Direct-to-consumer (DTC) genetic testing is a recent commercial endeavor that allows the general public to access personal genomic data. The growing availability of personal genomic data has in turn stimulated the development of non-commercial tools for DTC data analysis. Despite this new wealth of public resources, no systematic research has been carried out to assess these tools for interpretation of DTC data. Here, we provide an initial analysis benchmark in the context of a whole family, using single nucleotide polymorphism (SNP) data. Five blood-related DTC SNP chip data tests were analyzed in conjunction with one whole exome sequence. We report findings related to genomic similarity between individuals, genetic risks and an overall assessment of data quality; thus providing an evaluation of the current potential of public domain analysis tools for personal genomics. We envisage that as the use of personal genome tests spreads to the general population, publicly available tools will have a more prominent role in the interpretation of genomic data in the context of health risks and ancestry. Taking advantage of the growing body of statistical associations accumulated in the scientific literature, DTC providers have been able to offer personalized genomic ‘reports’ that present accessible scientific information of relevance to their customers’ observed genotypes. It is precisely in these genomic annotations where customers realize the value of their DTC product purchase.Direct-to-Consumer (DTC) genetic testing is a relatively new commercial endeavor offering access to personal genomic tests to the general public. Individuals wishing to learn about their genomes today enjoy a range of options. DTC providers typically offer chip-based genotyping of genome-wide markers, currently in the range of hundreds of thousands to a million single-nucleotide polymorphisms (SNPs). This current wealth of personal genomic data is likely to grow at an increasing pace as DNA sequencing become ubiquitous in personal genome testing. Genome sequencing allows elucidation of not just SNPs, but copy number variants (CNVs), insertions, inversions and many other genomic features currently underrepresented in personal genome analyses. Yet personal SNP data has proven to be a valuable resource for making personalized inferences about the risk of developing medical conditions, the probability of having certain phenotypic traits, and one’s likely ancestral origins3 is a wiki-styled resource that collects and annotates SNPs from the scientific literature and provides tools with which to associate these annotations to those observed in DTC genomic tests. openSNP4 is a public resource that collects genotypes from people willing to share them, allows annotation of phenotypes, and the search of occurrences of a particular SNP in scientific publications using Mendeley5. Although all resources, public or commercial, are limited by the reliability of the data available for any given marker, public and low cost resources have the potential of engaging community wide efforts (‘crowd-sourcing’) to an extent to which closed commercial applications cannot.A feature of DTC genomic test interpretation is that, being a commercial product, genomic annotations and analysis tools are proprietary and not freely available to the research community. This has motivated the parallel development of public resources and low cost genotype analysis tools. SNPedia6 or Gene Wiki+7, may offer a good solution for rapid, accurate and comprehensive community annotation of personal genomic data.We decided to explore the extent to which phenotype inference and genotype analysis can be carried out solely using existing public or very low cost resources. This is motivated by our belief that no DTC company will ultimately be able to match the rapid pace of genomic data accumulation and annotation that the research community is producing. Apart from SNPedia or openSNP, a wiki-based model, perhaps integrated with existing genetics resources such as the Gene WikiIn this paper we carry out a systematic analysis of DTC genomic data from a family of five blood relatives using mostly public annotations and tools. We present a) our findings related to the quality of the data, b) a comparison of the similarity between members of the family and an undisclosed individual of a different ethnic background and c) phenotype inferences as described by SNPedia trait annotations. We also incorporate analysis of DTC exome sequence data to supplement the genotype findings of one individual. We thus offer a pioneering methodical study of a whole family analyzed using only DTC data. Since comparable data could, in principle, be bought by any individual, this study benchmarks the personal genomic analyses available to non-experts using open, web-based tools.We analyzed DTC genomic data from a family of five of self-reported European ancestry, two males and three females across two generations .8 was taken as the reference genotype for SNPedia phenotype predictions. Two kinds of SNP chip were used in this analysis, the 23andMe9 versions 2 and 3. All family members except ‘Son’  for at least 4 generations. Principal component analyses of ancestry informative markers confirm tight parental clustering with Southern European populations . We thus10. We found that data downloaded from 23andMe at different times may vary, probably as a consequence of changes in genotype-calling algorithms. To ensure consistency in our analyses, we downloaded the most recent data, for all family members, on May 30th 2012.Whole family genotypes provide an additional genetic context that individual data analyses cannot offer, enabling enhanced error correction and inheritance state analysis23andMe genotype data for Mother, Father, Son, Daughter and Aunt. Son is 23andMe version 2 data and the rest of the family are 23andMe version 3 data.Click here for additional data file.In order to facilitate comparison between different genotypes, we excluded non-autosomal SNP data . All v3 chips had a total of 930,342 autosomal SNPs; the v2 SNP chip had 556,694. The reported ‘no call’ rate (shown as ‘- -’ in the downloaded data) varied slightly for each individual , but ove11, meaning that the chip can provide accurate data for more than 98% of those variants in any particular person. When an allele variant present in heterozygous state is "undercalled" (not observed), the locus may be reported as being homozygous for the other variant, leading to missed heterozygosity. Such sites may significantly impact the disease risks predicted for the individual. Under the simplifying assumption of uniform undercall probability, we estimated the number of heterozygous sites mistakenly reported as homozygous  is in disagreement with both parents: Father=CC, Mother=CC, Daughter=TT.For the remaining 6 relationships, only part of the genome is expected to be identical by descent (IBD). Fully incompatible sites can be numbered as "pseudo-MIEs" .The reduced numbers of MIEs and pseudo-MIEs between the Son and all other family members were due to the lower total number of SNPs assayed for the Son. The Daughter has more MIEs relative to the Mother than relative to the Father. This may be due to having inherited a few deleted segments leading to a hemizygous state: hemizygous sites are reported as homozygous for the allele present, leading to an accumulation of apparent MIE sites. For example, at chr2:41093584 (rs12465519) . The ISCThe genotypes observed for the Mother (M) and Daughter (D) in the range 41,092,148-41,101,972 of chromosome 2 are mutually incompatible . Genotypes for the Father (F) are shown for reference. Heterozygous sites are highlighted in grey. The simplest explanation for the cluster of incompatible genotypes is the presence of a deletion in the Mother''s genome, inherited by the Daughter. Both Mother and Daughter are thus hemizygous in this region. Right panel: inferred genotypes, showing the deleted segment inherited from Mother (blue) and the phased haplotype inherited from Father (yellow).Click here for additional data file.We compared the 23andMe genotypes of the family members at three levels: 1) pairwise all-against-all comparison, 2) inheritance state analysis in family quartets, and 3) analysis of population admixture.1. Pairwise comparison. We performed an all-against-all genotype comparison among all family members. As an external point of reference, we included in our analysis a male individual of Indian ethnic background .2. Inheritance state analysis. The availability of nuclear families with two or more offspring enables the identification of inheritance states10. These represent whether the offspring inherited the same alleles from both parents , the same allele from one parent but different alleles from the other , or different alleles from both parents . Some family genotypes are consistent with all inheritance states  and are thus non-informative. Some family genotypes are consistent only with a subset of the inheritance states. For example if both parents are heterozygous A/G, and both offspring are homozygous A/A, clearly the offspring inherited the same alleles from both parents, which is consistent only with the "identical" state. Some family genotypes are consistent with two inheritance states. By combining the evidence from individual SNPs along a chromosome, it is possible to identify contiguous blocks of consistent inheritance, bounded by recombination events in either parent . This is due to the largely uniform sampling of SNPs along the genome.3. Admixture analysis. Visualization of admixture for Mother and Father was done with ADMIXTURE12 in the context of other similar Southern European individuals.13. Mother was denoted as ES7 (red arrow) and Father as ES8 (blue arrow). Mother and Father seemed to be markedly different yet in and around the Portuguese and Spanish cluster of individuals.th June, 2012). Processing just the exome, only 925 genotypes were found annotated in SNPedia. It is not surprising that so few additional SNPs were found, as the exome comprises a very small percentage of the total genome. Two SNPs were discovered to have conflicting genotypes between the two platforms:rs12344615 reported as ‘AG’ and ‘GG’ andrs2290272 reported as ‘CT’ and ‘TT’ respectively. The most likely informative SNPs from the exome data are summarized as judged by their observed frequency in HapMap8.To leverage all genotype data contained in all different sources, we combined the SNP chip data v2  with those found in Son’s exome data. 10,203 genotypes were annotated in SNPedia when exome SNPs were pooled with the SNP chip version 2 . This was noted to be lower than expected if additional samples had been genotyped concurrently. 97% of the variants identified were within a known gene, 58% of them overlapped a protein domain and 5,565 were non-synonymous (15%) with serious predicted consequences on the protein product . Of these 5,565 potentially pathogenic SNPs, 413 had not been previously identified (verified against dbSNP release 132). This represents a normal figure for the private, novel, non-synonymous changes carried by most individuals. No more serious, novel changes were identified (such as stop codons gained or lost).For the analysis of similarities between genotypes only 23andMe data was analyzed. Although exome sequencing is not widely marketed yet within the DTC providers as an option, this is likely to change in the near future. With our current budget constraints, we were able to sequence Son’s exome and relevant SNP and variation data was added for further analysis. A BAM file was created out of 4 FASTQ files downloaded from a server from the Beijing Genomics Institute. A compressed VCF file was also created. A total of 2.54 Gigabases of sequence was aligned at high quality. Summary metrics of the exome were calculated using PicardWe used SNPedia’s Hilbert curve visualization tool to compare chromosomes between different individuals.This figure shows that the haploidentical states are well distinguished (contiguous green or yellow segments). Lacking information on the grandparents, it is impossible to distinguish between haploidentical maternal and haploidentical paternal for the mother-aunt comparison, hence the haploidentical states are shown as mixtures of green and yellow.Click here for additional data file.The next most similar graph can be seen to be the Mother/Daughter comparison. Much of the graph comparing Mother and Son is grey, representing SNPs present only on 23andMe v3 but absent from v2.Each file contains all SNPs in the individual matching an annotated SNP in SNPedia. SNPedia annotations contain a magnitude value  and a phenotype description of the condition of particular genotype affects.Click here for additional data file.19, which allows annotation of observed genotypes from DTC analyses with SNPedia-annotated phenotype associations. These are collected directly from the scientific literature. All family members were analyzed using Promethease and associated phenotypes were collected for further interpretation. SNPedia’s annotationMagnitude, denoting a subjective degree of importance for an observed trait as judged by the curator, was used to discriminate between traits that should be considered further in our analyses. A magnitude of 0 denotes a common genotype for which no associated phenotypic data is known. Magnitude >3 is defined as ‘probably’ interesting. The maximum number is 10. In order to compare family phenotype annotations, all SNPs or ‘genosets’ (groups of SNPs) with equal or greater than magnitude 3 were extracted and summarized inWe inferred phenotypes from the observed genotypes, by comparing to all available SNPedia SNP annotations. We analyzed family genotypes using