Motivation: In sequencing studies of common diseases and quantitative traits, power to test rare and low frequency variants individually is weak. To improve power, a common approach is to combine statistical evidence from several genetic variants in a region. Major challenges are how to do the combining and which statistical framework to use.General approaches for testing association between rare variants and quantitative traits include aggregating genotypes and trait values, referred to as ‘collapsing’, or using a score-based variance component test. However, little attention has been paid to alternative models tailored for protein truncating variants. Recent studies have highlighted the important role that protein truncating variants, commonly referred to as ‘loss of function’ variants, may have on disease susceptibility and quantitative levels of biomarkers. We propose a Bayesian modelling framework for the analysis of protein truncating variants and quantitative traits.Results: Our simulation results show that our models have an advantage over the commonly used methods. We apply our models to sequence and exome-array data and discover strong evidence of association between low plasma triglyceride levels and protein truncating variants at APOC3 (Apolipoprotein C3).Availability: Software is available from http://www.well.ox.ac.uk/~rivas/mambaContact:donnelly@well.ox.ac.uk Advances in DNA sequencing and customized exome arrays are quickly transforming the landscape of genomic studies of diseases and related quantitative traits . The functional prediction is made based on the translational consequence of a mutation against a reference transcript set, for example, Gencode. Protein truncating variants (PTVs) are typically subject to nonsense-mediated decay (NMD), a cellular mechanism that detects nonsense mutations and prevents the expression of truncated proteins, resulting in loss of function of that copy of the protein. To a first approximation, PTVs are thus likely to have the same functional consequence, namely, loss of function (though see below for a more nuanced treatment), so that variants in this class within a gene may naturally be combined in assessing their effect on a trait or phenotype of interest. A second reason for focussing on PTVs is that there is ample evidence from studies of Mendelian phenotypes, common diseases and quantitative traits  escape NMD and can act as loss of function, gain of function (by deleting an inhibitory domain) or neutral variants. The approach could be extended to allow more than two groups of PTVs, with PTVs in each group having the same effect, but for definiteness we focus in this article on the case of either one or two groups.We study the properties of the new method and compare it with other approaches. A simple alternative to our similar effects model is to use the mean trait value of the PTV carriers as the test statistic  and MAMBA dependency), identifies two candidate PTVs predicted to trigger NMD: the previously mentioned stop-gain mutation, p.R19X , and a donor essential splice variant c.IVS2 + 1G >A . After application of the SEM to the data, the posterior distribution indicates that plasma triglyceride levels of individuals with PTVs at APOC3 are 1.2 standard deviations (SD) lower than the population mean, indicating a strong effect , whereas SKAT and lm give clearly larger P-values, probably because they are not assuming similar effect sizes across the two variants. When we compare the ranks of genes in an exome-wide analysis, the SEM and SKAT rank APOC3 as #3 and #5, respectively. To illustrate the performance of our models, we focus here on data for g effect . The effg effect . We findn = 4443 samples, [http://www.oxfordbiobank.org.uk]). Quantification of plasma triglyceride concentrations in the Oxford Biobank was made after an overnight fast. Combined analysis of plasma triglyceride levels and variant genotype data for c.IVS2 + 1G >A  and p.R19X  from the Oxford Biobank cohort and the GoT2D samples demonstrates strong supporting evidence for protein truncating variant association to low plasma triglyceride levels  on the alternative hypothesis can be calculated by multiplying the prior odds (i.e. before the data) by the BF.We envisage that a primary application of this and similar methods will be in the analysis of quantitative traits measured in individuals for whom whole-genome or whole-exome sequence data are available, or perhaps for whom genetic data are obtained from an exome-SNP-array.For a single dataset one could use the size of the BF to indicate the strength of the evidence in favour of the alternative hypothesis. It can also be helpful to plot the posterior distribution on the mean trait value for individuals carrying PTVs.P-values. The interpretation of a P-value obtained in a particular experiment depends on both the alternative hypothesis and the power of the statistical method used. In the current context, power will change with n, the number of PTVs observed at the gene: there will be more power to reject the null hypothesis  at genes that happen to have more PTVs in the sample, and as a consequence, informally, a low P-value will be more informative for a gene with more PTVs than the same P-value would be for a gene with fewer PTVs. For this reason, simple ranking of genes by P-value is unlikely to be optimal. In contrast, BFs quantify the evidence for the alternative hypothesis as compared with the null , so can be compared naturally even for genes with different numbers of PTVs.Many studies will consist of two phases, discovery and replication. In this setting, analyses of the discovery data would aim to rank genes for follow-up according to the evidence for departures from the null hypothesis at that gene. In such a ranking exercise, the use of BFs has considerable advantages over the use of n, a P-value can then be obtained by asking for the probability, under the null hypothesis, of obtaining the same or a larger BF than the one actually calculated from the data. Under the SEM, the null distribution of the BF can be calculated explicitly, allowing a simple analytic computation of P-values. For more complicated alternative hypotheses, P-values can be obtained easily by simulation under the null.Although we see advantages in adopting a Bayesian perspective, our approach could be used in a frequentist context by calculating the BF and using this as a frequentist test statistic. Conditional on the number of PTV carriers at the gene, In the SEM setting, the statistical problem is a simple one, namely, of detecting a mean shift in a  number of observations. Our method is the obvious Bayesian approach to the problem, and when the resulting BF is used as a test statistic it is effectively equivalent to the obvious frequentist approach. We have, necessarily, used particular prior distributions for the change in mean trait value caused by the PTVs. In our method these are relatively broad, but they could be adjusted as our biological knowledge grows, as could the priors in the GEM model. As more genes are discovered where PTVs affect quantitative traits, the priors could be informed by knowledge of the effect sizes of PTVs at these genes. Two caveats are appropriate here. The first is that early discoveries are likely to correspond to genes where PTVs have larger effects, and the second is that a more precise specification of the prior will gain power if it reflects reality but could substantially lose power otherwise.P-value of What are the sample sizes required to obtain exome-wide significant results? Under the assumption that effect sizes are 2, 1.5 and 1 SDs from the mean, to get a As we generate more cellular genomics data, we will improve our understanding of the impact of PTVs on gene function. For example, combining DNA and RNA sequence data will enable better predictions of mechanisms underlying NMD, which will, in turn, give more accurate predictions of the biological consequences that the analyzed variants may have. Current annotation of loss of function variants is based on some reference transcript set, for example, GENCODE  and may result in gain of function. As our biological understanding of the NMD mechanism increases, we expect that it will add power to allow potentially different effects for PTVs escaping NMD, for example, by applying GEM-NMD (in which they may have similar effects or different effects) and SEM .APOC3 and lowering of plasma triglyceride levels.Finally, we have established and replicated an association between PTVs in


How various genetic effects in combination affect susceptibility to certain disease states continues to be a major area of methodological research. Various rare variant models have been proposed, in response to a common failure to either identify or validate biologically driven causal genetic variants in genome-wide association studies. Adopting the idea that multiple rare variants may effectively produce a combined effect equal to a single common variant effect through common linkage with this variant, we construct a pathway-based genetic association analysis model using both common and rare variants. This genetic model is applied to the disease status of unrelated individuals in replication 1 from Genetic Analysis Workshop 17. In this simulated example, we were able to identify several pathways that were potentially associated with the disease status and found that common variants showed stronger genetic effect than rare variants. In the search for causal variants, an abundance of research has been focused on relatively frequent variants that are assumed to be located near the true causal variants. This focus is based on a popular hypothesis that common single-nucleotide polymorphisms (SNPs) contribute to the genetic effects underlying complex traits, where, traditionally, common SNPs are defined to be the ones with minor allele frequency greater than 1%. Genome-wide association study is an approach that scans markers across the whole genome. It has been proven to be quite successful, as more than 2,000 common variants have been identified to be associated with common diseases or related traits. Once the genetic markers have been found, extensive