaled inverted chi-squared distribution with a scale parameter S and a degree-of-freedom ν, χ-2, following [b, gl, σgl2  and σe2, can be obtained by Gibbs sampling [gl. In this study, we consider not haplotype effect but the single marker effect for gl. The use of marker haplotypes instead of the single marker genotypes would cause slight modification of the model, but the procedure for estimation of effects and prediction of GBV is essentially the same.where y = y1, y, ..., yny = y1, y, ..., ynp, of each SNP to be included in the model is considered. Usually, a small value is given for p based on the assumption that many of SNPs have actually no effects for a trait. The prior distribution of gl is assumed to be a normal distribution with a mean 0 and a variance σgl2 in SSVS as in BSR, whereas the prior distribution of σgl2 is expressed as a mixture of two distributions corresponding to the inclusion and the exclusion of the SNP as follows:In SSVS (BayesB) method, the model (1) is also adopted but a prior probability, χ-2 when the SNP is included. When MCMC algorithm is applied for the estimation of the parameters in SSVS, gl and σgl2 are jointly updated with Metropolis-Hastings chain [assuming that the prior is gs chain . The GBVIn Bayesian estimation, the inferences about the parameters are made based on the posterior distributions. MCMC algorithms can be used for obtaining the posterior information of the parameters in BSR method as described above. However, the posterior mode of each SNP effect which is a point estimate maximizing the density function of the posterior distribution can be calculated instead of a posterior expectation by some other iteration algorithm including EM algorithm. In QTL mapping using BSR method, Yi and Banerjee  utilizedθ,The posterior distribution is given by combining a likelihood of the data and the prior distributions of the parameters. We denote parameters in BSR method as a vector form θ given the data of phenotypes, y, and genotypes of SNP data, U = , is denoted by g and written asThe posterior distribution of C means a constant and it should be noted that the likelihood of y given the model parameters and genotypes is a normal distribution with a mean Xb + σe2 and the prior of gl is a normal distribution with a mean 0 and a variance σgl2, the prior of which is the scaled inverted chi-squared distribution χ-2 as described above. The priors of b and σe2 are written by p(b) and p(σe2), respectively, which are assumed uniform distributions over suitable ranges of the values here.where σgl2 , as missing data and replace σgl2 by the conditional posterior expectation of σgl2, denoted by Following , we regaσgl2  maximizing the log-posterior distribution with σgl2 replaced by As M-step, we obtain the values of parameters other than glog gl, bj  and σe2, denoted as The mode of each parameter which maximizes the log-posterior can be given by solving an equation derived by making the partial derivative of the log-posterior with respect to the parameter equal to 0. Accordingly the modes of The EM algorithm for BSR is summarized as follows:σgl2 is estimated as gl, which is l = 1, 2, ..., N.1. E-step: gl , bj  and σe2 maximizing the log posterior distribution of parameters, 2. M-step: the values of -6, where E-step and M-step are repeated until the values of parameters converge. We stop this iteration when the change of values of parameters becomes small. For example, when σgl2, a mixture distribution combining χ-2 and 0 with probability p and 1-p, respectively, cannot be well treated with EM algorithm. To devise a cost-effective and EM-based method providing more accurate prediction for genomic selection with a higher degree of shrinkage, we develop a new modified BSR method incorporating a weight for each SNP depending on the strength of its association with a trait. In this method, we modify the model (1) by incorporating the variable γl indicating the inclusion of the lth SNP in the model or exclusion of the lth SNP from the model, where inclusion and exclusion of the SNP are indicated by γl = 1 and γl = 0, respectively. We assume that the prior probabilities of γl = 1 and γl = 0 are p and 1-p, respectively, as in SSVS. The modified model is written asIn SSVS, SNP effects can shrink more strongly than in BSR due to the assumption that only a small number of SNPs can be linked to QTL causing only a small portion of SNPs to have significant effects and many other SNPs to have negligible effects, which might result in the improvement of prediction accuracy for SSVS using a more parsimonious model. Although it was reported