the Promethease toolBased on observed results, the maternal hereditary line seems to carry greater risks related to diabetes and cardiovascular/thrombosis related conditions. In addition, both males (Son and Father) have greater risk of baldness as well as mixed results in terms of their ability to metabolize drugs. All the family shares a common trait of substantially increased odds of developing V617F-positive Myeloproliferative neoplasms.We have presented here, to our knowledge, the first systematic analysis of DTC genomic data using non-commercial or low cost resources, combining data from 5 blood-related family members. The purpose of this study does not lie in uncovering the phenotypic predictions or genetic findings in these individuals’ genomes. Instead we aimed to demonstrate to what extent, in principle, any individual can interpret their personal genome using only public resources with an affordable budget and no laboratory equipment. We stress that our goal is not to diminish the value of DTC industry services, which have catalyzed the access to, and interest in, personal genomics data in the wider public. However, as the adoption of DTC personal genomics tests becomes ever more widespread, we envisage community phenotype association and third party public tools to become more significant in the overall interpretation of personal genomics results.Although we found the no-call rate to be comparable between both versions of the 23andMe platform, Mother had a greater undercall rate than the other individuals. Also, results given by no-call rates suggest that there might be some intrinsic differences between the two chips. Nevertheless the current number of samples analyzed is not large enough to make this conclusion, as only five chips of data analyzed do not provide any basis for their overall performance. Therefore our error estimation rates presented here should only be considered in the context of this analysis and not as representative of the DTC company’s overall quality scores.10 is a powerful tool to assess genotyping quality.Based on the no-call rate and the relative ratios of homozygous and heterozygous sites reported, we computed an ‘undercall’ rate and used it to estimate the number of heterozygous sites mistakenly reported as homozygous. The frequency of such events is small (~0.2%). Nevertheless, the fact that up to 2,000 heterozygous variants may be missed is a reminder that interpretation of personal disease risks should be done with caution. Findings of potential medical relevance should always be verified for correctness. Wherever possible, it is beneficial to perform the analysis in the context of families: identification of MIEs and State Consistency ErrorsThe similar level at which identical genotypes is shared between the relatives and the non-CEU individual is consistent with the ethnically close background for the family members. This, however, does not suggest that Mother or Father are directly related. In fact, when performing an admixture analysis with unrelated individuals of Southern European descent  it is clWe also indicated that when comparing genotype similarities between siblings and parents, we found that Daughter was significantly closer to Father than Son was. Although the expectation was that both Daughter and Son should be equally similar to both parents, these unexpected results may be the reflection of bias in the subset of markers used in the DTC analysis. These results are not therefore indicative of Daughter’s genome being closer to either parent, as most of the genome is missing and hence any inference in this respect cannot be made. In the context of SNP analysis, however, it is worthwhile reporting such SNP differences as these will influence the overall results reported back to the DTC customer. This in turn may explain why observed susceptibility risks vary among family members when comparing their phenotypic annotations.10 but also to the general public, to predict shared phenotypes among family members. Within ‘identical’ blocks, siblings are essentially identical twins: this fact gains special personal meaning in the context of DTC genetic analysis.The identification of blocks of identical, haploidentical or nonidentical genotype between family members , vandalism rates are very low in areas of specialist academic interestIn this study we have not found any annotated genotype that is likely to raise significant health concerns among the family individuals. It is inevitable, however, that as more individuals investigate their own personal genomes, and more statistical associations are uncovered, genotype/phenotype correlations with serious health implications will be accessible through open access resources. Interpreting such information appropriately is bound to be difficult for individuals who are not expert geneticists; this poses special ethical challenges from the point of view of how to present the data. DTC companies have recognized this ethical issue by implementing additional access controls to some particularly sensitive annotations, such as those genotypes associated with a high risk of developing Alzheimer’s disease or some forms of breast cancer. To our knowledge, public annotation resources do not currently make such distinctions. We therefore urge caution when investigating personal genomic data for health risks, especially when using open access information. We recommend those who uncover personal genomic information of medical concern seek the advice of genetic counselors, who can interpret and advise on the context of what it really means to the tested individual.Five 23andMe genome analysis kits were purchased at two time points. Son’s kit was bought in May 2009 and was tested with 23andMe version 2 . The other 4 family members were analyzed in one batch, with kits bought in December 2010 and results returned in February 2011. The second batch used 23andMe’s chip version 3 with ~967,000 SNPs per genome analyzed. After discussion of results, consent was given by all family members of the family to publish their genotypes.Assume N SNPs were tested, and of these, fN are truly heterozygous. We wished to compute f (heterozygous fraction) from the observed numbers of homozygous, heterozygous and failed SNPs.For each SNP with a dbSNP ‘rs’ identifier, we assumed that 1) the SNP is biallelic, 2) it is present in diploid state. We further assumed a probability x of not observing a given allele , and assumed this probability was equal for both alleles, at all sites. Finally, we assumed that the probability of observing a wrong allele was zero .2). 2) If one allele was not observed , the SNP was called "HOM" . 3) If both alleles were observed, the SNP was called "HET" .If the true state of the SNP was heterozygous, the following could have happened. 1) If neither allele was observed , the SNP was called "NULL"  If the allele was not observed, the SNP was called "NULL" . Otherwise, 2) the SNP was called "HOM" .2+(1-f)xNULL’ = fx2)HET’ = f(1-2x-xHOM’ = (1-f)(1-x)+2fx = 1-x+f(3x-1)The expected frequencies for NULL, HET and HOM were:Since HOM + HET + NULL = 1, the undercall rate x was given by solving:3 + (1+HOM)x2 + (3HET+2HOM-3)x + NULL = 0x2), and the number of "missing" heterozygous sites could be computed by: missing = (f-HET)N. Finally, the number of heterozygous sites reported as homozygous was given by:The heterozygous fraction f was then given by: f = HET/(1-2x-xhet2hom = missing - N*NULL*HET/(HET+HOM)21. Local realignment was performed around indels with the Genome Analysis Toolkit (GATK v1.4)15 framework for variation discovery and genotyping using next-generation DNA sequencing data. Optical and PCR duplicates were marked in BAM files using Picard 1.6214. Original HiSeq base quality scores were recalibrated using GATK TableRecalibration and variants called with GATK UnifiedGenotyper. Indels and SNPs were hard-filtered according to Broad Institute best-practice guidelines22 to eliminate false positive calls and produce the final VCF.Raw reads were aligned to the reference GRCh37 using bwa 0.61The Fastq files represent the raw exome data for the son. The BAM files are derived from the fastq files by aligning the reads using a Burrows-Wheeler Aligner (BWA). The BAM file (.bam) is the binary version of a tab-delimited text file that contains sequence alignment data. The BAM file index (.bai) provides fast random access to the BAM file. The compressed VCF file (.vcf.gz) describes variant calls of the data in text format.Click here for additional data files.23. In our analysis only SNPedia annotations of >= 3 Magnitude were examined. Magnitude is a subjective score that helps prioritize SNPs according to their expected importance and the phenotypic annotation itself in the form of free text. Magnitude is assigned by SNPedia entry curators.Promethease, a SNPedia tool for phenotype inference, was used for assignment of SNPedia annotations to observed SNPs. SNPedia annotations contain manually curated SNPs that summarize phenotype associations observed in a particular population. Phenotype associations were inferred using SNPedia’s SNP ids, which correspond to dbSNPWe calculated similarity scores using our own Perl scripts and MySQL. Similarity between any given two individuals was calculated as the total number of matching SNPs plus half-matches divided by 2. Negative results were counted as the total number of conflicts plus the number of half-matches divided by 2. As the platforms used are different for Son as compared to the rest, Son can only be compared relative to himself and not against other individuals. Similarities between individuals were statistically tested using the R package for Pearson’s Chi-squared with Yates’ correction.Written consent for publication of their genotype and phenotype was obtained from all family individuals. This is a well written and interesting article that describes analyses on genome-wide genotyping information from a DTC company.Major concernsAlthough the analyses are carried out very well, the overall purpose and conclusion of the paper is rather mixed and somewhat unclear. The authors state that they “explore the extent to which phenotype inference and genotype analysis can be carried out solely using existing public or very low cost resources.” or as in the abstract “providing an evaluation of the current potential of public available analysis tools for personal genomics”.The discussion section focuses very much on the results of the different analyses, whereas that is (according to the purpose of the paper) not that relevant. Rather in that context it would be more interesting to speculate on other aspects  interpret the outcome of such tools) and to provide a real conclusion on “the extent to which phenotype inference and genotype analysis can be carried out solely using existing public or very low cost resources.”It is not clear whether the tools that were investigated compromise all public domain tools for these kinds of analyses. If the authors do not investigate all available tools, the authors should motivate their choice for the described toolsGiven the purpose of the paper, a table with the available tools and their “potential” would be very useful.Minor concernsThe fact that “Son” was done with a V2 assay complicates all comparisons in the paper. It seems to paper would be a lot more straightforward if the authors used a V3 assay for the son as well.Page 7, “Exome data summary statistics”, authors mention that the numbers they find are what is to be expected. Please add a reference to substantiate this.Page 10, the cond. prob. of a heterozygous call for a heterozygous SNP reads “1-2x-x2” and should probably be “1-2x-x^2”. Similar with the expected frequency for NULL’.YesIs the title appropriate for the content of the article?YesIs the abstract a suitable summary of the article?Yes, the article is well constructed, but could do with a conclusion and discussion that is more in line with the purpose of the paper. (see comments above)Is the article well constructed and clear?YesIs there adequate analysis, including information on how the data were analyzed ?The conclusions does not answer the questions that were asked at the outset of the paper.Are the conclusions sensible and balanced?YesHave any potential biases or competing interests been disclosed?SummaryI have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. 23andMe and several other companies have been offering Direct-to-consumer (DTC) genomic testing for several years now. In general, such tests seem to have a bad reputation amongst human geneticists because of the perception that individuals may not be able to interpret the findings in a useful way.