Investigation of the genetic architectures that influence the behavioral traits of animals can provide important insights into human neuropsychiatric phenotypes. These traits, however, are often highly polygenic, with individual loci contributing only small effects to the overall association. The polygenicity makes it challenging to explain, for example, the widely observed comorbidity between stress and cardiac disease.We present an algorithm for inferring the collective association of a large number of interacting gene variants with a quantitative trait. Using simulated data, we demonstrate that by taking into account the non-uniform distribution of genotypes within a cohort, we can achieve greater power than regression-based methods for high-dimensional inference.We analyzed genome-wide data sets of outbred mice and pet dogs, and found neurobiological pathways whose associations with behavioral traits arose primarily from interaction effects: γ-carboxylated coagulation factors and downstream neuronal signaling were highly associated with conditioned fear, consistent with our previous finding in human post-traumatic stress disorder (PTSD) data. Prepulse inhibition in mice was associated with serotonin transporter and platelet homeostasis, and noise-induced fear in dogs with hemostasis.Our findings suggest a novel explanation for the observed comorbidity between PTSD/anxiety and cardiovascular diseases: key coagulation factors modulating hemostasis also regulate synaptic plasticity affecting the learning and extinction of fear.The online version of this article (10.1186/s12888-018-1753-4) contains supplementary material, which is available to authorized users. Mammalian behavioral traits, such as burrowing and parenting in wild mice , 2, as wRecent studies of outbred mouse stocks , 9 with Here, we report the development of an analytical method to infer collective genetic associations of a group of SNPs with quantitative traits. In this method, which is analogous to a similar approach for binary case-control phenotypes –12, pre-In conventional association studies, loci highly associated with quantitative traits are first identified using linear regression-based methods, and the putative causal genes near the loci are tested for enrichment in curated databases of pathways. Studies targeting epistatic effects have largely focused on extending single-SNP methods, namely via exhaustive or selective testing of SNP pairs –15. StatWe first used simulated data to demonstrate that substantially higher power could be achieved by such collective inference than by independent loci inference of quantitative trait associations and RR-based multi-locus tests. We chose RR for comparison because it extends linear regression―the main approach used for most association tests using single variant data―in a manner analogous to how our approach uses penalizer-based regularization. We then applied our method to the recent data sets of behavioral traits in outbred mice  and dogsD = {ak, yk}, where n is the total number of individuals and k = 1, …, n; ak denotes the genotype count vector for individual k ; and yk is a continuous-variable phenotype of individual k. The log-likelihood of a statistical model is defined as:We formulated and implemented a collective inference algorithm adapted to genotype-quantitative trait data sets, as described in this subsection. We denote the data as A = ∑k ln Pr(yk) is the likelihood of the marginal distribution of the phenotype. We assume that the latter is distributed normally with mean μ and variance σ2. Maximizing L with respect to these two parameters leads to their estimates where wherei, j = 1, …, m, genotype indices a, b = 0, 1, 2, and the index representing phenotype-independent and -dependent effects, l = 0, 1. These parameters are set to zero if a = 0 or b = 0. The null hypothesis (no association between genotype ai and phenotype y) is then represented by the condition y. Maximization of L in Eq.  sample genotype frequency and covariance, respectively; the Kronecker delta symbol is defined as δ = 1 if a = b and 0 otherwise; and the corresponding quantities without the hat are population averages defined by:where a or b is zero makes the total number of unknown parameters equal to that for the sample genotype frequencies and covariances after taking into account constraints associated with their normalization conditions  and f0 = (1/2) ln[(1 + R0)/(1 − R0)] (Fisher’s transformation). To obtain the mean correlation R0 under the null hypothesis necessary in this formula, we repeated the inference 10 times with phenotype labels permuted and calculated the mean of the correlations. This mean value was typically close to zero and positive but negative for some pathways. We tested this null distribution  for a selection of SNP sets and calculated p-values by phenotype-label permutation directly as the fraction of replicates among ~ 1000 for which R < R0 . We tested the association of 102 pathways that had been added to the main database with fear conditioning (cued test) and found that the highest ranked pathways had 1 and λ2, which maximize the prediction score R under cross-validation. We then applied the inference for the second half under these penalizer values and calculated the squared correlation between the predicted and actual trait values. For comparison, we used the software GCTA . The presence of Signaling by G protein-coupled receptors , which contains the PIP2 hydrolysis pathway, suggested that the strongest association with cued fear arises from the group of genes involved in post-synaptic signaling by GPCRs during memory consolidation [r2 < 0.9) distributed over ~ 20 genes. None of these individual genomic loci were dominant in association strengths without interaction effects  and that during exposure to the context alone (context test) . Quantillidation . The PIPcts Fig. , indicatRemoval of N-terminal propeptides from γ-carboxylated proteins, P = 6.3×10−5; γ-carboxylation of protein precursors, P = 7.6×10−5). The γ-carboxylation of protein precursors pathway contained 10 SNPs after LD pruning near two coagulation factor-coding genes, F2 (thrombin) and F9, in addition to the genes Bglap, Ggcx, Gas6, and Proc. Recent studies have revealed that γ-carboxyglutamate-containing coagulation factors, particularly thrombin, in addition to playing central roles in hemostasis of peripheral blood [q protein activates phospholipase Cβ (PLCβ), which hydrolyzes PIP2 to generate second messenger molecules, inositol 1,4,5-triphosphate (IP3) and diacylglycerol (DAG), leading to the phosphorylation of cytosolic proteins by protein kinase C and mobilization of Ca2+, respectively [Transport of γ-carboxylated protein precursors from ER to Golgi apparatus, P = 9.6×10−5 in human PTSD versus P = 1.8×10−4 in the current study for cued fear).The second group of highly ranked pathways contained those involved in γ-carboxylated proteins, including their synthesis, transport in the endoplasmic reticulum (ER) and Golgi apparatus, and modifications Fig. , of whical blood , also real blood . PAR1, aal blood , 38 as aectively , 39. Togectively . These rectively , 40  were those for transcription and mRNA decay , which are likely relevant in the regulation of synaptic plasticity at the levels of transcription and translation, e.g., by the transport and storage of mRNAs in distal dendrites [The third group of pathways associated with cued fear , whose strong association was clearly collective in nature, containing a large number of variants of individually low association levels scattered across different chromosomes , which describes the action of the serotonin transporter encoded by Slc6a4, the target gene of numerous antidepressant drugs known as serotonin reuptake inhibitors [Slc6a4, whose individual association levels were negligible in contrast to their collective p-value , which contained SNPs near genes Mt1–4  , arising from SNPs near the Smox gene , containing SNPs near the Pla2g4a and Gpcpd1 genes  was highly polygenic, similar to Effects of PIP2 hydrolysis and Platelet homeostasis  Fig. . The hig.1) Fig.  were Intene Fig. , and Hydnes Fig. –c. In cosis Fig. .Fig. 7InNegative feedback regulation of mitogen-activated protein kinase (MAPK) pathway (P = 2.8×10−4), along with cell cycle pathways . Our finding of the association of MAPK signaling and its negative regulators is consistent with reported evidence for their roles in depression [Regulation of Frizzled by ubiquitination (P = 9×10−5) of Wnt signaling and Regulation of signaling by Nodal (P = 5×10−5) to be highly associated, respectively, with each trait  indicated signs of inflation under sex-based meta-analysis. We performed meta-analyses by sub-dividing the cohort into two groups of high and low immobility during the first 2 min and inferred the association with immobility during the last 4 min separately, so that only the component of depression traits induced by stress (forced swim) would be tested. This choice removed genomic inflation Fig.  and yielpression , 47. We ay P = 2.