Life-history studies of wild bird populations often focus on the relationship between an individual’s condition and its capacity to mount an immune response, as measured by a commonly-employed assay of cutaneous immunity, the PHA skin test. In addition, haematocrit, the packed cell volume in relation to total blood volume, is often measured as an indicator of physiological performance. A multi-year study of a wild population of house wrens has recently revealed that those exhibiting the highest condition and strongest PHA responses as nestlings are most likely to be recruited to the breeding population and to breed through two years of age; in contrast, intermediate haematocrit values result in the highest recruitment to the population. Selection theory would predict, therefore, that most of the underlying genetic variation in these traits should be exhausted resulting in low heritability, although such traits may also exhibit low heritability because of increased residual variance. Here, we examine the genetic and environmental variation in condition, cutaneous immunity, and haematocrit using an animal model based on a pedigree of approximately 2,800 house wrens.Environmental effects played a paramount role in shaping the expression of the fitness-related traits measured in this wild population, but two of them, condition and haematocrit, retained significant heritable variation. Condition was also positively correlated with both the PHA response and haematocrit, but in the absence of any significant genetic correlations, it appears that this covariance arises through parallel effects of the environment acting on this suite of traits.The maintenance of genetic variation in different measures of condition appears to be a pervasive feature of wild bird populations, in contradiction of conventional selection theory. A major challenge in future studies will be to explain how such variation persists in the face of the directional selection acting on condition in house wrens and other species. An animal’s condition is defined by the pool of resources available to allocate to the production and maintenance of traits that enhance fitness, and reflects an individual’s ability to acquire food, avoid predators, and resist disease . Body coIn studies of wild bird populations, the condition of both nestlings and breeding adults is often measured in the context of understanding life-history variation . For exaIn addition to morphological measures of condition, haematocrit, the packed volume of red blood cells in relation to total blood volume, is often measured as a presumed physiological indicator of condition in field studies of wild birds ,19. BecaTroglodytes aedon, in north-central Illinois, USA [Over the past 10 years, we have routinely measured condition, PHA response, and haematocrit in a wild population of house wrens, ois, USA -26. A muois, USA . The relois, USA .If, as our data on recruitment and subsequent breeding success would suggest, condition, immune response to PHA injection, and haematocrit in this population are subject to strong natural selection, we would anticipate that most of the underlying genetic variation in these traits would be exhausted . HoweverHouse wrens are small (10–12 g), insectivorous songbirds that are sexually monomorphic in size and plumage. As obligate cavity-nesters, house wrens readily accept nestboxes within which to build their nests. Upon arrival at the study area in north-central Illinois after spring migration, females select a male that is defending a nest site and, after completing the nest, lay a clutch of 4–8 eggs. About half of the females that successfully rear their first brood produce a second ,34,35. ON =700) of uniform construction [N =301) were located in three semi-isolated neighbourhoods within the study area [We conducted this study during the 2004–2006 breeding seasons on the Mackinaw study site  in a second-growth, deciduous forest bordering the Mackinaw River. Nestboxes (truction  were spatruction ). All neudy area . All actWe visited all nestboxes every 1–3 days, noting their contents, the behaviours of any wrens present, and male identity as revealed by unique combinations of coloured, Darvic leg rings. We trapped and individually ringed any previously uncaught females when incubating or males when provisioning nestlings. To obtain DNA for paternity analyses, we collected a blood sample from adults upon capture and from all nestlings. On brood-day 11 or 12 (brood-day 0 is the day the first egg of a clutch hatches), we ringed nestlings with a numbered aluminium band, weighed them on an electronic balance  to the nearest 0.1 g, and measured their right tarsus length with dial callipers to the nearest 0.1 mm. At the same time, we obtained a blood sample (≈75 μL) from the brachial vein in heparinized microcapillary tubes that were stored on ice in coolers in the field. Blood was taken to the laboratory later the same day to be centrifuged at 1,610 g for 60 s  to separate cellular and plasma components. We measured haematocrit as the percentage of whole blood constituted by red blood cells, using the mean of three measurements. Red blood cells were stored in lysis buffer at 4°C and plasma at −20°C until further analysis.−1 [We induced a cutaneous immune response in nestlings on brood-day 11 by injecting 50 μL of sterile phosphate buffered saline (PBS) containing phytohaemagglutinin  at a concentration of 5 mg mL−1  into the−1 ; but see−1 . We used−1 .2, 1X PCR Buffer II (Applied Biosystems), and 0.133 μM forward and reverse primers. We used a thermal profile that followed the touchdown protocol described in Johnson et al. [We isolated DNA from blood samples using a high-salt extraction protocol following Bruford et al. . Polymern et al. . Forwardn et al. , Mcyμ4 [n et al. , and LTMn et al. . When mon et al. , were usAttendant females matched nestlings at all loci with rare exceptions  and were assumed to be the genetic parent in all cases. At some nests, we were unable to obtain blood samples from attending adults, in which case, maternity of the attending female was assumed because intraspecific brood parasitism does not occur in our population ; paterniBased on these methods, we were able to construct a pedigree comprised of 2,768 house wrens, in which we were able to identify both parents of 1,661 individuals and only the mother of an additional 552 individuals. The pedigree spanned three generations, and included 229 paternal and 265 maternal identities. Not all phenotypic traits were measured on all individuals; sample sizes and mean values (±SE) for all traits are shown in Table Sex was determined by amplifying sex-specific introns of the CHD-1 gene. Polymerase chain reactions (PCR) were carried out with sexing primers 1237L and 1272H  using a A) for each trait . Nest identity was included as an additional random effect to minimise the potential for upward bias in genetic variance from common environment effects. Nest effects are assumed to be normally distributed with a mean of zero and a variance to be estimated (Vnest). Similarly, we assume that residuals from the model are uncorrelated and normally distributed, with the residual variance (VR) arising from unmodelled environmental effects and/or measurement error. Fixed effects included year , and sex  as three-level factors for all traits. In addition, we included a cubic regression of tarsus length as a fixed effect in the model of mass. By correcting for the average mass-tarsus length relationship, variance components (and derived parameters) from our model of mass can be viewed as pertaining to body condition . For each trait we determined heritability (h2) as the ratio of VA to VP  and the corresponding proportion of variance explained by nest effects (denoted n2). To test the genetic basis of hypothesised relationships among traits, we then fitted a multivariate model of all traits simultaneously with fixed effects as described above but no random effects. Note that the cubic function of tarsus length was included as a fixed effect on mass only, not the other response variables. This yielded an estimate of the phenotypic variance-covariance matrix  that we denote P. We then added individual genetic merit and nest effects back into the model to partition P into additive genetic (G), nest (N) and residual (R) matrices. Statistical inference was based on likelihood-ratio tests (LRT) for random effects and Wald F-statistics for fixed effects. For LRT of single random-effect terms in the univariate models, we assume the test statistic to be asymptotically distributed as a 50:50 mix of χ20 and χ21 [Data were analysed using animal models fitted in ASReml (version 3). An animal model is a form of linear mixed-effect model in which an individual’s genetic merit is included as a random effect allowing estimation of the additive genetic (co)variance provided pedigree data are available ,50. We f and χ21 . For comFixed-effect estimates for nestling house wren traits are shown in Table Neither tarsus length nor PHA response were significantly heritable Table . There wP contained significant among-trait covariance . This result was driven by significant positive phenotypic correlations between nestling body condition and each of the other three traits, tarsus length, PHA response, and haematocrit  versus variance only matrix  LRT; χ26 = 22.4, P = 0.001) and R . Among broods , nestling body condition was positively associated with both tarsus length and haematocrit, but was not correlated with PHA response , and pairwise genetic correlations were characterised by large standard errors in all cases , for example, has shown that heritability can vary with the age at which traits are measured, and residual variation was generally much higher in traits expressed early in development  [The absence of significant genetic variation in tarsus length was surprising, given that the heritability of tarsus length in birds typically hovers around 0.5 . Howeveremblance , so our emblance . A long-stlings) .Consistent with a number of other studies employing the animal model ,11,29,30Falco tinnunculus) employing the animal model that revealed a surprising high heritability of PHA response in fledglings (h2 = 0.47-0.55) [PHA response was not significantly heritable, consistent with other studies employing the animal model to estimate additive genetic variance ,62, as w47-0.55) ; as with47-0.55) ,31,32, tNest effects accounted for over half the phenotypic variation in nestling PHA response, a result consistent with previous studies in this population showing that hatching date and time of injection can influence PHA response of nestling house wrens ,25,41,66Nest effects had a major influence on nestling haematocrit, consistent with studies of other wild bird populations ,20,21. ATaeniopygia guttata) based on a pedigree of similar size as the one considered here [P <0.001) as determined by the animal model.In addition to the strong environmental effect on nestling haematocrit, haematocrit was significantly heritable, in contrast to cross-fostering studies of other species ,21 that red here , reporterG estimates. Increased precision could be obtained through larger sample sizes and/or the use of cross-fostering designs that better facilitate statistical separation of additive genetic from nest effects [There was a significant positive phenotypic correlation, but no genetic correlation, between nestling haematocrit and condition. This suggests that the phenotypic correlation arises because of parallel effects of the common nest environment  on the two traits, and not because of any underlying pleiotropic effect. The absence of genetic correlations in this study should be regarded with some caution, however, as our power to detect such correlations given our sample sizes is probably limited. This is reflected in the large standard errors associated with our  effects . Both ne effects , and wou effects . Althoug effects . Indeed, effects .Environmental effects played a paramount role in shaping the expression of the fitness-related traits measured in this wild population of house wrens, but two of them, condition and haematocrit, retained significant heritable variation. It is becoming increasingly evident that the maintenance of genetic variation in condition measures, in particular, appears to be a pervasive feature of wild bird populations, in apparent contradiction of conventional selection theory ,30,74. Ahttp://doi.org/doi:10.5061/dryad.jk2m0).The data supporting the results of this article are available in the Dryad digital repository doi:10.5061/dryad.jk2m0  (http://