resequencing of the nearby sites is done to seek out the true causal SNPs. This step, however, has not been as successful as genome-wide association study, which raises the question of the common trait/common variant assumption . DicksonBecause of the low penetrance of rare variants, the power to detect the effect of a single rare SNP is small. Modeling groups of rare SNPs is an efficient way to detect rare variant effects. Groups may refer to genes or pathways or other biologically meaningful units. A genetic pathway is a set of interactions between several genes that function together to affect a disease or trait . ExperimUsing the mini-exome data provided by Genetic Analysis Workshop 17 (GAW17), we aim to analyze the effects from rare and common variants at the pathway level to identify potential disease-associated pathways.The mini-exome data provided by GAW17 consists of 697 unrelated individuals from the 1000 Genomes Project. Phenotypes of these individuals include one binary trait of affection status and three continuous quantitative traits. Genetic information is available on 24,487 SNPs distributed across all autosomal chromosomes and comes from 3,205 genes. Clinical factors include age, sex, smoking status, and ethnic origin, where ethnic origin is reclassified into Asian, African, and European. Two hundred replications of the data were generated, with the genetic information, age, sex, and ethnic origin held fixed while the phenotype variables and smoking status were simulated across the 200 replications.p < 1 × 10−6) in each of the subpopulation groups were excluded from further analysis. We applied nonadjusted logistic regression models to each of the SNPs and the disease status before further modeling was carried out. To determine whether our models should adjust for the quantitative effects in addition to the other clinical factors, we explored the associations between the traits and the disease status using logistic regression.We used PLINK  to provii, i = 1, …, 697, we define the disease status as the outcome as follows:For each individual We applied the genetic model to the pathway level and defined risk scores representing either rare or common variant effects of each pathway using a collapsing methods (described in more detail later), where a threshold of 1% for the minor allele frequency was used to divide rare and common variants. We then combined the two different risk scores into a single risk score to assess whether a pathway was associated with the disease. We ran unadjusted and adjusted models. The unadjusted model uses only the genetic risk score in a model, and the adjusted model uses the covariates Age, Sex, Smoke, and the population in the model as well. To be more specific, we describe our modeling steps in what follows.Step 1. Apply the unadjusted logistic regression model to the rare variants effect. We construct a risk score, defined as the count or proportion of minor alleles of the rare variants within a pathway, to represent the rare variants effect [rare.s effect . We denoStep 2. Apply the adjusted logistic regression model to the common variants effect. First, we use the least absolute shrinkage and selection operator (LASSO) [ (LASSO)  for logiCX,j is the jth common SNP in a specific pathway, and:where j |jγ| <λ, where λ is a LASSO penalty parameter. Some of the estimated regression coefficients represents the adjusted covariates in the model. The model selection is based on maximizing the corresponding likelihood function subject to ∑ common.If none of the common variants are selected, then we conclude that no common variants effect is identified for this pathway. For the pathways with selected common variants, we then define a risk score as the summation of risk alleles . This risk score differs from the risk score for rare variants in that effect directions have been accounted for . We denoStep 3. Apply both the unadjusted and adjusted logistic regression models to the combined rare and common variants effect. The combining is done by using a weighted summation of the risk scores for rare and common variant effects. To decide the weights, we dichotomize the rare and common variant risk scores using their medians and then fit logistic regression models on the dichotomized variables to estimate the odds ratios. We then use the estimated odds ratios as the weights. Specifically, the final risk score for a given pathway is:r is estimated from:where ORr − 1) is 1 if ORr > 1 and 0 otherwise. I(·) is an identity function. The definitions of the common variant risk component are similar.and equals expStep 4. Correct for inflated type I error. We realized that the pathway effect selected from step 3 may have inflated type I error as a result of model selection. The asymptotic p-value based on standard normal distribution of the Z statistics does not consider such inflated type I error. To evaluate the pathway effect, we generate an empirical distribution based on the Z statistics of testing RSfinal. Such empirical distribution use all the Z statistics from step 3, except the top k pathway Z statistics . Then we estimate empirical p-values for the Z statistics of the top k pathway using this empirical distribution. Such empirical p-values can be used to evaluate whether the top pathway effects satisfy the same distribution as the rest of the pathway effect.http://www.broad.mit.edu/gsea/msigdb/index.jsp). To ensure that each pathway has a reasonable number of variants, in the analysis we use only gene sets or pathways with at least 5 genes .Pathways information is obtained in two steps: (1) by mapping all SNPs to genes based on the SNP information provided by GAW17; and (2) by mapping genes to pathways, where genes resulting from step 1 had been identified in predefined gene sets or pathways  to find out whether the nature of the disease is population specific. For those pathways identified as associated with the disease status, we use a linear regression model to assess whether the genetic risk score across different populations is different. We apply a multivariate model to disease status on population and to those identified pathway risk scores that have a different profile across population. We apply the likelihood ratio test to test whether the population effect remains significant.p-value was smaller than 1 × 10−6. This left us with 23,173 autosomal SNPs and 697 samples for the follow-up analysis. Univariate modeling for the effect of a single SNP yielded no significant signal at an adjusted significance level of 0.05/24,487 = 2.0419 × 10−6 after all clinical factors  were accounted for.We removed 1,314 SNPs for which the HWE test p < 6.47 × 10−5, corresponding to a Bonferroni-corrected significance level of 0.05). In comparison, only two pathways reached Bonferroni-adjusted significance  for their rare variant effects. The strongest signal of rare variant effect came from a pathway known as PENG_GLUTAMINE_UP ; however, significant effects from common variant(s) of this pathway were only suggestive .Of the 1,126 pathways, 772 had at least one common variant that was selected by the LASSO. Of these, 152 pathways had significant common variant effects (p < 6.47 × 10−5). There was a leap in p-values assessed through combined effects (unadjusted) between the seventh and eighth most significant pathways, which led us to assess empirical p-values for the top seven pathways. Statistics directly from the model for the seven pathways are given in Table When common and rare variant effects were combined, 162 out of the 772 pathways had a suggestive signal  reached a Bonferroni-corrected significance level (p < 6.47 × 10−5).Based on the empirical p < 2.2 × 10−16) . Likelihood ratio tests that assessed the effects of population on disease when genetic risk scores were taken into account gave p-values greater than 0.05 for five of the seven pathways, which suggests that prevalence differences in different populations are likely due to genetic differences.The disease prevalence in different populations was significantly different  were detected to be potentially associated with disease status. The associations still exist after adjusting for other factors, which is robust evidence for the pathway-based effect. Because of this result, we believe that modeling SNPs at the pathway level can enhance the power to detect genetic effects compared to modeling single SNPs. In common practice, investigators may have to take multiple steps to make good use of pathway information. These steps include using different methods to screen out potential pathways in the first stage and then validating those results.The top identified pathways show that both common and rare variants have consistent signals but with different magnitudes. For this study, the common variants showed stronger signals. This suggests that the underlying mechanism for genetic effects is likely to be a joint effect consisting of both common and rare variants.The significant difference of the disease prevalence across subpopulations is interesting and led us to explore potential factors affecting it. Using the likelihood ratio test with the genetic risk score in a nested model, we found that the disease risk was no longer significantly associated with population category. This suggests that the genetic component may partly explain this difference in disease prevalence. Different distributions of the disease-related genetic profiles may cause different disease prevalences across populations. For example, the African cohort has the highest genetic risk score, the European cohort has an intermediate risk score, and the Asian cohort has the lowest risk score, corresponding to the highest disease risk in the African cohort, an intermediate risk in the European cohort, and the lowest risk in the Asian cohort. Further analysis may be needed to identify other effects that may contribute to the different disease prevalences across the subpopulations.The authors declare that they have no competing interests.LC carried out the analysis and drafted the manuscript. PZH prepared the pathway data. WX designed the analysis plan. All authors participated in discussions of analysis plan and helped revise the manuscript.


