Background and objectives: Is there a trade-off between children ever born (CEB) and post-reproductive lifespan in humans? Here, we report a comprehensive analysis of reproductive trade-offs in the Framingham Heart Study (FHS) dataset using phenotypic and genotypic correlations and a genome-wide association study (GWAS) to look for single-nucleotide polymorphisms (SNPs) that are related to the association between CEB and lifespan.Methodology: We calculated the phenotypic and genetic correlations of lifespan with CEB for men and women in the Framingham dataset, and then performed a GWAS to search for SNPs that might affect the relationship between post-reproductive lifespan and CEB.Results: We found significant negative phenotypic correlations between CEB and lifespan in both women  and men . The genetic correlation was large, highly significant and strongly negative in women  in a model without covariates, but not in men (P = 0.777). The GWAS identified five SNPs associated with the relationship between CEB and post-reproductive lifespan in women; some are near genes that have been linked to cancer. None were identified in men.Conclusions and implications: We identified several SNPs for which the relationship between CEB and post-reproductive lifespan differs by genotype in women in the FHS who were born between 1889 and 1958. That result was not robust to changes in the sample. Further studies on larger samples are needed to validate the antagonistic pleiotropy of these genes. Both the theory of life-history evolution and the evolutionary theory of aging assume a trade-off between reproduction and survival: a cost of reproduction paid in lifespan . AlthougAlthough most of the attempts to measure the trade-off in humans are based on phenotypic correlations, the standard of evidence for the existence of a trade-off in evolutionary analyses of model organisms is a negative genetic correlation demonstrated as a correlated response to selection e.g. , 33). Su. Su33]).et al. [Two other types of genetic evidence, however, are available in humans. First, genetic correlations can be measured with pedigree analysis using methods developed for animal breeding. Using such methods, Gögele et al.  found a et al. [Second, genome-wide association studies (GWAS) can be done on populations where both the relevant traits and the single-nucleotide polymorphisms (SNPs) have been measured. In a GWAS done on more than 3500 women from Rotterdam, Kuningas The aims of this analysis of men and women in the Framingham Heart Study (FHS) were to add to the genetic information on reproductive trade-offs in humans by (i) first measuring the phenotypic correlation of lifespan with children ever born (CEB), (ii) second estimating the genetic correlation of lifespan with CEB and (iii) performing a GWAS to search for SNPs with effects on the relationship of lifespan to CEB. We found significantly negative phenotypic and genetic correlations between post-reproductive lifespan and CEB in women. We also found five chromosomal regions mediating the trade-off that were genome-wide significant in several statistical models but not when we added smoking as a covariate. Some of the genes in those five regions are associated with increased risk of cancer.Initiated in 1948 in the town of Framingham (MA), the FHS includes three generations of participants that continue to be measured. Beginning with 5209 men and women initially enrolled in the original-cohort, the study added 5124 offspring-cohort participants in 1971 that were mostly offspring of the original-cohort. In 2002, a third-cohort was added consisting of offspring of the second cohort. Original-cohort participants have been examined every 2 years , the offspring-cohort every 4 years . Participants are mostly of European ancestry . Data were de-identified by the FHS. Data-use and human subjects’ approval were obtained from the National Institutes of Health (dbGaP) and the Yale Institutional Review Board.nmen = 2579; nwomen = 2193), CEB , and age at menarche (n = 1355) and menopause (n = 2415) in women. In each regression, potentially confounding effects in lifespan were controlled by including education, country of origin and smoking status. To test for potential nonlinear effects, a separate regression was run with a quadratic term included for the main predictor traits. If quadratic terms were significant, this was explored further by examining the Cox regression model  using penalized splines (with 4 df) [Our sample included men and women who were born between the 1890s and the 1950s, except for age at menarche where the available sample was much smaller (i.e. 1923–56). Cox regression was used to calculate risk of death depending on age at first birth (th 4 df) .h(t) is assumed to be a linear combination of the covariates. Specifically, for a model containing p covariates The Cox proportional hazards model is a standard tool for survival analysis, in which the log of the hazard function Note that FHS reports CEB as a value from ‘0’ to ‘5’, where ‘5’ indicates having had five or more children. Several variables were pre-adjusted for age and year measured. For body mass index (BMI), systolic blood pressure (SBP) and total cholesterol, age and year effects were removed by taking residuals of each trait against age (measures between 20 and 60 years old) and year measured using a generalized additive model . All residuals for a subject were then averaged to obtain an average residual for each trait, which were then used for modelling. As demonstrated previously, the surface of the generalized additive model can be accurately estimated due to the large number of trait measurements .Our initial sample included 4123 women for whom data on age at death, CEB, education level, smoking history, estrogen use and BMI were available. We then removed 941 women who were born in or after 1941, a period when the correlation between lifespan and CEB was weaker, possibly because of the improvement of health care after World War II. We did so because to have a chance of detecting any significantly correlated SNPs in the GWAS, we needed to focus on a period where the phenotypic correlation is relatively strong. Nineteen women who died before the age of 50 years were also excluded, because their CEB records might represent incomplete observations. Because we excluded women who died before the age of 50 years, we are specifically studying the relationship of CEB to post-reproductive mortality. Of the remaining 3163 women, keeping only those who had genotype data reduced our sample size to 1810. We required this sample to have associated genotype data because we later used the same sample for the GWAS. Note that our phenotypic analysis used the year 1919 as a cut-off because the yearly ratio of individuals alive to individuals deceased increased to about 50% in 1919, and continued to rise thereafter.n = 680) out of a total of 1810 who satisfied specific criteria outlined above.For illustrative purposes, we also ran a multiple linear regression on a smaller sample for women, including only the deceased subjects who were born prior to 1919 (n = 712) out of a total of 1474 men satisfying similar criteria.We similarly ran a regression model on a smaller sample of men who have died (21DF = 2 × (LogLFULL − LogLREDUCED)) lacking the additive genetic component. Genetic correlations were also tested for significance by comparing likelihood values from full models to ones where the genetic covariance was fixed at zero.We estimated heritabilities and genetic correlations for traits from pedigrees using a mixed effects restricted maximum likelihood (REML) model in ASReml version 3.0 . We consSupplementary Fig. S4 summarizes the pedigree information for these women, grouped by cohort via the ‘pedantics’ package in R [Our genetic correlation analysis between CEB and lifespan included a total of 5133 females for whom age at death and CEB information were available. age in R . PedigreP-value >0.00001, Mendel error rate <2% and minor allele frequency >0.01. These SNP selection criteria are further discussed in the Supplementary Information.Our association results are based on 444 205 SNPs from the 500 K and 50 K Affymetrix samples that satisfied the following criteria: call rate >90%, Hardy–Weinberg equilibrium We used Cox proportional hazards models, as done in the phenotypic correlation analysis, to estimate the interactions between survival time past age 50 years, CEB and genotype. For censored individuals, we used their times of last observation past age 50 years as their censoring time.Supplementary Information.Several models were run under this setup, which we number to emphasize that they are nested models. Model 1 did not adjust for any covariates. We then added covariates to reduce confounding by variables that may be correlated with lifespan and CEB. Model 2 used education level. Model 3 further added BMI, estrogen use and cohort as covariates. Models 4a–d were intermediate steps in which one of the four additional covariates was added: blood pressure treatment indicator (Model 4a), total cholesterol (Model 4b), SBP (Model 4c) and smoking indicator (Model 4d). Model 5 included all four of these additional covariates. Models 4a–d were run retrospectively to pinpoint which covariate, when added, resulted in removing significance from all SNPs. A summary of the models fitted can be found in the Both genotypes and CEB were included as continuous variables to model an additive effect of the minor allele. We used both the raw genotypes provided by FHS as well as an imputed dataset. The imputation was done in several stages. First, we incorporated values imputed by MACH that were included in the FHS dataset. The MACH algorithm imputes missing genotypes based on shared haplotype stretches between subjects and HapMap data . Of the In addition to running the above five models on the full sample of 1810, we tested our models for robustness by mimicking an out-of-sample analysis. To that end, we randomly divided our sample into two