×10−4, alait Fig. –g.To gain further insight into the genetic pathways associated with fear-related traits, we additionally analyzed recent dog personality trait data reported for Labrador Retrievers by Ilska et al. . We chosHemostasis (P = 7.4×10−7), providing further support to the suggested contributions of coagulation factors to cued fear in mice  and LRR FLII-interacting protein 1 activates type I interferon production (P = 1.5×10−5). One possible route through which polymorphisms in these DNA damage and innate immune pathways could affect the fear response is via the neural development of cortical interneurons [Breakdown of nuclear lamina, P = 1.9×10−5). We found that Synthesis of inositol phosphates (IPs) in the nucleus was also relatively highly ranked for fear of humans/objects albeit without exceeding the Bonferroni threshold (P = 4.9×10−5), consistent with the high association between Effects of PIP2 hydrolysis and cued fear in mice  and Gαo (inhibitory) proteins, whereas the latter becomes more important after conditioning. Our finding of the high association of PIP2 hydrolysis, which is downstream of the Gαq protein pathway, suggests that genetic polymorphisms affecting the generation of second messenger molecules during the excitatory phase of long-term potentiation contributes significantly to the heritability of conditioned fear responses.One of our major findings on the genetics of behavioral traits is for fear conditioning in the cued test Fig. : γ-carboHemostasis pathway was also found to be most strongly associated with gene sets differentially expressed in blood from PTSD subjects [The role of the thrombin-PAR1 pathway in long-term potentiation and fear conditioning, furthermore, suggests a possible explanation for the commonly observed comorbidity of PTSD and cardiovascular diseases , 26: indsubjects . A seconsubjects , 53 as wsubjects . We founsubjects .The numerous other pathways highly ranked for the behavioral traits of mice and dogs Fig.  belong tAlthough the genetic architectures of mice/dogs differ markedly from those of humans, the overall picture suggested by our results on fear-related traits is likely to be relevant to human genetics, given the common evolutionary origin of fear responses shared by all mammals. Results using animal genetic data may also offer avenues for experimental validation. For instance, pharmacological experiments that target neuronal pathways involving γ-carboxylated proteases in mice  could beOur estimates of the variance explained by interacting SNPs Fig.  demonstrWe presented a novel method to infer collective association of a large number of variants with quantitative traits while taking into account interaction effects. Applications to mammalian behavioral trait data revealed pathways linking stress-related phenotypes and hemostasis: neuronal signaling by γ-carboxylated proteases. Our work provides evidence suggesting that behavioral traits are strongly influenced by large-scale interaction effects among genetic variants.Additional file 1:Table S1. Sample sizes of behavioral trait data considered for outbred mice8 and dogs5. Figure S1 Distributions of quantitative trait values used for association testing. See Table S1. c, e, h, and i have been log-transformed . Figure S2 Comparison of single-SNP p-values from linear regression (LR) and continuous discriminant analysis (CDA). Figure S3 Comparison of empirical p-values of groups of interacting SNPs estimated by permutation of phenotype labels (symbols) and the use of the null distribution of R for normally distributed data. Figure S4 Optimized prediction scores of 10 top-ranked pathways for fear conditioning . Figure S5 Population stratification of Labrador Retrievers using principal component (PC) analysis. Figure S6 Quantile-quantile plots of independent SNP p-values for Labrador Retriever data. Figure S7 Independent-SNP and collective association levels of SNPs in pathways highly ranked for fear in dogs. Figure S8 Top-ranked pathways for fear of humans/objects (dogs). Table S2 Additional pathways from Reactome database (Feb. 2018) ranked by association strengths with respect to fear conditioning (cued test). (PDF 1811 kb)Additional file 2:Table S3. Lists of highly ranked pathways for mouse and dog behavioral traits. Pathways with P < 0.05 are shown for fear conditioning (cued and context), prepulse inhibition, elevated plus maze, swim test, sleep duration and difference in light and dark periods, noise and human/object-oriented fear in dogs. (XLSX 120 kb)


Common strategy of genome-wide association studies (GWAS) relying on large samples faces difficulties, which raise concerns that GWAS have exhausted their potential, particularly for complex traits. Here, we examine the efficiency of the traditional sample-size-centered strategy in GWAS of these traits, and its potential for improvement. The paper focuses on the results of the four largest GWAS meta-analyses of body mass index (BMI) and lipids. We show that just increasing sample size may not make p-values of genetic effects in large  samples smaller but can make them larger. The efficiency of these GWAS, defined as ratio of the log-transformed p-value to the sample size, in larger samples was larger than in smaller samples for a small fraction of loci. These results emphasize the important role of heterogeneity in genetic associations with complex traits such as BMI and lipids. They highlight the substantial potential for improving GWAS by explicating this role (affecting 11–79% of loci in the selected GWAS), especially the effects of biodemographic processes, which are heavily underexplored in current GWAS and which are important sources of heterogeneity in the various study populations. Further progress in this direction is crucial for efficient use of genetic discoveries in health care. New insights into genetic predisposition to diseases, related traits, and survival could substantially contribute to improvement of healthspan, particularly in aging populations in developed countries12Despite the apparent GWAS successes56910Various strategies for overcoming GWAS difficulties have been discussed. One broad category of strategies emphasizes the role of genetic factors such as rare variants, epigenetic modifications, miRNA, etc.571213Why and how to improve the traditional GWAS strategy in case of complex traits?”). Then, the analyses of genetic predisposition to heterogeneous traits (as opposite to homogeneous traits defined above) relying on increasing sample size become problematic because “increasing the size of human disease cohorts is likely only to scale the heterogeneity in parallel”These strategies may or may not fit those which are widespread in current GWAS. For example, GWAS rely heavily on “the benefits of the large sample sizes achievable through collaboration”17In this paper, we examine the potential for improving the traditional GWAS strategy, which relies on increasing the sample size, in the case of inherently heterogeneous traits. The paper focuses on the results of the largest GWAS meta-analyses, so far. They include studies of lipids comprising nearly 100,000 individualsMethods”). To examine the efficiency of the traditional GWAS strategy, we first compared p-values between larger and smaller samples (see “For 13 of 38 SNPs (34.2%) the BMI p-values were either larger (5 loci) or had minor decrease (8 loci) in the larger sample compared to the smaller sample in the same studyFor the lipid meta-analyses, the p-values were either larger (5 loci) or had minor decrease (4 loci within two orders of magnitude) for nine of 76 loci (11.8%) in the larger studyρ  of GWAS in the same pairs of larger and smaller samples as above.At first glance, the results in ρ > 1) was found for 3 of 38 loci (7.9%). However, the estimates of ρ for these three loci did not attain statistical significance . ificance . The relρ did not attain significance . For GWAS in ref. ρ was significantly larger than 1. For 8 of 38 loci (21.1%) ρ was significantly smaller than 1 (ificance . The relr than 1 .ρ was significantly larger than 1. For 13 of 76 loci (17.1%) ρ was significantly smaller than 1.ξ1) for loci with ρ = ξ1/ξ2 < 1 because ξ1 < ξ2 in this case.Improving the efficiency of the analyses implies that fewer people are needed to achieve the same result as in the case of conventional (unimproved) efficiency. One potential source is improving the efficiency of the analyses in larger samples . The role of evolution in the winner’s course effect is, however, unclear, particularly in the selected BMI and lipid GWAS.More importantly, however, is that in biology any inferences should be considered from the viewpoint of evolution . Accordingly, the situation with ρ < 1 can be not only due to the winner’s course effect but it can naturally be due to heterogeneity.The evolutionary theory suggests that genetic predisposition to complex traits should be inherently heterogeneous. Deviation from ρ. It is not entirely clear, however, how to do that. One approach is to select the cut off based on significance of ρ < 1 for specific loci  implies that the efficiency ξ1  was larger by 10% in a larger sample than in a smaller one. This means that the efficiency of the analysis in the larger sample is 10% smaller than in the smaller sample. This is equivalent to underusing 10% of the available sample that in large samples with N > 100 K leads to underusing information on more than 10 K people. Therefore, by improving the efficiency ξ1, one can use 10% smaller sample in this case to achieve the same result as in the case of conventional (unimproved) efficiency. For example, assuming a 90% relative efficiency of GWAS  hold here.The situation with ρ = 1  is consistent with heterogeneity, the potential for improvement can likely be in exploring more rigorous approaches to explicate an inherent heterogeneity in genetic associations with complex traits which remains after handling cross-sample heterogeneity using genomic methods used in the referenced GWAS meta-analyses .Because deviation from ρ = 1 was significant for: (i) 19 of 38 loci (50%) for the BMI GWAS in refs ρ = 1 by 10%  is observed for 62% to 79% of loci  than in the smaller sample (N~240 K) . ImportaSecond, the analyses of samples of larger and smaller sizes showed that the efficiency of GWAS was larger in the larger samples for a small fraction of loci ranging from 7.9% (3 of 38 loci) for the BMI GWAS in refs Third, consistent increase of the relative efficiency was found for only one BMI locus.These results lead to three important conclusions. First, increasing the sample size of the study population in genetic analyses of complex traits does not necessarily decrease the estimates of the significance of genetic effects  but can actually increase p-values. Second, our results support the substantial role of heterogeneity in genetic predisposition to complex traits such as BMI and lipids. Importantly, this is an inherent (trait-specific) heterogeneity due to the elusive role of evolution in these traits (see below), which remains after handling cross-study heterogeneity using genomics methods in the referenced GWAS. Third, the results highlight the substantial potential for improving GWAS by explicating this inherent heterogeneity that may affect 11% (the most conservative estimate) to 79% (the less conservative estimate) of loci in the selected four GWAS.Why and how to improve the traditional GWAS strategy in case of complex traits ? A key argument for increasing sample size in GWAS of traits with moderate and small effect sizes is to have sufficient statistical power to detect genetic effects. This is because low power decreases the likelihood that a significant association actually reflects “a true effect”Evolutionary biology, epidemiology, and aging research argue that: (i) environmental exposures in modern societies are dramatically different than those in the past and (ii) complex traits may not be subject to direct evolutionary selection1718Sensitivity