The role of rare genetic variation in the etiology of complex disease remains unclear. However, the development of next-generation sequencing technologies offers the experimental opportunity to address this question. Several novel statistical methodologies have been recently proposed to assess the contribution of rare variation to complex disease etiology. Nevertheless, no empirical estimates comparing their relative power are available. We therefore assessed the parameters that influence their statistical power in 1,998 individuals Sanger-sequenced at seven genes by modeling different distributions of effect, proportions of causal variants, and direction of the associations  in simulated continuous trait and case/control phenotypes. Our results demonstrate that the power of recently proposed statistical methods depend strongly on the underlying hypotheses concerning the relationship of phenotypes with each of these three factors. No method demonstrates consistently acceptable power despite this large sample size, and the performance of each method depends upon the underlying assumption of the relationship between rare variants and complex traits. Sensitivity analyses are therefore recommended to compare the stability of the results arising from different methods, and promising results should be replicated using the same method in an independent sample. These findings provide guidance in the analysis and interpretation of the role of rare base-pair variation in the etiology of complex traits and diseases. There is now evidence that rare variants can contribute to the etiology of complex disease. Next generation sequencing technologies have enabled their detection in large cohorts, and new statistical methods have been proposed to ascertain their association with complex diseases and traits in order to improve power over single-marker analysis. Each of these new methods assumes a particular nature of the relationship between rare variants and complex disease, yet these hypotheses have been largely unverified. Therefore we sought to compare the power of commonly used and novel statistical methods for rare variants using Sanger sequencing data from 1,998 individuals sequenced at 7 genes by simulating several phenotypes under models spanning a spectrum of the common hypotheses concerning such associations. While all methods perform reasonably well under their own model-specific hypotheses, no single method gives consistently acceptable power when these hypotheses are violated. Unlike GWAS, wherein all variants can often be tested using the same method across the entire genome, the analysis and interpretation of sequencing studies will therefore be considerably more challenging. There is growing evidence that rare variants contribute to the etiology of complex diseases Identifying rare variants associated with disease requires large sample sizes since few individuals harbor such polymorphisms. In addition, for rare variants, the power of single-marker tests, such as those performed by genome-wide association studies (GWAS), is poor. Development of alternative methods is thus essential. Over the past two years, a growing body of methods Li and Leal These recently proposed models have often relied upon unverifiable (and sometimes unnecessary) hypotheses in order to simulate sequence data. Certainly, simulation of large sets of sequence data is a complex task and depends on hypotheses concerning the evolution of human genomic regions. The validity of any particular set of evolutionary hypotheses is unlikely to be consistently true across the The performance of these newly proposed models using real sequence data in a large sample has not been independently evaluated. We therefore tested the power of commonly-used statistical methods designed to assess the impact of rare variants on continuous and dichotomous traits in 1,998 individuals Sanger-sequenced at seven genes. We employed a variety of possible relationships between genotype and phenotype in order to fully investigate the performance of such models under different realistic scenarios.http://genetics.bwh.harvard.edu/rare_variants). , a weighting method , a variance-based approach We evaluated the comparative power of recently proposed rare variant association methods using Sanger sequencing data from 1,998 individuals.The first set of simulations, which are designed to act as positive and negative controls for each of the methods tested, assesses potential relationships between rare variants and continuous traits under the relevant hypotheses made in several models. Scenario 1 is a “null model”, which serves as a negative control. Scenario 2 acts as a positive control for all collapsing models. Scenario 3 depicts a mixture of rare and common variants. Scenario 4 is a positive control for SKAT and WOD, which are designed to perform well under a mixture of protective and deleterious variants. Scenarios 5 and 6 are positive controls for WE. Details for these six different scenarios are found in In the remaining scenarios, it is striking that all methods have relatively poor power under most hypotheses, even though our simulation design included large shifts in the mean phenotype in a large number of individuals. Almost all of these scenarios show power less than 50% in the majority of the methods. In scenario 3, the addition of common causal variants to the presence of rare causal variants did not improve the power, except for the SKAT method which demonstrates its advantage when combining common and rare variants. In Scenario 4, where bidirectional causal variants are present, only WOD1 and SKAT have power above 50%.Scenarios 5 and 6 test performance when rarer variants have stronger effects. While the VT method marginally outperforms the WE method in these scenarios, the WE method improves considerably when compared to the other scenarios where no relationship was assumed between MAF and effect.These results demonstrate that all methods perform well under their intended hypothesized relationship between rare variants and phenotypes, but their power can vary largely when there is departure from this main hypothesis.We next assessed to what extent power is influenced by the effect size and proportion of causal rare variants. In the next set of simulations, we varied these two parameters to explore more systematically how much they influence the strength of the signal between genes and complex traits. The proportions of causal variants varied from 10, 15, 20, and 30% of all rare variants, where the causal variants were chosen at random from the polymorphisms that had low frequency . We assumed seven possible values for the mean effects: 0.5, 0.75, 1, 1.25, 1.5, 2, and 2.5 standard deviations.The combination of seven effects, four proportions of causal variants associated with the trait, and seven genes, leads to 196 scenarios. In these scenarios, we first analyzed the results of each scenario using single-marker tests, and then next applied the seven rare variant methods  for gene-level analysis. Next, we applied all seven methods to dichotomous traits, created by selecting from the extreme quarters of the continuous trait distribution.Results here are restricted to analysis only of the assigned causal variants, and we report the proportion of these causal variants that reach statistical significance, after adjustment for multiple-testing, using a Bonferroni correction. Collapsing methods do not perform well when effects are small or moderate (<1.5 standard deviations). The only situation where the power was greater than 75% is when between 15% and 30% of the rare variants are causal, and effects are moderate-to-large . The SKAWe also evaluated the power of the rare variant methods when rare variants are assigned to have either deleterious or protective effects . In thisth percentiles of the continuous trait distributions. This design therefore tests power of rare variant methods for sampling designs targeting more extremes of the distribution.In order to assess the performance of these methods for dichotomous traits, we selected 500 cases and 500 controls from the extreme 25Again, power increases as the proportion of causal variants increases, and power remains low for smaller effects. In this particular case-control design, VT appears to have the lowest power compared to all other methods. The remaining methods, T1, T5, WE, and SKAT have power estimates that are in a similar range, but power from T1, T5, and WE seemed to outperform SKAT as the proportion of causal variants increases. Interestingly, power does not seem to be as strongly influenced by the magnitude of the effect, as is it for continuous trait results. This can be explain by the fact that when the effect is one SD away from the mean, on average, over 90% of the individuals that are carrying a causal allele will have their phenotype shifted and be classified as cases. In other words, between effects of 1 to 2.5 SD, there is not a large difference in the number of shifted individuals that are correctly classified as cases.The power was low for almost all methods when causal variants could be either deleterious or protective––as was observed for continuous traits. While many large-scale sequencing studies are now underway to identify rare variants associated with complex diseases and traits, our results demonstrate that assessing the association between rare variants