equal parts and fitted Models 1–5 to each part separately to check for consistency in significance of the top performing SNPs. A true out-of-sample performance check would include the calculation of prediction error based on a model fitted on a training set. Our method does not aim to validate prediction out of sample, but rather to ensure that a SNP discovered to be significant in one sample ought to be significant in another sample—a less stringent, but still important requirement of consistency. To minimize the effects of missing genotypes on each subsample, which would further lower our sample size in each of the two separate runs, we only used the imputed genotypes for this portion of our analysis. The downside of using imputed genotypes is the risk of imputation error. To verify that our risk of imputation error is low, we used the imputed SNP data to repeat our full-sample analyses for Models 1–5. Our aim was to show that our results for these models are similar, regardless of whether we used imputed or raw SNP data.To explore possible non-additive genotypic effects, we ran a separate Model 6 that used genotype as a categorical variable. The covariates used in Model 6 are identical to those used in Model 3, and any SNPs for which the homozygous minor genotype had fewer than 20 counts were excluded. We did not apply the half-sample testing to Model 6, because in many cases, the genotype counts in the homozygous minor allele category were too small to further subdivide the group for categorical modelling.Finally, we ran two additional models that are outside of the nested framework given above on the raw data only . A quadratic model was run to search for a possible nonlinear effect by adding a quadratic CEB term along with its interaction with genotype to Model 1. The ‘matching covariates’ model was run to provide a frame of reference to the reader; this model uses exactly the same covariates that were included in the phenotypic and genotypic correlation analyses—education, smoking indicator and country of origin.P = 0.030) had more children , but the significance of this effect depended on whether estimates were adjusted or not (P = 0.003), although this effect disappeared when other effects were controlled for , censoring was used to account for those who were still alive according to the latest medical records. Risk of mortality beyond age 50 years increased if women (adjusted incidence rate ratio (RR) = 1.045, children . When a children  support d or not . Mortalilled for . Full mor = −0.133, P = 0.0005; r = −0.079, P = 0.0355; Supplementary Fig. S2). An additional child cost 0.54 years of male lifespan (SE = 0.26 years). Again, the correlation varied by birth year, but the variations were less pronounced than for females . The observation that phenotypic correlations are dependent on birth year is consistent with previous findings that selection pressures changed over time in Framingham [In the analysis where only the 680 women were included in the range of birth years 1889–1918 in which all had died, the phenotypic correlation between CEB and lifespan was highly significant and negative , CEB , age at first birth  and menopause .In women , the herrG = −0.88, P = 0.01) in a model without covariates (Supplementary Table S2). When we included education as a random effect, the genetic correlation decreased to −0.70 but was still significant (P = 0.02). When we included either the mother or the father identifiers in place of education as a random effect, the genetic covariance remained large and negative, but was no longer significant . The model in which we adjusted for education, smoking status and country of origin also produced a large negative genetic correlation, but the correlation was not significant .In women, the genetic correlation of CEB with age at death was large, negative and significant , and borderline non-significant in the model with only the father identifier .The correlation between the quadratic term CEBn = 2540), we got rG = −0.46 (P = 0.14) and including only those women with pedigree depth of 2 or higher (n = 948), we got rG = −0.21 (P = 0.60); both correlations were no longer significant in the reduced samples.Furthermore, we looked to see if the genetic correlation between CEB and lifespan was robust to pedigree depth in the simplest model where no covariates were included. Including only those women with pedigree depth of 1 or higher . In men . All other male heritability and genetic correlation estimates were non-significant. Full model results for heritability can be seen in Supplementary Table S2.The genetic correlation of CEB with age at menarche was relatively large, positive and highly significant , unless otherwise indicated. For females, we found two SNPs that attained genome-wide significance using the full sample: ss66450977 on Chromosome 3 (close to EOMES) and ss66475987 on Chromosome 4 (close to ATP8A1). Their levels of significance decreased as additional covariates were included in the model; however, these SNPs were also significant in the matching covariates model , no SNPs were found to be significant for females , even whNo significant SNPs were detected for males in Models 1–3. As in the GWAS for females, the addition of more covariates decreased levels of significance, and therefore no further models were run.Supplementary Information.No significant SNPs were detected in a model that included a quadratic effect of CEB. Further details on the GWAS for females are in the rG = −0.88) was large. The effects of shared environment reduced the strength of the linear correlation and increased the strength of the quadratic correlation, and education mimicked the effects of a cost of reproduction in that increased level of education was associated with both fewer children and longer life: including education decreased the estimate of the genetic correlation.The phenotypic correlation between CEB and lifespan in women differed with birth year, demonstrating the importance of phenotypic plasticity on the relationships among life-history traits. Secular cultural and environmental changes affect that correlation and probably account for much of the variation among studies , 21, 22.Some of our genetic correlation estimates were below −1. This indicates that the estimated variance component is negative, known to be a possible result of REML estimation .rG = −0.69) yet no longer significant. This mirrors the pattern we observed in the GWAS; as covariates were introduced into the model, associations became insignificant.When we controlled for the effects of smoking, education, country of origin and maternal effects, the correlation was still negative  are known to affect lifespan, but these habits are not recorded, and therefore the genetic correlations that we see may be confounded by these unobservable factors.One can only find a genetic correlation when the phenotypic correlation is significant, and one can only find significant effects of SNPs on a phenotypic correlation when it differs from zero. Our chain of inference thus depends on genetic effects not being too masked by phenotypic plasticity.Supplementary Information. Although these SNPs were close in physical distance to their respective genes (<130 kb), further study of linkage disequilibrium would help to understand their possible association.We found several SNPs with nominally significant effects on the correlation of CEB with post-reproductive lifespan; two of them are near EOMES and RAD51B, genes that are related to cancer when under-expressed. The effect of the SNP close to EOMES reached genome-wide significance. The EOMES gene has been associated with multiple sclerosis and bladder cancer , 44. RADet al. [Voorhuis et al.  collatedet al. [P < 10−4) that were associated with decreased male fertility. Adachi et al. [P < 10−4) with possible links to endometriosis in Japanese females. Both were GWAS studies that did not find any genome-wide significant SNPs. Murray et al. [et al. [Several other recent genetic studies relate fertility to genotype. Kosova et al.  found 41i et al.  found 36y et al.  reported [et al.  examined [et al. .P < 0.05), whereas we started with a GWAS across 444 205 SNPs in models that relate each SNP to both CEB and lifespan (with a threshold of P < 1.13 × 10−7). We did not find Bonferroni-level significance with SNPs near the four gene regions identified in [Although the Kuningas Rotterdam study incorporated mortality in its analysis and was therefore more similar to our study , it diffified in .We have analysed phenotypic and genetic correlations between reproductive success and survival and have identified a small set of genes that may mediate a trade-off between them. This warrants further studies in other samples.The Framingham dataset has some shortcomings. In particular, women born before the start of the study would only have been included in the study if they survived until 1948–52 (when the study began). Therefore, our dataset does not include anyone who died during World War I, the 1918 flu pandemic, the Great Depression and World War II. If these catastrophic events affected women differently depending on their fertility and lifespan, then excluding these women from our analysis would bias our results. The issue is inherent in such observational studies of humans, and unfortunately cannot be avoided.We failed to find any significant SNPs when covariates  were included and when we did a rough check for consistency out of sample. It is unknown how often such checks modify significance of SNP associations, for many other published GWAS studies do not account for the effects of covariates or do out-of-sample predictions.S.G.B. and X.W. jointly worked on processing and cleaning the data and phenotypic correlation calculations. S.G.B. further calculated the genetic correlations and heritabilities. X.W. performed the GWAS. S.C.S. conceived of the study and drafted the initial manuscript. All authors contributed to the final manuscript.Supplementary data is available at EMPH online.


