M. musculus musculus and M. m. domesticus. Many generations of admixture enables high-resolution mapping of loci contributing to these sterility-related phenotypes. We identify complex interactions among sterility loci, suggesting multiple, non-independent genetic incompatibilities contribute to barriers to gene flow in the hybrid zone.Mapping hybrid defects in contact zones between incipient species can identify genomic regions contributing to reproductive isolation and reveal genetic mechanisms of speciation. The house mouse features a rare combination of sophisticated genetic tools and natural hybrid zones between subspecies. Male hybrids often show reduced fertility, a common reproductive barrier between incipient species. Laboratory crosses have identified sterility loci, but each encompasses hundreds of genes. We map genetic determinants of testis weight and testis gene expression using offspring of mice captured in a hybrid zone between DOI:http://dx.doi.org/10.7554/eLife.02504.001 Different species have often evolved from a common ancestor. In order to become distinct species, however, the different groups of descendants of that ancestor must have become isolated from one another at some point in their history so that they could no longer mate or reproduce. For example, a mountain or a river might create a physical barrier that keeps species apart, so that if the species meet up again they may struggle to mate or produce offspring. Furthermore, any ‘hybrid’ offspring that are produced may themselves struggle to survive or successfully reproduce.Examining the genes of the hybrid offspring that result when two recently separated species crossbreed could help us to understand how new species evolve. However, the challenges of finding enough suitable hybrids to compare means that few studies have so far investigated the genetic changes that occur to make reproduction between separate species difficult.Mus musculus musculus and Mus musculus domesticus—live alongside each other in a region of central Europe and can mate and produce hybrid offspring. Male hybrid mice are commonly less fertile than non-hybrids; this acts as a barrier to reproductive success that helps to maintain the separation between the two subspecies.Two subspecies of the house mouse—Turner and Harr captured wild hybrid mice, bred them in the laboratory, and studied their offspring. This strategy enabled them to measure fertility in mice very similar to wild-caught hybrids, but now all individuals can be measured at the same age and under the same environmental conditions.A method called a genome-wide association study can be used to survey the genes of individuals with a particular disease or physical characteristic in an effort to identify gene variants that are associated with that condition. In many species, the weight of a male's testes has been linked to their fertility—small testes mean the male is likely to be less fertile. Changes in how genes are ‘expressed’ in the testes can also reduce fertility.Turner and Harr used a genome-wide association study to investigate which genetic changes are linked to changes in testis weight or how genes in the testes are expressed in the offspring of hybrid mice. This revealed that many separate genetic regions are involved; including some that had not previously been identified. Turner and Harr then examined how these gene regions interact with each other. With the exception of one gene, all interacted with at least one of the other genetic regions that had been identified, forming a complex network of interactions.Although a genome-wide association study reveals which genes are altered in hybrid mice with small testes, it does not reveal which of these genes actually cause the changes in testis size and gene expression. However, the work of Turner and Harr greatly narrows down the candidates for further investigation.DOI:http://dx.doi.org/10.7554/eLife.02504.002 New species arise when reproductive barriers form, preventing gene flow between populations . RecentlFor example, ‘islands of divergence’ have been reported in species pairs from taxonomically diverse groups . These hAn alternative approach to identify genomic regions contributing to reproductive isolation is to map known reproductive barrier traits in naturally hybridizing populations. The potential for mapping in hybrid zones is long-recognized . Hybrid Despite these advantages, few studies have mapped barrier traits or other fitness-related traits in nature, due to the logistical challenges of collecting dense genome-wide genetic markers in species with admixed populations and well-characterized phenotypes. Examples include associations between pollen sterility and genomic regions showing reduced introgression in a sunflower hybrid zone  and lociMus musculus) are a promising model system for genetic mapping in natural populations  colonized Europe through different geographic routes and meet in a narrow secondary contact zone running through central Europe from Bulgaria to Denmark  to map testis weight and testis gene expression in 185 first generation lab-bred offspring of wild-caught hybrid mice . GWAS haOur hybrid zone GWAS identified genomic regions associated with variation in relative testis weight (testis weight/body weight) and genome-wide testis expression pattern, including regions previously implicated in hybrid sterility as well as novel loci. Motivated by the Dobzhansky–Muller genetic model of hybrid defects, we tested for genetic interactions (Dobzhansky–Muller interactions—‘DMIs’) between loci affecting testis weight or expression pattern. All loci except one showed evidence for interaction with at least one partner locus and most interact with more than one partner. The deviations in phenotype associated with most interactions were large–affected individuals have phenotypes below the range observed in pure subspecies–suggesting that these interactions indeed are hybrid incompatibilities. To our knowledge, this is the first GWAS for a reproductive barrier trait. Using natural hybrids provided high mapping resolution that will facilitate future studies to identify causative genes; for example, a majority of GWAS regions contain 10 or fewer genes. Moreover, this study provides the first genome-scale description of a hybrid incompatibility network in nature.musculus and domesticus and mice from the hybrid zone  and genome-wide testis gene expression pattern. Both of these phenotypes have previously been linked to hybrid male sterility in studies of offspring from crosses between rid zone .P = 2 × 10−16), indicating that there is a strong association between those two sterility phenotypes , thus the effect of hybrid defects does not obscure subspecies differences in expression.Testis expression PC1 (explaining 14.6% of the variance) is significantly correlated with relative testis weight  individuals had relative testis weight below the minimum observed in pure subspecies males and 21/179 (11.7%) individuals had expression PC1 scores below (PC1 = −46.97) the pure subspecies range.We identified four SNPs significantly associated with relative testis weight in three regions on the X chromosome using stringent thresholds determined by permutation . An addiWe identified 104 SNPs on the X and chromosome 1 significantly associated with expression PC1 using stringent permutation-based thresholds . An additrans associations (SNP is located on different chromosome from transcript), based on evidence from a study in F2 hybrids that trans expression QTL (eQTL) are associated with sterility while cis eQTL are predominantly associated with subspecies differences . The combined mapping results provide multiple lines of evidence for contributions of all 50 PC1 regions and 9/12 testis weight regions. The three testis-weight regions  not significantly associated with testis expression phenotypes are more likely to be spurious and are weaker candidates for future study.There was substantial overlap between mapping results for testis weight and expression PC1; 48/55 SNPs significant for relative testis weight (9 regions) were also significant for expression PC1. A permutation test, performed by randomly shuffling the positions of GWAS regions in the genome, provides strong evidence that this overlap is non-random . Most SNPs significant for testis weight and/or expression PC1 were significantly enriched for Power to identify pairwise epistasis in GWAS for quantitative traits is limited even with very large sample sizes, due to multiple testing issues e.g. . The DobWe tested for genetic interactions between all pairs of significant SNPs (FDR < 0.1) located on different chromosomes for testis weight and for expression PC1. We identified 142 significant pairwise interactions for relative testis weight, representing 22 pairs of GWAS regions . These rWe identified 44,145 significant interactions between SNPs for expression PC1. The 913 GWAS region pairs provide evidence that at least 144 autosomal–autosomal interactions and 18 X–autosomal interactions contribute to expression PC1 .We used deviations from population means for single SNPs and two-locus genotypes to estimate the phenotypic effects of GWAS regions and interactions .10.7554/As expected, interactions had greater effects, on average, than single loci for both phenotypes . We provide examples of autosomal–autosomal and X–autosomal SNP pairs with significant interactions for each phenotype in It is possible that some of the GWAS regions we mapped contribute to quantitative variation within/between subspecies, rather than hybrid defects. The lowest genotypic means for most interactions fell below the range observed in pure subspecies (relative testis weight: 19/22 (86.3%) region pairs; expression PC1: 877/913 (96%) region pairs; We performed simulations to assess the performance of the mapping procedure for different genetic architectures by estimating the power to detect causative loci and the false positive rate . We simuThe distribution of distances to the causal SNP for all significant SNPs located on the same chromosome  shows thAs expected, the power to detect one or both causative loci depended on the location , dominance, and frequency of both ‘causative’ alleles , Table 3It is important to note that our empirical results suggest that the two-locus models used to simulate phenotypes are overly simplified. We predict that involvement of a sterility locus in multiple incompatibilities would reduce the influence of allele/genotype frequencies of any single partner locus on power.To estimate the false discovery rate from simulations, we classified significant SNPs not located on the same chromosome as one of the causative SNPs as false positives. Choosing an appropriate distance threshold for false vs true positives on the same chromosome was not obvious given the distribution of distances to causal SNPs .The potential to leverage recombination events from generations of intercrossing in hybrid zones to achieve high-resolution genetic mapping of quantitative traits has been recognized for decades  and 50 genomic regions to