in  that BSRX, b, ul, gl and e are as described in the model (1). We assume that the priors of gl and σgl2 are not influenced by the inclusion (γl = 1) or exclusion (γl = 0) of SNP in the model (2) and are as adopted in BSR. The method with the model (7), but utilizing these assumption, is called wBSR, meaning a modified BSR incorporating SNP weight, in this study since the same EM procedure as used in BSR for searching the posterior mode of parameters can be applied for this method and it is equivalent to an EM-based BSR procedure proposed by [p = 1. We denote the variables indicating the inclusion of SNP effects in the model in a vector form as γ =  which are treated as variables to be estimated in wBSR.where posed by  when p =g is modified from (2) and written as gIn wBSR, the posterior distribution p(b) and p(σe2) are assumed uniform distributions. Applying the same argument as in EM algorithm used for BSR, σgl2 is replaced by its conditional posterior expectation, σl indicating the inclusion of SNP in the model is unobserved, thus, σl is also replaced by its conditional posterior expectation ξl which can be written, from (8) and under the assumption that the priors of gl and σgl2 are independent of σl, aswhere the priors γl -denotes γ with the lth component γl deleted andwhere γj (j ≠ l) is also unobserved. Therefore, we modify the expression for ξl by substituting γj with ξj for j ≠ l. Accordingly, the conditional posterior expectation of γl, ξl, is approximately obtained in E-step for l = 1, 2, ..., N following the formula:In this expression, however, where bj  and σe2 maximizing g, In M-step, the values of andgl, the value maximizing the posterior (8), γl and is given as γl = 0 andFor γl = 1. As γl is unobserved, we substitute ξl for γl in the expressions of γl = 1 is adopted for the iteration. In summary, for andξl given by (9) is an approximate posterior expectation of γl that might be different from the posterior probability of SNP to be included in the model. Therefore, ξl is referred to as the weight of the SNP that is regarded as an indicator of the strength of the association of the SNP with a trait. The SNP assigned a large weight with ξl taking values near one is considered to essentially contribute to GBV while the contribution of the SNP assigned a small weight with ξl taking values near the given prior value of p is regarded as negligible. The degree of shrinkage can be affected by the value of a prior probability p as well as the values of hyperparameters, ν and S, in ξ-2, the prior distribution for σgl2. The predicted GBV of wBSR is expressed as It should be noted that p based on simulated data sets. The population and genome were simulated following the way as in [-3 and 2.5 × 10-5 for marker locus and QTL, respectively. At least one mutation occurred in the most of all marker loci with such high mutation rate during the simulated generations. In the marker loci experiencing more than one mutation, the mutation remaining at the highest minor allele frequency (MAF) was regarded as visible, whereas the others were ignored, which caused the marker loci to have two alleles like SNP markers. The polymorphic QTLs at which mutation occurred only affected the trait, where the effects of QTL alleles were sampled from a gamma distribution with scale parameter 0.4 and shape parameter 1.66 and were assigned with positive or negative values with equal probabilities [We evaluated the accuracy for the prediction of GBV using wBSR with variable ay as in . In briebilities .In generation 1001 and 1002, the population size was increased to 1000. The population in the 1001th generation was treated as a training data, where the phenotypes of a trait and SNP genotypes of the individuals were simulated and analyzed with methods of genomic selection to estimate the SNP effects in the model. The phenotype of each individual in the 1001th generation was given as a sum of QTL effects over the polymorphic QTLs and environmental effects  sampled from a normal distribution with a mean 0 and a variance 1 such that the heritability in the population was expected to be 0.5. The population in the 1002th generation was used as selection candidates, where the individuals were only genotyped for 1010 and 10100 SNP markers in Data I and Data II, respectively, without phenotypic records and GBV of each individual was predicted using a model with SNP effects estimated based on the population in the 1001th generation. The true breeding value (TBV) of the individual in the 1002th generation was also simulated as a sum of QTL effects corresponding to the QTL