N = 32,225). We collected 8 further cohorts  for replication, and rs6448771 on 4p15 demonstrated genome-wide significant interaction with waist-to-hip-ratio (WHR) on total cholesterol (TC) with a combined P-value of 4.79×10−9. There were two potential candidate genes in the region, PCDH7 and CCKAR, with differential expression levels for rs6448771 genotypes in adipose tissue. The effect of WHR on TC was strongest for individuals carrying two copies of G allele, for whom a one standard deviation (sd) difference in WHR corresponds to 0.19 sd difference in TC concentration, while for A allele homozygous the difference was 0.12 sd. Our findings may open up possibilities for targeted intervention strategies for people characterized by specific genomic profiles. However, more refined measures of both body-fat distribution and metabolic measures are needed to understand how their joint dynamics are modified by the newly found locus.Recent genome-wide association (GWA) studies described 95 loci controlling serum lipid levels. These common variants explain ∼25% of the heritability of the phenotypes. To date, no unbiased screen for gene–environment interactions for circulating lipids has been reported. We screened for variants that modify the relationship between known epidemiological risk factors and circulating lipid levels in a meta-analysis of genome-wide association (GWA) data from 18 population-based cohorts with European ancestry , we found a genome-wide significant locus in chromosome 4p15 with a joint P-value of 4.79×10−9 modifying the effect of waist-to-hip ratio on total cholesterol. In the area surrounding this genetic variant, there were two genes having association between the genotypes and the gene expression in adipose tissue, and we also found enrichment of association in genes belonging to lipid metabolism related functions.Circulating serum lipids contribute greatly to the global health by affecting the risk for cardiovascular diseases. Serum lipid levels are partly inherited, and already 95 loci affecting high- and low-density lipoprotein cholesterol, total cholesterol, and triglycerides have been found. Serum lipids are also known to be affected by multiple epidemiological risk factors like body composition, lifestyle, and sex. It has been hypothesized that there are loci modifying the effects between risk factors and serum lipids, but to date only candidate gene studies for interactions have been reported. We conducted a genome-wide screen with meta-analysis approach to identify loci having interactions with epidemiological risk factors on serum lipids with over 30,000 population-based samples. When combining results from our initial datasets and 8 additional replication cohorts (maximum  Serum lipids are important determinants of cardiovascular disease and related morbidity http://www.genome.gov/26525384). To better understand the biological processes modifying lipid levels, several twin studies Epidemiological risk factors, such as alcohol consumption, smoking, physical activity, diet and body composition are known to affect lipid levels Interactions between genes and modifiable risk factors might help us develop new lifestyle interventions targeted to susceptible individuals based on their genetic information. The effects of genetic loci and risk factors have been studied widely separately, but to date no GWA studies for interactions on lipids have been reported.N = 32,225; We conducted a genome-wide screen for interactions between 2.5 million genetic markers and sex, lifestyle factors , and body composition (BMI and WHR) in association to serum lipid levels  in 18 population-based cohorts  in stage 1 analyses were taken forward to stage 2 analysis in eight additional cohorts . This interaction was tested in stage 3 in two further cohorts , the P-value for interaction was 4.79×10−9. The association between WHR and TC was strongest for individuals carrying two G alleles of rs6448771, for whom a one standard deviation (sd) difference in WHR corresponds to 0.19 sd difference  in TC concentration, while for individuals homozygous for the A allele the difference was 0.12 sd   gene.18 SNPs with suggestive interactions for at least one of the trait – epidemiological factor combinations (HR on TC  was stat09–0.16) . The eff09–0.16) . The SNPrs6448771 have variation in their transcript profiles. As WHR reflects adipose tissue function, we selected 54 individuals from Finnish dyslipidemic families with available fat biopsies and GWA data. We used linear regression to find genes that were differentially expressed in adipose tissue depending on the rs6448771 genotype. We found two potential candidate genes with nominally significant cis-eQTL effects, PCDH7  and CCKAR . The region with CCKAR has previously been linked with obesity P-value<0.01 (both trans- and cis-eQTLs). Among other diverse IPA-defined biological functions, there was an eQTL association enrichment among genes belonging to the ‘degradation of phosphatidylcholine’  and ‘degradation of phosphatidic acid’  functions, which are members of broader defined IPA categories “Lipid Metabolism” and “Carbohydrate Metabolism”. These pathways were up-regulated in individuals carrying the G allele of rs6448771, possibly indicating a role for rs6448771 in lipid and carbohydrate metabolism.Since the polymorphisms associated with complex phenotypes often influence gene expression, we examined whether individuals carrying different genotypes of P = 0.0016) and HDL-C  in our stage 1 meta-analysis and after adjusting for TC no residual interaction effect on LDL-C and a little on HDL-C remains (P = 0.834 and P = 0.131 respectively) when testing in data subset. Therefore we tested the SNP – WHR interaction also on a range of lipoprotein subclasses measured using NMR metabonomics platform N = 4624 mean age = 31.0; YFS, N = 1889, mean age = 37.6). The results show that the SNP has a positive interaction effect on large HDL particle concentration  and a negative effect on large very-low-density lipoprotein (VLDL) particles  and total triglycerides   .rs6448771, modifying the relationship between WHR and TC. The effect of WHR is estimated to be 64% stronger for individuals carrying two copies of the G allele than for individuals carrying two A alleles. The interaction explains around half a percent of the TC variance that is in par with the main effects of the strongest previously identified TC SNPs individually. This SNP also shows similar interaction effects on a cascade of more detailed lipid fractions suggesting broad involvement in lipid metabolism, which was also suggested by our eQTL association enrichment analysis with adipose tissue expression data.Our genome-wide scan for interactions between SNP markers and traditional epidemiological risk factors in population-based random samples found a genome-wide significant locus, PCDH7) gene, which produces a protein that is thought to function in cell-cell recognition and adhesion. The other candidate gene, cholecystokinin A receptor (CCKAR) regulates satiety and release of beta-endorphin and dopamine in the central and peripheral nervous system. It has been previously shown that rats with no expressed CCKARs developed obesity, hyperglycemia and type 2 diabetes PCDH7 and CCKAR in another dataset with genome wide expression data from blood leukocytes (N = 518) available. CCKAR could not be tested due to its negligible expression in blood leukocytes, and no association was found for the PCDH7  gene most likely indicating an adipose tissue specific eQTL for PCDH7 as a function of rs6448771.The eQTL analysis pointed towards two potential candidate genes in the region. The first one of these was protocadherin 7  were common and available for a large proportion of the study sample. This suggests that the contribution of two-way G×E interactions to lipid levels, at least for the risk factors we examined, is rather small, or that our current measures of risk factors may not be robust enough for identifying interactions. More specific measures of both phenotypes and interacting risk factors would give better statistical power in future screens of G×E interactions.rs6448771 modified the relationship between WHR and TC, but was not associated with either WHR or TC alone. This observation suggests that genome-wide screens for interactions may be complementary to the current large-scale GWAS efforts for finding main effects. Third, in addition to careful harmonization of both risk factor data and phenotypes, large sample sizes are needed to identify interactions. In our study, 43,903 samples were combined to robustly identify the interaction. Our data, however, suggest that the contribution of G×E interaction using current phenotypes appears limited. Finally, from clinical point of view, the interaction may open up possibilities for targeted intervention strategies for people characterized by specific genomic profiles but more refined measures of both body-fat distribution and metabolic measures are needed to understand how their joint dynamics are modified by the newly found locus.Our findings allow us to draw several conclusions. First, to our knowledge, this is the first time an interaction between a genetic loci and a risk factor has been identified in a genome-wide scan using a stringent statistical threshold for genome-wide significance. Second, in our samples, 18 studies, with a combined sample size of over 30,000 individuals, participated in the discovery phase of this analysis; 8 studies were available for replication with over 14,000 individuals. In the discovery stage, only population-based cohorts not ascertained on the basis of phenotype, with a wide variety of well-defined epidemiological measures available, were included. In the replication datasets, the NTR cohort was selected on the basis of low risk for depression and the Genmets samples were selected for metabolic syndrome. In further replication of rs6448771, the EPIC cases were ascertained by BMI. Descriptive statistics for these populations are detailed in Individuals were excluded from analysis if they were not of European descent or were receiving lipid-lowering medication at the time of sampling. TC, HDL-C, and TG concentrations were measured from serum or plasma extracted from whole blood, typically using standard enzymatic methods. LDL-C was either directly measured or estimated using the Friedewald Equation  Covariates and epidemiological risk factors were ascertained at the same time that blood was drawn for lipid measurements. BMI was defined as weight in kilograms divided by the square of height in meters. Waist circumference was measured at the mid-point between the lower border of the ribs and the iliac crest; hip circumference was measured at the widest point over the buttocks. Waist-to-hip ratio was defined as the ratio of waist and hip circumferences. Alcohol consumption and smoking habits were determined via interviews and/or questionnaires. Both behaviors were coded as dichotomous (abbreviations: ALC for drinker/abstainer and SMO for current smoker/current non-smoker) and semi-quantitative traits. Semi-quantitative alcohol usage (ALCq) was based on daily consumption in grams . Semi-quantitative smoking (SMOq) was assessed based on the number of cigarettes per day .Affymetrix, Illumina or Perlegen arrays were used for genotyping in the discovery cohorts. Each study filtered both individuals and SNPs to ensure robustness for genetic analysis. After quality control, these data were used to impute genotypes for approximately 2.5 million autosomal SNPs based on the LD patterns observed in the HapMap 2 CEU samples. Imputed genotypes were coded as dosages, fractional values between 0 and 2 reflecting the estimated number of copies of a given allele for a given SNP for each individual. Cohort specific details concerning quality