expression PC1 (on the X and 18 autosomes).2s  are much smaller than sterility QTL identified in F35.1 Mb; . Many GWWe identified candidate genes with known roles in reproduction in four testis-weight regions and 17 expression PC1 regions . However1 crosses between strains of domesticus and musculus have shown that hybrid sterility phenotypes and loci depend on the geographic origins of parental strains . However, one expression PC1 region (PC42) is ∼4 Mb proximal to Prdm9. Reductions in PC1 are observed in individuals that are heterozygous or homozygous for the domesticus allele at PC42. This pattern is partially consistent with sterility caused by Prdm9, which occurs in heterozygous individuals carrying sterile alleles from domesticus  is ∼41 Mb distal and low testis-weight is associated with the musculus allele. in mice . None ofmesticus . We did 2 cross between musculus and domesticus  identified for testis weight and expression PC1 overlaps an interaction affecting testis expression in F2 hybrids (2s) encompasses 12 protein-coding genes, including a gene with a knockout model showing low testis weight and sperm count (Arl4a) .There is concordance between some of the genetic interactions between loci identified here and interactions identified by mapping sterility phenotypes and testis expression traits in an Fmesticus   , and twoWe compared the positions of GWAS regions to 182 regions  with evidence for epistasis based on a genome-wide analysis of genomic clines in a transect across the house mouse hybrid zone in Bavaria , the samThree GWAS regions associated with testis weight and five expression PC1 regions are located on the X chromosome. The X-chromosomal regions surpass the stringent permutation-based significance thresholds and thus have strong statistical support. These results are consistent with evidence for an important role for the X in hybrid sterility from laboratory crosses between subspecies strains geographically diverse in origin  and evidmusculus derived X chromosome has been implicated repeatedly in genetic studies of sterility in F1 and F2 hybrids  in 2008  Figure . We incl−5).Males were housed individually after weaning (28 days) to prevent effects of dominance interactions on fertility. We measured combined testis weight and body weight immediately after mice were sacrificed at 9–12 weeks. We calculated relative testis weight (testis weight/body weight) to account for a significant association between testis weight and body weight  and comparable to (within 1 standard deviation) values observed in F1 hybrids from 4/7 interspecific crosses that showed significant reductions (mean plus one standard deviation 4.6–9.2 mg/g).We classify individuals with relative testis weight below the range observed in pure subspecies as showing evidence for sterility . To confWe measured gene expression in testes of 179 out of the 185 males from the mapping population. Freshly dissected testes were stored in RNAlater  at 4°C overnight, then transferred to −20°C until processed. We extracted RNA from 15–20 mg whole testis using Qiagen RNeasy kits and a Qiagen Tissue Lyser for the homogenization step. We verified quality of RNA samples (RIN >8) using RNA 6000 Nano kits (Agilent) on a 2100 Bioanalyzer .. caused by buffer leakage or dust particles) and quality control metrics from Feature Extraction protocol GE1_QCMT_Dec08.We used Whole Mouse Genome Microarrays (Agilent) to measure genome-wide expression. This array contains 43,379 probes surveying 22,210 transcripts from 21,326 genes. We labeled, amplified, and hybridized samples to arrays using single-color Quick-Amp Labeling Kits (Agilent), according to manufacturer protocols. We verified the yield (>2 μg) and specific activity (>9.0 pmol Cy3/μg cRNA) of labeling reactions using a NanoDrop ND-1000 UV-VIS Spectrophotometer . We scanned arrays using a High Resolution Microarray Scanner (Agilent) and processed raw images using Feature Extraction Software (Agilent). Quality control procedures for arrays included visual inspection of raw images and the distribution of non-uniformity outliers to identify large spatial artifacts  using BLAT . Males from the mapping population were genotyped using Mouse Diversity Genotyping Arrays  by Atlas Biolabs .apt-probeset-genotype (Affymetrix) and standard settings. We used the MouseDivGeno algorithm to identify variable intensity oligonucleotides (VINOs) (−6 (VIF = 1/(2),1−R where 2R is the multiple correlation coefficient for a SNP regressed on all other SNPs simultaneously). This procedure essentially removed SNPs in perfect LD. These filtering steps yielded 156,204 SNPs.We called genotypes at 584,729 SNPs using  (VINOs) ; 53,148 M. m. domesticus individuals  and 22 M. m. musculus individuals   .Structure  matrix as a random effect to correct for genetic structure in the mapping population. We estimated relatedness among the individuals in the mapping population in GEMMA using all markers and the –gk 1 option, which generates a centered relatedness matrix. For each single SNP association test we recorded the Wald test p value. Phenotypes tested include relative testis weight , testis expression principal component 1  and 5.83 × 10−5 (X chromosome); thresholds for expression PC1 were 1.66 × 10−8 (autosomes) and 1.01 × 10−5 (X chromosome). Next, we identified regions using a more permissive significance threshold based on the 10% false discovery rate  with each significant SNP. We combined significant regions <10 Mb apart into a single region.To estimate the genomic interval represented by each significant LD-filtered SNP, we report significant regions defined by the most distant flanking SNPs in the full dataset showing lmekin function from the coxme R package  identified using GEMMA. We tested all pairs of significant SNPs located on different chromosomes for each phenotype . To account for relatedness among individuals we used a mixed model approach, similar to the model implemented in GEMMA. We used the  package  to fit lWe performed simulations to evaluate the performance of our mapping approach under varying genetic architectures and allele frequencies. We simulated phenotypes using several genetic models of two-locus epistasis and parameters based on the empirical distribution of relative testis weight. The simulation procedure is illustrated in domesticus X-linked sterile alleles and 50 with musculus X-linked sterile alleles). The criteria used for choosing ‘causative’ SNPs were a minor allele frequency >0.05 in the mapping population and fixed in at least one subspecies. The ‘sterile’ allele could be polymorphic or fixed within subspecies but the alternate ‘non-sterile’ allele had to be fixed within the other subspecies—e.g. domesticus sterile alleles have frequencies 0.05–1.0 in the domesticus reference populations from France and Germany and the alternate allele at those SNPs are fixed in musculus samples from the Czech Republic and Kazakhstan. For each pair, the ‘causative’ SNPs were randomly selected from all SNPs meeting those criteria .We tested 100 autosomal–autosomal SNP pairs (SNPs on different chromosomes) and 100 X–autosomal pairs  for each individual from a normal distribution with the appropriate two-locus mean and standard deviation = 0.75. The standard deviation value, equivalent to 2.98 mg/g, was chosen on the basis of standard deviations in pure subspecies samples from the mapping population . This vH = 1.0; ), suggesIn total, 90,000 simulations were performed, (9 architectures × 100 SNP pairs × 100 data sets). We identified significant SNPs for each data set using GEMMA, as described above for the empirical data.2 hybrids from crosses between subspecies , and gene ontology (GO) terms related to male reproduction or gene regulation (plus children): meiosis GO:0007126; DNA methylation GO:0006306; regulation of gene expression GO:0010468; transcription GO:0006351; spermatogenesis GO:0007283; male gamete generation GO:0048232; gamete generation GO:0007276; meiotic cell cycle GO:0051321. Many genes with roles in reproduction reported in publications were not annotated with related GO terms, highlighting the limitations of gene ontology. Moreover, genes causing sterility might not have functions obviously related to reproduction.We used ENSEMBL  Biomart to download gene annotations for genomic regions significantly associated with relative testis weight. We identified candidate genes in significant regions with roles in male reproduction using reviews of male fertility , manual  review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.eLife posts the editorial decision letter and author response on a selection of the published articles . An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent  and 3 reviewers, one of whom is a member of our Board of Reviewing Editors.Thank you for sending your work entitled “Genome-wide mapping in a natural hybrid zone reveals hybrid male sterility loci and Dobzhansky-Muller interactions” for consideration at The Reviewing editor and the other reviewers discussed their comments before we reached a decision. We all feel that the design and direction of your experiment are important innovations and the work has the potential to be important in the field. However, there are a number of substantive points that need to be addressed. Moreover, the reviewers all felt that there were multiple aspects of analysis, presentation and interpretation that need to be improved.eLife to return the full reviews in addition to the editor's summary. However, in this case I think the range of comments is a useful reflection of the responses of readers and are likely to be helpful in revision.Below, you will find the full reviews. I should note that it is not standard practice at In terms of revising, the following major points need to be addressed:Analysis: Given the large effect of the X chromosome it seems critical to include X variants in the covariance matrix. We also believe that more could be made of analyses of the genetic architecture of the trait . Similarly the DMI model makes specific predictions about the direction of epistatic effects  which should be easy to address by polarising variants using an outgroup.Interpretation: The phenotype studied is not hybrid sterility (despite the Title). There is actually no direct evidence for an association between the trait studied and fitness. Indeed the statement that there were no significant results for sperm count is a little worrying. However, there is limited evidence that the phenotypes of the hybrids are typically outside the range of normal variation within the species. It is important to note these caveats.Presentation: Without fine-mapping or replication, the ability to identify/localise specific genes as being important in the trait is limited. For this reason, we feel the emphasis on long lists of rather weakly-supported candidate genes is misplaced. There are also issues with formatting, clarity of figures and citations. Please concentrate on these above points in your revision. The comments below should help you in understanding details of these issues.Reviewer #1:The genetic study of reproductive isolation and speciation has the potential to be transformed by the large-scale study of genetic variation in hybrid zones and mapping experiments. This paper presents the first (at least as far as I know) GWAS of a reproductive isolation phenotype (relative testis weight) between Mus musculus musculus and M. m. domesticus from a hybrid zone. The principle findings are:1) There are multiple (estimate of 26) regions across the genome contributing to the phenotype.2) Some of the identified regions overlap with (or are close to) regions identified as affecting reproductive traits from F2 crosses.3) Many of the regions harbour loci that also affect gene expression in the testis.4) There is some evidence for interactions between loci, consistent with the DMI model.These findings are of broad interest to the field and an interesting counterpart to the mapping results, though perhaps not unexpected and I feel not presented in as compelling a way as possible (see below). To be compelling, I think there has to be a more rigorous approach to validating the findings.Major points:1) A standard GWAS approach, using linear mixed models to account for relatedness between individuals is used. However, while human GWAS studies typically use 'genome-wide significant'  and require replication, the approach taken here is to use an FDR approach with a generous threshold of 10% and no replication. Permutation studies presented show that this threshold is somewhat permissive. More generally, there is no attempt made to either establish how well calibrated the FDR approach is, nor to validate findings through replication. To me this is a serious limitation, and while I understand the authors' desire to maximise the findings from the study, I think a more limited, but well-validated, set of loci would be both more credible and of wider value. In short, I think validation of association signals in a replication set of samples is needed.2) I would like to know more about the data. For example, how does relative testis weight vary with age (mice were killed across a range of ages), does it differ between the parental species? Likewise, more could be made from chromosome-scale analyses of variance; e.g. using GCTA. Similarly, it would be interesting to attempt to estimate dominance and epistatic variance components. Also, the DMI model makes specific predictions about the direction of interactions. I would like to know what fraction of identified interactions fit with a model in which derived allele (inferred using an outgroup) interactions are deleterious .3) Although each is largely minor, there are many comments about the methodology used; justifying methods and parameter choices. I am particularly concerned about the exclusion of the X chromosome from the relatedness matrix, not least because it apparently has such a large estimated effect. This needs to be addressed.Reviewer #2:The manuscript by Turner and Harr reports the genome-wide mapping of relative testis-weight loci segregating in a natural hybrid zone of Mus m. musculus and Mus m. domesticus. They combined the high resolution Mouse Diversity Genotyping array for GWAS analysis with microarray-based gene expression analysis of the whole testis to identify the genomic loci potentially involved in reproductive isolation between both species. The logic of the experiments is difficult to follow without first reading their previous paper . Even then, there are three main concerns: First and most important, the phenotype studied is complex and in respect to hybrid sterility rather vague. Testes weight (or relative TW) variation can reflect genetically determined variations without any defect, various defects in haploid phase of meiosis, intrameiotic arrests of various causes and various extent, or even a premeiotic block. If rTW is taken as the only phenotypic trait it is not surprising that the outcome is so complex and its interpretation necessarily difficult. If, at least, histological evaluation and, for example, sperm count were included, the information value would have been much higher.Second, the use of the term “hybrid sterility loci” seems as a kind of overstatement, because none of the examined males proved to be sterile. In fact, Turner et al. 2011 Evolution paper states 'breeding data do show that hybrids have similar fertility to pure subspecies pairs' text and . MoreoveThird, the Dobrzhansky-Muller incompatibilities, which result in lowering testes weight can favor the involvement of such loci in reproductive isolation. The authors found 149 such chromosomal region pairs but their documentation is missing, perhaps with the exception of Figure 5, which, however, seems to show only expression DMIs (?).In conclusion, the strength of the paper includes the first attempt to use GWAS on wild mice from a hybrid zone to infer the genetic networks involved in reproductive isolation of a young species pair. The main weak point is the inadequate phenotype selected for quantification and consequent overstatements in the interpretation of the obtained data.Reviewer #3:In this study, the authors use crosses among wild mice collected in the hybrid zone between M. m. musculus and M. m. domesticus to map genomic regions associated with reduced relative testis weight in hybrid males. This is a novel approach to identifying regions associated with hybrid male sterility in a system of general interest to the evolutionary genetics community. They found 26 regions across the genome, all of which interacted with at least one partner and most of which interacted with many. They also used GWAS to identify loci associated with variation in expression of testis-expressed genes. The approach seems appropriate and well executed.My substantive comments are regarding organization, clarity and the interpretation of results. These should be remedied easily with a short round of editing.The writing could be tighter and clearer, especially in the Abstract and Introduction. Be upfront about the advantages and disadvantages of the approach and clearly summarize the approach and major findings. Hybrid zone analyses reflect current processes in a zone of secondary contact that is relatively recent. Their significance to initial phases of reproductive isolation in allopatry is bolstered when there is overlap between this approach and QTL mapping studies. In addition, even if these specific regions were not critical in early phases, they give insight into the genetic architecture of traits that almost certainly were important.I was often confused in the manuscript regarding which results were being referred to when e.g. which regions are being referred to in the sentence “All significant regions are involved in”?Citations are a bit sloppy. For example, in the first paragraph of the Introduction, the authors posit that two approaches have recently substantially advanced understanding. I expected some citations of recent work and instead found only citations from Dobzhansky and Muller. Another example, the citations for “the long-recognized potential for mapping in hybrid zones...” contain only relatively recent papers. This could be easily remedied by adding “for review, see Reiseberg and Buerkle” given that that paper does review some of the older work in the system. There are many additional citations that should be considered for the statement that “islands may not always represent targets of selection...” All in all, I would suggest going through the manuscript more carefully and including citations more consistently and broadly.More information about the system would also be useful in the Introduction, e.g. what are the three subspecies of house mice, how long have they been diverged, how old is the hybrid zone, etc. What have we learned previously? The Abstract does not actually give the names of the two subspecies.Explain why there may and may not be overlap between loci uncovered using mapping in hybrid zones and mapping between allopatric populations.I think this section could be re-organized to make the whole approach clearer. Explain motivation for insight into nature and timing of fertility defects. Why consider testis expression changes in the context of infertility? Give more info on what was actually done. You associated specific loci with variation in what measure of expression? Help the reader more clearly connect your results to specific insight (this refers to paragraph 1 and 2 in this section).Candidate genes:First sentence, identified genes from which of the previous analyses? Make the methods for identifying specific genes more explicitly methodical. First, we looked at all genes in the 26 regions with... Then we focused on the genes that were implicated in both this analysis and...Success of GWAS:This section seems a bit unclear. Is it GWAS that was very successful or using many different approaches? What is the success here-identification of relatively few locit or better characterization of the architecture? Be upfront about the possible downsides .The entire simulation section needs more explanation and justification. Many tables and figures are devoted to this  but the explanation is very slight. Insight into the importance of which factors in the simulations? There are three criteria for true or false positives-which ones correspond to something you considered true and which ones false? What do the results mean for the interpretation of results?Genetic Architecture:This section seems weak. What do you find? How does this compare with previous studies? What does this study in particular add? The reference to the Snowball effect is too slight to be effective. Analysis: Given the large effect of the X chromosome it seems critical to include X variants in the covariance matrix. We also believe that more could be made of analyses of the genetic architecture of the trait . Similarly the DMI model makes specific predictions about the direction of epistatic effects  which should be easy to address by polarising variants using an outgroup.Inclusion of X genotypes:We repeated the mapping analysis, including X genotypes in the covariance matrix. The main effect of this change was a slightly more stringent P value cutoff to identify significant SNPs, which resulted in the identification of fewer genomic regions . The positions of the regions stayed essentially the same.Variance explained by individual chromosomes:gcta to try and estimate the contribution of individual chromosomes to phenotypic variance (i.e. relative testis weight). This analysis has been pioneered on large GWAS datasets on human quantitative traits, such as height. We faced several problems with this analysis. The first problem is a conceptual one, the second a technical.As suggested by reviewer 1, we used the software gcta is fitting assumes that SNPs have additive effects on the trait. While this is very likely a good approximation for traits such as human height, it is not appropriate for hybrid sterility traits, because the Dobzhansky-Muller model predicts epistatic interactions between loci are necessary to observe hybrid