(Genetics in Medicine (2010) 12, 839–843).Additionally, although genetic testing has an important and widely accepted role in the diagnostics of Mendelian disease and several other areas, many feel that the clinical utility of genomic testing for common disorders such as heart disease or hypertension has not been shown to date. For instance, the Evaluation of Genomic Applications in Practice and Prevention Working Group (EWG) found insufficient evidence to recommend testing for the 9p21 genetic variant or 57 other variants in 28 genes to assess risk for cardiovascular disease in the general populationBe that as it may, has become well known and the range of variants now tested by companies such as 23andMe is increasing; with the advent of relatively cheap exome and even genome sequencing, it seems quite likely that DTC exome and genome sequencing will be offered in the not too distant future. Therefore, it is important to understand several aspects about DTC testing, including the quality of the raw product , the depth, correctness, and utility of annotations provided by DTC companies or widely available to the public, and perhaps most importantly, to know how typical consumers of DTC products use the findings for their own health care or, say, for planning life-style modifications.This paper offers a detailed look at several of these aspects, and I think does an excellent job at providing the reader with a sense of what kind kind of data consumers of DTC products can expect. I think the paper would profit from a number of minor revisions. The paper essentially deals with two different topics. The first involves a number of computational quality control procedures that require a good deal of bioinformatics expertise (I doubt that much of this would be in the reach of most of the customers of DTC sequencing), ranging from a calculation of error rates, an analysis of Mendelian inheritance errors, genotype distributions, and admixture analysis. It was not entirely clear to me how the p-values reported for the SNP similarity analysis were calculated, and a more precise definition of how pseudo-MIEs were counted should be provided. The authors should provide full methodological details of how this analysis was performed. It is also something of a distraction that one of the samples was analyzed using the 23andMe v2 kit, while the rest of the family was analyzed using the v3 kit. I imagine simply that the Son was the person who “went first”, but the apparently different error characteristics of the two versions limit somewhat the findings of the paper.The second major aspect of this paper involves analysis that could be done by non-specialists, using tools such as SNPedia. It was not entirely clear to me why the authors performed the analysis in Figure 5. The heading called “Inference of Phenotypes Using SNP data” is misleading because actually what the findings reveal is a genotype, say, a risk for going bald. However, if the son is not currently bald, then he does not now have the phenotype “baldness”. Instead, the only thing that can be inferred from the genotype is an increased risk of baldness. This is a very important distinction, and it is very important especially for the general public to realize that having a genotype that is associated with an increased risk of some phenotype does not necessarily mean that one will actually develop that phenotype.It would be a nice addition to this paper to hear more about how the family reacted to these findings. Have the increased risks for some of the diseases mentioned led to life style changes? Did the family members report the findings to their physicians? Was a family member worried or uncomfortable about hearing the findings?I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


The Qatari population, located at the Arabian migration crossroads of African and Eurasia, is comprised of Bedouin, Persian and African genetic subgroups. By deep exome sequencing of only 7 Qataris, including individuals in each subgroup, we identified 2,750 nonsynonymous SNPs predicted to be deleterious, many of which are linked to human health, or are in genes linked to human health. Many of these SNPs were at significantly elevated deleterious allele frequency in Qataris compared to other populations worldwide. Despite the small sample size, SNP allele frequency was highly correlated with a larger Qatari sample. Together, the data demonstrate that exome sequencing of only a small number of individuals can reveal genetic variations with potential health consequences in understudied populations. Identification of specific genetic factors associated with disparate disease risk, incidence, or severity among populations is important for increasing access to genomic medicine Exome sequencing, in which the coding sequences of the genome are selected from fragmented DNA and analyzed using next-generation sequencing, has led to remarkable insights into the incidence and frequency of polymorphisms within the coding regions of the human genome ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/release/).We carried out exome sequencing of 7 randomly chosen Qataris from the 3 Qatari genetic subpopulations Overall, the data identified numerous potentially deleterious SNPs at significantly elevated frequency in the Qatari population compared to Europeans, Asians, Africans, and recently admixed Americans. These data demonstrate the power of exome sequencing, where sequencing of only a small number of individuals can be used to identify some of the potentially deleterious health-related alleles in a population and to identify allelic variation that differentiate these peoples from African, European, Asian, and American populations. The results provide insight into this population that, despite its importance in the story of human genetic variation Of the 126,924 sites polymorphic in the seven analyzed Qatari exomes (QE7) or that were fixed and present in 1000 Genomes, 20,857 were missense nonsynonymous SNPs predicted to be potentially deleterious by SIFT or PolyPhen2 . Of these 33, the genes containing 13 were described in the OMIM database of Mendelian Disorders vs a larger Qatari sample of n = 48, n = 174 or n = 193, depending on the number of available samples, using Pearson’s product-moment method . The highest allele frequency correlation between small and larger samples was for Asians (0.91 and 0.92) and Europeans (0.92), for each population, each continent. The allele frequency correlation for European and Asian populations was, in general, higher than that of African and American populations. By comparison, the Qatari allele frequency correlation was 0.89, higher than a “World mix 2″ but lower than a European or Asian population. The observed correlation was similar for 131 potentially deleterious SNPs validated by Affymetrix 5.0 genotyping in 149 Qataris  and significantly higher vs at least one, including 14,347 (56%) significantly higher in QE7 compared to four continents (Web Resource S1). Of the remainder, 2 (<1%) were significantly lower than four continents . These were two intronic SNPs in the gamma aminobutyric acid B receptor 1 (GABBR1) gene linked to nicotine dependence SNPs at significantly different allele frequency that make Qataris distinct from Europeans, Asians or Africans, were identified with an Fst threshold of population differentiation of Fst>0.25 and a Storey q-value multiple testing corrected p value of q<0.05 for the probability of observing  success . Out of Out of 25,803 potentially deleterious SNPs significantly higher or lower than the overall frequency in at least one continent, 1,853 were missense nonsynonymous SNPs predicted to be potentially deleterious by SIFT or PolyPhen2 . Of thesThe main objective of this study is to identify variants for further study in Qatar. For this purpose a set of variants were selected to be highlighted in Of the 9 potentially deleterious missense coding SNPs identified in ≥6 of 14 QE7 significantly higher in QE7 compared to one or more continents where the SNP was in a gene previously linked to human health, but the potentially deleterious SNP was different than previously reported, we selected 3 of potential medical relevance in Qatar to highlight  than the overall frequency in at least one continent. To identify these SNPs, we used a combination of a statistical test robust to the small effects of sample size together with an Fst statistic; the latter has been successfully applied to identify missense SNPs for adaptation to altitude in Tibetans Among the 392 missense potentially deleterious SNPs not in dbSNP nor observed in 1000 Genomes and hence potentially novel in the Qatari population, one was observed in all seven Qataris, 5 were observed at least once in the three population clusters , and 6 were observed at least once in two of the three population clusters. The remaining 380 novel potentially deleterious coding nonsynonymous missense SNPs were observed in only one of the QE7. Five of these novel potentially deleterious SNPs observed in two or more Qatari populations are within genes previously linked to disease, including a Phe374Leu mutation in the copper transporter protein CUTC involved in the copper metabolism vs at least one, including 1 SNP significantly lower than 3 continents, 3 significantly lower than 2 continents and 32 significantly lower than 1 continent. This list includes 2 SNPs previously linked to pigmentation of skin  Among the missense potentially deleterious SNPs with lower or higher frequency in Qataris, there were 36 SNPs lower in allele frequency than all 1000 Genomes Project overall continental frequencies  and significantly lower By identifying potentially deleterious polymorphisms from exome sequences of 7 randomly chosen individuals of Qatari ancestry, and by validating these polymorphisms in a larger population of Qataris using genome-wide SNP array and TaqMan analysis, we have demonstrated that at least some coding sequence variations of potential medical importance within a population can be uncovered by exome sequencing of only a small number of subjects. This strategy can be used as a screening approach to identify, with reasonable confidence, common genetic variations of potential medical importance within a population. While this cannot substitute for assessing large numbers within any given population, this study demonstrates that harnessing the power of next-generation genomics can be used to identify enriched SNPs of potential medical importance that are common in populations with reasonable confidence using a small sample. In this context, a significant subset of the health-relevant variation in large segments of world populations that have yet to be analyzed could be surveyed at minimal cost. Likewise, for global-scale genomics initiatives, many small samples that cast a wide net can complement the current approach of studying large samples of fewer populations in understanding global genomic diversity.Human subjects were recruited and written informed consent obtained at Hamad Medical Corporation (HMC), Doha, Qatar under protocols approved by the Medical Research Center & Research Committee and the Institutional Review Board of Weill Cornell Medical College in Qatar. In a previous study, we identified the three major components of ancestry in Qatar  based on a sample of 156 unrelated Qatari genotyped using the Affymetrix Genome-Wide SNP Array 5.0  as members of Q1 (Bedouin), Q2 (Persian/South Asian) Q3 (African) From the Qataris classified as Q1, Q2 and Q3, 7 individuals with Affymetrix 5.0 genotypes available  were randomly selected for exome sequencing. The sample included Qataris with >55% ancestry within one cluster  SNP genotypes were called in coding exons Available databases, including dbSNP, GATK VariantAnnotator SNPs were annotated in two ways: by SNP and by gene. First, SNPs involved in disease or drug metabolism were identified by querying each database with the dbSNP rsID. For OMIM SNPs, where there are nonsynonymous missense SNPs not linked to rsIDs, the database was queried using the gene name and amino acid substitution . Second, coding SNPs within genes involved in disease or drug metabolism were identified by querying each database with the gene that contained the coding SNP.Affymetrix 5.0 genotypes for the 156 Qataris . Individuals were placed in 4 continental groups , when compared to the 1000 Genomes allele frequencies across populations within Europe, Asia, Africa or America. While the approach of comparing to the overall frequency in populations within a continent is not expected to identify differences between Qatari and all populations within a continent, particularly for alleles with high variance across a continent, this approach is expected to identify differences between Qatari and at least a subset of continental populations, a strategy that balances comparison to a larger sample number while flagging alleles of potential interest in the Qatari population. The allele frequency estimates used in the frequency enrichment test were calculated from the 1000 Genomes SNPs in the October 2011 Integrated Phase 1 Variant Set Release of genotypes on 1,092 individuals  could be calculated, two approaches were used to assess whether the alternative allele was at a higher or lower frequency than expected when compared to the European, Asian, African and American continental 1000 Genomes allele frequency. First, an Fst statistic was calculated vs at least one continental population (FDR <0.05 and Fst >0.25) was combined into the full list of SNPs at significantly different allele frequency.This combined approach was used to assess 126,924 SNPs, where a threshold of Fst >0.25 Figure S1Population structure analysis of 156 Qatari genotyped on the Affymetrix Genome-Wide SNP Array 5.0.(DOC)Click here for additional data file.Figure S2Population structure analysis of 1,092 exomes from Europe, Asia, Africa and the Americas genotyped on 48 SNPs designed to distinguish Qatari of Bedouin, Persian/South Asian or African ancestry.(PDF)Click here for additional data file.Table S1SNPs used to identify the 3 Qatari Genetic Subpopulations.(PDF)Click here for additional data file.Table S2Ancestry proportion, sequencing and coverage of seven Qatari exomes.(PDF)Click here for additional data file.Table S3Population samples from 1000 Genomes Project Phase 1.(PDF)Click here for additional data file.Table S4Allele frequency correlation between small and large samples.(PDF)Click here for additional data file.Details S1Supplemental Details Regarding Predicted Deleterious Nonsynonymous Coding SNPs Identified in the Qatari Population.(PDF)Click here for additional data file.Methods S1(PDF)Click here for additional data file.References S1(PDF)Click here for additional data file.Web Resource S1SNPs genotyped in seven Qatari exomes.(PDF)Click here for additional data file.