and complex disease is a challenging task. Standard single-marker association methods exhibit low power and the power of the statistical methods tailored for rare variants varies tremendously depending on the true nature of the relationship between the rare genetic variants and the phenotype. These findings provide guidance in the design, analysis and interpretation of sequencing studies for complex disease.As it is still unknown how rare variants influence complex disease, we have simulated several phenotypes under models spanning a spectrum of the common hypotheses concerning such associations. It is likely that the nature of the relationship between rare variants and a phenotype varies from gene-to-gene. Our findings suggest that no single method gives consistently acceptable power across the range of these relationships, even in a large sample size. Analysis using different methods clearly imposes an additional multiple testing burden, which cannot be easily addressed. One, though somewhat cumbersome, way to solve this problem would be by derivation of empirical P-values taking into account the variety of methods tested. Another, more straightforward, approach would be to undertake replication in an independent sample, using the method which demonstrated best results at the discovery stage.In this paper, we have also developed a new method conceptually based on Liu and Leal's KBAC method Collapsing methods demonstrate increasing power when the trait varies with an increasing number of rare alleles. However, examples exist where protective and deleterious rare alleles are present in a gene Our study also provides empirical data to judge the value of dichotomizing a continuous trait and sequencing only its extremes. While our design included the extreme quarters of the distribution, thereby eliminating the need to sequence half the study population and consequently reducing sequencing costs substantially, we note that power was similar to that derived from the entire distribution particularly only when the proportion of causal variants was high and the effect sizes moderate. Nonetheless, sampling of the extremes remains an attractive study design, particularly if the sampled population is large and a more extreme sub-population is selected.Methods have been proposed to weight the relative importance of rare variants based on various parameters including their estimated deleterious effect on protein function The spectrum and frequencies of rare genetic variants are known to depend on ancestry and age of the population studied One of the strengths of our study is the use of Sanger sequencing data, rather than simulated genotyping data. We have been able to avoid the simulation of such data by using fully Sanger-sequenced data on nearly 2,000 individuals at seven genes. Therefore, no genotypic hypotheses were made to generate the sequence data. Furthermore, the sample size employed is among the largest sequenced datasets in the world at present. Despite the fact that gene 3 had more missing data and fewer variants, we note that the power results derived from this gene are similar to all other genes.We note that our simulations assumed no additive effects when an individual carries multiple rare variants. However, we note that very few individuals carry 2 or more rare variants . In addiIn light of our results, we recommend that single-marker tests should not be used alone when rare variants are present and are assumed to have small-to-moderate effects on the trait of interest. On the other hand, as power across all novel rare variants methods is generally low, the potential for identifying rare variant associations using gene-based analysis strategies requires improvement. Ideally, the true underlying nature of the association between the gene and the phenotype should determine the choice of statistical method, however, this relationship is almost always unknown. Therefore, performing sensitivity analyses, i.e., assessing different methods that perform differently under various conditions might be a helpful option in order to interpret the results. Furthermore we suggest that if one method identifies a gene of interest that replication of this result should be performed in an independent sample using the same statistical method. All methods seemed to perform adequately under their specific model hypotheses, but do not perform as well when these hypotheses are violated.In the next few years, advances in sequencing technology will enable the production of large quantities of sequence data on large numbers of individuals, allowing for the cost-effective identification of rare variants. These data will enable researchers to investigate the role that rare variants play in disease etiology, in addition to uncovering functional variants. Our results may provide guidance in the planning, analysis and interpretation of these large-scale initiatives.The work described in this manuscript represents a re-use of data and no new human interventions were conducted. No additional IRB approvals were sought for this specific portion of the work. The Committee on Ethics in Clinical Research, CHUV, Lausanne University, Lausanne, Switzerland approved the original protocols for sample collection.The subjects used in this paper are a subset of the CoLaus study, a population-based study of 6,188 Lausanne residents aged 35 to 75 years PLA2G7 from 1,998 individuals were provided by GlaxoSmithKline (GSK). Methods for performing the sequencing for the PLA2G7 gene and the additional 6 genes have been described Sanger sequence data for the exons and flanking regions of seven genes including All seven genes contained both rare and common variants: the number of polymorphic variants ranged from 29 to 128, and the proportion of variants with a MAF≤1% ranged from 81% to 93%. The majority of these variants were extremely rare, with an average of 55% of all variants across all genes being singletons. We developed two simulation sets to illustrate the power of a variety of commonly-held hypotheses about the possible effects of rare variants on complex traits. In the first set, we tested collapsing and weighting designs and a range of general concepts about the potential role of rare variants, whereas in the second set, we varied the effect and the proportion of causal variants in across a grid of values.We proposed different phenotype simulation scenarios to explore popular hypotheses regarding the mechanism by which rare variants could influence complex disorders, namely (a) the assumption that risk of disease increases with more rare alleles (collapsing design), (b) the assumption that the magnitude of the effect depends on MAF (such as equation (1) in common variant(s) is/are assumed to have a deleterious effect, and an individual is carrying at least one of these causal alleles, we randomly drew a phenotypic value from a normal distribution having a mean of −0.07 and a standard deviation of 1.01, which allows for an effect typically identified in GWAS studies of continuous traits In our simulation of phenotypes, the following rules were applied in all scenarios. We assumed that all non-carriers of a causal allele (deleterious or protective) variant have a normally distributed trait with mean zero and variance of one, using a standard normal random variable. When one or more Deleterious variants were randomly sampled from the pool of variants for each simulation. Rare variants were defined as those having a MAF 1%, and common variants were defined as >1%. While other thresholds can be used, GWAS have often used a 1% threshold to define rare variants By varying hypotheses about the sampling of causal variants and their effect, we created these 6 simulation scenarios. We randomly generated a set of 250 phenotypes per individual, per scenario, per gene. In each case, we randomly selected causal variants associated with the traits, and then randomly generated a set of phenotypes based on the corresponding parameters for each iteration.In our second series of simulations, we varied the proportion of causal rare variants and their average effect on the phenotype across a grid of values, i.e., where proportions  of causal rare (MAF≤1%) variants were combined with values  for the mean effects. We also report in Multiple-testing was taken into account for single-marker test analyses, using a conservative approach with Bonferroni correction for the number of single-nucleotide polymorphisms (SNPs) tested. As for other rare variant methods, permutation was used to control for type-I error in all statistical methods. Alpha level was set to 0.05.We also simulated dichotomous phenotypes by assuming selection from the extremes of a quantitative distribution. In each of the 196 scenarios presented above for continuous traits, we have defined cases as being the 500 individuals with the lowest continuous phenotypes, and the controls as being the 500 individuals with the highest continuous phenotypes. This study design allows direct comparison of the relative utility of sequencing only the extremes of a distribution, as compared to the entire distribution, which has considerable financial ramificationsText S1Details on the WOD method, additional details on how the phenotypes were simulated for each scenario, and QQ plots under the null hypothesis for all methods, and for genes 6 and 7.(DOCX)Click here for additional data file.