defects. The additive genetic model would be adequate if we would map quantitative variation in testis weight within a subspecies, but not for the transgressive phenotypes in the hybrids between subspecies.The model that http://gcta.freeforums.net/thread/27/partioning-autosomes).The technical problem arises when one deals with a highly structured population, such as our population from the hybrid zone. If there is an effect of population structure, SNPs on one chromosome will be correlated with the SNPs on the other chromosomes, hence estimates of variance explained individual chromosomes are overestimates. To correct for this, The results presented below were obtained by running the REML procedure on each chromosome separately, incorporating the first 10 principle components (Eigenvectors) calculated over the whole autosomal dataset to correct for population structure. Similar results were obtained when Eigenvectors were calculated from the respective chromosome to correct for population structure.The heritability estimates /Vp) are not reliable due to the complications stated above. However, in both sets of analyses, chromosomes 2, 17 and X explained most of the variation. These results are consistent with our mapping analysis, which identified significant GWAS regions located on those chromosomes.Ancestral vs. derived sterility alleles:M. m. musculus and M. m. domesticus populations to M. spretus, M. spicilegus and M. mattheyii. Each region comprises numerous SNPs that can be polarized and additional sites with shared polymorphisms between musculus and domesticus. Thus, it will not be possible to determine which alleles are ancestral vs. derived until future studies identify causal genes/mutations.We agree that determining if sterility alleles are ancestral or derived would be of great interest. However, significant SNPs identified by GWAS are unlikely to include the causal mutations for mapped phenotypes. Without knowing the causal variant, it is not straightforward to categorize sterility alleles as ancestral or derived. Nevertheless, we compared genotypes in significant regions from Interpretation: The phenotype studied is not hybrid sterility (despite the Title). There is actually no direct evidence for an association between the trait studied and fitness. Indeed the statement that there were no significant results for sperm count is a little worrying. However, there is limited evidence that the phenotypes of the hybrids are typically outside the range of normal variation within the species. It is important to note these caveats.We agree that that there was a need for additional explanation of links between the mapped phenotype and hybrid sterility. We discuss this issue in detail now and note caveats in the “phenotyping” section of Methods, and the “sterility-associated phenotypes” and “effect size” sections of Results. In addition, we mapped another sterility-associated phenotype: testis expression PC1.Presentation: Without fine-mapping or replication, the ability to identify/localise specific genes as being important in the trait is limited. For this reason, we feel the emphasis on long lists of rather weakly-supported candidate genes is misplaced. There are also issues with formatting, clarity of figures and citations.We agree. We simplified annotation of the GWAS regions, reporting only genes with strong evidence for roles in reproduction  and remoWe made several major changes to the organization of the manuscript to improve clarity. First, we edited the Introduction, incorporating suggestions from reviewers to better explain the motivation and logic of the study. Second, we separated Results and Discussion and added more subheadings. Third, we reduced the number of figures in the main text to four.Additional changes:We made several changes in response to concerns of individual reviewers, notable examples include:Significance thresholds: We clarified the motivation for reporting results using a permissive (FDR <0.1) threshold and using multiple lines of evidence to identify loci likely to be true positives. We report estimates of the false positive rate using stringent and permissive thresholds from simulations.Tests for interactions:Instead of testing for interactions with MCMCglmm, we used a mixed model approach similar to the GEMMA framework used to identify single-SNP associations.Overlap between GWAS regions and sterility QTL:We performed permutation tests to determine if associations between GWAS regions and previously reported candidate sterility regions were non-random. It was not possible to test for non-random concordance between genetic interactions identified in this study and those reported in F2 hybrids by permutation because individual GWAS regions have multiple partners and the number of possible pairwise interactions between SNPs varies widely across region pairs. In the absence of statistical support for the overlapping pattern, we decreased emphasis on this result by including the figure showing overlap as a supplemental figure.Yang, J., et al. 2011. Genome partitioning of genetic variation for complex traits using common SNPs. Nat. Genet. 43:519–525.


We identified and replicated 22 loci with significant associations with urinary traits, 15 of which are new . Two-thirds of the urinary loci also have a metabolite association in blood. For all but one of the 6 loci where significant associations target the same metabolite in blood and urine, the genetic effects have the same direction in both fluids. In contrast, for the SLC5A11 locus, we found increased levels of myo-inositol in urine whereas mGWAS in blood reported decreased levels for the same genetic variant. This might indicate less effective re-absorption of myo-inositol in the kidneys of carriers. In summary, our study more than doubles the number of known loci that influence urinary phenotypes. It thus allows novel insights into the relationship between blood homeostasis and its regulation through excretion. The newly discovered loci also include variants previously linked to chronic kidney disease , pulmonary hypertension (CPS1), and ischemic stroke (XYLB). By establishing connections from gene to disease via metabolic traits our results provide novel hypotheses about molecular mechanisms involved in the etiology of diseases.Genome-wide association studies with metabolic traits (mGWAS) uncovered many genetic variants that influence human metabolism. These genetically influenced metabotypes (GIMs) contribute to our metabolic individuality, our capacity to respond to environmental challenges, and our susceptibility to specific diseases. While metabolic homeostasis in blood is a well investigated topic in large mGWAS with over 150 known loci, metabolic detoxification through urinary excretion has only been addressed by few small mGWAS with only 11 associated loci so far. Here we report the largest mGWAS to date, combining targeted and non-targeted  Human metabolism is influenced by genetic and environmental factors defining a person’s metabolic individuality. This individuality is linked to personal differences in the ability to react on metabolic challenges and in the susceptibility to specific diseases. By investigating how common variants in genetic regions (loci) affect individual blood metabolite levels, the substantial contribution of genetic inheritance to metabolic individuality has been demonstrated previously. Meanwhile, more than 150 loci influencing metabolic homeostasis in blood are known. Here we shift the focus to genetic variants that modulate urinary metabolite excretion, for which only 11 loci were reported so far. In the largest genetic study on urinary metabolites to date, we identified 15 additional loci. Most of the 26 loci also affect blood metabolite levels. This shows that the metabolic individuality seen in blood is also reflected in urine, which is expected when urine is regarded as “diluted blood”. Nonetheless, we also found loci that appear to primarily influence metabolite excretion. For instance, we identified genetic variants near a gene of a transporter that change the capability for renal re-absorption of the transporter’s substrate. Thus, our findings could help to elucidate molecular mechanisms influencing kidney function and the body’s detoxification capabilities. Th. ThXYLB c stroke . Furtherfarction . In the farction , 71. Verr stroke . In thisr stroke ) possiblCPS1, AGXT2, DMGDH, SLC6A13, HPD, SLC5A11). Interestingly, in all but one case (SLC5A11) the genetic effect has the same direction in both fluids, thus indicating that urine can be regarded as “diluted plasma” to some extent. For 5 of the 14 loci, we considered the associated metabolic traits in blood and urine to be biochemically related. Here, the metabolites are either products of the enzyme coded by the candidate gene (NAT8: N-acetylated compounds), or they are linked through an enzymatic reaction other than the reaction catalyzed by the candidate gene’s product , or they belong to the same metabolite class . The observed associations of related but different metabolites in blood and urine may be indicative either for biochemical conversions before excretion, or simply be a result of differences in the composition of the metabolite panels covered by the various mGWAS. In case of the remaining 3 loci, we find no direct biochemical or metabolic relationship between the metabolites in both media, since AGXT associates with an unknown compound in blood, PNMT associates with amino acids in urine and HDL cholesterol in blood, and SLC6A20 targets loosely related amino acid derivatives.In total, 26 genetic loci that associate with urinary metabolic traits are known to date  gene and elevated urinary glycine levels. The strongest associated SNP rs715 was also identified in previous mGWAS with higher glycine concentrations in blood . Th. ThSLC5Ah sodium . SLC5A11trations , which wntestine . On the e kidney . The oppAs these examples demonstrate, mGWAS in urine extend our understanding of genetically influenced biochemical processes and can facilitate the knowledge transfer from blood to urine and vice versa. Currently, this transfer is limited by the comparatively low number of GIMs in urine (26) versus blood (>150). Further increasing the sample sizes of mGWAS in urine and the application of more sensitive MS-based metabolomics platforms as already used for blood mGWAS could compensate this bias.For this study, we used data from SHIP  and, for replication, from the KORA (Kooperative Gesundheitsforschung in der Region Augsburg) study. Both studies have been described extensively in the study design papers –86 and iSHIP is a longitudinal population study conducted in West-Pomerania, located in the northeastern part of Germany. 