HSP17.8 may be differentially expressed in different accessions of a crop species exposed to identical stress conditions. The ability of different genotypes to adapt to various stress conditions resides in their genetic diversity. Allelic variations are the most common forms of genetic variation in natural populations. In this study, single nucleotide polymorphisms (SNPs) of the HSP17.8 gene were investigated across 210 barley accessions collected from 30 countries using EcoTILLING technology. Eleven SNPs including 10 from the coding region of HSP17.8 were detected, which form nine distinguishable haplotypes in the barley collection. Among the 10 SNPs in the coding region, six are missense mutations and four are synonymous nucleotide changes. Five of the six missense changes are predicted to be deleterious to HSP17.8 function. The accessions from Middle East Asia showed the higher nucleotide diversity of HSP17.8 than those from other regions and wild barley (H. spontaneum) accessions exhibited greater diversity than the cultivated barley (H. vulgare) accessions. Four SNPs in HSP17.8 were found associated with at least one of the agronomic traits evaluated except for spike length, namely number of grains per spike, thousand kernel weight, plant height, flag leaf area and leaf color. The association between SNP and these agronomic traits may provide new insight for study of the gene's potential contribution to drought tolerance of barley.Small heat shock protein 17.8 (HSP17.8) is produced abundantly in plant cells under heat and other stress conditions and may play an important role in plant tolerance to stress environments. However,  Small heat shock proteins (sHSPs) with a molecular weight of 15 to 42 kDa are produced ubiquitously in prokaryotic and eukaryotic cells under heat and other stress conditions at various growth stages HSP17.8 plays a role in tolerance to heat HSP17.8 showed chaperone-like activity to protect citrate synthase from thermal aggregation at 43°C in the cyanobacterium Anabaena sp. PCC 7120 ArabidopsisHSP17.8 showed at least 1.5 times more expression in freezing-tolerant transgenic maize than in non-transgenic maize HSP17.8 transcripts were present at a higher level in drought-tolerant control plants of Katya cultivar than in non-tolerant Sadovo cultivar of wheat HSP17.8 showed expression exclusively in drought-tolerant barley genotypes e.g. Martin and Hordeum spontaneum 41-1, and not in drought-sensitive genotype e.g. Moroc9-75, under drought stress Heat shock protein 17.8 (HSP17.8), a member of class I cytosolic sHSPs, presents as a dimer under normal physiological conditions and is converted to high oligomeric complexes, ranging from 240 kDa to >480 kDa, after heat shock ArabidopsisPopulus trichocarpa), EcoTILLING identified 63 SNPs in nine different genes in 41 different populations HvCBF4 gene was significantly (P<0.001) associated with salt tolerance in 188 Tibetan barley accessions Lhcb1 gene were significantly (P<0.01) associated with several agronomic traits in 292 barley accessions HSP17.8 and its association with agronomic traits in barley.The ability of different accessions of a crop species to adapt to stress is a result of sequence variation in genes in the accessions of the given crop species. Single nucleotide polymorphisms (SNPs) and small insertions and deletions (indels) are the most common forms of genetic variation in natural populations, and is a reflection of evolution and adaptation that play a prominent role in the heritability of phenotypes HSP17.8 in a natural barley population comprising of 210 accessions collected from diverse geographical origins. Distribution of SNPs across different geographic origins , row types (two-row or six-row) and other possible categories (such as wild versus landrace) were investigated. Population parameters were estimated using SNPs found in different barley populations. An attempt was also made to assess potential effect of SNPs on protein function and their association with six agronomic traits in barley.In this study, EcoTILLING approach was used to detect genetic variations of Hordeum vulgare L.) accessions containing 171 H. vulgare landraces and 39 wild relatives of Hordeum vulgare ssp. spontaneum (hereafter named H. spontaneum) were obtained from the International Centre for Agricultural Research in the Dry Areas (Seeds of 210 barley (ry Areas . Of the 2), spike length (SL in cm), number of grains per spike (NGS), leaf color (LC in SPAD), plant height (PH in cm) and thousand kernel weight (TKW in g)—in replicated field experiments. Three randomly selected plants of each accession from each plot were characterized for the six traits using the following methods:The germplasm collection (210 accessions) was sown with three replications during two growing seasons (2009/2010 and 2010/2011) at the Experimental Station of Guangzhou University, Guangzhou, Guangdong Province, China . Eleven seeds of each accession were planted 30 cm apart in 1.5 m long, single-row plots. All accessions were evaluated for six agronomic traits—flag leaf area (FLA in cm2) of the uppermost, fully expanded leaf of the main tiller at flowering. FLA = leaf length×leaf width×0.75 1. Flag leaf area  was measured from the base of each main spike to the top of the spike, excluding awns, at maturity.3. Number of grains per spike (NGS) was counted at maturity and the average number of seeds from three spikes used for analysis.4. Leaf color  was determined at heading, before any symptoms of senescence were visible. The middle section of a randomly-selected flag leaf was evaluated between 09:00 and 12:00 h using a chlorophyll meter  and averaged over two measurements for analysis.5. Plant height  was measured from ground level to the base of spike at maturity.6. Thousand kernel weight  was calculated based on a sample of 250 seeds per plot.HSP17.8 as described in Wienholds et al. HSP17.8 from GeneBank (accession no. AK368988.1) with melting temperatures around 60°C by using Primer 5.0 software   . The regadaptors . M13 forGenomic DNA of barley accessions was extracted from 200 mg young leaf tissue following Guo et al. HSP17.8 was amplified by a nested PCR described by Wang et al. For nucleotide polymorphism screening with EcoTILLING, the target region of 4, 0.002% (w/v) Triton X-100, 0.2 µg/ml of bovine serum albumin, and 0.4 µl CEL I enzyme solution, was added to 10 µl of heteroduplex DNAs, and incubated at 45°C for 15 min. Digestion was stopped by adding 5 µl 0.25 M EDTA (pH 8.0), mixing thoroughly and then incubating on ice. Digested products were purified using isopropanol as described by Raghavan et al. 2O were added and heated at 85°C for concentrating sample to about 3 µl. Samples were loaded (0.8 µl/well) on denaturing 6.5% polyacrylamide gels on LI-COR 4300 DNA Analyzer. Two electronic image files were produced per gel run, one in the IRD700 channel and the other in IRD800 channel. Tiff images were manually scored using the GelBuddy program The PCR products were digested by CEL I as described by Raghavan et al. http://www.ebi.ac.uk/tools). The SIFT (Sorting Intolerant from Tolerant) and PARSESNP (Position-Specific Scoring Matrix) programs were used to predict the impact of missense mutations on protein function One representative genotype for each unique haplotype was reamplified by gene-specific primers using 40 ng of genomic DNA. The resulted PCR fragment was directly sequenced from both directions using an ABI 3730xl DNA Analyzer by a commercial company  to confirm the polymorphisms. Each polymorphic site was sequenced in more than one accession to confirm only two alleles segregating at any specific site. Sequences were analyzed using ClustalW software (Q) in the TASSEL v3.0 software (http://www.maizegenetics.net/tassel), where the SNP being tested was considered as a fixed effect, and the factor and matrix of subpopulation membership (Q matrix) were used as cofactors to account for population structure. Possible population structure was depicted using genotypic data of the 210 barley accessions and 21 genome-wide SSR markers (3 SSRs for each chromosome)   by Struc2), leaf color (LC in SPAD) and plant height (PH in cm) were measured for all 210 accessions  and four were transversions . Among the ten coding SNPs, six were nonsynonymous mutations s and four were silent synonymous mutations. Five of the missense mutations were predicted to have a severe effect on the HSP17.8 protein function.By using EcoTILLING, 13 polymorphic sites were detected in the targeted region of cessions , Fig. 1.HSP17.8 as measured by π (pairwise nucleotide diversity) was 0.00188 among the 210 accessions. Across different geographic regions, the range of π values spanned from 0.00049 for African accessions (40 accessions) to 0.00212 for Middle East Asian accessions (44 accessions). Similarly, π for H. spontaneum was higher than for H. vulgare landrace accessions. In addition, 2-row and 6-row barley had similar nucleotide diversity  were estimated. Tajima's D values were negative for all sub-populations except for Africa population. However none of the values was statistically significant (P<0.05)  , thus, nFor the 11 SNPs confirmed by sequencing, nine distinguishable haplotypes were detected across 210 accessions . The levHSP17.8 haplotypes differed markedly across geographical regions (H. spontaneum and H. vulgare landraces were widely separated into six haplotypes with only three common between the two groups (The frequencies of  regions . Haplotyo groups . When coo groups .HSP17.8) were rare alleles (frequency <1%), and three SNPs  or two SNPs (positions 525 bp and 565 bp) were in complete linkage disequilibrium, five SNPs  were excluded from further analysis. Although two SNPs (position 469 and 582 bp) were synonymous variations, they may be in complete linkage disequilibrium with other non-synonymous mutations, which could result in biological changes in the organism. Therefore, these two SNPs were further analyzed. Thus only six SNPs  were used in association analysis. Significant association (P<0.05) was observed for four distinct SNPs with at least one of the evaluated traits except for spike length, and four of the associations were highly significant (P<0.01) (P<0.01) with one SNP (position 469 bp in HSP17.8) in 2009/2010, which explained 3.9% of the phenotypic variation. NGS was significantly associated (P<0.01) with one SNP at position 599 bp, which explained 3.3% and 4.6% phenotypic variation in 2009/2010 and 2010/2011, respectively. One SNP (position 300 in HSP17.8) significantly associated (P<0.05) with PH was observed, accounting for approximately 2% phenotypic variation in 2009/2010 and 2010/2011. LC showed significant (P<0.05) association with three SNPs , which explained 2.8%, 3.9% and 2.4% of the phenotypic variation, respectively. TGW was significantly associated (P<0.05) with one SNP at position 300 bp in 2009/2010, which explained 2.6% phenotypic variation.Because two SNPs (positions 196 bp and 483 bp in (P<0.01) . These aHSP17.8, 171 H. vulgare landraces and 39 H. spontaneum accessions from 30 countries in three continents were characterized for allele diversity. EcoTILLING revealed 11 SNPs and nine haplotypes after analysis of 126,000 bp sequences in HSP17.8. Of the 11 unique SNPs, ten were in the coding region, which included four silent synonymous mutations and six missense mutations. Of the six missense mutations, four SNPs were predicted by the SIFT program to severely affect protein function, and one mutation was predicted by PSSM program to