It is common for humans and model organisms to exhibit sexual dimorphism in a variety of complex traits. However, this phenomenon has rarely been explored in pigs.2 cross. Of the 213 traits examined, 102 differed significantly between the two sexes , which indicates that sex is an important factor that influences a broad range of traits in pigs. We compared the estimated heritability of these 213 traits between males and females. In particular, we found that traits related to meat quality and fatty acid composition were significantly different between the two sexes, which shows that genetic factors contribute to variation in sexual dimorphic traits. Next, we performed a genome-wide association study (GWAS) in males and females separately; this approach allowed us to identify 13.6% more significant trait-SNP (single nucleotide polymorphism) associations compared to the number of associations identified in a GWAS that included both males and females. By comparing the allelic effects of SNPs in the two sexes, we identified 43 significant sexually dimorphic SNPs that were associated with 22 traits; 41 of these 43 loci were autosomal. The most significant sexually dimorphic loci were found to be associated with muscle hue angle and Minolta a* values (which are parameters that reflect the redness of meat) and were located between 9.3 and 10.7 Mb on chromosome 6. A nearby gene i.e. NUDT7 that plays an important role in heme synthesis is a strong candidate gene.To investigate the genetic contribution to sexual dimorphism in complex traits in pigs, we conducted a sex-stratified analysis on 213 traits measured in 921 individuals produced by a White Duroc × Erhualian FThis study illustrates that sex is an important factor that influences phenotypic values and modifies the effects of the genetic variants that underlie complex traits in pigs; it also emphasizes the importance of stratifying by sex when performing GWAS.The online version of this article (doi:10.1186/s12711-014-0076-2) contains supplementary material, which is available to authorized users. Drosophila, many traits ranging from gene expression levels [Males and females differ in many aspects, from their chromosomes (X vs. Y) to their body morphologies to their reproductive behaviors. In model organisms such as mice and n levels  to embryn levels  have been levels  and asthn levels , also vaDrosophila , which 2 cross, measured a variety of traits and obtained 60 K genotypes from more than 1000 F2 individuals. Using these data, we performed GWAS to identify the loci associated with various traits [Genome-wide association studies (GWAS) have been used to identify genetic loci that affect complex traits in pigs, such as traits related to body composition , hematols traits ,14. Howe2 pig experimental population. Data were obtained from a total of 921 F2 individuals of both sexes. All the male pigs were castrated when they were 90 days old; therefore, the influence of sex hormones was limited in this study. First, we evaluated sex-specific differences in the 213 traits using a linear regression model. Next, by comparing the genome-wide SNP-trait association signatures found in males versus females, we identified loci that displayed sexually dimorphic expression; these loci were associated with a variety of traits related to meat quality, fatty acid composition, food intake, and hematological parameters. This study emphasizes that sex is an important factor that can modify the effects of the genetic variants underlying complex traits in pigs.In this study, we used a sex-stratified GWAS to examine sex-specific differences in gene regulation using 213 traits in individuals from our large F2 cross; the design and management of this experimental population have been described elsewhere [1 pigs. Then, nine F1 boars were crossed with 59 F1 sows to produce 1912 F2 pigs (in six batches). All the F2 animals were raised at the Jiangxi Agricultural University experimental farm ; they were fed ad libitum and weaned at the age of 46 days. The male pigs were castrated at the age of 90 days. The castrated males and the females lived in mixed pens and were treated equally throughout the experiment. All the F2 pigs were slaughtered at the age of 240 ± 3 days; overall, we measured more than 400 traits in the F2 population. We obtained 60 K SNP genotypes from 1000 F2 pigs using porcine SNP60 Beadchips (Illumina). The procedures used to phenotype the pigs and obtain the 60 K genotypes have been described in previous publications [All the pigs used in this study were the product of a large-scale White Duroc × Erhualian Flsewhere . Brieflyications -17. All 2 pigs produced by a large-scale White Duroc × Chinese Erhualian F2 cross. These traits are related to a broad range of characteristics, including growth, fatness, meat quality, hematological parameters, and serum glucose and lipid levels  the phenotypic values of any traits that strongly deviated from normality . We assessed the significance of the effects of sex on the 213 traits examined using the lm function in R [polygenic function. The associations between the SNP genotypes and the traits were evaluated using mixed linear models (mmscore function). As in the previously described GWAS, sex and batch were fitted as fixed effects. We assessed the empirical significance of the difference in heritability between the two sexes using a permutation test [P values of the associations yielded by the GWAS after applying a Bonferroni correction. The suggestive significance level was set to 1/Nsnp, where Nsnp was the number of SNPs tested in the GWAS; this threshold allowed one false positive association per genome scan. The genome-wide significance level was set to 0.05/Nsnp.We log-transformed  and se are the standard errors of those SNP effects. Given the null hypothesis that SNP effects in males and female were equal, the z-statistic has a standard normal distribution. Single-tailed tests were used to evaluate the significance of any sex-specific difference. The significance level was set using the Bonferroni approach described above. To avoid spurious signals caused by low minor allele frequencies (MAF) in either sex, we exclude SNPs with MAF values of less than 5% .To identify genetic variants associated with strong sexual dimorphism, we tested for sex-specific differences in SNP effects using an approach similar to the one described in . Briefly−4; these traits were related to fatness, fatty acid composition, growth and meat pH values ; on average, males had 72 g more abdominal fat than did females  . P = 3.94 × 10−27) . For instance, meat quality, forward gait score at 223 days old, platelet distribution width and serum glucose levels were more heritable in males than in females  sexually dimorphic loci  were detected for hue angle (H*) value at 45 minutes post-slaughter  associated with the 213 traits. Of these SNPs, 70.5% were detected by both approaches. Notably, 2035 significant SNPs were detected only by the sex-stratified GWAS; this corresponds to 13.6% of the loci detected by the combined GWAS . This finding indicates that most of these new SNPs have sex-specific effects. Therefore, their signatures were weakened in the combined GWAS.We compared the significant signals obtained in the sex-stratified GWAS with the significant signals obtained in the combined GWAS. Between the sex-stratified GWAS and the combined GWAS, we identified a total of 16 996 significant SNPs . These experimental features have largely reduced the level of environmental noise and increased the ability to detect sexually dimorphic loci. Indeed, we were able to identify many extremely significant loci associated with traits related to meat quality [To study sex-specific genetic effects on complex traits in pigs, we conducted a sex-stratified GWAS using 921  F quality , fatty a quality  and bloo quality , which sSex hormones are important factors that mediate sexually dimorphic gene expression in mice . It shou−4); for instance, we found that the castrated males studied in our study tended to accumulate more abdominal fat, leaf fat and intramuscular fat and that they had higher levels of saturated fatty acids in their fat and muscle tissue than females. From the standpoint of food quality, this result indicates that pork from castrated male pigs might taste better since IMF content is related to meat juiciness and flavor. In contrast, females grew faster during the early stages of life (from 21 to 46 days) and had higher levels of unsaturated fatty acids in their tissues; this result indicates that pork from female pigs might be healthier since foods containing a higher proportion of unsaturated fatty acids are better for human health [Of the 213 traits examined, 102 demonstrated significant sex-specific differences. Some of these traits were related to growth, fatness, meat quality, serum lipid levels and hematological parameters. These results indicate that sex is an important factor that affects the phenotypic values of various traits in pigs, even in the absence of the influence of sex hormones. Some traits showed a highly significant degree of sexual dimorphism , the sex-stratified GWAS identified 13.6% more loci associated with the traits. This percentage of novel loci identified by the sex-stratified GWAS is similar to what has been found in studies of gene expression traits in humans . This relongissimus muscle in pigs can be explained by pigment content and myoglobin forms [nudix (nucleoside diphosphate linked moiety X)-type motif 7 gene (NUDT7), located at 10.1 Mb on chromosome 6 is of particular interest. This gene plays an important role in heme biosynthesis, and overexpression of NUDT7 has been associated with reduced heme biosynthesis in pig skeletal muscle [NUDT7 in female offspring can be induced by high levels of maternal folic acid during gestation [To identify sexually dimorphic loci associated with the traits being examined, we scanned the whole genome to compare the effects of SNPs in the two sexes. The most significant sexually dimorphic loci were found between 9.3 and 10.7 Mb on chromosome 6; they were associated with hue angle and Minolta a* values at 45 min post slaughter, which are color parameters that reflect meat redness. Meat color is an important feature for consumers since it reflects meat quality and freshness. It has been reported that ~90% of the variance in the color parameters of in forms . Moreovein forms . Presumal muscle . In miceestation , which iWe found that 41 (95.3%) of the 43 significant sexually dimorphic loci were autosomal. This result is not unexpected since many health-related traits, such as fat distribution in humans, have also been observed to be associated with sexually dimorphic loci located on autosomes . The mol−4) were associated with significant sexually dimorphic loci. These results suggest that even if phenotypic values differ significantly between the two sexes, it does not necessarily mean that strong sex-specific genetic controls are in place, and vice versa. Similar results have been reported for gene expression traits in humans [We checked whether the 22 traits that were associated with significant sexually dimorphic loci demonstrated significantly different phenotypic values for males versus females. We found that 12 54.4%) of the 22 traits did not have sex-specific phenotypic values (q >0.05). However, not one of the top five traits i.e. abdominal fat weight; C16:1, C18:0 and C20:3 levels in abdominal fat; and the saturated fatty acid content of muscle that demonstrated sex-specific phenotypic values (q <104.4% of tIn this study, we looked for evidence of sexual dimorphism in the phenotypic values of and the genetic variants associated with 213 traits related to a broad range of pig traits. Some of these traits, namely those related to growth, fatness, meat quality and palatability, are of economic importance. The results of this study have improved our understanding of the relationship between sex and complex traits. About half of the 213 traits examined in this study showed significant sex-specific differences in their phenotypic values, which demonstrates that sex is an important factor that influences the phenotypic values of quantitative traits in pigs even when the influence exerted by sex hormones is limited. The sex-stratified GWAS revealed the existence of a landscape of sex-specific genetic effects on a variety of pig traits and demonstrated that sex-specific regulation is an important aspect of the genetic architecture that underlies complex traits. Our results suggest that future GWAS should stratify their analyses by sex or incorporate a sex-by-genotype interaction to identify sexually dimorphic loci associated with complex traits in pigs and other organisms.