of genetic effects to the environment due to narrow norm of reaction to complex traits and specific properties of age-related traits weaken the conceptual basis of the hypothesis of “true” genetic effects on complex traits5A key to improve the efficiency of the analyses of genetic predisposition to complex traits without increasing sample size is to better handle heterogeneity. However, heterogeneity is the result of various processes that requires better understanding diversity of its sources. Common sources include: (i) processes associated with evolutionarily selected genetic patterns in populations, (ii) complex etiologies of human phenotypes, (iii) environmental influences, and (iv) age-related heterogeneity attributed to the elusive role of evolution in the development of age-related phenotypes. Clearly, different sources of heterogeneity require different strategies to work with. Common practice in GWAS is to handle heterogeneity attributed to population structures, using, for example, methods of principal component analysis61729Evidences of the importance of the role of age-related heterogeneity in genetic associations are accumulating in the field. For example, the analyses highlighted the role of age in genetic regulation of BMI123335Thus, refocusing the analyses to a realistic concept of complex, inherently heterogeneous traits reflecting elusive role of evolution in these traits, has substantial potential for improving the efficiency of GWAS. Parameter of the relative efficiency ρ could help in prioritizing SNPs for more comprehensive analyses. This is crucial not only for a better understanding of the genetic influences on these traits, but also for efficient use of genetic discoveries in health care.We identified SNPs for 38 loci associated with BMI which were reported in refs We also selected SNPs for 76 loci associated with lipids which were reported in refs To examine the efficiency of GWAS and its potential for improvement, we used two strategies. First, we compared the significance of the estimates  in larger and smaller samples. Second, we used a measure of the efficiency as detailed in the subsection below.We compared the results from the BMI meta-analyses2016The results for the selected 38 BMI loci were presented in ref. The results for the lipid loci were presented for the entire samples in each study, i.e., ref. b to the sample of size N, i.e., ξ = −log10(p)/N. The efficiency measure can be interpreted as the log-transformed p-value per unit observation, i.e., per person in this case. The estimate of the relative efficiency in two samples was defined as ρ = ξ1/ξ2. We used ξ1 for the larger sample and ξ2 for the smaller one. For a given trait-loci association, the relative efficiency ρ shows which sample had larger log-transformed p-value per unit (person). Then, if the efficiency ξ is larger in smaller sample than in larger sample then the analyses in larger sample are less efficient than in smaller sample.To better characterize the potential for improvement of GWAS, we derived a measure of the efficiency of GWAS meta-analyses , 38, 39.ρ is also a convenient characteristic of homogeneity/heterogeneity in genetic susceptibility to a given trait .


Telomeres cap the ends of linear chromosomes and shorten with age in many organisms. In humans short telomeres have been linked to morbidity and mortality. With the accumulation of longitudinal datasets the focus shifts from investigating telomere length (TL) to exploring TL change within individuals over time. Some studies indicate that the speed of telomere attrition is predictive of future disease. The objectives of the present study were to 1) characterize the change in bovine relative leukocyte TL (RLTL) across the lifetime in Holstein Friesian dairy cattle, 2) estimate genetic parameters of RLTL over time and 3) investigate the association of differences in individual RLTL profiles with productive lifespan. RLTL measurements were analysed using Legendre polynomials in a random regression model to describe TL profiles and genetic variance over age. The analyses were based on 1,328 repeated RLTL measurements of 308 female Holstein Friesian dairy cattle. A quadratic Legendre polynomial was fitted to the fixed effect of age in months and to the random effect of the animal identity. Changes in RLTL, heritability and within-trait genetic correlation along the age trajectory were calculated and illustrated. At a population level, the relationship between RLTL and age was described by a positive quadratic function. Individuals varied significantly regarding the direction and amount of RLTL change over life. The heritability of RLTL ranged from 0.36 to 0.47 (SE = 0.05–0.08) and remained statistically unchanged over time. The genetic correlation of RLTL at birth with measurements later in life decreased with the time interval between samplings from near unity to 0.69, indicating that TL later in life might be regulated by different genes than TL early in life. Even though animals differed in their RLTL profiles significantly, those differences were not correlated with productive lifespan (p = 0.954). TTAGGG in vertebrates) and attached proteins of the shelterin complex [Telomeres are located at the ends of linear chromosomes. They consist of non-coding nucleotide tandem repeats  characterize the change in bovine relative leukocyte telomere length (RLTL) across the lifetime in Holstein Friesian dairy cattle, 2) estimate genetic parameters of RLTL over time and 3) investigate the association of differences in individual RLTL profiles with productive lifespan.Blood sampling of Holstein Friesian cattle was approved by the Animal Experiments Committee (UK Home Office Project License Number: PPL 60/4278).Animals used in this study were Holstein Friesian dairy cattle of the Langhill herd that were kept at the Crichton Royal Research Farm in Dumfries . All animals in this herd belong to one of two distinct genetic lines (selected for high milk fat and protein yield vs. control). Furthermore, cows are randomly allocated to two different diets that contain either a high or low proportion of forage. These genetic lines and diets were set up over 30 years ago to accommodate genetic and nutritional scientific studies .We measured RLTL in 1,328 longitudinal samples of 308 female animals born between 2008 and 2014. Animals were approximately equally split between genetic lines and diets. All animals were blood sampled once at birth and then at least once more during their lifetime. On average, 4.3 samples were taken per animal. At the end of the study 244 out of 308 animals were dead and had recorded productive lifespan measurements. Productive lifespan was defined as the time between the animal’s birth and culling in days. Productive lifespan differs from longevity measurements in humans and natural populations, because dairy cattle rarely die of natural causes. However, we argue that productive lifespan is still biologically meaningful, because animals are not culled randomly but usually for fertility or health reasons.5’-CGG TTT GTT TGG GTT TGG GTT TGG GTT TGG GTT TGG GTT-3’) and tel 2b (5’-GGC TTG CCT TAC CCT TAC CCT TAC CCT TAC CCT TAC CCT-3’) primers [TEL and EB2M that were in turn used in following formula [TEL and CqB2M for the telomere and the B2M reaction respectively. Cq values of the individual samples were CqTEL(Sample) and CqB2M(Sample).DNA was extracted from whole blood samples using DNeasy spin columns (QIAGEN) and each sample had to pass internal quality control steps which were 1) yield and purity measured on a NanoDrop ND-1000 spectrophotometer (Thermo Scientific) had to fulfil the minimum requirements of: yield > 20 ng/μl, 260/280 ratio > 1.7 and 260/230 ratio >1.8 and 2) integrity gel scores had to be between 1–2 . RLTL wa primers  for the  primers . An iden primers . The sam formula  to calcuIndividual samples were measured on 25 qPCR plates in total which had 8 rows for each reaction. RLTL data were logarithmically transformed to achieve normal distribution . Because of the increasing scarcity of data points after the age of 60 months, this age was used as the cut-off for data visualisation. The pedigree included 11,003 animals spread over 27 generations. The animals with RLTL measurements were descendants of 40 sires and 241 dams.tijk = the ith RLTL measurement for animal j using a Legendre polynomial of the order k. BirthYearj represents the fixed effect of the year in which animal j was born; GeneticGroupj stands for the fixed effect of the genetic group of animal j; qPCR plate and qPCR row of a particular sample i of animal j was included as fixed effects (qPCRplateij and qPCRrowij); fixed effects regression coefficients are represented bybk, while ujk stands for the kth order random regression coefficients for the additive genetic effects of animal j; Pjkt represents the kth order of Legendre polynomial fitted to the measurement i of animal j at the age t in months; the random residual variance is etijk. Sampling intervals and age at sampling  differed among individuals.The following random regression model was used for the analysis of longitudinal RLTL data:Model (2) included fixed effects that remained statistically significant (p<0.05) after backwards eliminating all tested non-significant effects  and the genetic group of the animal. The fixed and random regressions, both modelled with polynomial functions, described the average RTL change across age, and individual animal deviations from the average, respectively. The latter pertained to the animal’s additive genetic effect. The animal’s permanent environment was also examined as a random factor but had a negligible effect see .We tested if the residual variance of different age groups differed significantly implying a heterogeneous variance structure. We first considered four different age groups  and then two different age groups (younger and older than 2 months) but did not find a significant difference in residual variance between any age groups see . TherefoThe Akaike information criterion (AIC) was used to assess 1) if the introduction of the random animal genetic effect improved the model fit compared to a model that only included fixed effects; this would suggest that animals differ in their intercept ; 2) if Legendre polynomials fitted to the random animal genetic effect improved the model fit further, thereby suggesting that animals also differ in their slope (RLTL dynamics). A difference of two units in AIC corresponds to an approximate