control filters, imputation reference sets and imputation software are described in Replication cohorts utilized genome-wide imputed data, as described above, where available. Details on the genotyping methods implemented in the replication samples are described in Proton NMR spectroscopy was used to measure lipid, lipoprotein subclass and particle concentrations in native serum samples. NMR methods have been previously described in detail 2. For the other analyses, the phenotypes were defined as the inverse normal transformed residuals resulting from the regression of the lipid measurement on age, age2, and sex.Triglyceride concentrations were natural log transformed prior to analysis. BMI and WHR were transformed to normality using inverse-normal transformation of ranks. For analyses where sex was the epidemiological variable of interest, the phenotypes were defined as the rank-inverse normal transformed residuals resulting from the regression of the lipid measurement on age and ageE×SNP was 0 using 1 df Wald tests. In family-based cohorts, linear mixed modeling was implemented to control for relatedness among samples Associations between the transformed residuals and epidemiological risk factors/SNPs were tested using linear regression models under the assumption of an additive  model of genotypic effect. The models regressed phenotypes on epidemiological factor, SNP, and epidemiological factor×SNP termsGC) http://www.sph.umich.edu/csg/abecasis/Metal/index.html) and the R http://www.genabel.org/). All results were concordant, reflecting a robust analysis. Results were selected for in silico replication if the meta-analysis P-value was less than 10−6. Results passing the threshold of suggestive genome-wide association  were selected for further replication by direct genotyping.The interaction terms from the regression analyses were meta-analyzed using inverse variance weighted fixed-effects models −8) reflects the estimated testing burden of one million independent SNPs in samples of European ancestry −8/3 = 1.67×10−8 corresponding to three principal components explaining 97.8% of the total variation of the risk factors (The commonly accepted genome wide level of significance (5×10 factors .www.ingenuity.com).” The Functional Analysis identified the biological functions and/or diseases that were most significant to the data set. Molecules which met the P-value cutoff of 0.01 for the rs6448771 – expression association in dataset of 54 Finnish individuals with both genotype and adipose tissue expression data, and were associated with biological functions and/or diseases in Ingenuity's Knowledge Base were considered for the analysis. Right-tailed Fisher's exact test was used to calculate a P-value determining the probability that each biological function and/or disease assigned to that data set is due to chance alone and Benjamini-Hochberg multiple test correction The functional analyses were generated through the use of Ingenuity Pathways Analysis  has been explained using waist-to-hip ratio (WHR). The analyses were ran in three strata based on the rs6448771 genotypes. The whiskers in the plot correspond to the confidence intervals of the effect estimates.Effect of waist-to-hip ratio on total cholesterol as a function of (DOC)Click here for additional data file.Table S1Cohort characteristics. The number of study subjects with available phenotype and genotype (lower line) and summary statistics (upper line) for every cohort and trait. For continuous traits mean (standard deviation) is presented. For dichotomous traits number of individuals with phenotype present (%) is presented. TC: total cholesterol (mmol/l); HDL-C: high-density lipoprotein cholesterol (mmol/l); LDL-C: low-density lipoprotein cholesterol (mmol/l); TG: triglycerides (mmol/l); BMI: body-mass index; WHR: waist-to-hip ratio; NA: not available.(DOC)Click here for additional data file.Table S2P-value<1×10−6 in Stage 1 analyses and replication of the SNPs. Best SNP per locus having P-value<1×10−6 in the Stage 1 analysis combining 19 cohorts. The bolded number is the genome-wide significant P-value. N: number of individuals; SE: standard error of the effect estimate, Beta; LDL-C: low-density lipoprotein cholesterol; TC: total cholesterol; TG: triglycerides; HDL-C: high-density lipoprotein cholesterol; ALC: alcohol usage (drinker/abstainer); WHR: waist-to-hip ratio; BMI: body mass index; SMO: smoking (current/not);; SMOq: semi-quantitative smoking ; ALCq: semi-quantitative alcohol .Loci having (DOC)Click here for additional data file.Table S3rs6448771 on total cholesterol (TC) by waist-to-hip ratio (WHR) tertiles and effect of WHR on TC by SNP genotype classes. Section A shows the combined effect of waist-to-hip ratio (WHR) on total cholesterol (TC) stratified by the rs6448771 genotype class from five Finnish cohorts  and section B shows the combined effect of the SNP on TC stratified by WHR tertiles from the same cohorts. The limit values for the waist-to-hip ratio (WHR) tertiles have been calculated using WHR values from all five datasets. Both analyses were ran using untransformed and standardized scales and were adjusted with age, age2 and sex. Beta: effect estimate; CI: confidence interval.Effect of (DOC)Click here for additional data file.Table S4Details of GWA data in discovery and replication cohorts. QC: quality control; MAF: minor allele frequency; HWE: Hardy-Weinberg equilibrium.(DOC)Click here for additional data file.Table S5Proportions of variance explained by principal components. Principal components analysis (PCA) was run for the seven risk factors used in the screening. PC: Principal Component.(DOC)Click here for additional data file.Text S1Short descriptions of the cohorts and a full list of acknowledgements.(DOC)Click here for additional data file.


Xiphophorus helleri, we show repeatable components of (co)variation in a suite of agonistic behaviour that is broadly consistent with a major axis of variation in aggressiveness. We also show that observed focal behaviour is dependent on opponent effects, which can themselves be repeatable but were more generally found to be context specific. In particular, our models show that within-individual variation in agonistic behaviour is explained, at least in part, by the relative size of a live opponent as predicted by contest theory. Finally, we suggest several additional applications of the multivariate models demonstrated here. These include testing the recently queried functional equivalence of alternative experimental approaches,  for assaying individual aggressiveness.Aggression occurs when individuals compete over limiting resources. While theoretical studies have long placed a strong emphasis on context-specificity of aggression, there is increasing recognition that consistent behavioural differences exist among individuals, and that aggressiveness may be an important component of individual personality. Though empirical studies tend to focus on one aspect or the other, we suggest there is merit in modelling both within- and among-individual variation in agonistic behaviour simultaneously. Here, we demonstrate how this can be achieved using multivariate linear mixed effect models. Using data from repeated mirror trials and dyadic interactions of male green swordtails,  Xiphophorus helleri.Aggression is widespread in animals and occurs most commonly among conspecifics in relation to competition for resources such as food, territory or mating opportunities sensuAggressiveness can be viewed as a latent characteristic that varies among individuals in a population . Rather than attempting to isolate these components, a more complete description of agonistic behaviours may result from application of an analytical framework capable of modelling both simultaneously. This integration can be achieved through the use of linear mixed effect models Multivariate models allow estimation and testing of correlation structures that are hypothesised to exist among behavioural traits. Just as variance can be decomposed into within- and between individual components for a single trait, covariance among traits can be similarly partitioned. Here we take this approach to explore the extent to which among-individual variation in a suite of specific agonistic behaviours can be viewed as arising from a single axis of variation in latent aggression. We also test the  relationships among behavioural traits expressed under two experimental settings commonly used for studies of aggression in fish, dyadic trials against a live opponent and mirror tests. This allows us to test whether individual aggression as inferred from mirror trials is a useful predictor of aggression when confronted by a live opponent, a question that has recently been raised in the literature Xiphophorus helleri, provides a well-known fish model for studies of dominance and aggression  individual variation in the latent character of aggression. Here we illustrate the application of univariate and multivariate mixed models to data from a captive population of swordtails by testing three specific hypotheses: 1) that there are repeatable among-individual differences in agonistic behaviour displayed by male swordtails across contexts , 2) that different traits used as indicators of aggression are positively correlated at the within-individual level  consistent with an important axis of among-individual variation in aggression, and 3) that observed behaviour directed towards a live conspecific is best explained not as a manifestation of focal aggression alone, but is also dependent on assessment strategies as predicted by contest theory.The green swordtail, All work was approved by the University of Edinburgh local ethical review committee and carried out under license granted by the Home Office (UK) under the Animals (Scientific Procedures) Act 1986. Criteria were in place to terminate any trial immediately in the event of physical injury or other overt signs of distress . No animal received injury as a result of the experiments and it was not necessary to terminate any individual trial. At the end of data collection all fish were released from the Animals (Scientific Procedures) Act 1986 (UK) following veterinary inspection and were re-homed as pets.Xiphophorus helleri, fish obtained were of four colour strains designated as “marigold”, “green”, “wagtail” and “red”, in order to facilitate individual identification during behavioural trials. These domestic fish have an unknown history of artificial selection, (probable) hybridisation with congenerics, and adaptation to captivity. In very broad terms we expect patterns of behavioural (co)variation to be similar to those that might be expressed by wild-counterparts subject to identical trials. Although we do expect that patterns of behavioural (co)variation will be broadly similar, we caution that results from this study should not be viewed as directly informative for the behavioural ecology of wild fish.Thirty commercially bred mature male swordtails were sourced from a tropical fish retailer. Nominally designated as green swordtails, Each fish was assigned at random to one half of a partitioned home tank . Individuals were thus physically  isolated from each other. Home tanks were enriched with rocks and plants, water temperature was maintained at 24°C, and a 12∶12 light∶dark cycle was imposed (lighting hours 0700-1900). Fish were fed on commercial flake food twice daily, supplemented with occasional feeding of live daphnia and frozen brine shrimp. Prior to the end of mirror trials, space constraints necessitated rehousing the fish in groups of 3–4. This change in housing regime is controlled for statistically in our analyses (see below).th February and 1st April 2010. Fish were subject to repeated trials of two different experimental protocols: a mirror test to assay aggression, and