To date, numerous genetic variants have been identified as associated with diverse phenotypic traits. However, identified associations generally explain only a small proportion of trait heritability and the predictive power of models incorporating only known-associated variants has been small. Multiple regression is a popular framework in which to consider the joint effect of many genetic variants simultaneously. Ordinary multiple regression is seldom appropriate in the context of genetic data, due to the high dimensionality of the data and the correlation structure among the predictors. There has been a resurgence of interest in the use of penalised regression techniques to circumvent these difficulties. In this paper, we focus on ridge regression, a penalised regression approach that has been shown to offer good performance in multivariate prediction problems. One challenge in the application of ridge regression is the choice of the ridge parameter that controls the amount of shrinkage of the regression coefficients. We present a method to determine the ridge parameter based on the data, with the aim of good performance in high-dimensional prediction problems. We establish a theoretical justification for our approach, and demonstrate its performance on simulated genetic data and on a real data example. Fitting a ridge regression model to hundreds of thousands to millions of genetic variants simultaneously presents computational challenges. We have developed an R package, ridge, which addresses these issues. Ridge implements the automatic choice of ridge parameter presented in this paper, and is freely available from CRAN. In modern biomedical studies, technological developments mean that data sets are now much larger than those considered in the past. The analysis of these high-dimensional data sets presents computational and statistical challenges. Specifically in genetics, genome-wide single nucleotide polymorphism (SNP) genotyping simultaneously types up to one million SNPs, and imputation can increase SNP density further. In the related fields of metabolomics and proteomics, information is captured about hundreds of thousands or millions of variables.In this paper, we are interested in the use of genetic information to predict either a continuous or a binary phenotype. Both of these outcome types arise in clinical settings. In the latter case, we consider both the problem of classification and the estimation of the probability of each of the two possible outcomes. We evaluate our method using SNP genotype data; however, the methods discussed here could usefully be applied to any high-dimensional regression setting.Recent developments and improvements in genotyping technology have led to an increase in the availability of genetic information. As genotyping becomes easier and cheaper, there is a growing interest in the use of genetic information to predict future phenotypes, and in particular in the prediction of disease phenotypes. The aim of genetic prediction is to construct a model to predict the phenotype of interest using the genotype data from individuals for whom the  phenotypic state is unknown. In a clinical setting, the value of this approach arises because genotypes are fixed at birth, but disease symptoms may not become apparent until later in life. If individuals who are at high genetic risk could be identified before symptoms become apparent, targeted interventions could be used with the aim of delaying or preventing the onset of disease. Drug response is known to be genetically heterogeneous . All other SNPs were given an effect size of 0. Thus the vector of effect sizes, Continuous outcomes analysed using linear regression. Two hundreds SNPs with MAF 10–15% were randomly selected to be causal SNPs; these causal SNPs were assigned an effect size drawn from a uniform distribution U. Case-control outcomes were generated by randomly selecting two haplotypes that were summed to generate a simulated genotype. An additive genetic model was assumed. Following such a model, the probability of an individual with that genotype being a case was generated as Binary outcomes analysed using the logistic model. Two hundreds SNPs with MAF 10–15% were randomly selected to be causal SNPs; these causal SNPs were assigned an effect size, or log odds ratio, drawn from a uniform distribution In both the simulation studies and the evaluation of the method using real data that follows, training genotypes were standardised to correlation form, and in the continuous case the responses were centred, prior to model fitting, as described in methods section. Coefficients were returned to the scale of the original data when the model was evaluated on test data.We compared the performance of RR using a priori, we evaluated the predictive performance when a range of proportions of predictors were included in the multiple regression.We used traditional multiple linear or logistic regression, which requires that a subset of SNPs be selected for inclusion in the model. To select which SNPs to include, univariate linear or logistic regression was used to estimate the strength of association of each predictor variable with the outcome. A proportion of the predictor variables that were most strongly associated with the outcome in univariate tests were included in a multiple regression model. When predictors were exactly correlated and both predictors cannot be included in a multiple regression model simultaneously, only the first predictor by SNP position was included. Because in real applications the number of causal variables is not known RR with the ridge parameter chosen using 10-fold cross-validation (RR-CV).HyperLasso regression  is a peElastic net  is a peResults are presented in Tablehttp://cran. r- project. org/web/packages/ridge). This package addresses the computational challenges that arise when fitting RR models to high-dimensional data such as genome-wide SNP data. For data sets that are too large to read into R, code written in C is provided and the corresponding R functions take file names as arguments. This circumvents the need to read large data sets into the R workspace whilst retaining a user-friendly interface to the code.We have developed an R package, ridge, which implements our method . BD is a complex neurobehavioural phenotype, characterised by episodes of mania and depression. The lifetime prevalence of BD is estimated to be in the region of 1%, and the heritability of BD has been estimated to be as high as 85%  was useHaving obtained directly typed and imputed SNPs such that we had the same SNPs in the two data sets, predictive models were fitted. In these data with a binary outcome, the logistic model was used to describe the relationship between genotypes and disease status.P-value cutoff) for a variable to be included. We evaluated predictive performance at a range of P-value thresholds. The number of SNPs that reached each significance threshold investigated is presented in TableP-values in our results revealed that these are not the top hits reported by the WTCCC [WTCCC, In performing variable selection followed by multiple regression, instead of including a pre-defined proportion of all predictor variables in the multiple regression model, we chose a significance threshold . However, this comes at a cost of a larger mean classification error, and as explained above, discarding many SNPs corresponds to discarding the information in those SNPs.Regression coefficients were estimated using RR with shrinkage parameter In Supplementary We have introduced a semi-automatic method to guide the choice of shrinkage parameter in ridge regression. Our method is particularly useful when the regression problem comprises more predictor variables than observations, a situation that often arises in genetic data. This is because existing ridge estimators such as that proposed by Hoerl et al.  cannot bDisease risk prediction using genetic information remains a challenging problem due to the high dimensionality and correlation structure of the data. RR is a technique that addresses these difficulties and has been shown to offer good predictive performance. As we demonstrated using a real data example, our method can feasibly be applied to genome-wide SNP data, and this is the setting in which we envisage it will be used. Although whole-genome sequencing of patients is not yet routine in the clinic, SNP genotyping is rapidly decreasing in cost and could feasibly be applied were prediction models available. Among penalised regression methods, RR has been shown to offer good predictive performance. Our method facilitates the application of RR to genetic data for the construction of prediction models.Our method has computational and practical advantages over competing methods. Because the choice of shrinkage parameter is semi-automatic, our method does not require computationally intensive CV. Nor is determination of causal variables necessary, as is the case when selecting predictor variables to include in a multivariate model fitted using OLSR. Using training data to build a model that is subsequently used to predict on new data relies on the obvious assumption that the two samples are drawn from the same population. The two samples must have in common both the disease phenotype being studied and the genetic architecture in terms of number of causal variables and their effect sizes. This is true of any prediction method and is not a problem limited to our approach. Prediction performance will degrade when this assumption is not met, but it can be a difficult assumption to verify, particularly for disease phenotypes with heterogeneous etiology.One challenge in the development of prediction models based on case-control data, as in our real data example, is that case-control samples contain an overrepresentation of cases compared to the number in the general population. Hence, population disease prevalence is implicitly overestimated in the fitted model. Methods have been proposed in the literature to adjust the baseline disease risk in the fitted model based on estimates of disease prevalence in the population. Proposed methods include intercept adjusted maximum likelihood estimation [Anderson, Using simulation studies, we demonstrate that our method outperforms competing penalised regression methods in terms of prediction error when data comprise more predictors than observations and there are many causal variables with small effects, a situation that is representative of genetic data. We demonstrate the good predictive performance our method by using data from two genome-wide association studies to construct and evaluate a prediction model.Because RR is a regression approach, the method can be extended to include additional, non-genetic covariates. For example, clinical information or PCs to correct for population structure could be included. It would be possible to extend the approach to investigate higher order interaction terms, albeit at an increased computational cost. Given the large number of predictor variables in a GWAS, it may be necessary to perform a variable selection step before investigating interaction effects.The authors have no conflict of interest to declare.