Utilizing the whole genomic variation of complex traits to predict the yet-to-be observed phenotypes or unobserved genetic values via whole genome prediction (WGP) and to infer the underlying genetic architecture via genome wide association study (GWAS) is an interesting and fast developing area in the context of human disease studies as well as in animal and plant breeding. Though thousands of significant loci for several species were detected via GWAS in the past decade, they were not used directly to improve WGP due to lack of proper models. Here, we propose a generalized way of building trait-specific genomic relationship matrices which can exploit GWAS results in WGP via a best linear unbiased prediction (BLUP) model for which we suggest the name BLUP|GA. Results from two illustrative examples show that using already existing GWAS results from public databases in BLUP|GA improved the accuracy of WGP for two out of the three model traits in a dairy cattle data set, and for nine out of the 11 traits in a rice diversity data set, compared to the reference methods GBLUP and BayesB. While BLUP|GA outperforms BayesB, its required computing time is comparable to GBLUP. Further simulation results suggest that accounting for publicly available GWAS results is potentially more useful for WGP utilizing smaller data sets and/or traits of low heritability, depending on the genetic architecture of the trait under consideration. To our knowledge, this is the first study incorporating public GWAS results formally into the standard GBLUP model and we think that the BLUP|GA approach deserves further investigations in animal breeding, plant breeding as well as human genetics. Predicting the yet-to-be observed phenotypes or unobserved genetic values for complex traits and inferring the underlying genetic architecture utilizing genomic data is an interesting and fast developing area in the context of human disease studies as well as in animal and plant breeding P-value. Thousands of significant loci associated with complex traits have recently been found for model organisms GWAS take the road to detect markers significantly associated with a trait by setting a stringent t-distribution By fitting all markers in a prediction model simultaneously, whole genome prediction (WGP) has largely promoted the usage of whole genome markers, also revolutionizing commercial breeding systems and showing good results both in simulation studies In the context of GWAS, it is not difficult to detect QTLs with large or moderate effects within large data sets for traits with high heritability So far, results of GWAS and WGP have mostly been considered independently from each other, depending on whether the aim was to decode the genetic architecture (GWAS) or to accurately predict the unobserved phenotypes or genetic values (WGP). However, both approaches require the same type of data: a subset of a population for which phenotypes and genotypes are available. Since it is well known that the genetic architecture of complex traits affects the accuracy of genomic prediction In this study, we propose a new approach of utilizing already existing knowledge of genetic architectures in form of significant QTL regions obtained in independent association studies to improve the accuracy of WGP. This includes a new strategy of building trait-specific genomic relationship matrices used in a best linear unbiased prediction (BLUP) approach.Besides the fact that the genetic architecture of a complex trait is known to affect the accuracy of genomic prediction as well as model selection We will demonstrate in this study, that the performance of WGP can be improved by including the publicly available GWAS results (in case the genetic architecture is important for the complex trait under consideration) and that WGP accuracy can be improved especially in situations where the prediction accuracy is limited by a small sample size of the data set or a small heritability.The remainder of the paper is organized as follows: We will first propose a generalized way of building genomic relationship matrices which are trait-specific. Based on this suggestion, we will illustrate with a dairy cattle and a rice data set that using already existing GWAS results from publicly available databases to build trait-specific genomic relationship matrices improves the accuracy of WGP compared to two well investigated WGP approaches: GBLUP To our knowledge, this is the first study proposing a formal way to improve the accuracy of WGP by directly incorporating results from publicly available GWAS results and which validates the effectiveness of the new approach using real data sets.Several approaches have already been proposed for building genomic relationship matrices by estimating the realized genomic relationship matrix G is defined asFollowing the approach of VanRaden I is an identity matrix and the matrix M contains the corrected SNP genotypes, with the number of rows equal to the number of individuals and the number of columns equal to the number of markers. Genotypes are coded as 0, 1 and 2, representing the number of copies of the second allele. For locus i, the original genotype is corrected for the allele frequency of the second allele at locus i in the base population by subtracting 2ip. We used a uniform value of ip = 0.5 for all SNPs to build the genomic relationship matrix in this study, since the accuracy of WGP is known to be unaffected by the use of different allele frequencies for correction I in equation (1), it is implicitly assumed that all loci contribute equally to the variance-covariance structure.Here, et al.TA, given byIn general, the variance contribution for different loci may be different D is a diagonal matrix with marker weights for each locus on the diagonal to represent the relative size of variance explained by the corresponding loci. In the present study, we propose to use a similar approach, in which only a subset of “important” markers are weighted accordingly, instead of assigning variable weights to the full set of available markers. This approach is computationally less demanding when building the covariance matrix. Since for most quantitative traits only a very small proportion of loci was found to have significant effects and a large number of other loci was found to have very small effects  in c and c1, thenM1, that isS is supposed to capture the genetic architecture part for the trait under consideration. Further note that T equals G for , and that it equals TAD = with T matrix given in m1 markers to build S, the overall weight ω for S, and a vector of marker weights S. Note that in the present study the vector of weights h was always rescaled after choosing its components by multiplying each entry byTo build the m1 markers and their corresponding weights can thereby be chosen very flexible, for example as (i) estimated marker effects or variances for a proportion of top markers from genomic prediction; (ii) estimated effects or variances for markers in the QTL regions detected by GWAS; or (iii) counts of how often a marker was reported to belong to a (significant) QTL region in the literature, thus allowing to incorporate prior knowledge of the underlying genetic architecture of the complex trait under consideration.In the following, we will consider these three parameters as variables which have to be specified within a study. The subset of T (instead of G or TA) as variance-covariance matrix in a genomic best linear unbiased prediction (BLUP) model. We will call this approach BLUP|GA .We finally propose to use T as proposed in h in T were chosen based on counts of how often a marker was reported to be within a significant QTL region during association studies previously carried out in the literature, a knowledge we will retrieve from publicly available QTL databases. We will compare the performance of BLUP|GA with the standard GBLUP approach In the following, we will present WGP results for a real dairy cattle and a rice data set using the methodology introduced above. Predictive ability of the WGP was measured via different cross-validation procedures, applying the BLUP|GA approach with genetic covariance structure given by the trait-specific variance-covariance matrix We considered 2,000 bulls of the German Holstein population which were genotyped with the Illumina Bovine SNP50 Beadchip. After quality control 45,221 autosomal SNPs were used in the study. We analyzed the traits milk fat percentage (FP), milk yield (MY) and somatic cell score (SCS) and used accurately estimated breeding values (EBVs) from the conventional breeding value estimation as quasi-phenotypes in the whole genome prediction models .Marker weights for the BLUP|GA approach were obtained by using publicly available GWAS results stored in the database animalQTLdb T matrix used in the BLUP|GA model is a mixture of the G matrix and the S matrix, we had to choose an overall weight ω for the S matrix. The accuracies of BLUP|GA increased for FP and MY when increasing ω from 0 to 1, with a drop in accuracy for ω approaching 1  to generate a “new” trait with lower heritability. The average accuracies of BLUP|GA and BayesB for 20 replicates of five-fold cross-validation for the original phenotypes as well as the artificial low heritability traits are shown in Oryza sativa from the Rice Diversity Panel data set  building the S matrix based on a list of important markers and their corresponding weights which are obtained from “existing knowledge”, (ii) forming the T matrix as the weighted sum of S and G using T matrix. SNPs used to build S should lie in trait associated chromosomal regions and their corresponding marker weights should represent their relative contributions. In this study, we obtained the list of important SNPs and their corresponding marker weights for different traits within a dairy cattle and a rice data set from QTL databases which are publicly available (The most important difference between BLUP|GA and any other WGP approach is that BLUP|GA can enhance the accuracy of WGP by modeling vailable .G matrix (ω = 0) nor the S matrix (ω = 1) alone, but rather the T matrix as a mixture of both, is the most appropriate variance-covariance matrix with respect to the predictive ability in the standard GBLUP approach.We showed that GWAS results are not only useful for follow-up studies in the context of association studies, but also for WGP. For two out of the three dairy cattle model traits, the accuracies of the BLUP|GA approach showed an “n” type curve , which sOur study also gives an answer to the question raised by human genetics “to what extent GWAS have identified genetic variants likely to be of clinical or public health importance” G matrix has been built and stored before running a job , suitable population structure and accurate phenotypes is usually needed to detect the genetic architecture of a complex trait Ns to achieve a certain accuracy will be different for different species and populations according to their effective population size (Ne) and genome length Ne = ∼100 Ne (100), 4 Ne (400) and 16 Ne , respectively. These training population sizes are large relative to the small value of Ne  As the effect size of detectable QTL decreases with the increase of population size, a training population with sufficient size . Other concepts like including e.g. pathway information might be promising as well and are left for further studies.The new approach presented in this study still offers room for further improvements, such as refining the SNP list and marker weights obtained from QTL lists or modifying the The BLUP|GA method provides a new tool to incorporate existing knowledge of the genetic architecture of complex traits explicitly into a genomic prediction model. Using the BLUP|GA model, we illustrated that the publicly available QTL lists detected by hundreds of GWAS and QTL mapping studies improved the performance of WGP compared to standard WGP methods within a dairy cattle and a rice data set, respectively. The accuracy of WGP could be improved for two out of the three model traits in dairy cattle and for nine out of 11 traits in the rice diversity panel. The publicly available GWAS results were shown to be potentially more useful for WGP utilizing smaller data sets and/or traits of low heritability, depending on the genetic architecture of the trait under consideration. BLUP|GA also improved the prediction accuracies compared to the traditional methods GBLUP and BayesB. To our knowledge, this is the first study incorporating public GWAS results into the standard BLUP model and we think that the BLUP|GA approach deserves further investigations in animal breeding, plant breeding as well as human genetics.A dairy cattle and a rice data set were analyzed in this study. Summary statistics for these sets and the considered traits are given in Genotypic data from the Illumina Bovine SNP50 Beadchip DGAT1) gene explains approximately 30% of the genetic variance in Holstein Friesian cattle The three traits, FP, MY and SCS, were considered due to their well-established distinct genetic architectures. For FP, a single mutation in the diacylglycerol acyltransferase 1  to the marker weights.We first retrieved regions of QTLs associated with the trait under consideration from the literature. For each reported QTL, we picked the SNPs from the genotype data set located in the corresponding QTL region. If a reported QTL region did not contain any SNP, we extended the QTL region by 300 kb at both sides to track the SNPs nearby. If a reported QTL region contained more than 1,000 SNPs, the corresponding QTL report was excluded from our analysis, since this QTL would not be informative with respect to the marker weights obtained in the next step. We thereby obtain a list of the most important SNPs as well as a list of corresponding QTL regions. For each SNP in this list, we then calculated its marker weight for the trait specific matrix http://www.animalgenome.org/QTLdb, Release 18, October 2, 2012), which is a comprehensive QTL database for domestic animals. This list included 5,920 QTLs on 407 traits from 331 publications. For each QTL, the estimated QTL intervals in base-pairs (bp), the associated trait, the significant P-value and other related information were given. For more details, we refer to Hu http://www.animalgenome.org/QTLdb. There were 279, 247 and 169 QTLs reported for FP, MY and SCS, respectively  was obtained from the Gramene database  The QTL list for y is a vector of phenotypic values; μ is the overall mean; g is a multivariate normally distributed vector of genetic values for all individuals in the model; X and Z are incidence matrices relating the overall mean and the genetic values to the phenotypic record. We assume T is the matrix from TA matrix were built according to equation (2) that proposed by Zhang et al.The statistical model for the genomic BLUP approach isA combined AI-EM restricted maximum likelihood algorithm  was used to estimate the variance components of the model via the DMU software package y, X, μ, M and e are as defined in Model 1 and s is a vector of normally distributed and independent SNP effects. The variance of the ith marker effect,The model for BayesB A five-fold cross-validation (CV) procedure Both accuracy and predictive ability in this study were defined as the Pearson correlation coefficient between observed phenotypic values (PHE) and predicted genetic values (PGV): ectively . The repFigure S1Computing times for GBLUP, BLUP|GA and BayesB. Computing times for GBLUP, BLUP|GA and BayesB  for population size N = 2,000 and m = 42,551 markers on an Intel Core i5-3470 CPU 3.2 GHz×4 with 16 GB RAM. For GBLUP and BLUP|GA, the computing time includes building the G matrix and solving the mixed model equations. For BayesB, the average time demanding for 10,000 iterations is shown.(TIF)Click here for additional data file.Figure S2Estimated marker effects for milk yield in dairy cattle. Estimated marker effects obtained with different population sizes (N). Dark blue dots represent the top 1% SNPs with the largest estimated marker effects.(TIF)Click here for additional data file.Figure S3Distribution of reported QTLs positions and marker weights obtained from rice QTL list. Reported QTLs associated with plant height (red), and panicle lenght (blue) retrieved from Gramene database  (TIF)Click here for additional data file.Table S1Accuracy and unbiasedness for traits with low heritability and small population sizes (based on the dairy cattle data set). The best result in each block is printed in boldface.(DOC)Click here for additional data file.Table S2SNP lists for the dairy cattle data set. This table (excel format) includes the name, chromosome, physical position, trait associated, number of QTL reports, and other important information for each SNP used in this study.(XLS)Click here for additional data file.Table S3SNP lists for rice diversity panel. This table (excel format) includes the name, chromosome, physical position, trait associated, QTL report, and other information for each SNP used in this study.(RAR)Click here for additional data file.