a dyadic interaction test against a live opponent (details below). Dyadic trials were completed prior to beginning mirror trials. Based on simulation-based power analyses , partitioned into two equal volumes with an opaque polystyrene divider. The fish was therefore visually isolated from a mirror placed at the right hand end of the tank . After afocal individual, and another as the opponent. Pairs were accepted provided the following conditions were met: 1) no dyad was used more than once ; 2) no dyads involving the two fish from a single partitioned home tank were allowed; and 3) no individual was in more than 8 dyads, with a maximum of 4 repeats as the focal individual. For each trial the two fish were transferred to an experimental tank partitioned into two halves by an opaque polystyrene divider and filled to a depth of 8 cm. The designated focal was placed in the left hand partition and the opponent in the right. Pebbles of (approximately) equal size and relative position were present in each side to provide spatial orientation and cover  number of approaches to the opponent; (2) number of attacks on the opponent; (3) number of tail beats, (4) time spent in lateral display to the opponent, (5) latency to first aggressive action ; and (6) latency to attack (recorded as 445 seconds if no attack was observed). In addition we recorded two behaviours putatively indicative of defence (or avoidance) rather than aggression. These were the number of retreats (a slow controlled movement away from the opponent) and the number of flees (a much more rapid movement away from the opponent which was only observed following an attack by the latter). Behaviours were defined operationally according to criteria presented in the ethogram  to discriminate between ethogram  and wereMorphological data used for analyses were collected on a single sampling occasion at the end of the behavioural testing period. All fish were anaesthetised using a solution of MS222 100%w/w , weighed by electronic balance (±0.01 g) and photographed. Standard length  and caudal sword length  were measured from digital images using the morphometric software tpsDig. To avoid subjecting fish to repeated anaesthesia, we assumed morphology to be constant within-individuals across the study period. Consistent with this assumption, data collected by placing non-anesthetised fish in a beaker of water placed on the electronic balance  provided no evidence of weight change during the course of the study (data not shown).http://www.wildanimalmodels.org/tiki-index.php?page=Modelsforsocialdominanceandcompetition. In order to better meet the assumption of residual normality all behavioural traits were square root transformed prior to analysis. In addition, to ease interpretation of the multivariate analysis we multiplied the square roots of latency to aggression and latency to attack by −1 such that for all traits higher values correspond to greater aggression.All data were analysed using linear mixed effect models solved by restricted maximum likelihood and implemented in ASReml (Version 3). For the interested reader a didactic presentation of the general approach taken, together ASReml code for fitting the models is available at i in trial k was modelled as:To test the hypothesis that behavioural traits associated with aggression are repeatable within individuals we fitted univariate models of each behavioural trait with the trait mean as a fixed effect and focal identity as a random effect. For traits assayed in the mirror tests we also included a fixed effect of housing regime (as a two level factor) in addition to the overall trait mean (μ), in order to account for any change caused by the switch from isolated to group housing such that trait y expressed by individual iFocal are not directly observed, but are assumed to be normally distributed with a variance to be estimated as VF, the among- individual variance component which is estimated by fitting the mixed model. We make the standard assumptions that residual errors (kε) are normally distributed and uncorrelated across observations, and estimate the residual variance VR . We then estimate the focal repeatability RF as the proportion of total phenotypic variance (VP) explained by individual identity such that RF = VF/VP (and VP is simply estimated as the sum of VF and VR).The repeatable individual effects i with opponent j as:O  to be estimated. We then estimated focal and opponent repeatabilities (RF and RO) respectively, as the ratio of the corresponding variance component to total VP . RO can be interpreted as the proportion of variance in a behavioural trait expressed by the focal individual that is attributable to the identity of its opponent For traits assayed in the dyadic interaction tests we modelled the observation from test k on individual Note that our hypotheses are entirely agnostic to the presence of carryover effects F) and opponent effects (rO). Furthermore, where focal and opponent effects are present, it is also possible to estimate the correlation between them, both within and between-traits. Thus one can test the within-individual correlation between, for instance, a focal individual's tendency to attack an opponent and an opponent's tendency to induce fleeing in a focal animal.We then fitted multivariate models to estimate the among-trait correlation structure of repeatable individual effects. Usefully this allows both the estimation of among-trait correlations for focal effects (rF and/or VO was significantly greater than zero (at α = 0.05) based on univariate analyses. We then modelled the full variance-covariance matrix, subsequently denoted I, among all individual-level  random effects that were supported by the univariate models described above. I was then rescaled to the corresponding correlation matrix rI. To better facilitate model convergence and allow standard errors for all correlations to be quantified, I was estimated without any constraints on parameter space (i.e. estimates of at r>|1| are possible in rI).We limited our multivariate analysis to that subset of behavioural traits for which VrI to the nearest positive definite matrix  as determined by the R function nearPD algorithm rI is estimated with uncertainty, statistical inference on its principal components is non-trivial  to measure size asymmetry in each trial. We then assigned trials to one of three classes based on the tertiles of this metric: 1 – focal i is smaller than opponent j (n = 35); 2 - focal and opponent of (approximately) similar size (n = 35); 3 - opponent is larger than focal (n = 34). The distributions of (untransformed) focal behaviour were visually compared across relative weight classes using box plots of the raw data, and statistically compared by refitting the univariate mixed effect models of transformed data after adding relative weight class (relWT) as a fixed effect (3 levels) such that:We fitted two additional sets of univariate linear mixed effect models to test whether focal aggression varies as a function of focal and/or opponent resource holding potential (RHP). Firstly we tested the prediction that focal behaviour should vary with asymmetry in RHP estimated as relative body size. Specifically we predict focal individuals will tend to exhibit more “escalated” behaviour when they are the larger member of the dyad, for example by spending more time displaying and/or by attacking an opponent more frequently and more rapidly. Weight and standard length were highly correlated see  and we cSecondly, we modelled the influence of RHP by including the partial regressions on absolute focal and opponent weight rather than the categorical relative weight class. Relying on a composite measure of RHP asymmetry has been shown to produce spurious results in terms of examining the RHP assessment strategy used sensuFor both sets of models we estimated the corresponding “adjusted repeatabilities” sensu for eachF and (where fitted) VO using one-tailed likelihood ratio tests, in which the log-likelihood of a model with focal identity (or opponent identity) included as a random effect was compared to the corresponding reduced model. The test statistic, calculated as twice the difference in model log-likelihoods is assumed to have a distribution corresponding to a 50∶50 mix of chi squared distributions having 0 and 1 DF respectively Χ21DF. We present P-values with no correction for multiple testing while acknowledging that the number of traits (and corresponding tests) raises concerns of Type I error. However traditional solutions  introduce further difficulties a priori expectation of non-independence among test statistics (i.e. traits were chosen precisely because they are hypothesised to reflect a single axis of latent variation in aggressiveness). Although not implemented here, one potentially useful strategy would be to formulate “global” statistical hypotheses about the structure of the I (or rI) matrix as opposed to its individual elements.In all models we assessed the significance of fixed effects using Wald F-tests implemented in ASReml. For univariate models we tested the significance of VF ranged from 5.4% to 42.5%, with the among-individual component of variance (VF) being significantly greater than zero (at α = 0.05) for 9 of the 12 traits tested  and, with a single exception, VO was not significantly greater than zero. The number of flees performed by a focal individual was the only trait for which non-zero opponent repeatability was statistically supported (RO = 0.174 (0.102), P = 0.031). Interestingly, this means that while the focal identity is not a significant predictor of its tendency to flee in dyadic trials, the identity of the opponent is.Univariate models confirmed our hypothesis that individuals show repeatable differences in behavioural traits chosen as putative indicators of underlying aggression. Across all traits tested estimates of focal repeatabilities Rs tested . In the s tested . RO estia priori as putative indicators of aggression, correlations were generally positive where significant  of 1.13 (0.397) between the repeatable focal tendency to attack and the repeatable opponent tendency to induce fleeing.Multivariate modelling also provided evidence of significant correlations among the individual level (repeatable) effects on different traits. Among behavioural traits assumed nificant . For ins fleeing . Since wrI and had moderate loadings of a consistent sign on most of the agonistic behaviours  of the correlation matrix revealed a first principal component that explained 45% of the variance in haviours . Some exhaviours .F = −0.746 (0.257), P<0.001 or rF = −0.824 (0.247), P<0.001 between weight or standard length respectively and √(no. attacks on mirror)).As expected, positive correlations were found among the three morphological traits weight, standard length and sword length ). There was little support for association between sword length and the repeatable components of behaviour, but in general large fish were more aggressive . For exa2,96.1 = 2.72, P = 0.072). Adjusted (for relative weight class) focal repeatabilities were similar to the unadjusted values (comparison of estimates in F - RF.adj) = −0.0025, Wilcoxon signed rank test P = 0.726). Adjusted opponent repeatabilities were slightly lower on average than the unadjusted values although the difference was marginally non-significant (comparison of estimates in O – RO.adj) = 0.019, Wilcoxon signed rank test P = 0.059).Plotting the behavioural data distributions by relative weight class suggests that a number of the traits observed in dyadic trials are influenced by relative body size . For exafocWTβ = 1.40 (0.715), P = 0.06). Adjusted (for absolute weight) focal repeatabilities were similar to unadjusted values (comparison of estimates in F - RF.adj) = −0.003, Wilcoxon signed rank test P = 