Littorina saxatilis, which has repeatedly evolved coexisting ecotypes adapted to either crab predation or wave action. We sequenced the transcriptome of snails of both ecotypes from three distant geographical locations  and mapped the reads to the L. saxatilis reference genome. We identified genomic regions potentially under divergent selection between ecotypes within each country, using an outlier approach based on FST values calculated per locus. In line with previous studies indicating that gene reuse is generally common, we expected to find extensive sharing of outlier loci due to recent shared ancestry and gene flow between at least two of the locations in our study system. Contrary to our expectations, we found that most outliers were country specific, suggesting that much of the genetic basis of divergence is not shared among locations. However, we did find that more outliers were shared than expected by chance and that differentiation of shared outliers is often generated by the same SNPs. We discuss two mechanisms potentially explaining the limited amount of sharing we observed. First, a polygenic basis of divergent traits might allow for multiple distinct molecular mechanisms generating the same phenotypic patterns. Second, additional, location-specific axes of selection that we did not focus on in this study may produce distinct patterns of genetic divergence within each site.Parallel patterns of adaptive divergence and speciation are cited as powerful evidence for the role of selection driving these processes. However, it is often not clear whether parallel phenotypic divergence is underlain by parallel genetic changes. Here, we asked about the genetic basis of parallel divergence in the marine snail  Both theoretical and empirical work show that strong divergent selection may facilitate adaptive divergence and speciation even in the face of gene flow . ParalleParallel phenotypic divergence, however, does not allow for inferences about the degree of genetic parallelism , even if divergence is relatively recent  and a southern European one that is more genetically distinct (Spain) , which lays eggs .Barcoded RNAseq libraries for the twelve pools were prepared using Illumina TruSeq RNA Sample Prep Kit v2 using 1 μg of total RNA input and 10 PCR cycles, as per the manufacturer's recommendations. They were sequenced in a single lane, using an Illumina HiSeq 2000 machine , at Edinburgh Genomics, Edinburgh, UK. Raw reads were filtered as described in Appendix S1 (Supporting information). A total of 148.7 L. saxatilis is available . We mapped the RNAseq reads to this reference genome using the mapper gsnap . Using these criteria, for a maximum of 8% mismatches, between 60% and 68% of the reads for each sample were retained . There was no pronounced difference between countries with regard to the average proportion of reads in a sample that could be mapped .We only kept reads that mapped to a single location in the genome and to the same reference contig as their paired-end read  and unlikely to contain more than one gene, probably representing one or a few exons. Differentiation between ecotypes should be increased not only at SNPs targeted directly by selection, but also at closely linked SNPs, because selection locally reduces effective gene flow between ecotypes .Because RNAseq data sets are characterized by a large variation in coverage depth across loci, the data were subsampled to an even coverage of 20 per sample  using the subsampling with replacement strategy in FST between ecotypes (crab 1–wave 1 and crab 2–wave 2) for each country. We also calculated FST within ecotypes (crab 1–crab 2 and wave 1–wave 2), as well as FST between countries. We are aware that popoolation2 is not intended for FST calculation from transcriptome data, where individual contribution to the pools may vary due to expression differences; however, our samples contained large numbers of individuals, and the replicates produced generally similar results (see below), supporting the validity of our approach.As we had two ‘crab’ and two ‘wave’ pools per location, we obtained two estimates of FST between ecotypes within each country. The distinction between ‘outliers’ and ‘nonoutliers’ is arbitrary, and the models underlying classical genome scan methods may be violated by demographic history and the ubiquity of purifying selection in the transcriptome causing variation in effective mutation rate .For the contigs that passed filters, our first aim was to identify those with exceptionally high ion rate . We therFST between ecotypes might rank more similarly in different locations than expected by chance. In theory, this could generate shared ‘outliers’ even without the effect of selection. We have analysed this effect in more detail and conclude that it is not likely to have a major impact on our results .Outliers shared between countries (‘shared outliers’) were those contigs that were identified as outliers in both focal countries for a given threshold. Some extent of sharing would be expected even if outliers were drawn randomly from the total set of contigs. Therefore, we also determined the expected number of shared loci if sharing was due to chance only, as well as its 95% confidence limits, using a hypergeometric distribution function . If the same SNP alleles are associated with the ‘crab’ ecotype in both countries, there should be a positive correlation. If SNP alleles are associated with the ‘crab’ ecotype in one, but the ‘wave’ ecotype in the other country, there should be a negative correlation. If different SNPs are associated with the divergence in the two countries, the correlation should on average be zero. Note, however, that positive and negative correlations will emerge at random if different SNPs are involved in the differentiation; therefore, it is only meaningful to study overall patterns instead of results for individual contigs. Significance was tested by bootstrap, analysing whether the mean Pearson's correlation coefficient of outlier contigs differed from that of all contigs . We also grouped loci into bins based on their correlation between countries and performed a chi-square test asking whether the distribution across bins was different from that observed for all loci .FST calculations and downstream analyses varied according to the stringency of settings during bioinformatic analyses . In addition, mean FST values increased with increasing minor allele count used for SNP identification, probably because SNPs with very small minor allele counts can necessarily only produce small FST values . For quantiles between 94% and 98%, more than 60% of outliers were consistently shared between replicate sample pairs, while the expectation for sharing due to chance is substantially lower (<10%) and decreases with increasing quantiles  should only have shared ‘outliers’ by chance. As shown in Fig. FST quantile above which a contig was considered an ‘outlier’ was arbitrary, we focused on contigs with FST above the 94% quantile, based on visual inspection of the plots .The average correlation of allele frequency differences per contig between countries was close to zero if all contigs were considered Fig. A. This iP < 0.01). In contrast, in the Spain–UK comparison, most contigs showed a negative correlation , suggesting that the same SNPs have diverged in different directions.However, shared outlier contigs showed a pattern deviating from the rest of the contigs. For the Spain–Sweden and the Sweden–UK comparison, positive correlations were more common than negative ones Fig. B, indicaLittorina saxatilis in the UK  should ensure that a wide variety of functions is covered. There may be a bias towards higher expression levels, as low-expression transcripts may often be missed in RNAseq unless sequencing coverage is very deep. These low-expression transcripts might include genes that contribute to divergence through differential expression . While we included various tissues from embryos as well as adult individuals, genes predominantly expressed in adult males might be absent and could be interesting for future studies. Our filtering may also have excluded genes from rapidly expanding gene families. Reads associated with such genes may have been mapped to multiple paralogues, especially given that we had to allow for a large number of mismatches (because of the high diversity in our study species), leading to their exclusion from the data set.A minor drawback of analysing sequence variation in transcriptome data is that some loci may show allele-specific differences in gene expression . If thesWe found genetic differentiation between countries and ecotypes consistent with expectations from previous studies  that are not captured by the outlier method used , there may be multiple different pathways towards similar phenotypic outcomes. Even though adaptation utilizing standing genetic variation may be expected , these might still be associated with the same functional pathways (While we focus on general patterns of parallelism here, our analysis has revealed many loci potentially under selection, some in the same direction in more than one region, some in opposing directions and some in only one location. It will be interesting to follow up individual outlier loci in more detail. A limitation for our study is that the With a more advanced genome assembly, it will also be possible to understand better the genomic architecture of outlier loci; and further studies, for example using mapping approaches in natural hybrid populations (