severely damage the function of the predicted protein. Because there is a 20% false-positive error in SIFT HSP17.8 in stress tolerance.To characterize genetic variation in HSP17.8  and average pairwise nucleotide diversity (π). Parameters θ and π are affected differently by natural selection. In this study, Tajima's D neutrality test revealed no evidence of natural selection for HSP17.8  but undeHSP17.8 was lower, and the level of haplotype diversity (0.526) was higher in this study. The average frequency of SNPs was 1 per 54.5 bp, which was similar to that found in Chen et al. (1 SNP/53.8 bp) H. spontaneum had a higher nucleotide diversity (π) and haplotype diversity than H. vulgare landraces in this study, which agrees with previous reports H. vulgare landraces would have gone through a population bottleneck during domestication, which resulted in a reduction in genetic diversityHSP17.8 in wild barleys and Middle East landraces might offer elite alleles for the improvement of stress tolerance in barley. To date, a number of wild barley and Middle East types have been used in barley breeding programs Compared to previous reports on barley Different ecotypes of barley germplasm show their specific traits in terms of ecology, morphology and physiology HSP17.8 in five barley accessions, namely Martin, HS4-1, Moroc9-75, Tadmor and WI2291, was up-regulated under drought stress with the expression fold changes of 15.67, 19.43, 23.92, 2.48 and 1.15, respectively 2 values of 0.138, 0.163, 0.169, 0.134 and 0.218, respectively, suggesting the high expression level of HSP17.8 is likely associated with PH, NGS, TKW, FLA and LC. In addition, BLAST search in GenBank (http://www.ncbi.nlm.nih.gov/genbank/) showed that HSP17.8 exhibited nucleotide sequence homology to a barley EST (GenBank accession AL510041) that was located in 63.4 cM on chromosome 4H HSP17.8 may have cis affects for the QTLs of PH, TGW, NGS, LC, and FLA located on chromosome 4H.In our previous studies, HSP17.8 gene were significantly associated with at least one agronomic trait investigated. Three SNPs at positions 469 bp, 525 bp and 599 bp of HSP17.8 were significantly associated with LC, one SNP at position 469 bp with FLA, one SNP at position 599 bp with NGS, and one SNP at position 300 bp with PH and TGW. Of the four SNPs associated with phenotypic traits, three at positions 300 bp, 525 bp and 599 bp in HSP17.8 were missense mutations which, according to SIFT, were predicted to severely affect protein function. Association analysis showed that the three missense mutations were deleterious mutations. Due to low minor allele frequency for two SNPs (positions 300 and 525), their association results should be interpreted with caution. However, one SNP at position 469 associated with phenotypic traits was a synonymous mutation that may not change the structure of the gene product. A plausible reason for this result is the hitchhiking effort of a locus in positive selection HSP17.8 is necessary to evaluate their possible influence on agronomic traits and usefulness as future selection markers in modern barley breeding program.Association mapping has recently emerged as an alternative approach to mapping QTL and genes associated with quantitative traits using a diverse collection of germplasm lines or breeding materials HSP17.8 among 210 barley accessions collected from 30 countries by EcoTILLING technology, and 5 SNPs with missense changes are predicted to be deleterious to protein function. Four SNPs significantly associated with agronomic traits identified in this study can be used as DNA markers in marker-assisted selection to improve these agronomic traits after further validation of their functions in individual cultivars.In this study, 11 SNPs were detected and nine unique haplotypes were identified in Table S1Details on barley accessions used in this study.(DOC)Click here for additional data file.Table S2Primer sequences used for PCR amplification of HSP17.8.(DOC)Click here for additional data file.Table S3SSR marker information used in evaluation of population structure.(DOC)Click here for additional data file.


Xenopus tropicalis, an incomplete and occasionally incorrect genome assembly compounds this problem. To facilitate cloning of X. tropicalis mutants identified in forward genetic screens, we sought to combine bulk segregant analysis and exome sequencing into a single step.Exome sequencing has transformed human genetic analysis and may do the same for other vertebrate model systems. However, a major challenge is sifting through the large number of sequence variants to identify the causative mutation for a given phenotype. In models like Here we report the first use of exon capture sequencing to identify mutations in a non-mammalian, vertebrate model. We demonstrate that bulk segregant analysis coupled with exon capture sequencing is not only able to identify causative mutations but can also generate linkage information, facilitate the assembly of scaffolds, identify misassembles, and discover thousands of SNPs for fine mapping.Exon capture sequencing and bulk segregant analysis is a rapid, inexpensive method to clone mutants identified in forward genetic screens. With sufficient meioses, this method can be generalized to any model system with a genome assembly, polished or unpolished, and in the latter case, it also provides many critical genomic resources. D. melanogaster and v7.1 [accession number AAMC02000000] available at http://www.xenbase.org) using Burrows-Wheeler Aligner with default parameters [For ng Btrim . We usedrameters . The maprameters .Using the two pileup files from WT and MUT as inputs, we created a script to analyze the alleles and their frequencies along each position of the genome with mapped reads. At each position, a minimum of 10 mapped reads in both WT and MUT pools were required for analysis. We identified a putative SNP if (i) the major allele frequency (MAF) in reads of WT is in the range of 50% and 75%; or (ii) WT major allele differs from that of MUT.For all putative SNPs, we counted the number of homozygous SNPs (nh) in the MUT pools where MAF ≥ 90%. Then the homozygosity ratio (r) of homozygous SNPs in a region is calculated as r = nh/nt, where nt is the total number of SNPs in the same region. For reference genome v4.1, we used each scaffold as a region. For reference genome v7.1, since the scaffolds are much longer, non-overlap sliding windows with a size of 0.5 Mb was used for a region. Detailed protocols and scripts for mapping sequence data and calling SNPs is available at our website , and SNP data is available per request.http://watcut.uwaterloo.ca/watcut/watcut/template.php) to generate primers flanking SNPs that create or abolish a restriction site (RFLP markers). Primers were designed to amplify at least 100–250 bp flanking a SNP so we could resolve genotypes on a simple 4% agarose gel. A list of all the primers used is described in Additional file RFLP markers were designed in regions with LOH. All the SNPs in these regions were evaluated with WAT-CUT (grinch was done using primers designed around Simple Sequence Length Polymorphisms (SSLPs) as identified by the meiotic map. PCR conditions were the same as above. Primers 148E4, 148 M1, s304-875 K, s304-219 K, 560E1, and 560B2 were designed around SSLPs with a florescent 5’-m13 tail on the Forward primer [Preliminary mapping of in vitro transcribed with T7 High Yield RNA Synthesis Kit (E2040S) from New England Biolabs to synthesize digoxigenin-labeled antisense probes. Pronephric markers clones were obtained from Open Biosystems: atp1a1, IMAGE: 6988026; slc7a8, IMAGE: 53828775: slc5a9, IMAGE: 5308256; smp-30, IMAGE: 6999181; hnf1-β, TNeu141k07; cdh16, TNeu004i09. Whole mount in situ hybridization was performed according to the standard protocol with minor modifications [We obtained clones from Open Biosystems (Thermo Scientific). The DNA templates were ications ,50. We sIn vitro fertilization and microinjection were done as previously described [http://tropicalis.yale.edu). Embryos were injected either at one or at two cell stage with 0.5 or 1 ng of MO solution or at 2 cell stage with up to 500 pg ccdc40 mRNA. Ccdc40 mRNA was co-injected with 100 pg GFP mRNA. After injections, embryos were left in 1/9X MR + 3% Ficoll for 1 hr and then transferred to 1/9X MR supplemented with 50 μg/ml of gentamycin. Embryos were raised at 22-27°C until the appropriate stage for fixation or evaluating phenotype. Pax8 translation blocking MO was obtained from Genetools, LLC. The sequence of the MO was: 5’ ATGCTGCTGTTGGGCATCTTCCTCC 3’. MO was co-injected with Alexa 488 dye (Invitrogen) as a lineage tracer.escribed  and protccdc40 transcript according to the manufacturer’s instructions. PCR was performed using oligonucleotides in the 5’UTR or at beginning of the transcript . A 1.5 μg aliquot of RNA was used to perform RT–PCR with SuperScript III Reverse Transcriptase (Invitrogen) using a gene specific primer located in the 3’UTR  were injected dorsally. Movies were captured using Canon EOS 5D Mark II DSLR camera.in situ hybridization; ST: Stage.SNP: Single nucleotide polymorphism; HTS: High throughput sequencing; WGS: Whole genome sequencing; BSA: Bulk segregant analysis; MUT: Mutant; WT: Wildtype; LOH: Loss of heterozygosity; RFLP: Restriction fragment length polymorphism; MO: morpholino; SEM: scanning electron microscopy; TEM: Transmission Electron Microscopy; SSLPs: simple sequence length polymorphisms.; WMSISH: Whole Mount The authors declare that they have no competing interests.FDV, DB and MKK conceived and designed the experiments and wrote the manuscript. FDV, DB and MKK carried out the wet laboratory experiments. YK analyzed the HTS. MJG and MKK designed the exon capture array. FDV and DB contributed equally to this project. All the authors read and approved the final manuscript.Additional file Figures 1–5 and Additional files Tables S1 and S2.Click here for fileMovie 1: Epidermal cilia driven flow imaging of Wildtype embryos.Click here for fileMovie 2: Epidermal cilia driven flow imaging of grinch mutant.Click here for fileEpidermal cilia driven flow imaging ofrescued grinch mutant embryo (left side injected).Click here for fileMovie 4: Epidermal cilia driven flow imaging of rescued grinch mutant embryo (right side injected).Click here for file