0.945) as were adjusted opponent repeatabilities (comparison of estimates in O – RO.adj) = 0.008, Wilcoxon signed rank test P = 0.529).When linear effects of absolute weight , rather than relative weight class were modelled there was a general pattern of decreasing aggression with increasing opponent size . The par1.40 0.71, P = 0.0XiphophorusOur analyses confirmed that there are consistent, repeatable differences among male swordtails in agonistic behaviour. Broadly speaking, and with several exceptions and caveats (discussed below), our multivariate models also provide qualitative and quantitative support for the hypothesised major axis of variation in aggression among fish used for this study. Thus, not only were multiple agonistic behaviours repeatable, but we also found evidence of significant correlations among behavioural traits. This is consistent with the premise that there is an important axis of among-individual variation in latent aggression (and that at least some of the traits studied are useful assays of this axis). These results agree with a number of recent studies of aggression (and dominance) in We also found support for the third of our hypotheses, namely that observed agonistic behaviours during dyadic contests should be dependent on assessment strategies predicted by contest theory Thus, within a single modelling framework we have explored the among- and within-individual sources of variance in agonistic behaviour, and find evidence consistent with personality variation and with the predictions of contest theory. This highlights the important point that the presence of consistent behavioural differences among-individuals does not negate an important role for plasticity Our analyses provide evidence for personality, defined for analytical purposes as a tendency for individuals to exhibit consistent differences in behaviour across multiple contexts rI is consistent with a major axis of variation in aggression, a latent character that may be thought of as one component of personality. However, this conclusion must be tempered by the fact that some correlation structure is an inevitable consequence of how traits are defined . It is also important to note that positive correlations were not ubiquitous among putative assays of aggression and in some cases estimates of rF were strikingly inconsistent with expectations. For example, an estimate of rF = 1.248 (0.423) between the number of retreats (dyadic tests) and number of attacks on the mirror is counter to the intuitive expectation that a more aggressive individual would retreat less. Furthermore, PC1 accounted for slightly less than half of the total variance in rI, which is arguably smaller than we might expect given all traits were chosen with the express intent of assaying aggression.We have also argued that the multivariate correlation structure captured in It is therefore an oversimplification to say that all repeatable (multivariate) behavioural variation assayed here corresponds to a single axis of personality variation . Two non-exclusive possibilities follow from this. Firstly, we may conclude that attempting to reduce (co)variation in multiple agonistic behaviours to a single dimension is inappropriate. Secondly, we may question whether all of the putatively agonistic behaviours included in our analysis are equally relevant or valid as measures of aggression.rI, but rather on the second (which accounted for a further 26% of the variance). Limited data available from open field tests shows that time spent stationary is repeatable (RF = 0.254 (0.109), P = 0.004) and correlated with an individual's inverse latency to aggression (rF = −1.269 (0.419), P = 0.003), and tendency to attack the mirror (rF = −0.729 (0.346), P = 0.074), though not with tendency to retreat. Thus, a post hoc interpretation might be that these traits are principally indicative of general activity level, a trait that has been used to investigate exploratory behaviour and/or boldness; F if the aim is to study a single aspect of personality), or to mimimise redundancy of effort .For instance, three traits, specifically latency to aggression, number of retreats, and number of attacks on the mirror do not load on the first principal component of rI is also informative for assessing the extent to which alternative experimental protocols reveal equivalent information about individual subjects. Assaying aggression by exposure to a stimulus designed to mimic a live opponent  has practical advantages, but the assumption that such stimuli elicit responses strictly comparable to those of a live opponent Xiphophorus predicted victory but not the level of aggression in subsequent dyadic trials, while Arnott and Elwood Astatotilapia burtoni, The structure of the correlation matrix F = 0.102 (0.338)), as is that between the tendency to attack a mirror and to attack a live opponent (rF = −0.037 (0.393)). It was also notable that larger individuals attacked the mirror less, while attacking opponents more in the dyadic trials. This difference is consistent with a mutual assessment strategy . This highlights the point that mirror trials can provide reliable information about an individuals's likely response to a live opponent, but careful choice of indicator traits may be critical and interpretation is not always straightforward. We also note that our interpretation with respect to stimulus type implicitly assumes that ranking of individuals is independent of observation period . Censoring the dyadic data to include only the first 180 seconds  indicates this is not unreasonable. Specifically, for those traits that were repeatable . We see no difficulty with this, particularly as the mechanistic basis of size-behaviour covariance is unknown. For instance, that large individuals tend to escalate conflicts can be explained as a consequence of the risks to themselves being (on average) less rI) can be readily conditioned on (or “adjusted” for) size. This is most readily done by inclusion of the size as a fixed covariate in the model, with the corresponding variance explained by size omitted from the estimate of VP. . However, it is important to recognise that this necessarily changes the biological interpretation of the analysis . Consistent with this view we found a strong positive correlation between an individual's repeatable tendency to attack and its tendency to induce fleeing. Although this treatment of correlated focal and opponent effects may be unfamiliar to some, it is really just a generalisation of a Bradley-Terry model F is non-zero then it follows logically that we expect non-zero RO . This is because an effect that predisposes to  winning when expressed in the focal individual, necessarily predisposes to  losing when encountered in an opponent O here, we suggest that modelling repeatable opponent effects on focal phenotype should have wide application. We also note that, regardless of statistical significance, including opponent identity as a random effect prevents pseudoreplication , and ensures the correct degrees of freedom are applied to tests of other model effects.Significant RBy comparison, our analyses provided wider support for context specific opponent effects of relative size. Where effects were statistically supported the predicted mean response was uniformly lowest (highest) for traits used to assay aggression (avoidance) when the focal individual was smaller (larger) than the opponent. Assuming size/weight to be a valid proxy for RHP, then escalated aggression is predicted when the contest is symmetric Xiphophorus are repeatable. We have also shown that repeatable, individual-level effects are correlated across traits both within- and across different experimental test types. This result is broadly consistent with the presence of an important axis of among individual variance in latent aggression. Furthermore, within the same analytical framework we also demonstrate context-dependent behavioural variation, and find evidence that focal individuals modify behavioural expression in response to the size of their opponent and relative to their own ability. Thus our modelling approach allows simultaneous testing of hypotheses relating to both the among- and within-individual components of (multivariate) behavioural variation. More generally, we highlight the utility of multivariate linear mixed effect models which, despite their limited application to date We have shown here that behavioural traits assayed through mirror tests and dyadic trials of male Figure S1Estimated power curves for the detection of trait repeatabilities from a sample size of 30 individuals with 2 (dotted line), 3 (dashed line) or 4 (solid line) repeat observations per individual. Power is estimated as the proportion of simulated data sets (with n = 200) for which the individual variance component is statistically significant at α = 0.05 based on a one-tailed likelihood ratio test (see main text for details). With a simulated repeatability of zero the power estimate is therefore an estimate of the type I error rate.(DOC)Click here for additional data file.Appendix S1Simple power analyses for detection of trait repeatabilities.(DOC)Click here for additional data file.Appendix S2Ethogram of behaviours assayed in mirror tests and dyadic interaction tests.(DOC)Click here for additional data file.


Imputation has been widely used in genome-wide association studies (GWAS) to infer genotypes of un-genotyped variants based on the linkage disequilibrium in external reference panels such as the HapMap and 1000 Genomes. However, imputation has only rarely been performed based on family relationships to infer genotypes of un-genotyped individuals. Using 8998 Framingham Heart Study (FHS) participants genotyped with Affymetrix 550K SNPs, we imputed genotypes of same set of SNPs for additional 3121 participants, most of whom were never genotyped due to lack of DNA sample. Prior to imputation, 122 pedigrees were too large to be handled by the imputation software Merlin. Therefore, we developed a novel pedigree splitting algorithm that can maximize the number of genotyped relatives for imputing each un-genotyped individual, while keeping new sub-pedigrees under a pre-specified size. In GWAS of four phenotypes available in FHS , we compared results using genotyped individuals only with results using both genotyped and imputed individuals. We studied the impact of applying different imputation quality filtering thresholds on the association results and did not found a universal threshold that always resulted in a more significant p-value for previously identified loci. However most of these loci had a lower p-value when we only included imputed genotypes with with ≥60% SNP- and ≥50% person-specific imputation certainty. In summary, we developed a novel algorithm for splitting large pedigrees for imputation and found a plausible imputation quality filtering threshold based on FHS. Further examination may be required to generalize this threshold to other studies. However, IBD-based genotype imputation has not frequently been applied to GWAS with family data. When phenotyped individuals exist who were not genotyped – perhaps due to limited genotyping resources or lack of a DNA sample – or poorly genotyped due to genotyping failure, poor quality DNA, or other reasons, IBD-based genotype imputation can be used to impute genotypes of these individuals and thereby increase sample size that potentially leads to better statistical power for genetic association studies.http://www.sph.umich.edu/csg/abecasis/Merlin/) Chen and Abecasis In the present investigation, we apply and evaluate the IBD-based imputation in the FHS that has recruited multiple generations of participants since 1948. The FHS sample consists of 14428 participants from 1538 pedigrees. Only 9274 have genotypes (Affymetrix 550K SNPs) and are