P=1.5 × 10−15) spanning 4 Mb and containing at least 36 genes. Other associated loci included interesting candidate genes on chromosome 1 (GRIK3 (glutamate receptor ionotropic kainate 3)), chromosome 4 (KLHL2 (Kelch-like protein 2)), chromosome 17 (CRHR1 (corticotropin-releasing hormone receptor 1) and MAPT (microtubule-associated protein Tau)) and on chromosome 18 (CELF4 (CUGBP elav-like family member 4)). We found no evidence for genetic differences in the common allelic architecture of neuroticism by sex. By comparing our findings with those of the Psychiatric Genetics Consortia, we identified a strong genetic correlation between neuroticism and MDD and a less strong but significant genetic correlation with schizophrenia, although not with bipolar disorder. Polygenic risk scores derived from the primary UK Biobank sample captured ∼1% of the variance in neuroticism in the GS:SFHS and QIMR samples, although most of the genome-wide significant alleles identified within a UK Biobank-only GWAS of neuroticism were not independently replicated within these cohorts. The identification of nine novel neuroticism-associated loci will drive forward future work on the neurobiology of neuroticism and related phenotypes.Neuroticism is a personality trait of fundamental importance for psychological well-being and public health. It is strongly associated with major depressive disorder (MDD) and several other psychiatric conditions. Although neuroticism is heritable, attempts to identify the alleles involved in previous studies have been limited by relatively small sample sizes. Here we report a combined meta-analysis of genome-wide association study (GWAS) of neuroticism that includes 91 370 participants from the UK Biobank cohort, 6659 participants from the Generation Scotland: Scottish Family Health Study (GS:SFHS) and 8687 participants from a QIMR  Berghofer Medical Research Institute (QIMR) cohort. All participants were assessed using the same neuroticism instrument, the Eysenck Personality Questionnaire-Revised (EPQ-R-S) Short Form's Neuroticism scale. We found a single-nucleotide polymorphism-based heritability estimate for neuroticism of ∼15% (s.e.=0.7%). Meta-analysis identified nine novel loci associated with neuroticism. The strongest evidence for association was at a locus on chromosome 8 ( Although the biological underpinnings of personality traits are not understood, genetic factors are clearly involved. Twin studies suggest that ∼40% of the trait variance for neuroticism is heritable,17 of which between 15 and 37% is explained by variation in common single-nucleotide polymorphisms (SNPs)18 and is potentially detectable using the genome-wide association study (GWAS) paradigm. The clear links between neuroticism, psychopathology and other adverse health outcomes—and the implications for global health that would result from a better understanding of its mechanisms19—provide a strong rationale for large-scale GWAS to identify its genetic architecture (and genetic aetiology).EysenckMAGI1 on chromosome 3 (P=2.38 × 10−8).18 Within two of the cohorts in this GPC study, common genetic variants explained ∼15% of the variance in neuroticism.18To date, individual GWASs of neuroticism have been limited by modest sample sizes and have delivered equivocal findings. Large meta-analyses of GWASs have also delivered modest findings. The recent Genetics of Personality Consortium (GPC) meta-analysis of neuroticism, which included 73 447 individuals from 29 discovery cohorts plus a replication cohort, identified only one genome-wide significant associated locus, at 20 and the QIMR  Berghofer Medical Research Institute Study in Adults (QIMR) cohort.14 The UK Biobank is the largest single GWAS sample of neuroticism to date and probably the most homogeneous in terms of ascertainment strategy and assessment methodology. In addition, we evaluated the genetic relationship between neuroticism and three major psychiatric phenotypes for which there are large, publically accessible GWAS data sets: MDD, schizophrenia and bipolar disorder (BD). Finally, we have compared our findings with those from the GPC meta-analytic GWAS of neuroticism,18 as well as the CONVERGE consortium for MDD.21In our study, seeking additional associated loci, we have conducted a meta-analysis that included GWAS results from the UK Biobank cohort, the Generation Scotland: Scottish Family Health Study (GS:SFHS) cohort22 The aim of UK Biobank aim is to study the genetic, environmental, medication and lifestyle factors that cause or prevent disease in middle and older age. Recruitment occurred over a 4-year period from 2006 to 2010. Baseline assessments included social, cognitive, personality (the trait of neuroticism), lifestyle and physical health measures. For the present study, we used the first genetic data release (June 2015) based on approximately one-third of UK Biobank participants. Aiming to maximise homogeneity, we restricted the sample to those who reported being of white UK ancestry and for whom neuroticism phenotype data were available (n=91 370).UK Biobank is a large prospective cohort of more than 502 000 residents of the United Kingdom, aged between 40 and 69 years.20 and QIMR cohorts14 to conduct a meta-analysis based on samples for which we could readily access individual genotypes and which were assessed using the same measure of neuroticism. The GS:SFHS sample comprised 7196 individuals and the QIMR sample comprised 8687 individuals. Individuals (n=537) who had participated in both UK Biobank and GS:SFHS were removed from the GS:SFHS sample based on relatedness checking using the genetic data.We also made use of data provided by investigators from the GS:SFHSNote that we were unable to incorporate the published data from the GPC as the neuroticism measure used in that study was derived from an item response theory analysis . In addition, there was no information on the sample size for each SNP  and the majority of participants in the QIMR cohort were included within the GPC meta-analysis.This study obtained informed consent from all participants and was conducted under generic approval from the National Health Service (NHS) National Research Ethics Service  and under UK Biobank approvals for application 6553 ‘Genome-wide association studies of mental health'  and 4844 ‘Stratifying Resilience and Depression Longitudinally' .23  using the 12 items of the neuroticism scale from the Eysenck Personality Questionnaire-Revised Short Form (EPQ-R-S)23 . Respond26 Briefly, after removing genotyped SNPs that were outliers or were multiallelic or of low frequency  <1%), phasing was performed using a modified version of SHAPEIT2 and imputation was carried out using IMPUTE2 algorithms, as implemented in a C++ platform for computational efficiency.28 Imputation was based upon a merged reference panel of 87 696  888 biallelic variants on 12 570 haplotypes constituted from the 1000 Genomes Phase 3 and UK10K haplotype panels.29 Variants with MAF <0.001% were excluded from the imputed marker set. Stringent quality control before release was applied by the Wellcome Trust Centre for Human Genetics, as described in UK Biobank documentation.30In June 2015, UK Biobank released the first set of genotype data for 152 729 UK Biobank participants. Approximately 67% of this sample was genotyped using the Affymetrix UK Biobank Axiom array  and the remaining 33% were genotyped using the Affymetrix UK BiLEVE Axiom array. These arrays have over 95% content in common. Only autosomal data were available under the current data release. Data were pre-imputed by UK Biobank as fully described in the UK Biobank interim release documentation.P<10−6, with MAF <0.01, with imputation quality score <0.4 and with data on <95% of the sample after excluding genotype calls made with <90% posterior probability, after which 8 268 322 variants were retained.Before all analyses, further quality control measures were applied. Individuals were removed based on UK Biobank genomic analysis exclusions (Biobank Data Dictionary item #22010), relatedness , gender mismatch (#22001: genetic sex), ancestry  analysis identified probable Caucasians within those individuals who were self-identified as British and other individuals were removed from the analysis) and quality control failure in the UK BiLEVE study . A sample of 112 031 individuals remained for further analyses. Of these, 91 370 had neuroticism scores. Genotype data were further filtered by removal of SNPs with Hardy–Weinberg equilibrium N=47 196) and males (N=44174) using linear regression (as above), with age, array and the first 8 PCs as covariates.Association analysis was conducted using linear regression under a model of additive allelic effects with sex, age, array and the first 8 PCs (Biobank Data Dictionary items #22009.01 to #22009.08) as covariates. Genetic PCs were included to control for hidden population structure within the sample, and the first 8 PCs, out of 15 available in the Biobank, were selected after visual inspection of each pair of PCs, taking forward only those that resulted in multiple clusters of individuals after excluding individuals self-reporting as being of non-white British ancestry (Biobank Data Dictionary item #22006). The distribution of the neuroticism score was assessed for skewness and kurtosis  and found to be sufficiently ‘normal' (both coefficients are between −1 and 1) to permit analysis using linear regression. GWASs of neuroticism were additionally performed separately for females 32 to the summary statistics to estimate SNP heritability (h2SNP) and to evaluate whether inflation in the test statistics is the result of polygenicity or of poor control of biases such as population stratification. Genetic correlations between neuroticism scores in the three cohorts  were tested, and genetic correlations between neuroticism, schizophrenia, BD and MDD were evaluated in the UK Biobank sample using LDSR,33 a process that corrects for potential sample overlap without relying on the availability of individual genotypes.32 For the psychiatric phenotypes, we used GWAS summary statistics provided by the Psychiatric Genomics Consortium (http://www.med.unc.edu/pgc/).36Univariate GCTA-GREML analyses were used to estimate the proportion of variance explained by all common SNPs for the neuroticism phenotype.N=8687 individuals), polygenic risk scores for neuroticism (PRS-N) based on the summary statistics from the UK Biobank GWAS were computed with PLINK 1.90 , for P-value thresholds (PT) 0.01, 0.05, 0.1, 0.5 and 1, following the procedure described by Wray et al.37 All subjects had GWAS data imputed to 1000G v.3 (http://csg.sph.umich.edu/abecasis/MaCH/download/). Only SNPs with a MAF ⩾0.01 and imputation quality r2 ⩾0.6 were used in the calculation of the PRS-N. Genotypes were LD pruned using clumping to obtain SNPs in approximate linkage equilibrium with an r2<0.1 within a 10 000 bp window. As QIMR participants were related, predictions were calculated using GCTA ,38 using the following linear mixed model: EPQ-N=intercept+β0 × covariates+β2 × g+e with g~N, where EPQ is neuroticism measured by EPQ (standardised sum score); covariates are age, sex, imputation chip, 10 genetic PCs and the standardised PRS ; e is error; and GRM is genetic relationship matrix. P-values were calculated using the t-statistic on the basis of the β and s.e. from the GCTA output. Variance explained by the PRS was calculated using: var(x) × b2/var(y), where x is the PRS, b is the estimate of the fixed effect from GCTA and y is the phenotype.In the QIMR sample . Linear regression models were used to examine the associations between the PRS-N and neuroticism score in GS, adjusting for age at measurement, sex and the first 10 genetic PCs to adjust for population stratification. The false discovery rate method was used to correct for multiple testing across the PRS-N at all five thresholds.40In the GS:SFHS sample, PRS-N based on the UK Biobank neuroticism GWAS results were created using PRSice from observed genotypes in 7196 individuals.http://www.sph.umich.edu/csg/abecasis/Metal). Data