Equus przewalski. The utility of the SNP array in genome-wide association was confirmed by mapping the known recessive chestnut coat color locus (MC1R) and defining a conserved haplotype of ∼750 kb across all breeds. These results demonstrate the high quality of this SNP genotyping resource, its usefulness in diverse genome analyses of the horse, and potential use in related species.An equine SNP genotyping array was developed and evaluated on a panel of samples representing 14 domestic horse breeds and 18 evolutionarily related species. More than 54,000 polymorphic SNPs provided an average inter-SNP spacing of ∼43 kb. The mean minor allele frequency across domestic horse breeds was 0.23, and the number of polymorphic SNPs within breeds ranged from 43,287 to 52,085. Genome-wide linkage disequilibrium (LD) in most breeds declined rapidly over the first 50–100 kb and reached background levels within 1–2 Mb. The extent of LD and the level of inbreeding were highest in the Thoroughbred and lowest in the Mongolian and Quarter Horse. Multidimensional scaling (MDS) analyses demonstrated the tight grouping of individuals within most breeds, close proximity of related breeds, and less tight grouping in admixed breeds. The close relationship between the Przewalski's Horse and the domestic horse was demonstrated by pair-wise genetic distance and MDS. Genotyping of other Perissodactyla  was variably successful, with call rates and the number of polymorphic loci varying across taxa. Parsimony analysis placed the modern horse as sister taxa to  We utilized the previously generated horse genome sequence and a large SNP database to design an ∼54,000 SNP assay for use in the domestic horse and related species. The utility of this SNP array was demonstrated through genome-wide linkage disequilibrium, inbreeding and genetic distance measurements within breeds, as well as multidimensional scaling and parsimony analysis. Association mapping confirmed a large conserved segment containing the chestnut coat color locus in domestic horses. We also assess the utility of the SNP array in related species, including the Przewalski's Horse, zebras, asses, tapirs, and rhinoceros. This SNP genotyping tool will facilitate many genetics applications in equids, including identification of genes for health and performance traits, and compelling studies of the origins of the domestic horse, diversity within breeds, and evolutionary relationships among related species. Horses have held a valued place in human civilization for over 5,000 years through service in war, agriculture, sport and companionship http://ncbi.nlm.bih.gov/genome/guide/horse). A significant SNP discovery component within the NHGRI project identified ∼750,000 SNPs from Twilight and ∼400,000 SNPs from seven horses of different breeds, enabling an estimate of the overall frequency of SNPs within the equine genome (∼1/1500 bp), and providing sufficient markers to construct a whole genome SNP panel for use in the domestic horse and related species The horse was selected by the National Human Genome Research Institute (NHGRI) for whole genome shotgun sequencing as a representative of the order Perissodactyla. The genome of the female Thoroughbred Twilight was sequenced to 6.8 fold coverage at the Broad Institute of Harvard and MIT accompanied by paired-end sequences from over 150,000 BAC clones performed at the Helmholtz Centre for Infection Research, and the University of Veterinary Medicine Hannover, Germany. This project has produced the EquCab2.0 assembly with a total contig length of 2.43 Gb, 96% of it assigned to chromosomes, and a predicted genome size of 2.67 Gb , or in both discovery horses and Twilight, were utilized . Of the 54,602 SNPs provided genotype data, and 53,524 SNPs were validated , indicating overall assay conversion and validation rates of 0.910 and 0.980, respectively. The validation rates were highest for SNPs that were observed in a single discovery breed (0.990) when compared to Twilight, or observed in any two discovery breeds (0.989) . 53,066 The number of polymorphic SNPs (MAF≥0.01) within a breed ranged from 43,287 to 52,085 (79% to 95%); 26,473 (48.5%) SNPs were polymorphic in every breed . 90% of Equus zebra hartmannae) completely failed to genotype. Individual genotyping rates were slightly lower across the Hippomorpha (mean 0.959), and dramatically lower across the Ceratomorpha (mean 0.246) when compared to the domestic horse (mean 0.996)  . Qualityn 0.996)  in the Hn 0.996) .Due to lower quality scores and lower genotyping rates in some species, the genotypes in the Perissodactyla were further filtered based on raw intensity scores, individual genotyping rates, and SNP genotyping rates see , suggestEquus przewalskii) to 0.834 in the Hartmann's Mountain Zebra (Equus zebra hartmannae). The number of validated loci ranged from 265 (0.8%) in the Somali Wild Ass  to 26,859 (50.8%) in the Przewalski's Horse  in the Norwegian Fjord to 47,669 (87%) in the Quarter Horse . Mean MA = 0.247 .2r) within and across breeds. As expected, LD was higher within a breed than across breeds. Initial LD declined rapidly across all horses with mean 2r dropping below 0.2 by 50 kb   were highest in the Thoroughbred and Standardbred , and lowest in the Hanoverian, Quarter Horse and Mongolian  . The aveMetric multidimensional scaling (MDS) of pair-wise genetic distances was used to visualize the relationships among the 335 horses from 14 breeds. Plotting dimension 1 versus dimension 2 resulted in tight clustering by breed, with the exception of the Quarter Horse, Hanoverian and Swiss Warmblood . The 7 SEquus przewalski, and both the modern horse and E. przewalski as a sister clade to all the other equids, which fell out into species groups  placed the modern horse as sister taxa to s groups .Pair-wise genetic distances were also calculated with all domestic horse breeds, and the Przewalski's Horses (n = 9) . MDS revThe relationships between the domestic horse breeds and the Przewalski's Horse are also demonstrated by parsimony analysis in MC1R) on ECA3 ASIP) (agouti) STX17) on ECA25 To demonstrate the utility for GWAS, within and across breed mapping was performed for 3 coat color loci. Phenotypes were inferred either from the genotypes of all 9 known coat color loci with consideration of known interactions, or from genotype only at the locus of interest to model a simple Mendelian trait. The three most common alleles in our data set included the recessive chestnut coat color locus , when black was considered as a simple recessive trait (ignoring epistatic interactions). The gray phenotype was not successfully mapped within the two breeds attempted.Within breeds the chestnut locus was successfully mapped in Quarter Horses (22 cases and 24 controls) and Thoroughbreds (11 cases and 26 controls) . The ASIThe assay conversion rate was lower on this equine array when compared to a similar assay designed for cattle or pigs (91.0% versus 92.6% and 97.5% respectively); however, SNP validation rates were slightly higher in the horse than in bovine or porcine  http://www.illumina.com/applications/agriculture/livestock.ilmn#livestock_overview). Breeds with recent or ongoing admixture, such as the Quarter Horse, Hanoverian and Swiss Warmblood, had the highest mean MAF and the largest numbers of informative SNPs, while the lowest mean MAF were in the Norwegian Fjord, Belgian and Icelandic horse. The relatively high number of informative SNPs in the Icelandic horse may reflect its use in SNP discovery. Despite low genetic diversity and high levels of inbreeding, the mean MAF in the Thoroughbred was higher than any other non-admixed breed, and the fraction of informative SNPs exceeded that of any other breed included in the SNP discovery effort with the exception of the Quarter Horse. The high level of SNP informativeness in the Thoroughbred breed likely reflects bias due to its use in both SNP discovery and as the reference genome sequence.The number and mean MAF of polymorphic SNPs varied between breeds. On average the number of SNPs informative in any given domestic horse breed was higher than informativeness of similar assays within given cattle and dog breeds Equus caballus. SNP validation rates in Equus species other than Equus przewalski were low, which may reflect species divergence as well as the very limited number of individuals genotyped in most species; genotyping a larger cohort within each species would be necessary to determine the true polymorphism rates. Further work is also necessary to determine the accuracy of genotyping calls in Equus sp. by reproducibility, concordance with other genotyping methods and confirmation of Mendelian inheritance with parent-offspring trio data We attempted to use the EquineSNP50 BeadChip to genotype a limited number of individuals from 18 other Perissodactyl species. Due to the SNP discovery design, it was unlikely that a large proportion of the markers in this assay would be polymorphic in other species; however, the identification of even several hundred useful markers in any of these species would provide a dramatic increase in the number of autosomal markers available for conservation genetics applications. A variable number of genotypes were produced across species, with higher genotyping rates and better quality scores in the more closely related Hippomorpha  than in the Ceratomorpha (rhinos and tapirs). After data filtering, the assay conversion rate of the remaining SNPs was fairly high, and quality scores in Hippomorpha species were similar to those in the domestic horse, suggesting it may be a useful tool for certain applications in species other than Measurements of genetic diversity, inbreeding and LD all reflect population demographic history. Our measurements of genome-wide LD within and across breeds agreed well with previous work based on ten randomly selected 2 Mb genome segments 2r values dropping below 0.2 within the first 50–100 kb. The short extent of LD in the Mongolian and Quarter Horse reflects the low level of inbreeding and high genetic diversity in both breeds. The short extent of LD in the Mongolian horse is likely a result of its age and large population size. This breed has been bred in domestication since approximately 2000 BC, and the current population size is ∼3 million individuals LD declined most rapidly in the Quarter Horse and Mongolian horse, with In contrast, LD was clearly the highest in the Thoroughbred, reflecting the breed's low diversity, high inbreeding, and closure of the studbook to outside genetic influence for more than 300 years. Previous work has demonstrated that approximately 78% of Thoroughbred alleles are derived from 30 founders, and that a single founder stallion is responsible for approximately 95% of paternal lineages The mean pair-wise genetic distance between individuals within a breed was 0.24, which is higher than reported in cattle, but lower than reported in sheep (0.21 and 0.25 respectively) MDS plots demonstrated that individuals within most breeds were tightly clustered in relation to other breed groups. This was true even for the Thoroughbred population where two geographically distinct sample origins were represented . The exceptions to this were the three breeds with recent and/or ongoing admixture; the Quarter Horse, Hanoverian and Swiss Warmblood. In addition, the Hanoverian and Quarter Horse, and to a lesser extent the Swiss Warmblood, had larger variation along dimension 1 than other breeds, suggesting that the admixture may be resulting in significant population substructure. The Andalusian breed was not tightly clustered in dimension 6, suggesting population substructure as well. This is consistent with the practice of some American breeders crossing Andalusians (from Spain) with closely related Lusitano horses  in their breeding programs. Close relationships between some breeds were also visualized, including the clustering of the Standardbred and the French Trotter apart from the other breeds in dimension 3. This may be the result of the influence of the Standardbred on the French Trotter, or similar selective pressures for the trotting phenotype in both breeds. The Norwegian Fjord, Icelandic, Mongolian, and Belgian clustered together in the first 3 dimensions, and Icelandic and Norwegian Fjord horses clustered tightly together in all 6 dimensions. This may reflect the suggested influence of Mongolian genes in the development of the Norwegian Fjord and subsequent development of the Icelandic horse from Scandinavian stock imported to Iceland Equus spp. using over 40,000 SNPs, high bootstrap support distinguishes Equus caballus from Equus przewalskii while also making a clear distinction between those species and the zebras and asses. With further work, the use of random nuclear SNPs