4,308 inhabitants in that region participated in the first phase “SHIP-0”. For the GWAS presented here, metabolically characterized urine samples and genotype data were jointly available for 3,861 study participants . KORA is a population study conducted in the municipal region of Augsburg in southern Germany. The KORA F4 cohort comprises 3,080 subjects. For the study presented here, both genotype and urine samples from 1,691 participants  were available. In both studies, all participants have given written informed consent and the local ethics committees  have approved the studies.1H NMR spectra were recorded at the University of Greifswald, Germany using a Bruker DRX-400 spectrometer . Spectra were acquired at 300K and a frequency of 400.13 MHz using a standard one-dimensional NOESY-PRESAT pulse sequence with water peak suppression, as previously described in [In SHIP-0, non-fasting, spontaneous urine samples were collected from the study participants. In contrast, KORA F4 study participants were overnight-fasting prior to urine sample collection. All urine samples were stored at -80°C until the analysis. In preparation for the recording of NMR spectra, 75μl of phosphate buffer were added to 675μl of urine to set the pH to 7.0 (+/- 0.35). The deuterated buffer contained 0.5 mM sodium trimethylsilylphosphate (TSP) to provide a reference substance for the annotation of the NMR spectra. One-dimensional ribed in .10 transformed. Individual data points more than three times the standard deviation away from the mean were removed to avoid spurious associations. Finally, we considered only metabolic traits with at least 300 valid data points for the GWAS. In total, the final SHIP-0 data set comprises 1,518 targeted metabolic traits  that were tested for genetic associations.The Fourier-transformed and baseline-corrected NMR spectra were manually annotated by spectral pattern matching using the Chenomx Worksuite 7.0 by Chenomx, Inc.  to deduce absolute urinary metabolite concentrations. The panel of targeted metabolites comprises 60 compounds . In the discovery mGWAS, we used both metabolite concentrations (59) and pairwise ratios thereof  from the SHIP-0 data as phenotypic traits. Prior to analysis, individual metabolite concentrations were normalized to the annotated creatinine levels, and both concentrations and ratios were logLikewise, we processed the NMR spectra originating from the KORA F4 data set that was used for replication. Due to the smaller data set size, we lowered the burden for non-missing values to 100. The replication data set contains 53 metabolites and 1,359 pairwise ratios. It covers all metabolic traits that significantly associated in the discovery GWAS.http://www.urr.cat/FOCUS) [10 transformed. In comparison to the targeted data set, we chose a less stringent removal of extreme values. Here, individual data points more than four standard deviations away from the mean were removed.The same Fourier-transformed and baseline-corrected NMR spectra that were used for the targeted analysis were binned at a segment width of 0.0005 ppm. All signal intensities were normalized to the TSP reference peak. We used the FOCUS software (t/FOCUS)  for the PHWE < 1.0×10−6, 8,623 in SHIP and 32,033 in KORA), or had a genotyping rate below 95% , or displayed minor allele frequencies (MAF) below 5% . After the exclusion, 620,456 autosomal SNPs remained in the SHIP-0 data set and 593,830 autosomal SNPs in the KORA F4 data set. Both SHIP and KORA genotypes were imputed in a two-stage process (pre-phasing followed by imputation). According to data from the 1000 genomes project  ≥ 0.8.Both SHIP and KORA samples were genotyped using Affymetrix Human SNP Array 6.0 gene chips. SNPs were called using the Birdseed2 algorithm. In both data sets, the total genotyping rate was above 99%. 909,508 SNPs were genotyped in the SHIP-0 cohort, and 906,716 SNPs in the KORA F4 cohort. We excluded SNPs that violated the Hardy-Weinberg equilibrium . We considered associations to be genome-wide significant if the resulting P value was below the Bonferroni-adjusted significance threshold of 5×10−8/15,379 = 3.25×10−12. In case of associations with ratios, we imposed an additional significance criterion on the P-gain as suggested by Petersen et al. [P-gain describes the observed increase in strength of association when compared to the association with the individual metabolic traits from which the ratio was computed (min(P(M1)/P(M1/M2), P(M2)/P(M1/M2))). A conservative Bonferroni-type lower limit on the P-gain at a significance level of 5% is given by ten times the number of tested ratio pairs. Thus, associations with ratios were considered to be significant if the P-gain exceeded 15,180 in case of targeted metabolic ratios and 138,610 in case of non-targeted ratios. The analysis of the PLINK output files was performed using in-house R (v3.0.2) code (available at http://www.gwas.eu).In both the SHIP-0 and KORA F4 cohorts, we used PLINK (v1.07)  to compun et al. . P-gain P-value  [http://www.gwas.eu).Annotation data for genetic variants as well as linkage disequilibrium (LD) data from the 1000 genomes project  were reipa.org) . Full liipa.org)  were obtP-value and P-gain criteria) were assigned to distinct genetic regions (loci), based on a physical distance threshold of 1 Mb. Each of the resulting 23 genome-wide significant loci was then projected to candidate genes using an evidence-based procedure. To this end, we used the “block annotation” feature of SNiPA on the LD-extended (r2 ≥ 0.8) list of associated variants at each locus. This feature provides a condensed view of genes that are linked to any of the significantly associated variants or their LD-proxies via genomic proximity, eQTL association, or regulatory elements. Additionally, the block annotation highlights missense and pathogenic variants. Based on these data, we defined the following criteria to identify candidate genes: 1) Genomic proximity: genes that harbor or are in close proximity (<5kb) to any of the variants in the list. 2) eQTL association: genes where altered expression levels have been discovered to associate with any of the variants in the list. 3) Regulatory elements: potentially regulated genes that are associated with a promoter/enhancer/repressor element containing a variant of the list. Further evidence for potential involvement of a gene was assumed if 4) the variant list contains a missense variant for a protein product of this gene and 5) if an intragenic variant in the list is annotated as pathogenic in one of the phenotype databases contained in SNiPA. For each gene, we counted how many of the aforementioned criteria are met. Thus, the maximum evidence count for a candidate gene is five. If evidence-based gene selection was ambiguous, the gene with the most plausible biological function was chosen . It does so by comparing the association signal between a SNP and all non-targeted traits (pseudo-spectrum or association spectrum) to the 1H-NMR spectra of metabolites in a reference set, and assigning a score to each pseudo-spectrum to NMR spectrum match. The metabolites most likely to underlie the genetic association are the ones with the highest scores. We applied metabomatching to each SNP showing a significant association with an NMR feature or feature-ratio, using the 180 metabolites listed in the urine metabolome database [Metabomatching  is an audatabase  with an database  present S1 FigHIBCH, for which no match was found). Top panels contain the pseudo-spectrum of the SNP with the strongest association within its locus, bottom panels show the NMR spectrum of the most likely candidate metabolite(s). For pseudo-spectra, only peaks with-log(P) > 1.3 (P < 0.05) are displayed, and peak color indicates effect direction, with blue for β > 0 and orange for β < 0. The most likely candidate metabolite is not necessarily the metabolite of top rank: instead, it is manually selected among top-ranked metabolites. For some loci, the pseudo-spectrum indicates the involvement of more than one compound. For CPS1 (b) and HPD (o), 2-compound metabomatching, which ranks compound pairs rather than individual compounds, produces viable candidates. For SLC6A19 (g), NAT2 (k), and PNMT (r), the pseudo-spectra allow too many viable compound pairs even for 2-compound metabomatching, so these matches were manually selected.The subfigures (a)-(s) show the most likely candidate metabolite as suggested by metabomatching for each locus in (PDF)Click here for additional data file.S2 FigP-gain criterion . The variants are colored according to the degree of linkage disequilibrium (LD) to the lead SNP. The plot symbols and border colors indicate functional annotations (e.g. effects on transcripts) as well as results from other association studies. Plots were created with the SNiPA web service (available at www.snipa.org) using data from the 1000 genomes project  and Ensembl 75. Bottom left: Top associated metabolic trait in SHIP-0 grouped by genotype of the lead SNP . Bottom right: Quantile-quantile plots displaying the observed vs. the expected distribution of P-values for associations to the top-associated metabolic trait.Top: The regional association plot displays SNPs in a locus and the strength of association to the top-associated metabolic trait Tables  and 2. T(PDF)Click here for additional data file.S3 FigXYLB) and rs17118 (non-synonymous variant in XYLB) with glycolate. The urinary concentration increases per copy of the effect allele (indicated by the yellow arrow). Glycolate is a downstream product of the PFK-mediated D-xylulose pathway. Thus, the associated variants could decrease the enzymatic activity of XYLB’s gene product , which would lead to an increased D-xylulose metabolism via PFK. Figure adapted from [The mGWAS identified significant associations of SNP rs3132440 Click here for additional data file.S4 FigCarbamoyl-Phosphate Synthetase 1 (CPS1) can lead to high ammonia levels in blood (top). We detected significant associations of variants in CPS1 and elevated urinary glycine levels (indicated by the yellow arrow). We hypothesize that the excess ammonia is converted to glycine via the Glycine Cleavage System (bottom).A deficiency of (PDF)Click here for additional data file.S1 TableP < 0.05/38 = 1.32×10−3), which is the sum of replication attempts using targeted traits and non-targeted traits (SLC7A9) and the lysine/valine ratio could not be replicated. Instead, the second-strongest, still significant association of this SNP with the concentrations of valine was successfully replicated.In case of association to ratios, the metabolite that shows the stronger association signal is listed in the numerator. Tests: number of conducted association tests on the SNP. The significance level was adjusted for 38 tests (d traits . In the (DOCX)Click here for additional data file.S2 TableETNPPL, SLC6A19, DMGDH, ABO, and PNMT), we were unable to replicate the top associations in the KORA F4 data set. These associations are marked with an asterisk (*).Non-targeted traits are reported as chemical shifts . To replicate the non-targeted association results, we selected the NMR peaks or ratios thereof that were closest to the NMR features used in the SHIP-0 data set. For five loci ((DOCX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 TableExpression rate  for the most likely effector gene per locus as determined in 16 different tissues.(DOCX)Click here for additional data file.