were available across all 3 studies for 7 207 648 of the original 8 268 322 variants from the UK Biobank analysis. The total sample size included in the meta-analysis was N=106 716 .Inverse variance-weighted meta-analysis of UK Biobank, GS:SFHS and QIMR results was performed, restricted to variants present in all three samples, using the METAL package . PC analysis of the 12 EPQ-R-S items showed that all items loaded highly on a single component, and the internal consistency (Cronbach's α) coefficient was 0.84 (N with data=401 695) gave very similar results , chromosome 3, chromosome 4, chromosome 8, chromosome 9 (two loci), chromosome 17 and chromosome 18  and b.GRIK3 ; this was negligible in the context of sample size (λGC1000=1.003) ), rather than inflation due to unconstrained population structure (LD regression intercept=0.982 (s.e. 0.014)). Estimates of heritability using GCTA were similar to those using LD score regression .Overall, the GWAS data showed modest deviation in the test statistics compared with the null (λ0=1.003) . LDSR32 P=1.02 × 10−15) at which there is an extensive LD block spanning 4 Mb (attributable to an inversion polymorphism that has suppressed recombination) containing at least 36 genes. Similar findings to those from the UK Biobank data set in a GWAS primarily assessing the genetics of well-being have also recently been posted in a non-peer-reviewed format.46We observed a total of 8 independent loci exhibiting genome-wide significant associations with neuroticism  with the47 potentially explaining the greater prevalence of depressive and anxiety disorders in women.48 This suggests the possibility of sex-related genetic heterogeneity. We therefore conducted secondary analyses looking for sex-specific neuroticism loci in women (N=47 196) and men (N=44 174) respectively. To minimise heterogeneity, this analysis was restricted to the UK Biobank samples. SNP heritability (measured by LDSR) for each sex was comparable ; male h2SNP=0.135 (s.e.=0.0237)), and was highly correlated between the sexes (genetic correlation=0.911 (s.e.=0.07); P=1.07 × 10−38) at a level that was not significantly different from 1 (P=0.21). In both sexes separately, the chromosome 8 locus was associated at genome-wide significance but no other single locus attained significance. Overall, we found no evidence for genetic differences in the common allelic architecture of neuroticism by sex.Neuroticism scores are in general higher in women than in men and it has been postulated that neuroticism may play a stronger aetiologic role in MDD in women than in men,P=3.31 × 10−19) and a smaller, but significant, correlation between neuroticism and schizophrenia  (P=0.15). Similar results based solely on the UK Biobank data set have been reported recently in a non-peer-reviewed format.49LDSR showed strong genetic correlation between neuroticism and MDD  . We founP=4.04 × 10−09); between UK Biobank and QIMR the genetic correlation was 0.74 ; and between GS:SFHS and QIMR the genetic correlation was 1.16 . Note that the true maximum for a genetic correlation is bounded by 1. That the LD score estimate is greater than this reflects the imprecision in the estimate as indicated by the large s.e., in the context of which we interpret this as evidence for high but imprecisely estimated genetic correlation between the two samples.The LDSR-calculated genetic correlation for neuroticism between the three samples was strong: between UK Biobank and GS:SFHS the genetic correlation was 0.91  did show a trend for association .In contrast to the finding of the GPC meta-analysis, we did not identify a genome-wide significant association close to SIRT1 gene  and the other in an intron of the LHPP gene .21 Neither of these index SNPs were associated with neuroticism within the UK Biobank sample .The recently published CONVERGE consortium study of Chinese women with recurrent and melancholic MDD identified two loci contributing to risk of MDD on chromosome 10: one near the n=73 447) identified only a single genome-wide significant locus.18The identification of nine independent loci showing genome-wide significant associations with neuroticism within our combined meta-analysis represents a significant advance. In contrast, a recent meta-analysis of neuroticism conducted by the GPC , whereas the GPC study assessed neuroticism scores using different instruments across cohorts, with an item response theory approach to harmonise scores.http://www.braineac.org/) brain expression quantitative trait locus resource. This identified ERI1 as the only protein coding gene within the locus whose expression was associated with the index SNP in brain, but only nominally so (P=0.019) and not at a level that would reliably point to this gene as likely explaining the association.The most significant associated locus on chromosome 8, which was independently associated at genome-wide significance for both men and women, spans a 4-Mb region of extended LD (the result of an inversion polymorphism) containing at least 36 genes . The extCRHR1, encoding corticotropin-releasing hormone receptor 1. In the presence of corticotropin-releasing hormone, CRHR1 triggers the downstream release of the stress response-regulating hormone cortisol. CRHR1 is therefore a key link in the hypothalamic–pituitary–adrenal pathway that mediates the body's response to stress and that is abnormal in severe depression.45CRHR1 per se has also been shown to be involved in anxiety-related behaviours in mice and has also been genetically associated with panic disorder in humans.50The locus on chromosome 17 , which includes the glutamatergic kainate receptor KLHL2 that encodes a BTB-Kelch-like protein. KLHL2 is an actin-binding protein and has also been reported to be part of a complex that ubiquitinates NPTXR, the neuronal pentraxin receptor,61 among other targets. Expression of KLHL2 has been reported to be enriched in brain, and it is localised to cytoplasm and processes of neurons and astrocytes, being found at sites of ruffles and other actin network-containing membrane outgrowths.63 The associated region at this locus is short (∼150 kb), and although several other genes lie within 500 kb of the peak association at this locus, none is as promising a candidate as KLHL2.On chromosome 4, rs62353264 lies a short distance upstream of PTPRD. This gene encodes a receptor-type protein tyrosine phosphatase known to be expressed in brain and with an organising role at a variety of synapses,64 including those that play a role in synaptic plasticity. PTPRD is also known to harbour variation associated with restless legs syndrome.65 This is a credible candidate but particular caution is required given the distance between the associated locus and this gene.The associated region in chromosome 9p23 at ∼11.2–11.7 Mb contains no protein-coding genes; the nearest gene on the telomeric side, with its 5′-end located ∼650 kb from the associated region, is 18 in the two homogeneous subsets of the data they tested, and considerably greater than some earlier reports of ∼6%.67 Despite differences in the distribution of neuroticism by sex, SNP-based heritability was similar for both men and women and the genetic correlation between sexes was not significantly different from 1, suggesting a similar common variant architecture for both, and that differences in trait scores between the sexes are likely to result from structural variants, rare alleles and/or environmental exposures.In addition to identifying genome-wide significant loci, our study contributes further to understanding the genetic architecture of neuroticism and its relationship to other disorders. Our SNP-based heritability estimate for neuroticism was ∼0.15, as estimated using GCTA, and only slightly lower using LDSR. This is consistent with the estimates reported by the GPCPT up to at least 0.5) contributed to the variance captured by the signal.PRS analysis of neuroticism within the GS:SFHS and QIMR samples supported the expected highly polygenic architecture of neuroticism; despite the large discovery UK Biobank sample—but consistent with the modest number of GWS findings identified in this large sample—extremely weakly associated alleles at relaxed association thresholds  initiative that seek to use fundamental and quantitative characteristics to investigate the aetiology of psychiatric disorders across traditional nosological boundaries in order to develop a more biologically informed system of psychiatric classification.70By comparing the overall association analysis results in our study with those from the Psychiatric Genomics Consortia, we identified a strong genetic correlation between neuroticism and MDD (0.64), and a weaker but still significant genetic correlation with schizophrenia (0.22), although not with BD. These findings are line with evidence suggesting that neuroticism and MDD—as well as, to a lesser extent, neuroticism and schizophrenia—share genetic risk factors in common.36 Given the high genetic correlation between neuroticism and MDD, combining the two data sets in a meta-analysis may be a plausible strategy to optimise the power of population samples in the search for a proportion of MDD loci, although noting that the two phenotypes are not perfectly genetically correlated. The MDD loci identified in a recent study of Chinese women with recurrent (N=5303) and melancholic (N=4509) MDD by the CONVERGE consortium21 did not overlap with any of the loci reported here; given the apparent modest power to detect genome-wide significant loci in our sample, population differences between the studies and substantial differences between the phenotypes, the absence of overlap does not provide any evidence against the validity of the CONVERGE study finding. Given that neuroticism is a personality trait established as phenotypically and genetically strongly associated with MDD, the identification of several new genome-wide significant loci for neuroticism represents an important potential entry point into the biology of MDD.Our findings are of interest in the context of the limited success to date of GWAS studies of MDD. A recent mega-analysis of genome-wide association studies for MDD  failed to identify any genome-wide significant SNPs, suggesting that much larger samples are required to detect genetic effects for complex traits such as MDD.Overall, our findings confirm a polygenic basis for neuroticism and substantial shared genetic architecture between neuroticism and MDD, and to a lesser extent with schizophrenia, though not with BD disorder. The identification of nine new loci associated with neuroticism represents a significant advance in this field and will drive future work on the neurobiology of a personality trait that has fundamental importance to human health and well-being.