part of the SNP Health Association Resource (SHARe). Among the rest, we imputed genotypes for those who have at least one genotyped relative using IBD-based imputation After imputation, we evaluated how different imputation-quality filtering measures affected the results of GWAS with top SNPs for several phenotypes including Alzheimer disease, circulating levels of fibrinogen, high-density lipoprotein cholesterol (HDL) and uric acid. Using plausible imputation-quality thresholds, we conduct GWAS using the sample consisting of both genotyped and imputed individuals and compare with the GWAS results using genotyped individuals only.After splitting and trimming 122 pedigrees with bit size −6 and MAF less than 0.05, we identified 8 independent loci (either on different chromosomes or at least ×10−7 (Bonferroni correction of 403640 SNPs). In addition, the locus close to LIPC on chromosome 15 for HDL – which did not reach genome-wide significance – was included because it reached genome-wide significance in the GWAS incorporating imputed individuals. The results for 9 selected loci are presented in We first performed GWAS of Alzheimer disease, fibrinogen, HDL and uric acid including the 8998 well-genotyped individuals only. After filtering out SNPs with call rate less than 90%, HWE p-value less than 10S4 present the results  for Alzheimer disease, fibrinogen, HDL and uric acid. In each plot, the horizontal line presents the results from GWAS using genotyped individuals only. P-values from combined sample are more significant than that of GWAS with genotyped individuals only for most combinations of the person- and SNP-specific certainty thresholds, except for rs4681  and rs10186236 . No combination of person- and SNP-specific certainty threshold gives uniformly best results; also, there is no clear relation between certainty thresholds and improvement in p-value.Using GWAS top SNPs as positive controls, we evaluate how the association results change with various thresholds for person-specific certainty and SNP-specific certainty used to incorporate genotypes of imputed individuals with that of genotyped individuals. To incorporate genotypes of imputed individuals with that of the genotyped individuals for GWAS, we choose the combination of person-specific certainty threshold 0.5 and SNP-specific certainty threshold 0.6 as a trade-off between quantity (sample size) and quality. This combination generally gives slightly better results than most of the other combinations based on our evaluations using those top SNPs. −6 and MAF <0.05) are applied as in 550K GWAS, so we have the same SNPs for comparison. The results for the top SNPs and the genomic control parameter λλ estimates (1.02–1.03) show that no systematic inflation in test statistics is observed. The increase in sample size varies from about 300 to 600, which leads to slight decrease in the standard error estimate of the beta coefficient. Among the 9 independent loci, 7 loci improve their statistical significance after including imputed sample. 10 p-value scatter plots of GWAS using combined sample (Y axis) against GWAS using genotyped sample (X axis). The figure shows that except for Fibrinogen, most of the SNPs that reach genome-wide significance have smaller p-values from GWAS using combined sample, as we reported.We use the combination of person-specific certainty threshold 0.5 and SNP-specific certainty threshold 0.6 to combine genotype data of imputed with genotyped individuals. With the combined data, we redo the GWAS for Alzheimer disease, fibrinogen, HDL and uric acid, additionally adjusted for imputation status to account for the potential phenotypic difference between genotyped and imputed samples. The same filters  generally having more genotyped relatives, among whom, the most informative ones are retained in the split sub-pedigrees created using our algorithm. When regressing imputation certainty on the numbers of well-genotyped 1st, 2nd, and 3rd degree relatives, the bit size and the number of members in sub-pedigree, the numbers of genotyped 1st and 2nd degree relatives and the number of members in sub-pedigree are positively associated with imputation certainty with p-values st degree genotyped relatives. As the proposed algorithm relies on the relationships within a large pedigree, results are sensitive to any pedigree misspecification in nature.The fact that the Third Generation cohort has better imputation certainty over the previous two generations is a result of more Third Generation individuals having at least one genotyped parent, or more genotyped relatives. The proportion of imputed Third Generation subjects having at least one parent is 91.9%, versus 0.2% and 32.3% for imputed individuals in the Original and Offspring cohorts, respectively. The average sum of genotyped 1The imputation works well as we only have 23 and 368 SNPs with MAF difference (maximum 0.218) greater than 0.1 and 0.05, respectively, between well-genotyped and filtered imputed samples. The 23 SNPs have average 498 Mendelian errors, which suggests additional useful criterion for selecting SNPs for imputation. If the whole 3121 imputed sample is used, MAF will be in general underestimated. This reassures the necessity of using imputation certainty filter and the validation of our GWAS results using incorporated genotype data.rd) and 96.2% (top 4th), respectively, and the improvement does not seem to be associated with high imputation certainty. The lack of improvement may be due to heterogeneity in phenotypes between imputed and genotyped individuals and/or noise in the imputed genotypes. Even though family-based imputation can increase the sample size that leads to power increase by theory, it also introduces noise due to the uncertainty in the imputed genotypes. There is no imputation certainty threshold combination that consistently gives better results than other combinations. The thresholds (person-specific certainty >0.5 and SNP-specific certainty >0.6) we have adopted for our sample seem working well. It may be applicable for other studies, but examination of the sensitivity is still warranted when applied to a different study. In addition, as shown in When incorporating imputed genotype data with observed genotype data, we consider various combinations of thresholds of person-specific certainty and SNP-specific certainty to filter out genotypes and individuals with lower imputation certainty. Although we have observed improved statistical significance for most combinations of thresholds for most of the top SNPs, there are still a few cases (rs4681 for fibrinogen and rs10186236 for HDL) with no improvement for any threshold combinations. λ) remains similar compared with GWAS using genotyped individuals only. Among the 7 loci, APOC1 for Alzheimer disease has the smallest number in sample size increase  and HDL becomes genome-wide significant after including the imputed sample. Similarly, the association between rs13148356 in SLC2A9 and uric acid becomes genome-wide significant after imputation indicates that rs13148356 is also a likely truly associated variant missed by analyzing genotyped individuals only. The associations of rs4681 (FGB) with fibrinogen and rs10186236 (DPP10) with HDL are slightly weakened, but the latter association has not previously been reported.With both imputed sample (person-specific certainty >0.5 and SNP-specific certainty >0.6) and genotyped sample included in GWAS of Alzheimer disease, fibrinogen, HDL and uric acid, the statistical significance is strengthened for 7 out of 9 independent genome-wide significant loci, or for 98 out of 146 genome-wide significant SNPs, while the inflation measured by genomic control factor  form clusters of un-genotyped individuals with their closest (first degree) un-genotyped relatives; (ii) construct sub-pedigrees based on clusters; (iii) check the bit size of the sub-pedigrees; and (iv) apply trimming if the bit size is greater than desired. Details of the algorithm and a hypothetical example of applying the algorithm are described below.For each pedigree that needs size reduction,Clustering un-genotyped individualsCompute the number of un-genotyped first degree relatives for each un-genotyped individual.Form clusters by grouping un-genotyped first degree relatives together until all un-genotyped persons are clustered. We suggest starting from the un-genotyped individual with fewest un-genotyped first degree relatives. It is more efficient and can avoid large clusters that require more trimming that may leave insufficient genotyped individuals for imputation given a user specified bit size limit.Constructing sub-pedigrees based on un-genotyped clustersInclude genotyped blood relatives (kinship coefficient >0) of each cluster member.Include parents of current members (if not included).Remove un-genotyped founder couples that only have one child.Checking bit sizeIf bit size is less than but not close to user specified, add genotyped blood relatives of spouse. If bit size is greater than user specified, do trimming. Otherwise, sub-pedigree preparation is complete.Trimming sub-pedigrees with bit size greater than user specifiedFor each genotyped individual in a sub-pedigree, compute the following scores using kinship coefficients. A genotyped individual with more closest cluster members (score1 below) is more important for imputation and should not be trimmed if not needed. The order of importance of the scores is score1, score2, score3, score4, followed by score5.score1: number of the closest cluster members (not necessarily first degree)nd closest cluster membersscore2: number of the 2rd closest cluster membersscore3: number of the 3th closest cluster membersscore4: number of the 4th closest cluster membersscore5: number of the 5Identify roots  that are not cluster members.Rank the roots by scores.Remove person or persons with the minimum rankIf a whole sibship is removed or only has one sib left, remove their founder-parents, too.Compute bit size and repeat step 4.4 until bit size is less than user specified.If bit size is less than user specified within 2 bits, trimming is done; otherwise, add back latest removed genotyped persons, so that the bit size is as close to and less than user specified.If a sub-pedigree is a subset of another sub-pedigree, remove it.http://cran.r-project.org/web/packages/kinship2/), the original pedigree (top left panel) has 34 pedigree members and bit size of 36. A grey cell represents an individual not offering consent to participate in a study of interest, a black cell represents an individual to be imputed, and an empty cell represents a genotyped individual. The number under each cell is the individual ID. There are 7 individuals to be imputed in this pedigree  and they form 4 clusters in the following order, the 1st by 107, the 2nd by 132 and 120, the 3rd by 135, the 4th by 104, 106 and 108. The other 3 panels in nd cluster. Sub-pedigree 2 with bit size 20 is formed by the 3rd cluster. Sub-pedigree 3 with bit size 20 is formed by the 4th cluster and contains the sub-pedigree formed by the 1st cluster. ID 132 appears in sub-pedigrees 1 and 2, as ID 132 belongs to the 2nd cluster, genotypes imputed based on sub-pedigree 1 will be used for ID 132. The pedigree splitting and trimming scripts written in R are available on readers’ request.Example: Consider the example pedigree presented in Each resulting sub-pedigree is centered on one or more un-genotyped individuals with as many closely related genotyped relatives as possible. Between sub-pedigrees, there may be overlaps of genotyped individuals or un-genotyped individuals. The imputation is performed using one pedigree at a time; therefore, overlapping is not an issue. But the original pedigrees should be used in association analyses, not the split pedigrees.The FHS was initiated in 1948 with the