significance of p<0.05. Within the range of two units the simpler model was preferred over the more complicated –57. In tAll statistical analyses were conducted with the ASReml software version 4.1 .th order polynomial with the nth order polynomial residual. The standard error was calculated in parallel by using the standard errors associated with the solutions for the same calculation. Eigenvalues were calculated to estimate the amount of variance between animals that is due to 1) the intercept and 2) the shape of individual curves. Eigenfunctions were calculated to analyse the direction of each effect.The fixed curve that illustrates RLTL dynamics at a population level was calculated as the sum of the products of the Legendre polynomial order residuals for a given age and the corresponding fixed regression coefficients. This was repeated across all ages in the trajectory. Random regression models allow the calculation of an individual profile of RLTL change over age for each animal as a deviation from the population mean (fixed curve). The model output provides estimates (solutions) for each animal and each order of polynomial fitted in the model. The random curves were calculated simply by summing solutions for each animal and test month across all products of the nA) for each month was calculated using following formula [The additive genetic variance  were clustered using the R library kmlShape  in five Raw RLTL measures ranged from 0.693 to 1.727 with a mean of 1.082. The coefficient of variation was 0.162. The model that included the animal identity as a random effect fitted the data significantly better than a model including only the fixed effects (delta AIC = 204.97) suggesting that animals differed significantly in their average RLTL across time. Fitting animal identity with pedigree information further improved the model fit (delta AIC = 55.46). Fitting an individual curve for each animal  additionally increased the model fit (delta AIC = 3.24), meaning that monthly RLTL dynamics also differ among individual animals. A quadratic Legendre polynomial fitted marginally better than a linear function (delta AIC = 2.07) and had the advantage that the same order of Legendre polynomial was fitted to the fixed and the random effect which facilitates interpretation of the results.The fixed curve as described by the Legendre polynomial captured the expected initial decline of RLTL in early life and a relative stability of RLTL later in life . The curExamples of individual animal RLTL curves are shown in Monthly heritability estimates for RLTL ranged from 0.356 to 0.470 (SE = 0.045–0.104) and were slightly higher between 20 and 50 months of age than in the beginning of life or at older ages. Considering the SE, heritability estimates remained relatively stable over life .The genetic correlation between RLTL measurements at birth and at different stages of the animals’ lives are shown in Productive lifespan ranged from 17 to 2,823 days . To test the association between RLTL profiles (intercept and shape) and productive lifespan, RLTL profiles were clustered into groups depending on the similarity of their RLTL change pattern. Five clusters were formed to capture no telomere change and mild and moderate changes in both directions early in life (attrition vs. elongation). Animals differed more in their intercept than in their direction and amount of change. Of all animals 32% shortened their RLTL slightly in early life, while 29% did not show obvious RLTL change at all  which is visualised in This is the first study exploring individual RLTL profiles of farm animals across time and the largest longitudinal telomere study outside the human literature so far. Our results suggest that individual cattle differ in their RLTL dynamics over life. Although most of the difference between animals is explained by a different average RLTL (intercept) (94.7%), a small proportion is due to different shapes of RLTL profiles (5.3%). This is an important observation that justifies the further investigation of differences in telomere profiles in association with traits of interest such as health, fertility and mortality. The only other study we are aware of that used random regression models for the analysis of longitudinal telomere data did not report a significant difference in telomere dynamics among Seychelles warblers , which mAt a population level RLTL shortened in the beginning of life. The fixed curve calculated in the present study suggests an average RLTL increase later in life. However, this is probably due to the symmetry of a quadratic function and might not reflect biological changes. Therefore, we argue that at a population level telomeres shorten in the beginning of life and remain relatively stable thereafter. Some previous longitudinal studies in baboons and birds support these results, though they did not use random regression models for their analyses ,6160,61,6160,61.In the present study we report the first heritability estimates for telomere length across all species that were calculated using random regression models. Random regression model estimates do not only inform about the proportion of the variance that is due to additive genetic effects, they also demonstrate how this proportion might change over time. It is known that telomere length is affected by many different genes –26. EpigWithin an animal, the genetic correlation between consecutive RLTL measurements decreased as the time interval between measurements increased. This suggests that RLTL might be under different genetic control at different life stages. As mentioned before, epigenetic changes during ageing ,64 mightNot much is known about telomere length and its association with productive lifespan in cattle so far. In cross-sectional studies bovine telomere length declines with age and during the lactation period ,69,70. AS1 File(DOCX)Click here for additional data file.


All common genome-wide association (GWA) methods rely on population structure correction, to avoid false genotype-to-phenotype associations. However, population structure correction is a stringent penalization, which also impedes identification of real associations. Using recent statistical advances, we developed a new GWA method, called Quantitative Trait Cluster Association Test (QTCAT), enabling simultaneous multi-marker associations while considering correlations between markers. With this, QTCAT overcomes the need for population structure correction and also reflects the polygenic nature of complex traits better than single-marker methods. Using simulated data, we show that QTCAT clearly outperforms linear mixed model approaches. Moreover, using QTCAT to reanalyse public human, mouse and Arabidopsis GWA data revealed nearly all known and some previously undetected associations. Following up on the most significant novel association in the Arabidopsis data allowed us to identify a so far unknown component of root growth. Currently available methods for phenotype to genetic markers association need to account for population structure. Here, Klasen et al. devise a statistical method called Quantitative Trait Cluster Association Test (QTCAT) that overcomes the need for population structure correction. Since the advent of high-throughput genotyping methods, genome-wide association (GWA) is the emerging tool for studying the genetics underlying natural phenotypic variation. GWA studies have been applied across a wide range of species where it enabled fine-scale genetic mappingPresumably the most widely used method for GWA is the linear mixed model (LMM), which tests the association of the phenotypes to each individual marker, while taking into account a so-called random effect5678910However, the use of a random effect to correct for population structure and for an infinitesimal genetic background changes the hypothesis tested. Instead of testing whether a particular locus has an effect on the phenotype, it tests whether a locus has an effect on the phenotype that is neither explained by population structure nor by the genetic background. This is a drastic restriction to the hypothesis, which eventually leads to failures in the identification of causal loci13To minimize these negative effects of population structure correction several improvements have been suggested, including relaxation of the (in many cases) unrealistic assumption of an infinitesimal genetic background by a more explicit modelling of the underlying genetics15161719Here we introduce a new GWA method, called Quantitative Trait Cluster Association Test (QTCAT) that accounts for the correlation between the markers, while associating them to the phenotypes and thereby avoids the problems introduced by population structure . In addition, QTCAT also avoids the need to correct for any genetic background, as it simultaneously associates all markers to the phenotype. Using simulations based on populations with high levels of population structure, we showed that QTCAT is superior to LMM approaches, finding more loci with small effects, more extreme allele frequencies and higher involvement in population structure. Moreover, using simulations of experimental mapping populations without any population structure showed that QTCAT is also superior to common linkage mapping. To evaluate the performance of QTCAT on real data, we reanalysed previously published GWA data from Arabidopsis, mouse and human populations. Although nearly all previously reported associations were identified, QTCAT found additional associations in all cases. The most significant novel association found in an Arabidopsis data set for variation in root meristem zone lengths revealed an association of two candidate genes. The loss of function for one of the two genes resulted in a reduction of meristem zone length and therefore exemplified the great potential of candidate gene identification by QTCAT.The polygenic nature of quantitative traits makes multi-marker association methods superior to single-marker associations, as they combine all markers into one model to search for the subset of markers that together explains the phenotype best15y is a vector of n phenotypic observation, X is an n × p design matrix of covariates (markers), β is a vector of p effects and n residuals. p, the number of markers, is usually much larger than the number of individuals n, resulting in high-dimensional linear model where p≫n. However, as usually only a few markers have an effect on the phenotype, the model is sparse with many elements of β being equal to