PPARD in the QTL interval on SSC7 was identified as the causative mutation for ear size. However, on account of the large interval of QTL, the responsible gene on SSC5 has not been identified. In this study, an intercross population was constructed from the large-eared Minzhu, an indigenous Chinese pig breed, and the Western commercial Large White pig to examine the genetic basis of ear size diversity. A GWAS was performed to detect SNPs significantly associated with ear size. Thirty-five significant SNPs defined a 10.78-Mb (30.14–40.92 Mb) region on SSC5. Further, combining linkage disequilibrium and haplotype sharing analysis, a reduced region of 3.07-Mb was obtained. Finally, by using a selective sweep analysis, a critical region of about 450-kb interval containing two annotated genes LEMD3 and WIF1 was refined in this work. Functional analysis indicated that both represent biological candidates for porcine ear size, with potential application in breeding programs. The two genes could also be used as novel references for further study of the mechanism underlying human microtia.The quantitative trait loci (QTL) for porcine ear size was previously reported to mainly focus on SSC5 and SSC7. Recently, a missense mutation, G32E, in  Sus Scrofa Chromosome (SSC) 1, 4, 5, 6, 7, 8, 9, 11, 12, 16, and X PPARD in the QTL interval on SSC7 was identified as the causative mutation for ear size HMGA2 as the closest gene with a potential functional effect on the phenotype The large-ear feature of pigs  have historically been favored by owners in many areas of China, and as a result, most Chinese pig breeds have medium to large sized ears Mapping of QTL in genetic research of complex traits has led to a new approach, genome-wide association study (GWAS), which has been widely applied in studies of humans and livestock. QTL are often mapped to large intervals (≥10–20-cM) by genome scanning using microsatellite markers All animals used in the study were treated following the guidelines for the experimental animals established by the Council of China. Animal experiments were approved by the Science Research Department of the Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS) .2) than Large White pigs (about 165 cm2)  The protocol involved three steps:y is the vector of phenotypes of F2 individuals; b is the vector of fixed effects ; w is the vector of body weights of the individuals (considered as a covariate); c is the vector of litter effect ); a is the vector of random additive genetic effects with a∼N ; X, T, and Z are incidence matrices relating records in y to fixed and random effects; p is the regression coefficient of body weight; and e is the vector of residual errors with e∼N, where I  is the identity matrix and σe2 is the residual variance. The vector of residuals *y is estimated asb, p, c and a, respectively.Step 1: Data were analyzed using the mixed model:g is the vector of genotypes, k is the regression coefficient and e* is the vector of random residuals.Step 2: The residuals are used as the dependent trait and the associations are tested using a single locus regression analysis:ith SNP Ti2 is calculated as:k, respectively. The deflation factor λ is estimated as λ = median, where 0.456 is the median of χ(1)2ith SNP with the trait is examined by comparison of T12/(1)2.Step 3: In the GC procedure, the unadjusted test statistic factor of the P-value was divided by the number of tests performed The relationship matrix was calculated from the corrected pedigree. The DMU 10 likelihood ratio LH1/LH0. LH1 corresponds to the likelihood of the pedigree data assuming that the boar is of Qq genotype, and LH0 corresponds to the likelihood of the pedigree data assuming that the boar is of QQ or qq genotype. Boars were considered to be Qq when Z>2; QQ or qq when Z<–2; and of undetermined genotype if 2>Z>–2. According to the MASS analysis, Q-bearing chromosomes in F1 sires and Minzhu founder chromosomes segregated and haplotype-sharing analysis was performed using all 35 significant SNPs on SSC5.The genotypes of F1 boars were determined using marker-assisted segregation analysis (MASS) P-value was also calculated for the significance of each hap-score. A positive/negative score for a particular haplotype indicated that a haplotype is associated with increased/decreased risk of a given trait. The global score statistic index, which has an asymptotic distribution with degrees of freedom (df) and the P-value, was calculated to test overall associations among haplotype blocks and traits.Haplotype block detection was performed using all of significant SNPs on SSC5. The Haploview v4.0 program 2) as breed character , Landrace (about 200 cm2), and Large White (about 165 cm2) were treated as control groups  was reported to be the closest gene with a potential functional effect on the QTL marker for ear size on chromosome 5, and its SNP (g.2836 A>G) showed the strongest association with ear size in the standard association test HMGA2 was also located within the canine orthologous chromosome region, which was shown to be associated with ear type and size HMGA2 gene-targeting mouse model showed a dwarf phenotype that resulted from suppression of mesenchymal cell growth HMGA2 gene was the closet annotated gene with the most significant SNP and could be regarded as a good candidate for ear size to study further.The results of the GWAS of ear size are shown in peroxisome proliferator-activated receptor delta (PPARD) are located in the 971-kb region. Similar to our results, a QTL for ear size was previously mapped to an interval of 55.08-Mb (12.74–67.82 Mb) on SSC7 using a genome scan of a White Duroc×Erhualian intercross population PPARD was reported to be the responsible gene for ear size at the SSC7 locus and the G32E was identified to be the casual mutation PPARD, indicated that GWAS results identified the QTL efficiently.To detect other significant SNPs except for those on SSC5, a conditional analysis was performed using the most significant SNP H3GA0016181, as a fixed effect. The Q-Q plot and Manhattan plot obtained from the conditional analysis are shown in Figure S3 and S4 in 2) were calculated and haplotype blocks were inferred in the F2 population. Within the 10.78-Mb region, four haplotype blocks were identified as 427-kb, 447-kb, 404-kb, and 496-kb, respectively  and WNT inhibitory factor 1 (WIF1). Thus the two genes could be regarded as good candidates for porcine ear size.Erhualian pigs have undergone selection for extraordinary large ear size WIF1and LEMD3 were also treated as candidate genes for ear size from a GWAS in dog WNT5B gene, a member of the WNT family, was evaluated as a candidate for oculoauriculovertebral spectrum, including, unilateral or bilateral ear abnormalities (microtia) LEMD3 and WIF1 could indirectly regulate skin homeostasis, cartilage development, and fat metabolism, and could stand out as major determinants of ear size in pigs. Although the mechanism behind the size diversity of normal ears is different from that of abnormal ears, our finding could be used as a novel reference to further study the mechanisms leading to microtia in humans.Similar to present result, PPARD on SSC7. Finally, combining linkage analysis, haplotype sharing and selective sweep, the responsible gene region was refined to an about 450-kb region encompassing the LEMD3 and WIF1 on SSC5. Exploration of the two genes at associated loci via additional genetic, functional, and computational studies revealed both as the biological candidates, which are expected to lead to novel insights into polymorphism is porcine ear size and may underlie the human genetic deformity of microtia.In summary, this work describes GWAS of porcine ear size. First, through a genome-wide scan, 35 SNPs on SSC5 and 7 on SSC7 were determined to be significantly associated with porcine ear size. Further, with the most significant SNP as a fixed effect, conditional analysis was done and showed a 971-kb significant region containing the reported causal gene File S1Supporting figures.Figure S1, The Large White and Minzhu phenotypes. Minzhu pig (right panel) has larger and floppy ears. In comparison, Large White pig (left panel) exhibits smaller and fully erectness ears. Figure S2, The Erhualian and 3 western commercial pig breeds phenotypes. The ear sizes of Erhualian, Large White, Landrace and Duroc are about 400 cm2, 165 cm2, 200 cm2 and 180 cm2, respectively. Figure S3, Quantile-quantile (Q-Q) plots for ear size trait. SNPs for which the test statistic exceeds 25 are represented by triangles. Figures A and B are the Q-Q plots from GWAS and conditional analysis with the most significant SNP H3GA0016181 as a fixed effect, respectively. Figure S4, Manhattan plots of conditional analysis with the most significant SNP H3GA0016181 as a fixed effect. The x-axis shows the chromosomes (SSC) 1–18 and x. The y-axis shows the -log10 values . The thresholds for genome-wide significance and chromosome-wide significance are 6.68 . Figure S5, Haplotypes block analysis in the 17.73-Mb region on SSC5 with a lower genome-wide significance threshold of 1.03E-06 obtained with the HAPLOVIEW 3.31 program. Solid lines mark the blocks identified.(DOCX)Click here for additional data file.File S2Supporting tables.Table S1, Distribution of SNPs after quality control and average distances on each chromosome. Table S2, Primers of 30 SNPs for selective sweep analysis in the 10.78-Mb region on SSC5. Table S3, Primers of 10 SNPs for selective sweep analysis in the 10.78-Mb region on SSC5. Table S4, Genome-wide significant (P<1.03E-06) SNPs associated with ear size.(DOCX)Click here for additional data file.