in equid phylogeny studies should prove superior to the existing studies that use either mitochondrial SNPs, or SNPs from just a few nuclear genes In parsimony analysis of only Equus ferus) Equus przewalskii) is a sister species to the Tarpan. This close relationship between the domestic horse and the Przewalski's Horse is also likely a result of relatively recent gene flow between these lineages since divergence from a common ancestor. While Equus przewalskii and Equus caballus have a different number of chromosomes , they can interbreed and produce viable offspring. Since their discovery by the western world in the late 1880s, the question of admixture of the Przewalski's Horse and domestic horse has remained a topic of debate and controversy. Known introgressions took place in the early years of the propagation program that prevented the extinction of the species Equus przewalskii and Equus caballus in the wild, as the range of the Przewalski's Horse and the domestic horse overlapped in China, Russia and Mongolia Equus ferus and Equus przewalskii are considered different species based on chromosomal number differences, surviving Przewalski's Horses today are truly Equus przewalskii and Equus caballus hybrids The horse is thought to have been domesticated from the now extinct Tarpan  predicted a 98% validation rate. SNP quality scoring was conducted by Illumina Inc. and included estimates of sequencing quality on the Illumina platform based on flanking characteristics such as G-C content, proximity of known SNPs and unique flanking sequence. After scoring, SNPs requiring a single-bead type (Infinium II) were preferred and of these, the highest scoring 60,000 were passed to the array design regardless of genomic location.i.e. full-sibling pairs). The remaining individuals from any given breed were selected to be no more related than second cousins  based on pedigree analysis.Twilight and the 7 SNP discovery horses from the NHGRI equine genome sequencing project  were selected as controls Equus caballus in 53 individuals from 18 species evolutionarily related to the domestic horse, including domestic and wild asses, zebras, tapirs and rhinoceroses .Individual genotyping rate was defined as the proportion of loci that produced a genotype in that individual. SNP conversion rate was calculated as the number of SNPs that produced a genotype/number of SNPs included in the assay. Validation rate was calculated as the number of polymorphic SNPs /number of converted SNPs. Mendelian errors in each of the 18 nuclear trios were calculated using PLINK Additional filtering criteria were applied to the genotyping data in extant Perissodactyla, First, calls with low signal intensity were identified by combining the X raw and Y raw intensity values for both allele A and allele B, and removing genotypes failing to reach the threshold value (set at 1000). These loci were then removed and re-coded as no calls (00) in the ped file for further analyses. After intensity filtering, individual genotyping rates were determined. Mean and standard deviations for individual genotyping rates were determined for both the Hippomorpha and the Ceratomorpha. Individuals with genotyping rates below 2 standard deviations from the mean were excluded from further analyses. Markers that genotyped in >90% of the remaining individuals were considered true markers and are reported as loci producing a genotype in the species .2r values between all pairs of SNPs with inter-SNP distances of less than 4 Mb both within a given breed (a minimum of 18 horses per breed) and across all breeds. Pair-wise LD was calculated for each chromosome within breed using the LD plot function in Haploview 2r values in 50 Kb distance bins across all chromosomes for individual breeds and for all horses. Inter-SNP distances of greater than 4 Mb were ignored. Extent of LD was regarded as the persistence of LD until falling below two-fold background LD. Background LD within breed is largely affected by sample size and effective population size.Genome-wide LD was estimated by calculating Genetic distance (D) between pair-wise combinations of individuals was calculated using PLINK where D = 1−[(IBS2+0.5*IBS1)/N]: IBS2 and IBS1 are the number of loci that share either 2 or 1 alleles identical by state (IBS), respectively, and N is the number of loci tested. Metric multidimensional scaling (MDS) analysis of pair-wise genetic distance (6 dimensions) was used to identify the relationships between breeds with PLINK (–mds-plot 6).2r>0.2 (–indep-pairwise 100 25 0.2). The resulting set of 17,947 SNP loci was used to calculate F for all individuals within each breed.Individual inbreeding coefficients (F) were estimated with PLINK. SNPs were pruned for linkage equilibrium using pair-wise genotypic correlation in 100 SNP windows sliding by 25 SNPs along the genome; SNPs were pruned at Genetic distance (D) between pair-wise combinations of individuals was calculated and MDS analysis of pair-wise genetic distance (6 dimensions) was used to identify the relationships using PLINK (as above). D and MDS calculations for the domestic horse and Przewalski's Horse group included all domestic horses as well as the control horses.http://tree.bio.ed.ac.uk/software/figtree/).Autosomal genotypes from 344 individuals of the 14 modern breeds, the 9 Przewalski's Horses, and a domestic ass, were pruned in PLINK for a minimum per-SNP genotyping rate of 0.9 (–geno 0.1) and minor allele frequency of 0.05 (–maf 0.05). Alleles of the 46,244 remaining SNPs were coded AA = 0, AB = 1, and BB = 2 for parsimony analyses in TNT  with a domestic ass designated as the outgroup. Traditional searches were applied using subtree pruning-regrafting (SPR) branch swapping for 100 replicates followed by 100 replicates of the tree bisection-reconnection (TBR) method. New technology searches were them performed , at default settings. Bootstrap support was calculated using 500 pseudoreplicates with traditional search methods (SPR-TBR). The resulting tree was visualized in FigTree , ASIP , STX17 (gray), SLC36A1 (champagne), MATP (cream), PMEL17 (silver), KIT [exon skipping] (sabino), EDNRB (overo) and KIT [inversion] (tobanio) MC1R, ASIP, or STX17) to model a simple Mendelian trait. To infer coat color phenotype based on the 9 known loci:All horses were genotyped for all known coat color loci including MC1R genotype. Horses homozygous for the mutant MC1R allele (M/M) were classified as chestnut. Those with one or more copies of the wild type allele  were considered black-pigmented.The base color of the horse was determined by ASIP were classified as black, while those with one or two copies of the wild type allele were considered bay.Considering only non-chestnut horses, those homozygous for the exon 2 deletion in All other modifying loci were then considered which would result in dilution  or white patterning , and horses were assigned appropriate coat color phenotypes .STX17 exon 6 duplication were classified as gray, due to the epistatic dominance of the gray coat color over all other loci.All horses with the Norwegian Fjord horses were all considered to have the dun coat color due to fixation of this coat color in the breed and the lack of a genetic test for dun.Coat color association analyses were performed in PLINK Unstructured genome-wide association analysis was performed using chi-square tests for allelic association (–assoc). Stratified genome-wide association analysis was performed using the Cochran-Mantel-Haenszel (CMH) test (–mh). Horses were clustered for the CMH test on the basis of the pair-wise population concordance (PPC) test in PLINK, which clusters individuals based on the likelihood of concordant or discordant ancestry. A p-value for merging in the PPC test was set at p = 0.01 . Manhattan plots of all results were generated using Haploview MC1R, ASIP and STX17 loci on ECA3, 22 and 25, respectively, were determined with fastPHASE MC1R, the number of chromosomes in each breed that contained the chestnut allele, the number of SNPs on which the haplotype is based, and the length and coordinates of the shared haplotype are available in ASIP or STX17 loci.Haplotypes containing markers at the Figure S1Distance between informative SNPs across all 31 autosomes. Informative SNPs were defined as having MAF>0.05 across all 14 breeds.(TIF)Click here for additional data file.Figure S22r values between all pairs of SNPs with inter-SNP distances of less than 600 kb as described in Decline in genome-wide linkage disequilibrium across and within breeds. Genome-wide linkage disequilibrium (LD) was estimated both within a given breed, and across all breeds, by calculating (TIF)Click here for additional data file.Figure S3Multidimensional scaling with 14 domestic horse breeds. Metric multidimensional scaling analysis of pair-wise genetic distance was used as described in (TIF)Click here for additional data file.Figure S4Multidimensional scaling of Przewalski's Horse and domestic horse breeds. Metric multidimensional scaling analysis of pair-wise genetic distance was used as described in (TIF)Click here for additional data file.Figure S5Multidimensional scaling of Przewalski's Horse and domestic horse breeds. Metric multidimensional scaling analysis of pair-wise genetic distance was used as described in (TIF)Click here for additional data file.Figure S6MC1R genotype and chestnut as a simple recessive trait (b). Phenotypes were inferred as described in Mapping of the chestnut coat color locus across breeds based on phenotype inferred from all nine genotyped coat color alleles with known interactions (a), or based solely on (TIF)Click here for additional data file.Figure S7ASIP genotype and black color as a simple trait (b). Phenotypes were inferred as described in Mapping of the recessive black coat color locus across breeds based on phenotype inferred from all nine genotyped coat color alleles with known interactions (a), or based solely on (TIF)Click here for additional data file.Figure S8Mapping of the gray coat color locus across breeds based on gray color as a simple dominant trait. Gray phenotype was inferred as described in (TIF)Click here for additional data file.Figure S9MC1R locus on ECA3. For all breeds with the exception of the Standardbred, the length of minimal homozygosity for the chestnut allele is depicted. In the Standardbred, where no homozygotes were observed, the shortest length of the haplotype containing the chestnut allele is depicted. The number of chromosomes in each breed that contained the chestnut allele ranged from 7 in the Andalusian, and 10 in the Standardbred and Norwegian Fjord, to 41 in the Saddlebred and Thoroughbred, 48 in the Belgian and 75 in Quarter Horse, with an average of 27.8 across all 14 breeds. The number of SNPs on which the haplotype is based ranged from 16 in the Andalusian to 75 in the Standardbred, with an average of 37.8 across all 14 breeds. The length of the shared haplotype ranged from 1.08 Mb in Andalusian to 4.157 Mb in French Trotter, with an average length of 2.16 Mb across all 14 breeds. The complete list of chromosome numbers, numbers of SNPs on which the haplotype is based, and the length and coordinates of the shared haplotype are available in MC1R and the likely position of the centromere are depicted on the figure. Similar analyses resulted in no shared haplotypes for either ASIP or grey.Conserved haplotype at the (TIF)Click here for additional data file.Table S1Validation rates for the assayed SNPs. The number of selected SNPs from the seven discovery horses and Twilight that provided genotypes (converted), the number of validated SNPs in the equine sample set, and the validation fraction are indicated. SNPs with the highest validation rate were those originally ascertained in two breeds, especially if one of the horses was Twilight. SNPs with the lowest validation rate were originally ascertained in three breeds but were not present in Twilight's sequence.