zero.where the continuous response variable This is a common model selection problem with the additional complication that the markers are correlated. As a consequence, highly correlated markers are exchangeable as members of the ‘best subset', implying that there is not one unique subset of markers that explains the phenotype best. To address this, QTCAT searches for clusters of highly correlated markers that are significantly associated to the phenotype instead of searching for individual markers. For this, QTCAT follows an earlier suggestion by MeinshausenThis idea fits particularly well to the challenges of GWA. Each of the final clusters, called quantitative trait clusters (QTCs), combines markers, which cannot be distinguished for their individual contribution to the phenotypic variation. Important to note, this refers to markers, which are highly correlated to each other. Markers, which are not highly correlated, will not be able to explain the same part of the phenotypic variation and will not become part of the same QTC. Moreover, during the hierarchical testing procedure, the significant association of a QTC will suppress potential associations of other clusters to the same part of the phenotypic variation, ensuring that a particular part of the phenotypic variation is only explained by one QTC. With this, false associations, including erroneous associations introduced by population structure, are effectively avoided even without any further correction terms .p≫n) we implemented the hierarchical testing as a repeated sample-splitting procedure23To make this method applicable to high-dimensional linear models (h2) of 0.7. The second phenotype was similar though the effects were drawn from a normal distribution instead, to avoid the effects of predominant major loci. The third phenotype was more complex and based on 150 loci with effects that were randomly drawn from a normal distribution and an h2 of 0.4. We associated these phenotypes to the genotypes using QTCAT, as well as two different LMM approaches ; however, the precision for the third and most complex phenotype was only 92.5% and thus considerably lower. This drop in precision of the LMM is likely to be due to the high genetic complexity in regard to sample size and might be overcome with more individuals. Moreover, the precision of LMM* was drastically lower throughout all simulations  and was also even lower compared with the LMM, which is in contrast to previous reports on human data13α-level for the first two phenotypes as compared with both LMM approaches , whereas the average number of false positives was very low for both methods . However, at the same time the number of false positives was drastically increased to 3.93 as well. This was not only more as compared with the number of false positives of QTCAT , but accounts for nearly one third of all loci found by the LMM. Similar to that for LMM, the average number of true positives estimated by LMM* was significantly lower as compared with QTCAT , whereas the average number of false positives was already significantly increased when using FWER for multiple testing correction .To analyse these results in more detail, we explored the outcome of the associations to the first phenotype at an P-value=3.82e−10, Wilcoxon test; P-value=0.02765, Wilcoxon test; although considering only loci with similar effects (between 1.1 to 1.6) to be independent of effect size; P-value=8.697e−10, Wilcoxon test; P-value=0.005335, Wilcoxon test; again only considering loci with similar effect sizes).Comparing QTCAT with the LMM, 76% of the loci were identified by both methods, whereas 22% were found exclusively by QTCAT and only 2% were found by the LMM alone. Consequently, the results of QTCAT explained significantly more of the variance than the results of the LMM  and significantly less false positives . Even though there is no causal connection between phenotype and genotype in these cases, this can result in false positive associations. If non-genetic factors co-occur with population structure, population structure correction can avoid these false associations. As QTCAT does not rely on population structure correction, such factors need to be explicitly modeled to guarantee stringent error control.All common genetic mapping methods include two main steps: first, detection of associations and, second, precise localization of a mapping interval around each association that has a high probability of containing the causal variantStatistical solutions for high-dimensional regression based on correlated covariates are still in their infancy and further improvements are likely to follow soon. Our simulations showed that QTCAT is still very conservative, indicating that such further development could lead to even more powerful methods. However, already in the current form QTCAT properly accounted for polygenic inheritance and helped overcoming the need for population structure correction. In any case, independent of the actual method, associating clusters of highly correlated markers (QTCs) will always be superior to single-marker association, as they are more consistent with the nature of quantitative traits.h2 of the simulated traits was 0.7. The second set of phenotypes was similar with the exception that the effects were drawn from a normal distribution. Finally, the third phenotype was based on 150 causal loci with effects that were randomly drawn from a normal distribution with a h2 of 0.4.GWA data were simulated from a set of 214,051 single-nucleotide polymorphism markers, which were genotyped for 1,307 diverse Arabidopsis accessions showing strong population structureer)For linkage mapping we simulated a recombinant inbred line (RIL) population using the genotypes of two diverse Arabidopsis accessions  running a Linux 64 bit operating system. Clustering of 214,051 markers and 1,307 individuals took ∼17 h using QTCAT's clustering algorithm running in parallel on four cores . The actual association step performed by QTCAT's HIT algorithm took ∼1 h and 45 min, also running in parallel on four cores. However, QTCAT could easily be run on many more parallel cores, as the algorithm is tailored for direct distributed computing to achieve substantial computational speed-up.pepr2-1  sucrose and 0.8% (w/v) agar. The seeds were stratified for 2 or 5 days, respectively, at 4 °C in the dark and subsequently germinated upright in long-day conditions (16 h light/8 h dark) at 21 °C. Three/six days old seedlings were mounted on microscopy slides in liquid growth medium supplemented with 20 μM propidium iodide (Sigma Aldrich) and analysed using a Zeiss 700 inverted confocal microscope equipped with a × 20 air objective. Image identities were randomized before meristem measurements using the Fiji software (http://fiji.sc/Fiji) as described previouslypepr2-1 plants were analysed with ANOVA F-tests, while accounting for the different experiments. Normal distribution of data and homogenous variance was given.Arabidopsis seeds of Col-0 and 2-1 ref.  genotypehttp://github.com/QTCAT/qtcat/.QTCAT is available as open source R package at How to cite this article: Klasen, J. R. et al. A multi-marker association method for genome-wide association studies without the need for population structure correction. Nat. Commun.7, 13299 doi: 10.1038/ncomms13299 (2016).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary Figures 1-405, Supplementary Note 1 and Supplementary References


P=1.57 × 10−11, effect allele frequency (EAF) 0.006); and rs145556679 (triglycerides levels beta −1.13 (SE 0.17), P=2.53 × 10−11, EAF 0.013). Our findings add empirical support to the contribution of low-frequency variants in complex traits, demonstrate the advantage of including population-specific sequences in imputation panels and exemplify the power gains afforded by population isolates.Next-generation association studies can be empowered by sequence-based imputation and by studying founder populations. Here we report ∼9.5 million variants from whole-genome sequencing (WGS) of a Cretan-isolated population, and show enrichment of rare and low-frequency variants with predicted functional consequences. We use a WGS-based imputation approach utilizing 10,422 reference haplotypes to perform genome-wide association analyses and observe 17 genome-wide significant, independent signals, including replicating evidence for association at eight novel low-frequency variant signals. Two novel cardiometabolic associations are at lead variants unique to the founder population sequences: chr16:70790626 (high-density lipoprotein levels beta −1.71 (SE 0.25),  Isolated populations can provide useful information on low-frequency variants for dissecting genetic architecture of complex traits. Here, Zeggini and colleagues show enrichment of rare and low-frequency variants and 8 novel low-frequency variant signals for cardiometabolic traits in two Greek isolated populations Genome-wide association studies (GWAS) for complex medical traits have to date been designed and powered for the discovery of common-frequency variants with small to modest effect sizes. It is becoming increasingly clear that rare and low-frequency variants also play an important rolen=9,554,503 with MAC≥2) by functional class  enrichment of rare and low-frequency (MAF≤5%) coding and regulatory region variants (P<1.00 × 10−5) of variants annotated as intergenic and upstream/downstream.Of all autosomal SNVs found in the MANOLIS 4 × WGS data 0.52 million (5.81%) were unique compared to the UK10K and 1000 Genomes Project reference panels . Most vavariants , which in=1,476) and Pomak  cohorts were each genotyped in two tranches (z-scoresP with effect-size based methodshttps://github.com/wtsi-team144/metacarpa and http://www.sanger.ac.uk/science/tools/metacarpa). Using simulation, we show that under typical levels of sample overlap (up to 10%) METACARPA reduces false-positive rate inflation by 8%, while conserving power  for association with high-density lipoprotein (HDL)  . We concP≤5.00 × 10−8) that replicate within  or across the isolates studied . We identify 9 previously-reported GWAS signals (http://hapmap.ncbi.nlm.nih.gov), 5 do not have HapMap proxies (r2>0.8), and 3 are not present in the 1000 Genomes Project reference panel haplotypes. Three signals were identified in MANOLIS, four in Pomak and one across both isolated populations. Five signals fall just above our Bonferroni-adjusted genome-wide significance threshold (P<3.33 × 10−9) for the effective number of traits tested and are therefore considered tentative. When assessing the fine-mapping potential of these data  0.006) , who are on average 11 years younger than non-carriers, P=6.00 × 10−3 (We identify a new association with HDL cholesterol at chr16:70790626 (beta −1.71 (SE 0.25), ) 0.006) . This va) 0.006) . This is0 × 10−3 .P=2.53 × 10−11) and with very low-density lipoprotein cholesterol (VLDL) levels (beta −1.13 (SE 0.17), P=2.90 × 10−11)  . Conditional analysis confirms that rs145556679 is independent of R19X  (DSCAML1) gene, which is involved in cell adhesion in neuronal processes and is expressed in heart, liver, pancreas, skeletal muscle, kidney and brain14r2=0.0005 in MANOLIS) has previously been implicated with TG levels in the Amish founder populationWe identify a cardioprotective signal , which is associated with decreased triglycerides (TG) (beta −1.13 (SE 0.17), × 10−11) . This vaF=0.005) . HoweverF=0.005) . This va× 10−12) . rs14555P=1.35 × 10−8, EAF 0.010) , and has a higher frequency in the Pomak (MAF 0.038) and 1000 Genomes Project EUR populations (MAF 0.014) compared to MANOLIS (MAF 0.01). rs140087759 has no proxies with r2>0.8 in MANOLIS and is not present in the WHR GWAS summary statistics from the Genetic Investigation of ANthropometric Traits (GIANT) study (https://www.