Systolic blood pressure and diastolic blood pressure are known risk factors for cardiovascular diseases and understanding their genetic basis will have important public health implications. For rare variants, it is extremely challenging to make statistical inference for single-maker tests. Therefore, joint analysis of a set of variants has been proposed. In this paper, we applied recently proposed methods "test for testing the effect of an optimally weighted combination of variants" and "variable weight-TOW" to determine genetic regions that are associated with blood pressure. Then least absolute shrinkage and selection operator, as well as sparse partial least square methods, were used to identify significant markers within a gene or in intergenic regions. We investigated the effect of rare variants and common variants, and their combined effect. It is well known that high blood pressure is an important risk factor for cardiovascular diseases. Elevated blood pressure is a complicated trait that affects more than 30% of the adult population ,2. An inIn this paper, we focused on identifying whether a gene is associated with blood pressure. We applied recently proposed tests called "test for testing the effect of an optimally weighted combination of variants (TOW)" and "variable weight-TOW (VW-TOW)"  to deterBoth the real and simulated data that were made available for Genetic Analysis Workshop 18 (GAW18) were used. We focused on the genotype data on chromosome 3 for unrelated individuals. The baseline data for the covariates and the phenotypes were considered. We considered the first time point of systolic blood pressure (SBP) and diastolic blood pressure (DBP) as the traits. We also used a composite of the 2 phenotypes called the mean arterial pressure, which is defined as (2/3)*DBP + (1/3)*SBP. For the genotype data, we mapped single-nucleotide polymorphisms (SNPs) to the genes; the remaining SNPs that do not belong to any genes, were grouped as intergenic regions. A total of 2286 regions (consisting of 1224 genes and 1062 intergenic regions) that include all the SNPs were defined. The regions were further divided into "rare" or "common" based on minor allele frequency (MAF) threshold of 0.01.ith individual, where p and M denote number of covariates and variants, respectively. The effects of the covariates on TOW and VW-TOW are recently proposed methods that allow covariates and account for direction effects for causal variants. Let andith individual respectively. Let p value of p values are evaluated by permutation.The methods are based on the optimal weighting scheme, which is defined as After identifying the significant genomic regions, we further investigated the SNPs that have important contribution to the phenotypes for the significant regions by variable selection methods Lasso and SPLS, which are available in the R package: "RV tests." Because this package does not allow covariates, we adjusted the effect of environmental factors using the linear model shown in equation (1). Instead of the observed trait, the residuals from the linear model are treated as the phenotype.http://hgdownload.cse.ucsc.edu/). Then the genes or intergenic regions were further divided into subregions ("rare" vs. "common") based on a threshold of MAF = 0.01.Step 1: Map the SNPs to gene and intergenic regions based on the annotation file refGene.txt.gz  and covariates (baseline measures) data for the unrelated individuals. Remove the participants that have missing variables in phenotype or covariates data.Step 3: TOW and VW-TOW are applied to identify the regions that are associated with the traits.Step 4: Apply Lasso and SPLS to the regions to discriminate the associated variants from noise (using the R package "RV tests").The sample used in our analysis is made up of 142 independent individuals. After removing missing variables, 129 subjects were analyzed. There are, in total, 1,215,296 markers on chromosome 3; approximately one-sixth of the markers were removed as a result of zero variation across the 129 independent samples.p value, based on 10,000 permutations for each region. Figure p value plot for DBP, where the x-axis denotes the position of the genes in original order on chromosome 3. The p values for intergenic regions are not included in Figure The association tests (TOW and VW-TOW) were applied to each genetic region for SBP, DBP, and mean arterial pressure (MAP) on chromosome 3. Both tests produce an empirical p values, regions that have strong association with the traits are picked according to the ranking of the p values. We decided to set the significant level threshold to be 0.001, so as to be more selective. Genes are only selected if they satisfy this criterion for the trait using both TOW and VW-TOW. Table After obtaining all the As mentioned earlier, there is a cluster of regions  level. Table In stage I, we focused on the top significant genes on chromosome 3, which are MAP4, FLNB, and ABTB1, with common and rare variants combined. We analyzed all 200 replicates with the target genes to assess the power of TOW and VW-TOW. MAP4 has large effect on both SBP and DBP, whereas FLNB and ABTB1 have small effects on SBP only. We adjusted the phenotypes by all the covariates at baseline. Table In stage II, we assessed the performance of Lasso and SPLS by analyzing all 200 replicates on MAP4 with all the variants. There are 6 target SNPs in MAP4, but 1 of the SNPs is removed because of monomorphism. The location numbers of the 5 SNPs are 48040283, 47957996, 47956424, 48040284, and 47913455. Both Lasso and SPLS are variable selection methods. With the careful selection of the penalty parameters for both methods, on average approximately 5 variants are selected with every replicate. Table Most recently proposed methods assign large weights to rare variants and small weights to common variants, resulting in low power. On the other hand, TOW and VW-TOW assign corresponding weight, which can account for the direction effect, to individual variants. The methods outperform some currently popular methods, such as Combined Multivariate and Collapsing (CMC) and sequence kernel association test (SKAT), in various scenarios . In addiOverall, we were able to detect some significant genes based on association tests (TOW and VW-TOW) with SBP, DBP, and MAP. Although we used Lasso and SPLS only as variant selection methods, they can also be used to do the association test for genotype with complex traits. However, both Lasso and SPLS are very computationally intensive. In addition, our analysis is focused on the independent subjects only, which limits our sample size. For future study, it is essential to incorporate family structure that not only increases the size of the sample available for analysis, but also the number of variants. Because SBP and DBP are correlated, it is deficient to analyze them separately. MAP, which is a combination of SBP and DBP, has better power than SBP and DBP separately.The authors declare that they have no competing interests.XFL conducted statistical analyses and drafted the manuscript. JB conceived the study and assisted in drafting the manuscript. All authors read and approved the final manuscript.


Artificial selection provides a powerful approach to study the genetics of adaptation. Using selective-sweep mapping, it is possible to identify genomic regions where allele-frequencies have diverged during selection. To avoid false positive signatures of selection, it is necessary to show that a sweep affects a selected trait before it can be considered adaptive. Here, we confirm candidate, genome-wide distributed selective sweeps originating from the standing genetic variation in a long-term selection experiment on high and low body weight of chickens.Using an intercross between the two divergent chicken lines, 16 adaptive selective sweeps were confirmed based on their association with the body weight at 56 days of age. Although individual additive effects were small, the fixation for alternative alleles across the loci contributed at least 40 % of the phenotypic difference for the selected trait between these lines. The sweeps contributed about half of the additive genetic variance present within and between the lines after 40 generations of selection, corresponding to a considerable portion of the additive genetic variance of the base population.Long-term, single-trait, bi-directional selection in the Virginia chicken lines has resulted in a gradual response to selection for extreme phenotypes without a drastic reduction in the genetic variation. We find that fixation of several standing genetic variants across a highly polygenic genetic architecture made a considerable contribution to long-term selection response. This provides new fundamental insights into the dynamics of standing genetic variation during long-term selection and adaptation.The online version of this article (doi:10.1186/s13059-015-0785-z) contains supplementary material, which is available to authorized users. Adaptation is a dynamic evolutionary process where populations improve their fitness by accumulating beneficial alleles at loci controlling adaptive phenotypes. The polymorphisms contributing to adaptation can either be present as standing genetic variation at the onset of selection or emerge through mutations. A longstanding challenge in quantitative and evolutionary genetics has been quantification of the relative contributions from standing and emerging variation to long-term selection response , 2. WhilSubjecting populations to artificial selection provides an accelerated evolutionary process that may result in extreme phenotypes with accompanying changes across the genome –4. UsingSelection on variants that have been present in a population for some time prior to the onset of selection can result in a different genomic footprint of selection than that of selection on new mutations. The term ‘soft’ sweep has been introduced to distinguish the patterns resulting from selection on standing genetic variation or recurrent new mutation from the ‘hard’ sweep patterns observed after selection on single new mutations –9. The pIn this study, we utilise an experimental population developed by bi-directional selection from a common, segregating founder-population to estimate the contribution of standing genetic variation to long-term selection-response. For several reasons, this population is highly useful for separating the contributions from standing genetic variation and new mutations even when molecular data on the base population is missing , 4. FirsThe base population for the Virginia lines was established in 1957 by intercrossing seven partially inbred lines of White Plymouth Rock chickens. The genetic variation entering the population thus represents a sample of the polymorphisms present when these partially inbred lines were founded. Since the founder population was established, the high (HWS) and low (LWS) body-weight lines have been bred with one new generation per year by single-trait, bi-directional selection for 56-day body weight –12. The 15 Advanced Intercross Line (AIL) for the selective-sweep regions, allowed us to estimate the contribution of these sweeps to the adaptive trait: 56-day body weight. We found that the standing genetic variation at a large number of loci in the base population made a major contribution to the selectable additive variance during adaptation. Further, the results also show that fixation across several loci has been an important dynamic property in the selection on the highly polygenic genetic architecture of the selected trait in the base-population. Together they suggest that fixation across many loci containing variants present in the base population has been an important contributor to the gradual, continued, long-term response to selection in the Virginia lines.This study describes a genome-wide approach to explore the contributions from a large number of selective-sweeps  to selec15 generation of the AIL. Thirty-eight of these regions were covered by a single marker and 61 regions by multiple markers. The physical length of the regions covered by multiple markers was in the range of 0.02 to 6.7 Mb and were distributed across most autosomes in the chicken genome  thresholds [15 AIL, 8 at a 5 % and 16 at a 20 % FDR. Thus, we confirmed that a large number of the candidate selective sweeps identified previously [To estimate how many of the independently segregating regions contributed to 56-day body weight in the AIL Fresholds , 14. Theeviously  were adaP) for the selected trait . This shows that the length of the identified selective sweeps is significantly associated with the effect of the genetic variant, or variants, located in that sweep.Larger selective sweeps are expected to contain polymorphisms with stronger effects because they are expected to be fixed more rapidly . We test15 population, none of the intercross chickens were HWS/HWS or LWS/LWS homozygous across all 16 regions. We therefore instead predict the joint contributions of the adaptive selective sweeps to the divergence between the lines based on the average allele-substitution effect for HWS alleles across the selective sweep regions shown to contribute to 56-day body weight. The average estimates for the sweeps that were significant at 5/20 % FDR were 15.7 ± 2.5 (P = 6.7 × 10−10) /16.9 ± 1.6 (P <1 × 10−16) g, respectively. The predicted total contribution of