genotype and utilized for evaluation of the accuracy of predicted GBV but was regarded as unknown and unavailable in the estimation of SNP effects in the models. The accuracy was measured by the correlation between the predicted GBV and TBV.p = 0.01, 0.05, 0.1, 0.2, 0.5 and 1.0, we simulated 100 and 20 data sets under the scenario of Data I and Data II, respectively. The accuracies of the GBVs predicted by BSR and SSVS based on MCMC algorithm were also evaluated on the same data sets in comparison with wBSR. In MCMC iteration, we repeated 11000 cycles using a burn-in period of the first 1000 cycles. The values of parameters were sampled every 10 cycles for obtaining the posterior means. In SSVS, we investigated the accuracies of predicted GBVs for p = 0.01, 0.05, 0.1, 0.2 and 0.5 in Data I but for p = 0.01, 0.05 and 0.1 in Data II due to large computational time required for MCMC algorithm. SNP markers with MAF less than 0.05, which were less than 10% of all SNPs, were not used for the estimation of effects and the prediction of GBV. We set ν = 4.012 and S = 0.002 for MCMC-based BSR and wBSR with p = 1.0 that is equivalent to an EM-based BSR proposed by [ν = 4.234 and S = 0.0429 for SSVS and wBSR with other values of p. These values of ν and S were determined following [For the evaluation of the accuracies of the predicted GBVs obtained by wBSR with posed by , and ν =The accuracies of the predicted GBVs obtained by several methods for genomic selection were evaluated in 100 simulated data sets of Data I and in 20 data sets of Data II, where we assumed that 1010 SNP markers and 10100 SNP markers were available on a whole genome for Data I and Data II, respectively. The results of the simulations were summarized in Table p = 0.5 in the given settings of ν and S  were considerably different in Data I. MCMC-based BSR provided significantly better predicted GBV with accuracy of 0.748 than EM-based BSR with accuracy of 0.697 considering the standard errors based on 100 repetitions as shown in Table p = 0.5 in comparison with MCMC-based BSR in Data I although different values of ν and S were assumed. In Data II, SSVS with p = 0.01 could predict GBV most accurately with the accuracy of 0.887. The accuracy of wBSR was influenced by the value of p also in Data II, which was 0.843 at p = 0.01 and attained to 0.857 at p = 0.05 but much reduced to 0.665 at p = 0.5  without parallel computing implementation. Although the computational time required by MCMC-based BSR was less than that by SSVS, it still took more than 25 minutes and more than three hours on average in the analysis of a single data set of Data I and Data II, respectively. The iteration times in wBSR until attaining to convergence based on the criterion adopted here ranged 30 to 120 depending on the simulated data.In this study, EM algorithm for the estimation of SNP effects in BSR method for genomic selection was described following the algorithm proposed in QTL mapping . Moreovegl, was analytically evaluated instead of MCMC-based numerical calculation, where the prior of gl was assumed to be a mixture of a distribution with a discrete probability mass of zero and a double exponential distribution. Although no comparison between this method of SSVS based on the analytical integration and wBSR proposed here was made in this study, the simulation experiments showed that wBSR was also effective in computational time based on EM algorithm, which is a simple algorithm without integral calculation, and performed better than MCMC-based BSR, thus, wBSR could be regarded as a simpler method for genomic selection with practical prediction accuracy and computing efficiency as well as the SSVS method utilizing analytical integration (fBayesB).A fast non-MCMC algorithm for SSVS method, called fBayesB, was proposed in . In thisp, a prior probability of SNP to be included in the model. The accuracy was considered to also change along with the values of hyperparameters, ν and S, in χ-2, the prior distribution for σgl2. These prior parameters given a priori determine the degree of shrinkage of estimation for SNP effects and affect the accuracy of the prediction of GBV as well as the property of data analyzed. We adopted here the values of ν = 4.234 and S = 0.0429 for SSVS and wBSR with p < 1.0 and ν = 4.012 and S = 0.002 for MCMC-based BSR and EM-based BSR (wBSR with p = 1.0) since we considered the same scenario in simulations as that used by [ν and S were theoretically calculated as suitable values for SSVS and BSR. However, the suitability of these values of ν and S might be affected by the structure of