Characterization of population structure and genetic diversity of germplasm is essential for the efficient organization and utilization of breeding material. The objectives of this study were to (i) explore the patterns of population structure in the pollen parent heterotic pool using different methods, (ii) investigate the genome-wide distribution of genetic diversity, and (iii) assess the extent and genome-wide distribution of linkage disequilibrium (LD) in elite sugar beet germplasm.r2 >0.8 is required to detect marker-phenotype association explaining less than 1% of the phenotypic variance, our observation of a low proportion of SNP loci pairs showing such levels of LD suggests that the number of markers has to be dramatically increased for powerful genome-wide association mapping.A total of 264 and 238 inbred lines from the yield type and sugar type inbreds of the pollen parent heterotic gene pools, respectively, which had been genotyped with 328 SNP markers, were used in this study. Two distinct subgroups were detected based on different statistical methods within the elite sugar beet germplasm set, which was in accordance with its breeding history. MCLUST based on principal components, principal coordinates, or lapvectors had high correspondence with the germplasm type information as well as the assignment by STRUCTURE, which indicated that these methods might be alternatives to STRUCTURE for population structure analysis. Gene diversity and modified Roger's distance between the examined germplasm types varied considerably across the genome, which might be due to artificial selection. This observation indicates that population genetic approaches could be used to identify candidate genes for the traits under selection. Due to the fact that We provided a genome-wide distribution map of genetic diversity and linkage disequilibrium for the elite sugar beet germplasm, which is useful for the application of genome-wide association mapping in sugar beet as well as the efficient organization of germplasm. Beta vulgaris subsp. vulgaris) is a member of the family Amaranthaceae )The objectives of this study were to (i) explore the patterns of population structure in the pollen parent heterotic pool using different methods, (ii) investigate the genome-wide distribution of genetic diversity, and (iii) assess the extent and genome-wide distribution of LD in elite sugar beet germplasm.A total of 502 diploid sugar beet inbreds from the pollen parent heterotic pool were examined in this study. Among them, 264 accessions were yield types and 238 sugar types. All plant materials used in this study are proprietary to KWS SAAT AG . All 502 sugar beet inbreds were genotyped by KWS SAAT AG, following standard protocols, with 328 SNPs markers, which were distributed across the genome. A total of 26, 33, 41, 35, 40, 42, 39, 32, and 40 of these markers map to linkage group A to I, respectively (unpublished data). This data set comprises no inbreds or markers with more than 20% missing data.K = 1-10 subgroups using the linkage model neglecting prior information. Each run consisted of a burn-in period of 100,000 steps followed by 100,000 Monte Carlo Markov Chain replicates, assuming that allele frequencies are uncorrelated across clusters. Five replications were performed for each K value. To determine the most probable value of K, an ad hoc criterion was used , where CIf not stated differently, all analyses were performed with the statistical software R .K = 1 to K = 10 and showed no obvious optimum  were examined by MCLUST based on 1-150 PC, PCo, and lapvectors. Our results suggested that the number of subgroups varied between one and nine . The above mentioned methods also had with >85% a high correspondence of assignment with each other (data not shown). These findings suggested that these methods might be time-saving alternatives to STRUCTURE analyses, if the assignment of genotypes to subgroups is of interest and the numbers of subgroups is known.When the number of subgroups was set to two according to the results of PCA, PCoA, and LAP, we observed for 10-40 PC, 10-50 PCo, and 1-100 lapvectors Results of earlier studies revealed that cultivated sugar beet genotypes are genetically distinct from wild beet genotypes ,9. MoreoThe results of the STRUCTURE analysis revealed the presence of two subgroups in the entire pollen parent germplasm set  and oil concentration (high vs. low oil) in maize had lead to phenotypically but also genotypically divergent populations . Due to CV of MRD estimates among all pairs of inbreds decreased with increasing number of SNPs , we exas Figure . This isCV of MRD  the rather high density of markers examined in our study compared with earlier studies and (ii) different regression methods used to measure the decay of LD. The observation of slower LD decay for yield type inbreds than for sugar type inbreds, which might be due to the different selection history as outlined above, resulted in smaller effective population sizes Ne calculated for the yield type inbreds than the sugar type inbreds  linked SNP paris .8 Table . Such hivariance . This inWe observed different LD levels along the linkage groups of sugar beet  in the entire germplasm set(a) Log likelihood, (b) Δ.Click here for filePrincipal coordinate analysis, Laplacian eigenfunctions analysis, and Principal component analysis of the entire elite sugar beet germplasm set. (a) Principal coordinate analysis based on modified Roger's distance (MRD) estimates, (b) Laplacian eigenfunctions analysis, and (c) Principal component analysis of the entire elite sugar beet germplasm set. PC 1 and PC 2 refer to the first and second principal components/coordinates, respectively. LAP 1 and LAP 2 refer to the first and second lapvectors, respectively. The numbers in parentheses refer to the proportion of variance explained by the corresponding axes. Symbols identify the germplasm types and colors the STRUCTURE subgroups. SG 1 and SG 2 are the two subgroups identified by STRUCTURE based on the maximum membership probability threshold.Click here for fileNumber of subgroups identified by MCLUST. Number of subgroups identified by MCLUST based on different numbers of (a) principal components, (b) principal coordinates, and (c) lapvectors, and the cumulative proportion of explained variance of (d) principal components, (e) principal coordinates, and (f) lapvectors.Click here for fileCorrespondence between the known germplasm types of the sugar beet inbreds and the assignment by MCLUST. Correspondence between the known germplasm types of the sugar beet inbreds and the assignment by MCLUST based on different numbers of (a) principal components, (b) principal coordinates, and (c) lapvectors when the number of subgroups was set to two.Click here for fileGenome-wide distribution of gene diversity of yield and sugar type inbreds. Green and red lines indicate gene diversity of yield and sugar type inbreds, respectively. Dashed lines indicate the average gene diversity of the corresponding germplasm type. Vertical lines at each point indicate standard error multiplied by 100 which were calculated by bootstrapping across genotypes. Vertical lines at the x axis indicate genetic map positions of the SNP loci on the nine linkage groups.Click here for fileModified Roger's distance (MRD) between yield and sugar type inbreds across the genome. Dashed lines indicate average MRD across the genome and dotted lines average MRD for each linkage group. Vertical lines at each point represent the standard error multiplied by 10 which were calculated by bootstrapping across genotypes. Vertical lines at the x axis indicate genetic map positions of the SNP loci on the nine linkage groups.Click here for filer2) against genetic map distance (cM) between linked loci pairsPlot of linkage disequilibrium measured as squared correlation of allele frequencies (. (a) yield type and (b) sugar type inbreds. The red line is the nonlinear regression trend line of r2 vs. genetic map distance. The dashed line indicates the 95% quantile of r2 between unlinked loci pairs.Click here for filer2) for all linked loci pairs within 5 cM segments across the genomeAverage linkage disequilibrium measured as squared correlation of allele frequencies (. Green and red lines indicate average r2 for yield and sugar type inbreds, respectively. The vertical line at each point represents the standard error.Click here for file


The analysis of shared haplotypes among the 13 cases defined a critical interval of 1.6 Mb with 25 predicted genes. We re-sequenced the genome of one case at 38× coverage and detected 3 non-synonymous variants in the critical interval with respect to the reference genome assembly. We genotyped these variants in larger cohorts of dogs and only one was perfectly associated with the HNPK phenotype in a cohort of more than 500 dogs. This candidate causative variant is a missense variant in the SUV39H2 gene encoding a histone 3 lysine 9 (H3K9) methyltransferase, which mediates chromatin silencing. The variant c.972T>G is predicted to change an evolutionary conserved asparagine into a lysine in the catalytically active domain of the enzyme (p.N324K). We further studied the histopathological alterations in the epidermis in vivo. Our data suggest that the HNPK phenotype is not caused by hyperproliferation, but rather delayed terminal differentiation of keratinocytes. Thus, our data provide evidence that SUV39H2 is involved in the epigenetic regulation of keratinocyte differentiation ensuring proper stratification and tight sealing of the mammalian epidermis.Hereditary nasal parakeratosis (HNPK), an inherited monogenic autosomal recessive skin disorder, leads to crusts and fissures on the nasal planum of Labrador Retrievers. We performed a genome-wide association study (GWAS) using 13 HNPK cases and 23 controls. We obtained a single strong association signal on chromosome 2 , an inherited monogenic autosomal recessive skin disorder, leads to crusts and fissures on the nasal planum of Labrador Retrievers. We performed a genome-wide association study (GWAS) and mapped the causative mutation to a small interval on chromosome 2. We re-sequenced the entire genome of one HNPK affected dog and detected three protein-changing mutations in the critical interval. We genotyped these three variants in larger cohorts of dogs and only one showed perfect genotype-phenotype association, prompting us to consider this variant, a missense mutation in SUV39H2, as the most likely causative variant for HNPK. SUV39H2 is an enzyme involved in histone methylation and epigenetic silencing, a process determining the regulation of gene expression during keratinocyte differentiation. A loss of SUV39H2 function is predicted to result in delayed differentiation, which is compatible with the histopathological changes that we observed in biopsies from nasal epidermis of HNPK affected Labrador Retrievers. Thus, our results provide the most likely causative mutation for a hereditary disease in dogs that can now be eradicated by selective breeding. In addition, our results also provide the first hint that SUV39H2, whose  The outermost layer of the skin, the epidermis, is a stratified squamous epithelium which forms a barrier against the environment. It is renewed throughout lifetime by continuous proliferation of keratinocytes in the lower layers of the epithelium juxtaposed to a basement membrane. Once the cells exit the cell cycle, they start to move outwards and go on to differentiate while building up the stratified architecture of the epidermis. Epidermal renewal is a tightly controlled process that relies on well-orchestrated and finely tuned changes in the expression of genes encoding cell cycle regulators and structural components such as keratins or adhesion molecules. In their final stage of terminal differentiation, the keratinocytes lose their nuclei and cytoplasmic organelles to become corneocytes, which form the outermost tightly sealing layer of the epidermis. Corneocytes from the surface of the epidermis will eventually be sloughed into the environment. A complete turnover of the nonglabrous epidermis takes about 40–56 days in healthy humans, 8–10 days in mice and about 22 days in dogs Much progress has been made in the understanding of gene expression in keratinocytes during all stages of differentiation and a number of involved signaling pathways are now known Spontaneous animal mutants with hereditary defects in the normal differentiation of the epidermis provide an opportunity to identify further components of the complex regulatory network required to establish specific patterns of gene expression. Due to their special population structure purebred dogs are particularly well suited for genetic analysis Hereditary nasal parakeratosis (HNPK) of Labrador Retrievers has been reported to consist of a defect in the differentiation of the specialized epidermis of the nasal planum In this study we used a genome-wide association study (GWAS) and whole genome re-sequencing approach (WGS) of an affected Labrador Retriever to identify the genetic lesion responsible for HNPK.