Genomic imprinting is an epigenetic mechanism that can lead to differential gene expression depending on the parent-of-origin of a received allele. While most studies on imprinting address its underlying molecular mechanisms or attempt at discovering genomic regions that might be subject to imprinting, few have focused on the amount of phenotypic variation contributed by such epigenetic process. In this report, we give a brief review of a one-locus imprinting model in a quantitative genetics framework, and provide a decomposition of the genetic variance according to this model. Analytical deductions from the proposed imprinting model indicated a non-negligible contribution of imprinting to genetic variation of complex traits. Also, we performed a whole-genome scan analysis on mouse body mass index (BMI) aiming at revealing potential consequences when existing imprinting effects are ignored in genetic analysis.−4 to 1.842 ×10−4 when imprinting was considered in the analysis, implying that one third of marked variance would be lost if existing imprinting effects were not accounted for. When both marker and pedigree information were used, estimated heritability increased from 0.176 to 0.195 when imprinting was considered.10,021 SNP markers were used to perform a whole-genome single marker regression on mouse BMI using an additive and an imprinting model. Markers significant for imprinting indicated that BMI is subject to imprinting. Marked variance changed from 1.218 ×10When a complex trait is subject to imprinting, using an additive model that ignores this phenomenon may result in an underestimate of additive variability, potentially leading to wrong inferences about the underlying genetic architecture of that trait. This could be a possible factor explaining part of the missing heritability commonly observed in genome-wide association studies (GWAS). Genomic imprinting, an epigenetic process, is the preferential or differential gene expression in a parent-of-origin fashion , 2. If th2). Therefore, h2 is usually interpreted as the proportion of phenotypic variance explained by additive variance. This parameter is also the expected fraction of the selection differential transmitted from the parental to the offspring generation, and is crucial in artificial selection ).a, d and a can be assigned to three possible genotypes A2A2, A1A2/A2A1 and A1A1 respectively, in a biallelic locus. With imprinting, on the other hand, the resulting parent-of-origin effect makes the two reciprocal heterozygotes different from each other. Hence, the four genotypes A2A2, A1A2, A2A1 and A1A1 should be all uniquely identifiable and four different genotypic values are needed in this case. This constitutes the basic configuration of a model incorporating imprinting.In a standard quantitative genetic model without imprinting, three genotypic values −a, d1, d2 and a are assigned to the four genotypes as shown in Fig. p and q are the allele frequencies of A1 and A2 alleles, respectively, these four genotypes will have frequencies q2, pq, qp and p2 if Hardy-Weinberg equilibrium holds, and basic statistics gives the mean and variance of the genetic values as μG=a(q−p)+2pqd and d=(d1+d2)/2 as dominance effect in the conventional sense and i=(d2−d1)/2 as the imprinting effect, so that d1=d−i and d2=d+i, which was also suggested by ).ussed in , 64.Our results indicated that existing imprinting effects should not be ignored in genetic analysis. Meanwhile, it is also important to make a distinction between the terms “imprinting effect” and “parent-of-origin effect”. These terms are often used exchangeably in much of the epigenetic literature. However, a parent-of-origin effect referring to different genetic contribution of different parents to offspring is a broader concept than an imprinting effect. Genomic imprinting is the most important source of parent-of-origin effects, but not the only one. For example, maternal effects observed in swine production is a well known form of parent-of-origin effect that is not known to involve any epigenetic mechanisms; reciprocal effects observed in poultry breeding is another type of parent-of-origin effect. In the imprinting model that was adopted in our analysis, all inferences were performed at the DNA level using SNP markers. Hence, not all “putatively” imprinted loci were necessarily caused by imprinting at the epigenetic level. Moreover, other factors can lead to the detection of spurious imprinting effect that is actually caused by other types of parent-of-origin effect e.g., –67) or e or e67])We were inspired by studies that proposed equivalent one-locus imprinting models for quantitative genetic analysis. These studies defined paternal and maternal gene substitution effects. As such, imprinting does contribute to additive variance and a partition of additive variance into unimprinted and imprinted components is available. This variance decomposition hints that heritable genetic variation induced by epigenetic mechanisms, especially genomic imprinting, may have a considerable impact on the underlying genetic architecture of some complex traits, but it is largely neglected in many studies. Specifically, narrow sense heritability, especially marked “bottom up” heritability in GWAS, may be underestimated if one ignores imprinting when it is present. We tested this using a genome-wide association study performed on mouse BMI data. Results indicated that the portion of phenotypic variation explained by significant SNP markers increased drastically when imprinting effects were considered.Moreover, the imprinting regression model used here detects differences between paternally and maternally inherited alleles regardless of whether the biological mechanism is imprinting or not. Hence, this model might be capturing other (either genetic or epigenetic) mechanisms that produce nonequivalent contributions of paternal and maternal genomes as well. Therefore, it would be more appropriate to refer to this model as a model incorporating parent-of-origin effects, and such that, it can be applied to a number of situations. For example, in the human genome, more than 50 % of the genes have shown preferential expression of the paternal or maternal allele due to various mechanisms , indicat


The switch to a modern lifestyle in recent decades has coincided with a rapid increase in prevalence of obesity and other diseases. These shifts in prevalence could be explained by the release of genetic susceptibility for disease in the form of gene-by-environment (GxE) interactions. Yet, the detection of interaction effects requires large sample sizes, little replication has been reported, and a few studies have demonstrated environmental effects only after summing the risk of GWAS alleles into genetic risk scores (GRSxE). We performed extensive simulations of a quantitative trait controlled by 2500 causal variants to inspect the feasibility to detect gene-by-environment interactions in the context of GWAS. The simulated individuals were assigned either to an ancestral or a modern setting that alters the phenotype by increasing the effect size by 1.05–2-fold at a varying fraction of perturbed SNPs (from 1 to 20%). We report two main results. First, for a wide range of realistic scenarios, highly significant GRSxE is detected despite the absence of individual genotype GxE evidence at the contributing loci. Second, an increase in phenotypic variance after environmental perturbation reduces the power to discover susceptibility variants by GWAS in mixed cohorts with individuals from both ancestral and modern environments. We conclude that a pervasive presence of gene-by-environment effects can remain hidden even though it contributes to the genetic architecture of complex traits. Diseases such as diabetes, cardiovascular disease, and obesity have become highly prevalent in the developed world in a period of just a few generations. For example, more than one third of U.S Citizens are obese  has been confirmed for several traits. However, the discovered GxE effects explain just a minor fraction of variance, suggesting that most interaction effects remain hidden. The poor availability of reliable environmental data constitutes one the major hurdles to detect GxE interactions. Genetic variation of common nature can be interrogated systematically with commercial genotyping arrays, but the availability of counterpart environmental information is often patchy and inconsistent, impeding a systematic interrogation of GxE effects  contribution may be, we have performed a simulation study of a quantitative trait under “ancestral” and “modern” environments. Our main aim was to define the range of realistic conditions in which GRSxE interaction effects can be detected in the absence of evidence for individual GxE for the contributing alleles. The environmental perturbation and genetic architecture of the trait are based on recent inferences from human GWAS data. We demonstrate that a wide range of perturbation effects is consistent with current observations from GxE studies, although our investigations also show that these effects may heavily reduce the power to detect causal alleles by GWAS.PV = 1). In all simulations, we approximate the genetic architecture based on two recent inferences regarding the genetic basis of complex traits in humans. First, the trait is controlled by 2500 causal SNPs of common nature . This number of genes resembles the number of susceptibility variants inferred for several complex traits . Genotypes were simulated assuming Hardy-Weinberg equilibrium. For example, for a SNP with EAF = 0.4 in a simulation of 10,000 samples, we would assign a value of 0, 1, and 2 phenotype-increasing alleles to ~1600, 4800, and 3600 individuals, respectively. At this point of each simulation, we know the number of alleles that every individual carries at each site, as well as the total genetic variance each SNP explains. We can then easily calculate the effect size (β) of each SNP. The effect of the ith SNP on the trait is given by its contribution to the genetic variance of the trait  equals ~0.36. To achieve the desired phenotypic variance (PV = 1), we assigned a random environmental component (VE) to every individual, drawn from a normal distribution with mean 0 and variance 0.64 (EV = 1 − VG). In summary, we simulated a quantitative trait with heritability 36% that results from the additive gene action over 2500 independent causal SNPs of common frequency.For example, a variant that explains 0.28% of the VANCESTRAL = 0.06 transforms to βMODERN = 0.09). The strength of the GxE interaction is proportional to, first, the factor of perturbation and, second, the proportion of SNPs that become perturbed in the “modern” environment. For example, physical activity was shown to attenuate the association between rs9939609 in FTO and body mass index (BMI) by ~30 to 95%  while others have a more “modern” lifestyle (they consume sugary beverages and engage in other obesogenic behaviors). In reality there will be a gradation, but the dichotomous model serves for purposes of illustration of the potential consequences for disease for contemporary societies of the transition to a western lifestyle, that may have induced GxE effects Gibson, . SpecifiE) was drawn equally in both “ancestral” and “modern” environments. In other words, the “modern” environment