(DOCX)Click here for additional data file.Table S2Breeds included in the study. Breed, number of individuals, and geographic origin of horses genotyped in this study. *The control horses consisted of the 7 SNP discovery horses listed in (DOCX)Click here for additional data file.Table S3Tests of Mendelian inheritance in trios. Mendelian errors in each of the 18 nuclear trios were calculated and results are reported as Mendelian agreement as described in the (DOCX)Click here for additional data file.Table S4i.e., MAF>0) across all 14 breeds.Inter-SNP spacing by chromosome. Polymorphic SNPs were defined as having at least one heterozygous individual ((DOCX)Click here for additional data file.Table S5E), are indicated and calculated as described in Allele frequencies and genetic diversity in the domestic horse. The mean and median MAF , the number of polymorphic (MAF≥0.01) and informative SNPs (MAF>0.05), and genetic diversity as determined by heterozygosity (H(DOCX)Click here for additional data file.Table S6Proportion of validated SNPs from each discovery breed across all breeds genotyped. The impact of discovery breed on SNP validation rate across the genotyped sample set was determined as described in (DOCX)Click here for additional data file.Table S7Species, genotyping rates, sample and locus GC scores for unfiltered data in extant Perissodactyla. Genotyping rates and GC scores reported using Illumina's default calling parameters as described in (DOCX)Click here for additional data file.Table S8Quality scores, SNP conversion and SNP validation rates and mean heterozygosity for extant Perissodactyla after filtering for intensity and genotyping rate. Genotyping rates, conversion and validation rates and observed hetrozygosities after filtering the data as described in (DOCX)Click here for additional data file.Table S9Inbreeding coefficients in each breed. Individual inbreeding coefficients (F) were estimated with 17,947 SNPs that were pruned for linkage equilibrium as described in (DOCX)Click here for additional data file.Table S10Mean pair-wise genetic distances in domestic horse populations. Genetic distance (D) between pair-wise combinations of individuals was calculated as described in (DOCX)Click here for additional data file.Table S11Mean pair-wise distances between Przewalski's Horse and domestic horse populations. Genetic distance (D) between pair-wise combinations of individuals was calculated as described in (DOCX)Click here for additional data file.Table S12MC1R and ASIP) only, using known inheritance models as described in Chi square analysis for mapping of known coat color loci across the 14 breeds. Chestnut, black and gray phenotypes were either inferred from the genotypes at 9 known coat color loci, or from the genotype of a single locus (designated as (DOC)Click here for additional data file.Table S13CMH analysis for mapping of known coat color loci across the 14 breeds. Phenotypes were inferred from multi or single locus genotypes as described in (DOC)Click here for additional data file.Table S14MC1R and ASIP) only, using known inheritance models as described in Chi-square tests of association for coat color loci within breeds. Chestnut, black and gray phenotypes were either inferred from the genotypes at 8 known coat color loci, or from the genotype of a single locus (designated as (DOC)Click here for additional data file.Table S15MC1R Haplotype Analysis on ECA3. Genotype data was phased as described in Summary of (DOCX)Click here for additional data file.Text S1Supporting text includes additional information on: filtering of genotypes in Perissodactyla, the relationships between domestic horse breeds and between the domestic horse and the Przewalski's Horse, and genome wide association analysis.(DOCX)Click here for additional data file.


Assuming this principle, it is also possible to define the variances of the prior distribution of the 'big' and 'small' SNP. The relatively few large SNP explain a large proportion of the genetic variance and the majority of the SNP show small effects and explain a minor proportion of the genetic variance. We name this method MixP, where the prior distribution is a mixture of two normal distributions, i.e. one with a big variance and one with a small variance. Simulation results, using a real Norwegian Red cattle pedigree, show that MixP is at least as accurate as the other methods in all studied cases. This method also reduces the hyper-parameters of the prior distribution from 2 (proportion and variance of SNP with big effects) to 1 (proportion of SNP with big effects), assuming the overall genetic variance is known. The mixture of normal distribution prior made it possible to solve the equations iteratively, which greatly reduced computation loads by two orders of magnitude. In the era of marker density reaching million(s) and whole-genome sequence data, MixP provides a computationally feasible Bayesian method of analysis.Genome-wide breeding value (GWEBV) estimation methods can be classified based on the prior distribution assumptions of marker effects. Genome-wide BLUP methods assume a normal prior distribution for all markers with a constant variance, and are computationally fast. In Bayesian methods, more flexible prior distributions of SNP effects are applied that allow for very large SNP effects although most are small or even zero, but these prior distributions are often also computationally demanding as they rely on Monte Carlo Markov chain sampling. In this study, we adopted the Pareto principle to weight available marker loci, i.e., we consider that  Genomic selection (GS) is currently being adopted by the dairy cattle breeding industries around the world . Genome-π) of the SNP have no effect and π SNP have an effect with a t-distributed prior that is more thick tailed than the normal distribution, i.e. it allows for a few SNP with very large effects and many SNP with small ones. Based on the work of   π) of thn et al.  used a maccuracy , which sx): x, where 0 < x ≤ 50. If x = 50, the effects are all equal (50% of the effects cause 50% of the variance). In this study, we tried to apply this principle for the prior distribution of the SNP effects, i.e. the x% of the SNP with the largest effects cause (100 - x)% of the genetic variance (gV). Given that π (= x/100) and using the Pareto principle, the variances of the large and small SNP effects are, respectively:The Pareto principle, or the 80:20 rule, is often observed in economy and sociology . It statM is the number of SNP, such that π.where The aim of this paper is to present this novel approach using the Pareto principle applied on individual marker loci (MixP), and to compare it with other single SNP based GWEBV prediction methods in a real Norwegian Red cattle (NRF) pedigree, and on a real wheat dataset.For the estimation of SNP effects, we used the Iterative Conditional Expectation (ICE) algorithm of Meuwissen et al. . The ICEribution ,12 that ribution .The normal and Laplacian distributions are reported to yield similar results ,13. The i gis the effect of SNP i; π is the probability that the SNP effect belongs to the distribution of larger variance; μ and variance π is known. The model for the records is:where y is a (n × 1) vector of n records, μ is the overall mean; bi is a (m × 1) vector of the m 'standardized' SNP genotypes, i.e., ip; i gis the effect of the ith SNP genotype; e is the vector of environmental effects, with k], let μ and for current solutions of all other SNP effects, except SNP i, i.e.where i given The expectation of the effect of SNP Li 2is defined with i gassuming that the prior variance of i gis where i gassuming that the prior variance of i gis either πLi1) or to the second distribution ((1 - π)Li2). As described above, the calculation of Li 1requires the inversion of the Vi 1matrix, but the Appendix shows how to avoid this matrix inversion.Thus, Equation (3) is applied to every SNP in turn, whilst updating the values of -5 times the sum of the squares of the solutions. We refer this method as MixP.Iteration is continued until the sum of the squares of the changes become less that 10χ2 distribution is 4.2. GBLUP is also used, as described in 18], whicπ, obtained by 10-fold cross-validation, were quite high, indicating that many markers had a large effect on the traits. The curve relating prediction accuracy versus π was fairly flat for values of π >0.2 for all four yields (results not shown). Results of π = 0.2 for MixP are also listed in Table π is not known in the wheat data, five alternative values were evaluated  in the BayesB method. Their accuracies are very flat, i.e., no accuracy difference is greater than 0.01 within each trait. Estimates of π that give the top estimates for BayesB are also listed in Table Table -5. Of all the scenarios, GBLUP is the fastest. The speed of MixP is in the same order of magnitude of that of GBLUP. BayesB is more than two orders of magnitude slower. The MixP algorithm typically converged within 50 iterations, and always converged in more than 4000 datasets.Table Here we applied the Pareto principle to estimate genome-wide EBV (GWEBV). Because a simple prior distribution was used, i.e. a mixture of normal distributions, an iterative method to calculate the BayesB type of GWEBV was obtained, which was called MixP. MixP yielded accuracies that were very similar to BayesB Figures  and 2. Tπ values for grain yield were high (between 0.16 and 0.49), it appears that grain yield was also affected by many QTL, which explained the small differences in accuracy between MixP and GBLUP . We will assume here that the total genetic variance was known. An estimate of g Vcould be obtained easily using the GBLUP model and a variance component estimation, which would provide an approximately unbiased estimate. If the total genetic variance was known, the number of parameters would be reduced to 1 (π), which could be estimated by cross-validation 18], we cN is the number of training records, L is the genome size, 4eL Nis the actual number of chromosome segments from population genetics results, v is the ratio of effective and actual segments, so 4eLv Nis the effective number of segments. This proportional reduction of the genome size and heritability can greatly reduce the simulation time, i.e., only around 1/30 of the computer time was needed here. Thus, using where The main advantage of MixP over BayesB is that MixP is more than two orders of magnitude faster Table . This isLet us assume the following model which fits a single SNP effect:y is a (n × 1) vector of records; b is a (n × 1) vector of standardized SNP genotypes, g is the marker effect of a single SNP (Var(g) = σ2), and e is a vector of error effects where C is a constant and V can be calculated using only scalars:where And since :we have:g:where y'V-1y requires only some products of vector (y'y and y'b) and the calculation of Thus, the calculation of The authors declare that they have no competing interests.XY carried out the simulations, performed the analysis, and drafted the manuscript. TM designed the study and co-authored the manuscript. All authors read and approved the final version.