−5), we retained 106,681 markers for the final genome-wide allelic association study. Three best-associated SNPs in the GWAS had identical raw p-values of 4.4×10−14 , were non-informative (MAF<0.05), or showed a strong deviation from Hardy-Weinberg equilibrium in the controls  for the causative mutation and flanking chromosomal segments. We analyzed the cases for extended regions of homozygosity with simultaneous allele sharing. Two genome regions, of which the larger one coincided with the associated interval on CFA 2, fulfilled our search criteria. In this interval on CFA 2, all 13 affected dogs were homozygous and shared identical alleles over 110 consecutive SNP markers. We concluded that the causative mutation should be located in the ∼1.6 Mb critical interval between the closest heterozygous markers on either side of the homozygous segment . In the corresponding human interval with its presumably more complete annotation 25 genes and loci are contained . In ordeSUV39H2:c.972T>G remained perfectly associated in a very large cohort of more than 500 dogs. As expected for the causal variant, we found it exclusively in Labrador Retrievers, but not in 139 dogs from diverse other breeds. The SUV39H2:c.972T>G variant represents a missense mutation in the SUV39H2 gene, encoding the “suppressor of variegation 3-9 homolog 2 (Drosophila)”, a histone 3 lysine 9 (H3K9) methyltransferase. The variant results in the change of an asparagine residue in the catalytically active SET domain to a lysine (p.N324K). The SET domain has been named according to the first proteins, in which it has been identified, Suvar(3)9, enhancer of zeste, and trithorax. SIFT and Polyphen-2 predict that the p.N324K variant affects protein function We genotyped the three remaining non-synonymous variants in larger cohorts of dogs . Only onWe re-investigated the histopathological changes in the nasal planum of HNPK affected Labrador Retrievers. The changes in hematoxylin and eosin (HE) stained sections were compatible with earlier descriptions In addition to the re-examination of the previously published histopathological changes, we analyzed whether keratinocyte proliferation is altered in HNPK affected dogs. We performed quantitative immunofluorescence microscopy analyses for the proliferation marker Ki67 in paraffin sections of the nasal planum of 6 HNPK affected dogs and 6 non-affected control Labrador Retrievers. The number of Ki67 positive cells was not significantly increased in HNPK dogs compared with controls. There was no significant difference in the overall hyperproliferation and hyperplasia indices . The ratio of Ki67 positive cells in the basal layer to Ki67 positive total cells was also comparable between groups (p = 0.55). This analysis confirmed that HNPK is not due to hyperproliferation of keratinocytes.We also examined the expression pattern of the differentiation marker desmoglein 1 (DSG1). The expression of this adhesion molecule extends from the suprabasal layer to the last viable layers of the epidermis SUV39H2:c.972T>G variant is the actual causative variant. However, the strictly recessive mode of inheritance suggests a complete loss-of-function allele, which excludes a large fraction of regulatory variants with minor effects. It also has to be considered that our variant detection relied on short read mapping to an imperfect reference genome. Thus, we will have missed variants, which are located in genome segments that are not contained in the reference genome, such as gap regions. We may also have missed non-synonymous variants in genes that are not or not correctly annotated in the dog reference genome.In this study we identified a missense variant of SUV39H2 as candidate causative genetic defect for HNPK in Labrador Retrievers. It has to be cautioned that our analysis focused on non-synonymous variants. We thus cannot formally rule out the possibility that a synonymous or non-coding regulatory variant in absolute linkage disequilibrium with the The identified missense variant is actually within a plausible functional candidate gene. SUV39H2 catalyzes the addition of methyl groups to the lysine 9 residue of histone 3 (H3K9) and transforms an unmethylated H3K9 into trimethylated H3K9me3 in vivo is much less clear. Addressing this question has been difficult as mammals express a large number of closely related histone modifying enzymes including eight genes encoding specific H3K9 methyltransferases Suv39h2 deficient mice and mice deficient in the closely related Suv39h1 do not display any obvious phenotype While there is extensive knowledge on the biochemical activity of SUV39H2 and its role in heterochromatin formation, its specific physiological function Suv39h1 and Suv39h2 exhibit severe deficiencies in genome stability and regulation of telomere length. Consequently, the double knockout mice have a greatly impaired viability and delayed growth Suv39h2 deficient mice do not display an overt phenotype as the Suv39h2 function may be replaced by other redundant H3K9 methyltransferases.In contrast, the double knockout mice deficient in both Suv39h2 deficient mice do not display a similar phenotype. However, it can be argued that the nasal planum of mice is extraordinarily small and that subtle alterations are very difficult to assess.The clinical symptoms of HNPK in Labrador Retrievers are exclusively restricted to the epidermis of the nasal planum. It is currently unclear why a defect in a broadly expressed histone modifying enzyme leads to a very tissue-specific clinical phenotype. We speculate that this may be due to the tissue-specific expression profiles of the partially redundant H3K9 methyltransferases, with the epidermis of the nasal planum being the only tissue where the loss of SUV39H2 cannot be compensated. We also cannot explain at this point why in vivo histopathological analyses strongly suggest that SUV39H2:p.N324K is the causative defect for HNPK. Further studies with additional differentiation markers and RNA-seq experiments will help to clarify which downstream effector molecules are altered by the lacking chromatin modification in SUV39H2 deficient cells resulting in an impaired keratinocyte differentiation process.Our morphological analyses clearly demonstrate that the HNPK phenotype is not due to hyperproliferation of keratinocytes. Thus, it seems highly probable that the HNPK phenotype is caused by an altered terminal keratinocyte differentiation. Similar expression patterns of the differentiation marker DSG1 in cases and control dogs provide further evidence that this delay in differentiation might be primarily localized in the outermost layers of the epidermis. SUV39H2, a chromatin modifying enzyme, is a logical candidate for being involved in the regulation of cell differentiation. Although we do not yet have complete functional proof for its role in keratinocyte differentiation, our genetic data together with the detailed In conclusion, we have identified a missense variant in the histone methylase SUV39H2 as most likely causative for HNPK in Labrador Retrievers. This provides a first indication of an involvement of this particular enzyme in the epigenetic regulation of keratinocyte differentiation.All animal experiments were performed according to the local regulations. The dogs in this study were examined with the consent of their owners. The study was approved by the “Cantonal Committee For Animal Experiments” .We collected EDTA blood samples from Labrador Retrievers. We specifically used 26 cases and 164 controls, which could be unambiguously phenotyped based on photographs or direct inspection by veterinarians during the collection of the LUPA atopic dermatitis cohort We isolated genomic DNA from EDTA blood samples with the Nucleon Bacc2 kit . Genotyping was done on illumina canine_HD chips containing 173,662 SNP markers either at the Centre National de Génotypage, Evry, France or at the NCCR Genomics Platform of the University of Geneva. Genotypes were stored in a BC/Gene database version 3.5 (BC/Platforms).−5). The final dataset consisted of 36 dogs and 106,681 SNPs. We performed an allelic association study and determined an empirical significance threshold by performing 100,000 permutations of the dataset with arbitrarily assigned phenotypes. We also used PLINK to search for extended intervals of homozygosity with shared alleles as described previously We used PLINK v1.07 SUV39H2 gene corresponds to the accessions XM_535179.3 (mRNA) and XP_535179.2 (protein). We analyzed the functional effects of variants in silico with SIFT and Polyphen-2 We used the dog CanFam 3 and the human 37 assemblies for all analyses. We used BLASTN searches to define the orthologous human chromosomal regions corresponding to the associated interval on CFA 2. For the candidate gene inspection we used the human annotation provided by NCBI (build 37.5). All numbering within the canine http://sourceforge.net/projects/picard/). We used the Genome Analysis Tool Kit . We obtained a total of 502,733,347 paired-end reads or roughly 38× coverage. We mapped the reads to the dog reference genome using the Burrows-Wheeler Aligner (BWA) version 0.5.9-r16 Additionally, we searched for structural variations  within the critical interval using the software Pindel We used Sanger sequencing to confirm the illumina sequencing results and to perform targeted genotyping for selected variants. For these experiments we amplified PCR products using AmpliTaqGold360Mastermix (Applied Biosystems). PCR products were directly sequenced on an ABI 3730 capillary sequencer (Applied Biosystems) after treatment with exonuclease I and shrimp alkaline phosphatase. We analyzed the Sanger sequence data with Sequencher 5.1 (GeneCodes).n = 6) and non-affected control (n = 6) Labrador Retrievers were collected, fixed in 4% formalin and embedded in paraffin. Paraffin slides were generated and HE stained by standard methods. The immunofluorescence staining of Ki67  and DSG1 was carried out as described previously 2 and CaCl2 (“PBS+”) for 90 min at room temperature and incubated with primary antibody  overnight at 4°C. The appropriate secondary antibody was applied for 120 min at room temperature. Nuclei were counter-stained with Hoechst 33258 and slides were mounted with fluorescence mounting medium (DAKO) and stored protected from light at 4°C until further analysis. Each IF staining was performed twice with the full set of slides (n = 12) on different days to ensure reproducibility. Unspecific IgG instead of primary antibody was used as negative control.Biopsies of the nasal planum of HNPK affected , the Ki67 positive cells in the basal layer and in total as well as the Hoechst 33258-positive cells in the basal layer were counted in five microscopic fields of each dog. Hyperproliferation , hyperplasia , and the Ki67 basal : Ki67 total ratio were calculated.www.ncss.com) and applying multivariate analysis of variance (MANOVA). A p-value<0.05 was considered statistically significant.The data set of the IF staining results was analyzed using NCSS software  Photo of the nose of an HNPK affected Labrador Retriever illustrating the clinical symptoms. Note the crusts on the dorsal aspects of the nasal planum. (B) Non-affected control dog. Note the smooth, moist, and shiny nasal planum. (C) HE staining of a biopsy from the nose of an HNPK affected Labrador Retriever at 400× magnification illustrating the pronounced hyperkeratotic parakeratosis. Note the large number of retained nuclei in the stratum corneum. (D) Non-affected control dog. Note the complete absence of nuclei in the stratum corneum.(TIF)Click here for additional data file.