analyzed data such as the number of SNPs involved, especially for BSR including all of SNPs in the model. Therefore, we performed additional analyses with MCMC-based and EM-based BSR for Data I and Data II using the different values of ν and S. We adopted the same setting of ν and S as used in SSVS , which should cause less shrinkage for the estimate of SNP effect, in the additional analysis with both types of BSR in Data I. In Data II, the Jeffreys' prior p(σgl2) ∝ 1/σgl2 corresponding to ν = 0.0, yielding strong shrinkage for very small SNP effect but weak shrinkage for large effects [ν and S as in SSVS, the accuracy of EM-based BSR (wBSR with p = 1.0) much increased from 0.697 to 0.744 with standard error (s.e.) of 0.006 while the increase in the accuracy of MCMC-based BSR was slight, where the accuracy was changed from 0.748 to 0.754 with s.e. of 0.006. In another additional analysis of 20 repetitions of Data II using the Jeffreys' prior, the accuracies of both types of BSR were decreased in comparison with the original prior setting of ν and S. We obtained the accuracy of 0.834 with s.e. 0.017 for MCMC-based BSR and the accuracy of 0.809 with s.e. 0.016 for EM-based BSR with the Jeffreys' prior. Although there seems to be the possibility of further improvement of the accuracy by choosing the priors yielding more suitable degree of shrinkage for the estimates of SNP effects, it is generally difficult to construct such desirable prior for σgl2.As shown in Table  effects , was tesp, v and S would be to evaluate the accuracies obtained by varying the values of these hyperparameters in small steps over the suitable ranges, for example, 0 <p < 1, 0 <v < 5, 0 <S < 1. In genomic selection applied for the actual data, cross validation might be a method of choice for determining the suitable values of these hyperparameters. A number of replications in the estimation of a large number of SNP effects are necessarily required for finding the optimal values. When replicated estimations are required, the advantage of EM-based wBSR method over MCMC-based methods with respect to the computational time would be much more remarkable.An actual strategy to determine the optimal values of gl and σgl2. These investigations would be described elsewhere.In , EM algoIn large-scale genotyping data used for genomic selection including tens of thousands SNP genotypes for thousands of individuals, a large number of SNP genotypes may still be missing. EM-algorithm allows the missing SNP genotypes to be inferred with posterior expectations of the indicator variables of genotypes given the information of the adjacent SNPs or pedigree information. A step for the inference of missing genotypes can also be included in our EM-based method of genomic selection. Although the inference of missing genotypes with EM-algorithm has been shown to be effective for increase in power of QTL detection, how prediction accuracy is affected by the inference of missing genotypes in genomic selection remains to be investigated. This topic should be addressed in the further study.We developed a program implementing EM algorithm for estimating SNP effects, described here, in genomic selection and applied the program for the simulation study. The information of this program is provided below (see Availability and requirements).In this research, we described EM algorithm for a Bayesian method, BSR, that included effects of all SNPs in a regression model as covariates in genomic selection and was so far based on MCMC algorithm. Moreover, we devised a modified version of BSR method called wBSR by incorporating the weight assigned to each SNP according to the strength of its association with a trait, for which EM algorithm was also applicable. As results of simulation experiments, it was shown that the accuracy in predicting GBV by wBSR was improved in comparison with MCMC-based BSR. Although the accuracy of wBSR was inferior to SSVS, wBSR was regarded as a practical and cost-effective method taking great computing advantage over MCMC-based Bayesian methods into account.hayatk@affrc.go.jp). The sample input files and a brief manual of the program can be also provided.The source code of the program used in the simulation study was written with Fortran 77 and a Windows version of the executable program is available on the request to the first author (TH devised EM algorithm for Bayesian methods in genomic selection, developed a program for simulations and drafted the manuscript. HI assisted in developing a program and drafted the final manuscript. Both authors read and approved the final manuscript.