induces an increase in the VP after perturbation that is entirely dependent on the genetic component of the trait, thus increasing the VG and heritability. Models entailing an increase in VE could be similarly explored, but we do not do so here. Moreover, we note that although we only simulate scaling effects (at the SNP level), since only a small portion of variant effects is perturbed, there are also rank effects at the phenotype level.All causal SNPs do not account for the same proportion of genetic variance in the simulated trait. Therefore, the degree of GxE we induce also depends on the actual effect size of the perturbed SNPs. We explored two different models of SNPs that become perturbed. In model 1, the SNPs were chosen at random, whereas in model 2 they were chosen from those explaining most of the variance . Importantly, the random environmental component . In other words, “scenario C” corresponds to a situation in which half of the society lives in an “ancestral” environment , whilst the other half follows a “modern” lifestyle that increases the effect size of perturbed alleles. Importantly, we do not “know” which environment each individual lives in, in the sense that this information is not included in the discovery GWAS. For both scenarios, we performed a two-stage genome-wide screen in which the quantitative phenotype is regressed against the allele dosage at each SNP. In the discovery screen, we assay the 2500 simulated SNPs in a sample of 50,000 individuals. SNPs that achieve j = β0 + βG * χ(G) + βE * χ(E) + β(G * E) * χ(GE) to estimate the regression coefficient β(G * E), with Yj, χ(G)i, and χ(E)i recording the phenotype, allele dosage (or GRS) and environment of the individual j, for individuals 1,…, 40,000.A central focus of our study lies in the evaluation of the power to detect the GxE effects in our simulated trait. We aimed to evaluate the performance of two different approaches, namely (i) power of detection through the examination of individual SNPs and (ii) by means of unweighted genetic risk scores (GRS) that sum up the number of causal alleles for each individual . To do so, for each scenario we simulated two cohorts of 20,000 individuals each that are sampled from the “ancestral” and “modern” environments, respectively. For each simulated individual, we know its phenotype, the number of causal alleles at each SNP , the total number of causal alleles over all selected loci (GRS) and the environment it belongs to . In each simulation of 40,000 individuals, we tested the interaction between genetic component and environment by means of a multiple linear regression: Y4–7, and Supplementary Table In summary, we explored two different ways to select SNPs that undergo perturbation and three different procedures to choose the actual variants upon which we test for gene-by-environment interactions. For each of the six resulting combinations , we explored 400 combinations of parameters. Specifically, the percentage of SNPs that experienced perturbation ranged from 1 to 20% (20 steps of 1%), and the factor of perturbation ranged from a 1.05–2-fold change in effect size (20 steps of 0.05-fold increments). We performed five different replications for each of the 400 combinations, and thus 2000 simulations for each of the six combinations. Results are summarized as heat maps that interpolate relevant parameters across a continuous range of values , increasing the phenotype by 0, ~0.075 and 0.15 in individuals with zero, one and two causal alleles, respectively. If it becomes perturbed in the “modern” environment by the strongest perturbation possible , the effect size would increase from ~0.075 to 0.15. Thus, the variant would increase by 4-fold the percentage of phenotypic variance it accounts for, hiking from ~0.3 to 1.2% .The actual effect size of each causal allele depends on the frequency and variance explained by the causal variant. For example, we set the strongest contribution in the “ancestral” environment at ~0.3% of the variance explained. If that allele has a frequency of 0.5, it would present an effect size of 0.075 . Again, the effect is more pronounced under Model 2  the genetic effect sizes of perturbed SNPs  for any SNP in the GxE analyses, and all of these required a >1.5-fold change in the effect size  for all the ranges from 1.3 to 2-fold change in the genetic effect size. Indeed, tiny increments in the effect size, such as a 1.2-fold change, resulted in ~100% of the simulations detecting GRSxE effects at the P < 0.05 significance level . Only the parameter space correspondent to <1.1-fold changes for <5% of the causal variants consistently resulted in non-significant GRSxE interaction terms , GxE effects tend to remain undetected at the SNP level . GWAS on cohorts with 100% of the individuals being “perturbed” under model 1 led to an increased ability to detect variants associated to the trait Figure . The numHighly divergent patterns were observed when we perform a preliminary GWAS upon a mixed sample of individuals drawn equally from the “ancestral” and “modern” environment (“scenario C”). Under Model 1, the number of variants detected by GWAS still remained close to ~90 only if the 50% of GWAS individuals coming from the “modern” environment had only been perturbed slightly  that sum up the total number of causal alleles in a single figure. Moreover, we find that the ability to detect perturbed SNPs in a GWAS preliminary to the GxE analysis depends on the mixture of samples coming from each environment. Genome-wide screens performed upon homogeneous cohorts of perturbed individuals show increased power to detect significant gene-by-environment interaction effects. In contrast, GWAS upon heterogeneous mixtures of “unperturbed” and “perturbed” individuals present a decreased ability to detect significant SNPs, thus inhibiting the task of detecting GxE effects.The validity of the insights gained from this study depends on the plausibility of our model of environmental perturbation, and the extent to which we mimic the reality faced by current GWAS studies. Certainly, it is difficult to evaluate the consequences of the “modern” perturbation in the case of actual human phenotypes because the heritability and phenotype distributions correspondent to the “ancestral” lifestyle are unknown. However, there is increasing evidence that the switch to a western lifestyle may have been coupled with a change in the genetic effects of causal alleles Gibson, . Human cOur model postulates one of the simplest instances of GxE in which individuals are assigned to a binary environmental state that would roughly correspond to “ancestral” and “modern” lifestyles. A more realistic scenario of environmental perturbation should summarize the varying fraction of “modern lifestyle” followed by each person into an individual-specific measure, or “exposome” . Conversely, GRSxE analyses are always significant when β-s are multiplied by 1.3-fold or more, or for the whole range of perturbation parameters when the “modern” environment affects the SNPs that explain most of the variance in the trait . These results confirm that a widespread presence of GxE effects is not at odds with the lack of evidence when individual variants are assayed, despite of a substantial presence of interaction effects.An important aspect of our simulations lies in the choice of variants that are perturbed by the “modern” environment. We observe that it is easier to detect GxE effects when the variants that are perturbed coincide with the alleles that explain most of the genetic basis of the trait, as in model 2. This makes sense considering that these perturbed variants not only present the largest effect sizes, but also have multiplied it in the “modern” environment. The same mechanism explains the increment in the number of variants detected by GWAS when the genome-wide screen is performed entirely upon perturbed individuals, as in “scenario B.” For real traits with widespread GxE effects, it may be key to perform GWAS selecting for perturbed individuals. The selection of those individuals following a “modern” lifestyle would unravel specific pathways that respond badly in face of perturbation, thus enabling a more detailed understanding of the etiology of the diseases of affluence. Nonetheless, it may be inherently complex to design “perturbed-only” GWAS, owing to the difficulty in defining what exactly constitutes the perturbed environment. The sampling of individuals could also be confounded by the fraction of cases that are entirely due to purely environmental causes without any major role of gene-by-environment interactions linked to “modern” life.P < 10−12 when assayed in a GWAS with 20,000 individuals that follow the “ancestral” lifestyle. In contrast, the significance worsens (P < 10−7) when this variant is assayed upon a mixture of 10,000 “ancestral” individuals and 10,000 individuals in which 10% of the SNPs have increased their effect size by 1.5-fold. Eventually, the variant remains completely unnoticed in a mixed GWAS when the effect size increases by 2-fold in the individuals following “modern” lifestyle (P~10−4). As a consequence, these variants are not found among the top candidate list in our simulated meta-analysis GWAS.The simulations in which the preliminary GWAS is performed upon cohorts with a mixed environmental exposure (“scenario C”) show a remarkable trend regarding SNP discovery. The combination of “ancestral” and “modern” environments does not compromise the detection of causal variants when perturbation effects are tiny or restricted to a small fraction of the causal SNPs. However, larger perturbations decrease the ability to detect new variants, and statistical power eventually collapses for the strongest range of effects in our simulations. This result makes sense because gene-by-environment interactions add variance and heterogeneity in the estimates of SNP effects. We show the results for a causal variant that explains 0.3% of the variance in an “ancestral” population Figure . This alIt is difficult to evaluate the extent to which pervasive gene-by-environment effects have compromised the power to discover associated variants by GWAS. The number of discovered variants correlates with sample size . This procedure follows the usual practice consisting of screening polymorphisms for HWE. Nonetheless, confounding of population structure with environmental variability, further complicating the detection of GxE in real studies, remains a possibility. Third, we explored the presence of interactions through unweighted GRS that do not take into account the effect size of each variant. Since only a few variants present notably large effects Figure , in realIn summary, the present study constitutes a preliminary evaluation of a realistic mechanism by which gene-by-environment interactions may have altered the genetic etiology of human traits. A widespread presence of realistic GxE effects could only be detected by genetic risk scores calculated upon all variants discovered by GWAS. The extent to which these effects have shaped real human traits remains as an open question, and should be studied in future research.GG conceived the original idea. UMM and GG designed the study. UMM performed the simulations. UMM and GG interpreted the data and wrote the paper.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.