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files)In MANOLIS we also observe an association between waist-to-hip ratio (WHR) and rs140087759 (beta 1.19 (SE 0.21), F 0.010) , locatedP=3.18 × 10−8, EAF 0.043) (ENSR00000596922)PSD4. It is located 1 kbp upstream of novel transcript AC016683.5. rs13382259 is associated with the expression of PAX8 in tibial nerve . The allele frequency of rs13382259 is lower in the MANOLIS (MAF 0.024) compared with the 1000 Genomes Project EUR populations (MAF 0.05) and the Pomak population (MAF 0.05). The signal is not associated in MANOLIS (P=0.53) and is not present in the genome-wide summary statistics for the International Consortium for Blood Pressure (ICBP)r2>0.8) are present in the International HapMap Project data (http://hapmap.ncbi.nlm.nih.gov) and three are present in ICBP summary statistics but none were significantly associated with DBP.In the Pomak population, we identify an association between diastolic blood pressure (DBP) and rs13382259 (beta 0.55 (SE 0.1), F 0.043) , which rP=1.21 × 10−8, EAF 0.037) . The allele frequency of rs6131100 is higher in the MANOLIS (MAF 0.083) and 1000 Genomes Project EUR populations (MAF 0.053) compared to the Pomak population (MAF 0.039). rs6131100 is not associated with FGBMIadj in MANOLIS (P=0.91), and is not present in genome-wide summary data available from the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) study (www.magicinvestigators.org)1920We also identify an association between fasting glucose levels adjusted for BMI (FGBMIadj) and rs6131100 (beta −0.79 (SE 0.14), F 0.037) . rs61311P=3.00 × 10−8, EAF 0.008) (AC092594.1). The closest protein-coding gene is OSR1, 121 kbp away, a widely-expressed transcription factor implicated in embryonic heart, kidney and urogenital developmentP=0.19). It has a higher allele frequency in the 1000 Genomes Project EUR population (MAF 0.014). No proxies are present for rs79748197 in the Pomak population and this trait was not examined in the Haemgen RBC studyIn the Pomak cohort, we also observe an association with white blood cell count (WBC) and rs79748197 (beta −1.16 (SE 0.21), F 0.008) , which rP=4.83 × 10−11, EAF 0.004) (MBE1 and MBG1) and a non-coding RNA gene (AC104389.28). The G-allele of rs557129696 is not seen in the 1000 Genomes EUR population. Numerous associations with red blood cell traits, anaemia and thalassemias have been linked to this chromosome 11 region252627Lastly, rs557129696 is associated with haemoglobin levels (HGB) (beta −2.03 (SE 0.31), F 0.004) . The varF 0.004)  of previAC104389.. The G-aP=2.70 × 10−8, EAF 0.075) in both isolated populations . rs112037309 has a higher frequency in the 1000 Genomes Project EUR population (MAF 0.096) compared with the Pomak (MAF 0.073) and MANOLIS (MAF 0.074) populations. We were unable to look up this variant in large GWAS studies as weight is not one of the traits included as part of the Genetic Investigation of Anthropometric Traits (GIANT)Weight was associated with rs112037309 (beta 0.29 (SE 0.05), ulations . rs11203We provide here a first characterization of the genetic architecture of the MANOLIS isolated population and report 9.5M SNVs, of which 6% are absent from previous sequenced panels and are enriched for predicted functional consequences. Our complex trait association findings highlight the advantages of whole genome sequencing in founder populations: two lipid traits and the HGB signals we identify are driven by variants unique to the MANOLIS cohort or extremely rare in other worldwide populations. The remaining five novel associations are present in European populations (1000 Genomes Project EUR MAF ranging from 0.014 to 0.096) but are not significantly associated in GWAS meta-analyses of cosmopolitan populations. This can be due to a number of reasons in addition to winner's curse, that is, larger effect sizes in the discovery isolate cohort. For two of these signals, the variant and its proxies are not present in the HapMap reference panel and therefore these variants are not represented in GWAS conducted to date. Three of the associated variants are represented in HapMap and show no evidence of association outside the isolate; this can indicate that the index variant is in LD with the causal variant in the isolate but not in the cosmopolitan population. Furthermore, the effect and therefore the power to detect associations can be increased in isolates due to the environmental and phenotypic homogeneity when compared to other worldwide populations, in addition to extended LD.Our study demonstrates the power benefits of using a large, sequence-based imputation reference panel. Six of the eight new associations reported here have been detected for the first time as a consequence of improved imputation accuracy. The cost-effective hybrid WGS and imputation approach in founder populations serves as a good model for further low-frequency variant discovery, which can enhance our understanding of the biological processes underpinning complex traits of medical relevance.www.helic.org) MANOLIS (Minoan Isolates) collection focuses on Anogia and surrounding Mylopotamos villages on the Greek island of Crete. All individuals had to have at least one parent from the Mylopotamos area. The HELIC Pomak collection focuses on the Pomak villages, a set of isolated mountainous villages in the North of Greece. Recruitment of both population-based samples was primarily carried out at the village medical centres. The study includes biological sample collection for DNA extraction and lab-based blood measurements, and interview-based questionnaire filling. The phenotypes collected include anthropometric and biometric measurements, clinical evaluation data, biochemical and haematological profiles, self-reported medical history, demographic, socioeconomic and lifestyle information. The study was approved by the Harokopio University Bioethics Committee and informed consent was obtained from every participant.The HELIC  variants were used to calculate the pairwise identity by descent; there were 624,403 such pairs for 1,118 individuals. Samples were selected based on a maximal Sequencing and variant calling. Sequencing was performed at 4 × average depth using Illumina HiSeq 2000 sequencers. The data was aligned to the 1000 Genomes Project phase 1 reference assembly using BWAhttp://broadinstitute.github.io/picard). Variants were called using samtoolsVariant QC and Haplotype creation. Post-VQSR, variants were filtered so as to yield a sensitivity threshold of 90% for INDELS (VQSLOD<3.1159) and a threshold of 94% for SNPs (VQSLOD<5.4079).Variants were excluded if they were multi-allelic, monomorphic, singletons, indels, had a missingness >3% or a HWE P<1.00 × 10−4. Any variant from the HELIC MANOLIS data for which the alleles differed from the 1000 Genomes Project and UK10K datasets at the same position was excluded. Phasing was performed using SHAPEIT v2.r727 (ref. 727 ref. . Followi727 ref.  info sco727 ref. . Average727 ref. .http://mathgen.stats.ox.ac.uk/impute/impute_v2.1.0.html). For UK10K the haplotypes were prepared and described previously1A large reference panel was constructed by combining the WGS haplotypes from HELIC MANOLIS , 1000 Genomes ProjectIMPUTEv2 refs , 34 was The human genome was split to functional regions as follows: coding and UTR sequences were extracted from GENCODE annotations Using Ensembl variant effect predictor (VEP) (n=3718) and/or the 1000 Genomes Project (n=1092) reference haplotype dataset  or those unique to HELIC MANOLIS . To compare the distributions of consequence terms in the shared and the unique datasets a two-sided proportion test was used for each MAF bin, using the consequence and MAF bins described above. Fold enrichment was calculated with the following equation:EM,c is the fold enrichment of consequence c in M MAF bin; M MAF bin with consequence c in the unique and the shared dataset respectively; M MAF bin in the unique dataset and the shared dataset respectively. Bonferroni correction of the P was applied to account for multiple testing  at the Wellcome Trust Sanger Institute, Hinxton, UK. The two datasets for each cohort were phased, imputed and analysed separately .Quality control (QC) for the samples genotyped using the OmniExpress genotypes has been previously describedAdditional HELIC MANOLIS and Pomak samples were genotyped on the Illumina HumanCoreExome-12-v1.1 (Illumina) at the Wellcome Trust Sanger Institute, Hinxton, UK. Genotypes were called with GenCall and zCall . In MANOP<1.00 × 10−4 or imputation information score <0.4 was excluded. There was good genotype concordance between the 249 overlapping samples in the imputed and WGS (Each cohort and array was phased and imputed separately . Before  and WGS .P<0.05), the phenotype was stratified accordingly. Following trait-specific exclusions and adjustments, outliers were filtered out based on 3, 4 or 5 SD away from the mean. Traits not normally distributed were transformed to normality using either an inverse normal or log transformation. For all traits age and ageThirty-one phenotypes encompassing cardiometabolic, anthropomorphic and haematological traits were prepared separately for each cohort and array . If gendGEMMA. Association analysis