these regions to the 1,242 g line-difference between HWS40 and LWS40 . When we included the eight additional regions selected at a 20 % FDR, the estimate increased to 23.4 % of σ2P. Thus, the associated selective-sweep regions contributed to approximately half of the additive genetic variance in this population . Accordingly, the confirmed selective sweeps have been major contributors to the selectable additive genetic variance during this selection experiment. Further, this result also illustrates that fixation at these adaptive selective-sweep regions depleted about half of the total additive genetic variance for 56-day body weight during the first 40 generations of divergent selection.The next step was to estimate the contribution of the associated sweeps to the genetic variance in the F kinship , was h2BThe joint contribution by the 16 associated selective sweeps to the additive genetic variance in the base population is dependent on their allele frequencies at onset of selection. These frequencies are, however, unknown and cannot be empirically determined as no biological samples were collected from the Viriginia lines prior to generation 40. Here, we therefore estimate their contribution to the additive genetic variance in the base population based on theoretically assumed allele frequencies.15 generation, where the HWS alleles were present at frequencies were in the range of 0.2 to 0.8  the seven intercrossed lines contributed randomly fixed adaptive alleles from the White Plymouth Rock breed ; and (2) only one of the founder lines contributed the adaptive allele . Under the assumption of randomly distributed adaptive alleles across the founder lines, the contribution was estimated to fall between 75 % and 92 %, which was very similar to the estimate based on the AIL FThis study utilises our ability to differentiate the genomic patterns resulting from selection on standing genetic variation from those that emerge from selection on new variations –9. In ea15 individuals, it is likely that several loci with similar or smaller effects than those confirmed here contributed to the trait but remained undetected. Second, although the current study covered 99 candidate selective sweeps, it was only a sample of all candidate selective sweeps present in the Virginia lines. Accordingly, we would expect to have missed some soft sweeps in the earlier mapping study [15generation. Obtaining a complete understanding of the genetic architecture of selection response due to standing variation in the base population of the Virginia chicken lines will require additional candidate selective sweeps.In total, 8/16 candidate selective-sweep regions were associated with 56-day body weight at 5/20 % FDR. In the original selective-sweep mapping study , simulatng study  since itng study  were misP; Fig. 2P despite the average minor allele frequencies of the associated sweeps being rather high , suOur observation is consistent with the theoretical work by Robertson, who in 1960 showed that alleles of small effect can be fixed by chance in the opposite direction to that of selection . As the 15 generation of the AIL explained from 42 % to 51 % of the total additive genetic variance in this intercross, and we estimate that they contributed from 50 % to 92 % of the total additive genetic variance for 56-day body weight in the base population. The loci that were fixed for alternative alleles in the divergent lines after 40 generations of selection are thus likely have contributed a significant fraction of the selectable genetic variance present in the base population. This supports the theoretical expectation that standing variation will be an important contributor to the initial selection response for a population subjected to a novel selection pressure [The adaptive selective sweeps in the Fpressure . However15 generation. Further, the continued response to selection  and low (LWS) lines. The HWS and LWS lines were founded in 1957 from a base population obtained by crossing seven partially inbred lines of White Plymouth Rock chickens. Since then, they have been subjected to bi-directional selection for a single trait, high or low 56-day body weight (BW56), respectively, and currently the lines have reached generation 58  and 170 g (SE: ± 5 g), respectively. In total, 907 individuals were hatched in generation F15. Out of these, 852 survived until 56 days of age, when their body weight was measured. More details on the phenotypes of the founder lines and the AIL are provided in Table A complete description of the development of the Advanced Intercross population can be found in the publications describing the analysis of data from the F the AIL , 30. TheBlood samples were collected using a sterile needle and syringe, and then transferred to a tube containing disodium EDTA. DNA was extracted from whole blood samples using a Qiagen DNeasy kit.et al. [15 birds with recorded 56-day weights (n = 852) were sent for genotyping at the 316 selected SNPs using the GoldenGate assay  at the SNP technology platform in Uppsala (Sweden).In an earlier study, Johansson et al.  identifiIn total, 27 individuals and 64 SNPs failed to fulfill one or more of the following quality control criteria: call rate of individuals >0.9, call frequency of SNPs >0.9, and minor allele frequency >0.05. They were removed from the subsequent analysis, resulting in a final dataset consisting of genotypes for 252 SNPs in 825 birds. In total, the failed genotyping resulted in a loss of 26 initially targeted regions, resulting in a final coverage for 106 regions , 72 of w15 generation. This was to define clusters that segregated independently in the F15 generation in order to identify the number of independent, targeted selective sweeps that contribute to 56-day body weight in the lines. The criteria used for clustering markers were to assign adjacent markers that were no more than 50 cM apart into the same cluster and limit the range of each cluster to cover no more than 100 cM. According to these criteria, 99 independent regions were defined for use in the association analyses. The genetic distance between the markers in cM  was estimated using the function est.map from the R/qtl package in R [0 founders, we decided that the obtained estimates of the genetic distances were sufficiently precise for re-clustering of the markers.After ordering the genotyped markers based on their physical locations on the galGal4 assembly, we redefined the clusters based on their genetic linkage in the Fage in R  where th15 generation of the AIL. To estimate how many of the independently segregating regions contributed to 56-day body weight in the AIL F15 generation, we initially selected one representative marker from each of the 99 independently segregating clusters of markers, as defined above, in the F15 generation , Z is the associated incidence matrix, and ε is a normally distributed residual error ε ~ N. The relationship matrix A was constructed using the R/pedigree package [2 = σ2A/(σ2A + σ2E).To estimate the heritability in the narrow sense for 56-day body weight, we used a linear mixed model: package . The lin package  and the 15 genotypes in an ‘ATCG’ basis without information about the line origin of the respective alleles. Therefore, we first transformed the genotypes into a ‘–1 0 1’ basis by comparing the genotypes in the F15 to that of the HWS/LWS F0 founders; ‘–1’ was used to represent the case when both alleles were of LWS line origin, ‘0’ that the individual was heterozygous, and ‘1’ that both alleles were of HWS line origin. In this way, positive estimates of the additive effects in the association analysis indicate that the HWS-derived allele increased weight, and negative additive effects that the LWS-derived allele increased weight .The GoldenGate genotyping assay report Fy is the phenotype, β0 is an intercept term, β1 is the sex effect and w the associated indicator vector, β2 is the set of additive sweep effects modelled as fixed effects and X is the associated design matrix coded as −1, 0, 1 for the line origin of the marker genotypes, and ε is a normally distributed residual error. The number of markers within each region was limited and no problem of confounding between the fixed and random effects was detected.The dataset was not sufficiently large to separate the effects of the linked markers within the 99 selective-sweep regions. Therefore, the first step in the association analysis was to simplify the subsequent analyses by selecting one marker in each of these regions to represent their joint effects in the subsequent multi-locus analysis. To select these markers, we used a within-locus backward-elimination approach in a linear model framework. The additive genetic effects of all markers in the selective-sweep region to be evaluated were then included together with the fixed effects of the sex for the bird. The analysis was thus based on the following model:Using backward elimination from the full model, we then identified the individual marker within each region that had the most significant effect, without requiring a particular significance for it at this stage of the analysis. This analysis was performed using custom written scripts in R.15 generation of the AIL. The statistical analysis was chosen with the background knowledge that the genetic architecture of body weight in this population was highly polygenic [15 population may be considered minimal. Thus, a large portion of the difference in family means should be due to the joint effects of the many selective-sweep regions studied. When a large multi-locus mixed model was fitted to the data and fixed effects of markers across multiple selective-sweep regions were included together with a polygenic random effect in the model to account for family effects, there was a strong confounding between the fixed and random effects. This confounding may be explained by an assumption of linear mixed models being violated. A linear mixed model y = Xβ + Zu + e assumes that there is no correlation between a column in X, or a linear combination of several columns, with the true random effect Zu. This is a direct consequence of the basic assumption that the covariate and residuals in an ordinary linear model are independent. This might occur where the number of columns in X is large, and thus we deemed this analysis as unsuitable for use in the multi-locus analysis. However, as population structure might be of concern [et al. [The objective of the confirmation study was to identify the set of selective-sweep regions that jointly contribute to 56-day body weight in the Folygenic , 25, 30  concern , 35, we  [et al. .The bootstrap based approach was implemented in a backward-elimination model-selection framework across the genotyped selective sweeps. Before doing the multi-locus analysis, we evaluated whether it was statistically appropriate to perform this analysis across all the 99 regions that segregated independently in the linkage analysis. For this, a standard measure to identify potential high-order collinearity, the ‘variance inflation factor’ or VIF, was used. Consistent with the linkage analysis, there was no large pair-wise correlation between the 99 markers. However, some marker genotypes could  be written as a linear combination of the genotypes at all the other markers which would lead to collinearity problems in a multi-locus statistical analysis. Therefore the markers for the affected selective sweeps  were removed from the subsequent analyses.18 [The true size of the model  is unknown in advance and could range substantially from nearly half of the tested regions to a few individual associations. To compare models with such a wide range of variables to include, we opted to use an adaptive model selection criterion controlling the FDR , 14 deve18 . The addTo produce Fig. Two alternative approaches were used to predict how much of the population difference of 1,242 g between the parental HWS and LWS from generation 40 (Table 15 generation to the observed additive genetic variance in the base population for a particular set (θ) of allele frequencies at these loci, we calculated the total genetic variance explained for them as:A2(θ) is the total additive genetic variance across the 16 loci at the assumed allele frequencies, θ. ∑ is the sum over the 16 loci, and pi, qi, and αi are the HWS and LWS allele frequencies and allele-substitution effects for each of the loci.To estimate the contribution of the 16 significantly associated sweeps in the AIL Fi) in θ were equal to 1/7. Second, when the adaptive alleles were randomly fixed across the seven founder lines. Here, the minor allele frequencies (pi) across the 16 loci were drawn randomly 10,000 times from a uniform distribution in the range of 1/7 to 6/7 to generate a set of θ from which average estimates of the additive genetic variance was calculated.We evaluated two theoretical allele-frequency distributions across the 16 loci. First, when the adaptive, minor allele at each of the 16 sweeps was fixed in one of the partially inbred founder lines. Here, all the 16 individual allele-frequencies (pThe proportions of the additive genetic variance in the base population contributed by the sweeps were then obtained by dividing the estimates obtained via the procedure described above with the product of the realised heritability and the total phenotypic variance observed in the fourth generation of the selection lines .