We propose a two-stage design for the analysis of sequence variants in which a proportion of genes that show some evidence of association are identified initially and then followed up in an independent data set. We compare two different approaches. In both approaches the same summary measure  is used for each gene in the initial analysis. In the first (simple) approach the same summary measure is used in the analysis of the independent data set. In the second  approach a more specific hypothesis is formed for the second stage; the summary measure used is the count of minor alleles in only those variants that in the initial data showed the same direction of association as was seen overall. We applied the methods to the simulated quantitative traits of Genetic Analysis Workshop 17, blind to the simulation model, and then evaluated their performance once the underlying model was known. Performance was similar for most genes, but the simple strategy considerably out-performed the alternative strategy for one gene, where most of the effect was due to very rare variants; this suggests that the alternative approach would not be advisable when the effect is seen in very rare variants. Further simulations are needed to investigate the potential superior power of the alternative method when some variants within a gene have opposing effects. Overall, the power to detect associations was low; this was also true when using a more powerful joint analysis that combined the two stages of the study. Genome-wide association studies focus on genetic loci with common minor allele frequencies (MAFs > 0.05) and are not designed to detect the effects of rare variants. However, rare variants contribute to complex diseases and might be detectable using resequencing data. Several methods for analyzing this type of data have been proposed -4, but tUsing the simulated Genetic Analysis Workshop 17 (GAW17) data , we condi in gene j, let ijkx be the number of copies of the minor allele  carried by individual k . Suppose that gene j includes jn sequence variants; then a summary measure for gene j in individual k is formed by counting the number of minor alleles:For each sequence variant jkX and adjusting for population in three categories . Genes showing evidence of association with the trait at a significance level of 0.01 were carried forward to the second stage of the analysis.For each replicate, we randomly split the sample into an initial set of 349 (50%) subjects and an independent set consisting of the remaining 348 subjects. We tested association between each of the three simulated quantitative traits Q1, Q2, and Q4 and each gene in the initial set using a linear regression model, regressing the trait on N genes were carried forward to the second stage. These genes were then tested in the same way in relation to the trait, using the remaining data for that replicate. Genes were regarded as showing association in this second stage if they met the Bonferroni-corrected significance level of 0.05/N. Each analysis (first and second stage) was repeated for each trait in the 200 replicates.Suppose that, for a particular trait and replicate, N genes identified in the initial data set were tested in a different way in the remaining samples. Instead of summing all minor variants in the gene, we used information on which variants were positively or negatively related to the trait in the initial data set. Specifically, for each of the N genes, the direction of association between the sum of the variants and the trait in the initial set was ascertained . If they were positively correlated, we identified the subset of individual variants that were positively related to the trait in this data set .We investigated an alternative strategy in which the ijI be an indicator variable such that ijI = 1 if variant i is positively related to the trait and ijI = 0 otherwise (including for variants not observed in the data set). We calculated a new summary measure for each gene, summing over just these variants:Let In the second stage, we tested association with the trait by regressing the trait on N. Results are presented for each quantitative trait for each gene that showed consistent evidence of association in the first-stage analysis (in at least 120 of the 200 replicates). Results are also presented for the genes that were simulated to be associated with each trait. Analyses were conducted using programs written for this purpose in R .As in the simple analysis, we regarded genes as showing association in this second stage if they met a significance level of 0.05/N genes passing stage 1 but applying a Bonferroni correction for the original number of 3,205 genes. For this design , Skol et al. [We also considered the increase in power that could be obtained by carrying forward the samples from the first stage into the final analysis, that is, by carrying out a joint analysis of the total data set for the l et al.  has showFLT1 gene reaches this level of significance in 195 replicates, indicating a power of 97.5% with a sample size of only 349. The next two columns in Table KDR using the alternative strategy compared to the simple strategy .Table PRR4, all these genes are on the list of 695 genes found by others to be consistently spuriously associated with the simulated disease affection status, which was correlated with Q1 [In the list of our 12 top findings for Q1 , they were never significant in the follow-up analysis after applying the Bonferroni correction in the simple or alternative method. Seven other genes associated with Q1 in the simulation model were not detected in our analysis using any approach. Although all met the stage 1 significance level of 0.01 in at least two replicates (ranging from 2 for PLAT and PDGFD to 70 in 200 replicates for VNN1). However, using the simple two-stage method, only VNN1 ever reached our criterion for significance in stage 2 , and only VNN1 and SIRT1 ever reached significance under the alternative method . Even when carrying out the joint staged analysis, VNN1 was significant in only 20 simulations, and no other gene was significant in more than 3 simulations. For this trait, power to detect associations was very low, and there were few false positives.All 13 genes simulated to be associated with Q2 passed the initial threshold in at least one replicate , which is consistent with expectation. This suggests that the strategies have correct statistical type I error rates and that the false-positive results observed for Q1 are a result of correlation between genes.FLT1 and KDR) contain 11 and 10 associated variants, respectively, with a total MAF greater than 0.1. Only two other associated genes, BCHE and SREBF1, have as many associated variants , but for these genes the total MAF of the associated variants is still low. The next most frequently detected gene was VNN1, which, along with VNN3, is one of only two other genes (both associated with Q2) for which the total MAF of the associated variants exceeds 0.1. Figure β coefficient) was smaller in these genes.Using any strategy, we detected only a small number of the genes that were simulated. As shown in Figure KDR was remarkably lower than the power to detect FLT1  against its frequency. The KDR variants with strong effects (β > 0.3) are all extremely rare, in fact with no more than three instances of each allele observed in the total data, whereas three of the FLT1 variants with strong effects are much more common. The rarity of the KDR variants has a drastic effect on power, although this is less marked in the combined analysis.Unsurprisingly, the critical gene characteristics determining the power to detect an effect are the number of associated variants per gene, their total MAF, and the size of individual variant effects. However, the power to detect FLT1 and VNN1 but not for KDR. This is likely again to be because most of the associated variants in KDR are rare; the variants that are observed in the initial data set and that are the basis of the “specific hypothesis” of the alternative strategy may not be observed at all in the second data set. Conversely other rare variants seen only in the second data set will not be included in the test because of lack of initial information on their relevance.The two strategies for follow-up analysis performed similarly for KDR), the alternative method worked just as well as the simple strategy, even though less genotyping was required because fewer variants needed to be analyzed. Our analyses were conducted blind to the simulation models, but in fact in these models minor alleles were only ever associated with increases in trait values. Clearly, in this situation the alternative method we considered would lose some of its potential advantages. Variants with no relationship to the trait would still be removed from the summary statistic (with probability 0.5), but there would be no variants with opposing effects to remove.The ability to resequence the genome or exome opens up new possibilities for the discovery of trait-associated variants whose frequencies are too rare for them to be likely to be discovered using genome-wide association studies. However, sequencing a large number of samples is likely to remain expensive for the foreseeable future. We considered ways in which such approaches might be made more efficient by applying two-stage strategies. In our approach, only a small fraction of the genes  would have to be resequenced in the second set of samples. We investigated whether further efficiency savings or possible improvements in power could be made by considering only certain variants within those genes, on the basis of the direction of association in the initial sample. We expected that the method based on specific variants might improve power by removing from the gene’s summary statistic the noise variants that were not related to the trait (or even variants having an opposing effect); we also expected that power would be lost if variants were present in the training data set that were not found in the test set (private or very rare variants). Except in the case of one gene , the approach we used to restrict the analysis to variants showing a beneficial (or deleterious) effect while reducing the impact of neutral variants was similar in spirit to the data-adaptive summation method of Han and Pan  , because the stage 1 samples would be used both to define and to test the hypothesis.The authors declare that there are no competing interests.JHB carried out the main programming and analyses; JN carried out some analyses and produced tables and figures. Both authors designed the study and wrote the manuscript.