was performed separately for each cohort and array using the imputed genotypes. GEMMAP values from the likelihood ratio test (p_lrt) are reported. For meta-analysis within and across cohorts we developed and used METACARPA.METACARPA. When meta-analysing GWAS, both the P values and effect sizes can be meta-analysed on a per-variant basis. Both meta-statistics are weighted sums., for example, for effect sizes:K that should be meta-analysed, k and wk is a study-specific weight. For P values, we transform to z-scores using where z-scores are transformed back to P with the complement of the previous transformation: Then, zk)=1 by construction and K × K variance-covariance matrix describing this ‘inter-study relatedness'. Lin and SullivanVar, Ωz for the P meta-analysis is Ωη for the effect-size meta-analysis is where For weights, it is shownK and Ωη is the estimated covariance matrix of the effect sizes between studies with general term where P meta-analysis, the general term of Ωz does not contain a factor accounting for unequal sample size. The following weight vector does:For the s is a vector containing the sample sizes of all studies. The general term of the weight vector is i.where P values. This result was confirmed by comparing the approximated value with an iterative maximum likelihood estimator.We implemented this method in C++ using the Boost libraries. For tetrachoric correlation, we use the approximation of DigbySimulation and benchmark. This implementation was tested by repeatedly drawing two random sets of 2,000 samples each from the UKHLS GWAS dataset (EGA accession EGAD00010000890), with increasing sample overlap. Phenotypes were drawn from a standard normal. The two studies were associated separately using GEMMAP value-based meta-analysis−8, and the power to detect a single associated SNP. Effect SNPs were chosen randomly for each simulation, MAF and effect sizes were constrained so that the effect SNP explained 1% of phenotype variance.−5%/sample to 5.5 × 10−5%/sample (8.3%). While for typical (0.5–5%) levels of overlap, power to detect a single SNP is conserved, for substantial levels of overlap (5–10%) it drops at an approximate rate of 0.05%/sample. For extensive levels of overlap (10–75%), the increase in false-positive rate slows further and stabilizes around 9% for overlaps greater than 50% for both corrected methods , false-positive rate grows linearly for both the two uncorrected and the two corrected methods . However methods , whereasz-scores. Although both methods systematically underestimated sample overlap, tetrachoric correlation performed poorly compared to Pearson's when all SNPs were under the null . Binary and sources are freely available .P≤5.00 × 10−8 in a 500 kbp window around any given signal. Variants that were genome-wide significant in the within-cohort meta-analysis and not within 500 kbp of the across cohort meta-analysis signal were also considered. Replication is demonstrated in the within-cohort meta-analysis by nominal significance (two-sided P≤0.05) in the same direction in both datasets . For the across cohort meta-analysis both within-cohort meta-analysis have P≤0.05. To determine the Bonferroni corrected genome-wide significance level, for each cohort array we used the eigenvalues of the correlation matrix of the 31 traits testedP threshold to control FWER at 5%, using a Bonferroni correction, is 5.00 × 10−8/effective number of independent phenotypes. We selected the cohort array with the maximum number of independent phenotypes for the calculation, which was 5.00 × 10−8/14.99=3.33 × 10−9.Variants were prioritized for validation by direct genotyping from the meta-analysis across cohorts, keeping only the most significant SNV with Prioritized variants were independently genotyped in as many of the imputed samples as possible using the Sequenom iPLEX Assay and the Sequenom MassARRAY System (Agena Bioscience) .Sixty two variants were directly genotyped in a maximum of 2,778 samples. Concordances of the major and minor alleles were calculated separately. The minor allele concordance and the positive predictive value (PPV), which is the fraction of true positives for the minor allele calls, were used to assess the imputed genotype quality. Phenotypes were prepared again only for the samples with directly typed genotypes and the association and meta-analysis were repeated.Concordance and PPV were calculated as follows:The proportion of variants that had both concordance and PPV>90% were: MANOLIS CoreExome 54.8%; MANOLIS OmniExome 53.2%; Pomak CoreExome 50%; Pomak OmniExome 54.8%. All variants reported here pass validation .r2=1 with rs112037309 . Conditional analysis confirmed these represent the same signal.For the weight signal with rs112037309 we validated a proxy rs17262443 which has https://www.ebi.ac.uk/ega/home): EGAD00010000518; EGAD00010000522; EGAD00010000610; EGAD00001001636. We have also contributed the 249 HELIC MANOLIS whole-genome sequences to the Haplotype Reference Consortium (http://www.haplotype-reference-consortium.org).The following HELIC genotype and WGS datasets have been deposited to the European Genome-phenome Archive .Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary Figures, Supplementary Tables, Supplementary Notes, Supplementary Methods and Supplementary References


Large sample sets of whole genome sequencing with deep coverage are being generated, however assembling datasets from different sources inevitably introduces batch effects. These batch effects are not well understood and can be due to changes in the sequencing protocol or bioinformatics tools used to process the data. No systematic algorithms or heuristics exist to detect and filter batch effects or remove associations impacted by batch effects in whole genome sequencing data.We describe key quality metrics, provide a freely available software package to compute them, and demonstrate that identification of batch effects is aided by principal components analysis of these metrics. To mitigate batch effects, we developed new site-specific filters that identified and removed variants that falsely associated with the phenotype due to batch effect. These include filtering based on: a haplotype based genotype correction, a differential genotype quality test, and removing sites with missing genotype rate greater than 30% after setting genotypes with quality scores less than 20 to missing. This method removed 96.1% of unconfirmed genome-wide significant SNP associations and 97.6% of unconfirmed genome-wide significant indel associations. We performed analyses to demonstrate that: 1) These filters impacted variants known to be disease associated as 2 out of 16 confirmed associations in an AMD candidate SNP analysis were filtered, representing a reduction in power of 12.5%, 2) In the absence of batch effects, these filters removed only a small proportion of variants across the genome (type I error rate of 3%), and 3) in an independent dataset, the method removed 90.2% of unconfirmed genome-wide SNP associations and 89.8% of unconfirmed genome-wide indel associations.Researchers currently do not have effective tools to identify and mitigate batch effects in whole genome sequencing data. We developed and validated methods and filters to address this deficiency.The online version of this article (doi:10.1186/s12859-017-1756-z) contains supplementary material, which is available to authorized users. Recent reductions in the cost of whole genome sequencing  (WGS) haGenotyping arrays preceded WGS and were the standard assay for variant calling and genome-wide association studies (GWAS). Batch effects are well studied in the context of genotyping arrays –7 and ofBatch effects in WGS come with the additional complexity of interrogating difficult to characterize regions of the genome, and common approaches such as the Variant Quality Score Recalibration (VQSR) step in GATK  and procGiven that no standardized algorithms or heuristics currently exist to identify or address the issue of batch effects in WGS, batch effects have generally been handled by adopting stringent QC measures. The Type 2 Diabetes Consortium  used a sIn addition to removing unconfirmed and likely spurious associations induced by batch effects, researchers must also determine that a batch effect exists. Identifying a method to detect batch effects that have an impact on downstream association analyses is crucial as researchers need to know upfront whether WGS datasets can be combined or if changes in sequencing chemistry will result in sequences that can no longer be analyzed together. This has been done with principal components analysis  for SNP The powerful technique of haplotype inference has evolved orthogonal to the established approaches to correct for batch effects –29. HaplLarge-scale WGS efforts are thriving, however few guidelines exist for determining whether a dataset has batch effects and, if so, what methods will reduce their impact. We address both these deficiencies and introduce new software  that can help identify batch effects. We demonstrate how to identify a detectable batch effect in WGS data via summary metrics computed using genotype calls, their quality values, read depths, and genomic annotations, followed by a PCA of these metrics. We describe our strategy to eliminate unconfirmed genome-wide significant associations (UGAs), which are likely enriched for spurious associations, induced by batch effects. Our aim was to develop filters that removed sites impacted by a detectable batch effect with high specificity so as not to eliminate a large number of variants genome-wide. The filters we developed do not remove all UGAs impacted by batch effects and come at the cost of a reduction in power of 12.5%, however when applied in conjunction with standard quality control measures (see Methods) they can substantially mitigate the impact of batch effects.P < 5E-8) following that correction, 2) Impose a differential genotype quality filter, and 3) Set genotypes with quality scores less than 20 to missing, then filter any site missing 30% or more of its genotypes (we refer to this filter as “GQ20M30”). Application of this three-step filter substantially reduced UGAs . When applied to data for an Age-Related Macular Degeneration (AMD) study without a detectable batch effect, these filters removed only a small number of variants genome-wide (type I error rate of 3%). An AMD candidate SNP analysis revealed that these filters reduced power by 12.5%. Finally, an independent Rheumatoid Arthritis (RA) dataset with a different known source of detectable batch effect confirmed our proposed filters were effective (reduced UGAs 89.8%).We recommend the following three-step combination of filters to reduce UGAs: 1) Use haplotypes to correct errors in genotypes, then remove associations no longer achieving genome-wide significance  clearly revealed a batch effect Fig. . SimilarN = 918 samples) and samples sequenced in years 2013 and 2014 as group 2 (N = 313 samples).This detectable batch effect could not solely be attributed to vendor, library preparation, sequencing chemistry, or size exclusion step . Removing smaller regions of the genome had only a modest effect on %1000 g and affected both groups similarly as the difference in %1000 g between the two groups remained between 3 and 4 %. Masking difficult regions had little influence on the GQ. There was some impact on median read depth – after filtering out GQ less than 90 the median read depth metric was significantly different between groups . Filtering did not impact the Ti/Tv ratio metrics in non-coding or coding regions. Differences between groups for the percent heterozygous metric improved after repeat masked regions were removed  but remained unchanged for all other filters. This analysis suggested that filtering variants based only on excluding difficult regions was not an effective strategy.To test the hypothesis that only particularly difficult-to-sequence regions of the genome were subject to batch effects, we computed our metrics after removing repeat-masked regions  (53.02% GC) was high for this study at 1.07 as was genomic inflation corrected for small sample size (λ1000) at 1.25  added to the above-described filters. Most of these annotations removed only a few sites after our proposed filters were applied  to 1.06  to 1.05  to 1.14  and post filtering was 1.01 (λ1000 = 1.02), reflecting a slight improvement in genomic inflation  and 250 controls from the same batch. These samples had the same vendor, chemistry, and were jointly genotyped in a single run. We verified the absence of a batch effect by performing a PCA on the quality metrics as described previously and saw no detectable batch effect as the samples were completely overlapping  and 678 controls with a detectable batch effect  and 2016 . Indeed, after performing PCA using our quality metrics as described above on these samples, we observed a detectable batch effect explained by chemistry  or VCF files  and compOur initial efforts to perform association analyses in the presence of batch effects revolved around masking difficult to sequence regions, however we found this approach ineffective. In our Batch GWAS we did not see enrichment for UGAs in the repeat regions. This observation led us to develop and validate site-specific filters that target UGAs that arise from batch effects. We pursued the differential GQ filter because we observed in multiple datasets a systematic shift in GQ when sequencing chemistries changed. The LD filter was effective because the factors that led to batch effects are largely expected to be independent of the local LD structure. Thus the genotypes at UGA variants were not compatible with the surrounding haplotypes and these genotypes were corrected. The GQ20M30 filter addressed a need for a minimal quality threshold on the site. While we explored increasing the stringency on this filter, we found 30% missingness to be a reasonable tradeoff between retaining sites and removing batch effects. Therefore we recommend, in addition to standard GWAS QC, the LD filter, differential GQ filter, and the GQ20M30 filter while bearing in mind that these filters will reduce power to detect confirmed associations. We have also found that these filters may not be effective in the case of a severe batch effect – in this instance it may be necessary to adapt a more stringent filter such as GQ20M05, which will result in further reduction of power.Our method to eliminate spurious calls can be applied when case and control status is completely confounded with batch. However, in this report we have focused on common variants. Effective strategies for rare variants still need to be addressed, though new algorithmic approaches are being developed . We descp-values than overall. This was reflected in the small gains in genomic inflation factors post filtering (eg in the Batch GWAS from 1.07 to 1.06) despite the large percent of UGAs filtered (97.2% in the Batch GWAS). We chose to focus on GWS unconfirmed associations since practically scientists want to prioritize these for further research and validation.A final limitation of our methodology is that we have focused mostly on filtering out GWS associations and therefore we were much more effective in filtering in the genome-wide significant range of We showed that the quality metrics we developed can determine whether a batch effect exists within a dataset and released software that allows researchers to quickly assess the quality of their sequencing data. After testing existing WGS filters, we recommended our filtering strategy which combines (1) an LD filter, (2) differential GQ filter, and (3) GQ20M30 filter. This combination of filters removed 97.2% of the unconfirmed genome-wide significant associations in the Batch GWAS and 89.8% in the RA Batch GWAS. An AMD GWAS with no batch effect featured a Type I error rate of 3% and an AMD candidate SNP analysis revealed a reduction in power of 12.5% as 2 out of 16 confirmed AMD associations were filtered.Batch effects in WGS data are not well understood and perhaps because of this, we were not able to find an existing method or develop a novel method that removed all sites impacted by batch effects without impacting the power to detect true associations. While we focused on creating targeted filters that removed a small percent of the genome, in practice these need to be used in conjunction with standard quality control measures (for example removing sites out of Hardy-Weinberg equilibrium), which can result in very stringent filtering. In the case of a severe batch effect, such as the chemistry change present in the RA Batch GWAS, more stringent filtering was necessary even after applying standard quality control and our proposed filters as almost 40,000 UGAs remained after filtering. In order to fully address batch effects, disentangling the impact of changes in sequencing chemistry and bioinformatics processing on association analysis will be necessary.Batch effects will arise as independent groups attempt to combine sequencing data generated and processed from different sources – this collaboration is necessary particularly to attain power to detect new disease-associated variants. Large-scale resources are spent by research, industry, and government organizations creating databases that cannot easily be merged. Our experiments and tools will help researchers integrate this rich mine of genetic data.Samples were collected under appropriate consent approved by the Western Institutional Review Board through multiple ongoing collaborations. For all samples DNA was extracted from whole blood. The size exclusion step was performed using gel or SPRI and library preparation methods varied between different Illumina techniques: PCR-based, PCR-free, and PCR-plus. Thus multiple parameters varied between years and vendors and no single parameter was found to correspond to the observed batch effect in our samples. Sequencing was conducted on Illumina X 10 and HiSeq machines between the years of 2010 through 2016 using Illumina, Beijing Genomics Institute (BGI), DeCODE, Broad Institute (Boston), and Human Longevity Inc. (HLI) as sequencing vendors . During variant calling GATK HaplotypeCaller performed a local de-novo assembly of the reads. Due to this, the effective read depth at the time of variant calling could be different than the read depth in the original alignments and the read depths in the original alignments were reported in the final VCF.All samples were processed using the same sequence alignment and variant calling pipeline. Short read data were aligned to GRCh38 using BWA-MEM  and the We developed a software package: genotypeeval freely available on Bioconductor as part of the R Project  to compuDifficult to sequence regions were assessed using the following annotation tracks: 1. repeat-masked regions , 2. low-The 19 known AMD SNPs sites from  were evap-value <1xE-5). Close relatives and individuals related to multiple individuals  were removed. Association analysis was performed using logistic regression of phenotype on additively coded genotypes, and the first five eigenvectors from PCA analysis [p-value <5xE-8 were considered genome-wide significant (GWS).PLINK 1.9  was usedanalysis  were incThe Batch GWAS analysis used 815 subjects in total (642 in Batch 1 and 173 in Batch 2). GWAS as outlined above was performed and any GWS association was considered an unconfirmed genome-wide significant association (UGA) – this assumption was made because of the relatively small sample size and because there were no known confirmed associations for the phenotypes included in the sample. We identified 1901 UGA SNPs and 5469 UGA indels for a total of 7370 UGAs.Genotype calls with genotype quality score computed by GATK HaplotypeCaller less than 20 were set to missing. With the GQ20Mx filter, sites with greater than x% missing genotype rate were filtered. For example, in the case of the GQ20M10 filter, sites with greater than 10% missing genotype rate were filtered.The jointly genotyped VCF file generated by GATK was analyzed using Beagle Version 4.1 , to obtap-value cutoff of 1E-4. Homozygous reference, heterozygote, and alternative genotypes were tested independently at a given site and the site filtered if any of the three tests were significant.Genotype qualities were dichotomized at GQ60. A chi square test with the variables batch  and dichotomized GQ60 was used to test for differential genotype quality with a 1 ~ Uniform) and group 2 genotype quality scores from a continuous uniform distribution with added normal noise  + Normal). Sigma was tested at 1, 5, and 10. Mu varied from 0 to 20 and sample size was tested at 250, 500, and 1000. The simulations were repeated 1000 times each.Simulations to assess power were performed by drawing group 1 genotype quality scores from a continuous uniform distribution Additional file 2: Supplemental Tables S1-S11.(PDF 112 kb)Additional file 3:Sample level summary statistics and annotations calculated by genotypeeval. (CSV 102 kb)