enrollment of 5209 men and women  from Framingham, MA, who underwent biennial examinations In 2007, genome-wide genotyping of SNPs was performed for 9274 individuals using an Affymetrix 550K SNPs platform; 8998 participants were well-genotyped, that is, call rate greater than 90% and heterozygous rate within +/−5 standard deviation range from mean. Among un-genotyped individuals and 276 poorly-genotyped (not well-genotyped) individuals, 3121 (from 928 pedigrees) with at least one genotyped blood relative and with consent for genetic studies, can be included for genotype imputation. The 3121 individuals included 1990, 946 and 185 Original, Offspring and Third Generation cohorts, respectively. All individuals included in this study provided written informed consent, and study protocols were approved by the Institutional Review Boards of Boston University. Merlin The characteristics for each trait for the sample of genotyped and imputed individuals are presented in GWAS of continuous traits  were performed using a linear mixed effects model with the additive coding of SNP genotypes as a fixed effect and with individual-specific random intercepts correlated according to the kinship coefficient to account for residual familial correlations −6, using split sub-pedigrees and original pedigrees with bit size not greater than 20. The imputed genotype dosage data is used in association analysis. The maximum of the posterior genotype probabilities from genotype imputation was used as an indicator of the imputation certainty at each SNP for each imputed individual, which we also called SNP-specific certainty.Based on the genotypes of 8998 well-genotyped individuals with Affymetrix 550K SNPs, we imputed genome-wide genotype data for the additional 3121 individuals . Imputation was performed for 403640 autosomal SNPs with good genotyping quality, that is, call rate>0.97, MAF>0.01 and Hardy Weinberg Equilibrium (HWE) p-value>10For each individual, we compute the proportion of SNPs with the maximum posterior probability greater than 0.95, which is used to define person-specific certainty. Applying person-specific certainty threshold of 0.9 retains individuals with the proportion greater than 0.9. In contrast, applying SNP-specific certainty threshold of 0.6 retains individuals with imputation certainty greater than 0.6 for each SNP – and different individuals may be included for different SNPs.To evaluate the effects of including imputed individuals in association testing, various quality filtering thresholds of imputation certainty  are considered to incorporate imputed individuals and their imputed genotypes with observed genotypes. Each incorporated genotype dataset is used to test the association of 8 GWAS top SNPs for Alzheimer disease, fibrinogen, HDL and uric acid that have been previously reported and serve as positive control. We then selected the incorporated genotype dataset that gave the most robust (in the sense that in most cases the results are better than results from using genotyped sample) and improved (in the sense that in most cases the results are better than results from using other filtering thresholds) results at the 8 SNPs to conduct GWAS of Alzheimer disease, fibrinogen, HDL and uric acid adjusting for the same covariates with imputation status as an additional covariate to account for the potential phenotypic difference between genotyped and imputed samples.Figure S110 plot of rs4420638 at various certainty thresholds for Alzheimer disease.–log(TIFF)Click here for additional data file.Figure S210 plot of rs4681 at various certainty thresholds for Fibrinogen.–log(TIFF)Click here for additional data file.Figure S310 plot of rs3764261, rs1919484, rs10186236, rs1800588 at various certainty thresholds for HDL.–log(TIFF)Click here for additional data file.Figure S410 plot of rs16890979, rs2231142, rs1165205 at various certainty thresholds for uric acid.–log(TIFF)Click here for additional data file.Figure S5Regional association plot of rs4420638 for Alzheimer disease using incorporated genotype data.(TIFF)Click here for additional data file.Figure S6Regional association plot of rs4681 for Fibrinogen using incorporated genotype data.(TIFF)Click here for additional data file.Figure S7Regional association plot of rs3764261 for HDL using incorporated genotype data.(TIF)Click here for additional data file.Figure S8Regional association plot of rs1919484 for HDL using incorporated genotype data.(TIF)Click here for additional data file.Figure S9Regional association plot of rs10186236 for HDL using incorporated genotype data.(TIF)Click here for additional data file.Figure S10Regional association plot of rs1800588 for HDL using incorporated genotype data.(TIF)Click here for additional data file.Figure S11Regional association plot of rs16890979 for uric acid using incorporated genotype data.(TIF)Click here for additional data file.Figure S12Regional association plot of rs2231142 for uric acid using incorporated genotype data.(TIF)Click here for additional data file.Figure S13Regional association plot of rs1165205 for uric acid using incorporated genotype data.(TIF)Click here for additional data file.


Due to the lack of statistical power and confounding effects of population structure in human population data, genotype-environment interaction studies have not yielded promising results and have provided only limited knowledge for exploring how genotype and environmental factors interact to in their influence onto risk.P = 0.00032), which is related to abdominal obesity. Data suggested that the genotypes could have different effects on the phenotype (supra-iliac skinfold thickness) in different environmental settings . We then defined the genotype groups of individuals with similar genetic profiles based on the additive genetic relationships among individuals using SNPs. We observed the norms of reaction, and the differential phenotypic response of a genotype to a change in environmental exposure. Interestingly, we also found that the gene clusters responsible for cell-cell and cell-extracellular matrix interactions were enriched significantly for genotype-area interaction.We analyzed 49 human quantitative traits in 7,170 unrelated Korean individuals on 326,262 autosomal single nucleotide polymorphisms (SNPs) collected from the KARE (Korean Association Resource) project, and we estimated the statistically significant proportion of variance that could be explained by genotype-area interactions in the supra-iliac skinfold thickness trait (This significant heritability estimate of genotype-environment interactions will lead to conceptual advances in our understanding of the mechanisms underlying genotype-environment interactions, and could be ultimately applied to personalized preventative treatments based on environmental exposures. Rapid advances in population genetics in recent years have led to significantly improved insight into human complex traits. Although a large number of genetic loci for many complex traits and diseases have been identified using genome-wide association studies (GWAS), the associated variants explain only a small percentage of the overall heritability. Many coG×E is defined as a phenomenon that phenotypes respond to genotypes differently according to different environmental factors. For example, a specific aspect of the environment might have a greater effect on some genotypes over others. Alternatively, there may be a change in the order of merit of a series of genotypes when they are measured under different environmental conditions. This caSince G×E can obscure both genetic and environmental effects, the study of G×E is essential for improving accuracy and precision when assessing both genetic and environmental factors. It can Despite this importance, there are only a small number of replicated, biologically plausible, and methodologically sound examples of G×E with demonstrated clinical relevance and sufficiently high statistical power. MoreoveIn the context of the etiology of obesity, single-gene cases cannot account for latent genetic predispositions that are revealed only upon exposure to an obesogenic environment. InsteadH. sapiens genomic sequence (version GRCh37.p5).The U.S. National Center for Biotechnology Information (NCBI) site was used as the source of the Data collected by the Korean Association Resource (KARE) project was used for this study. The participants in the KARE project were recruited from two community-based cohorts, Ansung  and Ansan (urban city), in Gyeonggi Province of South Korea. The Ansung and Ansan cohorts consisted of 5,018 and 5,020 participants, respectively, 40−69 years old and born between 1931 and 1963. This Institutional Review Board of the Korea National Institute of Health approved this study, and all participants provided written informed consent for participation. Based on Cho et al. (2009), we excluded individuals with low call rates (< 96%), sample contamination, gender inconsistencies, cryptic relatedness, and serious concomitant illness, retaining 8,842 subjects [P < 10E−6) were excluded, leaving a total of 326,262 markers to be examined.The genomic DNA was genotyped on an Affymetrix Genome-Wide Human SNP array 5.0 containing 500,568 SNPs. Markers (GRCh37) with a high missing gene call rate (> 5%), low minor allele frequency (MAF) (<0.01), and significant deviation from the Hardy-Weinberg equilibrium  and in each gender group separately.All individuals were measured for 49 quantitative traits related to obesity, blood pressure, hyperglycemia, diabetes, liver function, lung function, and kidney function. A summary of trait descriptions is provided in Yang et al. (2013). We adjuWe defined three environmental factors in each statistical model: gender, geographical area, and age. For gender, males were coded as A and females as B. The two cities were designated as 1 for Ansung and 2 for Ansan. Age was classified into three different groups: those born in 1931−45 (A), 1946−55 (B), or 1956−63 (C), representing individuals whom experienced the Korean War (1950−53) in their childhood or when older, in their early childhood, and those born after the war, respectively. Most Koreans suffered severe nutritional deficiency during the war. Hypothesizing that nutrition affects phenotypic characteristics, the age groups were defined to determine if different nutritional statuses at a young age interacted with genotypes of a specific trait.j and k can be estimated using the following equation:As implemented in the GCTA  tool, the genxij refers to the number of copies of the reference allele for the ith SNP of the jth individual, and pi is the frequency of the reference allele. We estimated the additive genetic relationships between all pairs of individuals from SNP data, and removed one from each pair of individuals with an estimated relatedness > 0.025. Finally, we retained 7,170 “unrelated” individuals for analysis, consisting of 3,261 male and 3,909 female samples and 2,928 rural (Ansung) and 4,242 urban (Ansan) residents  between individuals estimated from SNPs, and ge is a vector of genotype-environment interaction effects for all individuals, with Ag = Age for pairs of individuals in the same environment, and Age = 0 for the pairs of individuals in different environments. The environmental effects were fitted as fixed effects in the model: a vector of fixed effects (β) with its incidence matrix (X). Because GCTA estimates the variance of the genotype-environment interaction for one environmental factor, three different models were defined separately and analyzed for each environmental factor: gender, age, and area . The phenotypes were corrected previously for age and gender, and standardized to z-scores in each area cohort data separately to eliminate the necessity to include the other two fixed effects . The phenotypic variance (g) = Agge) = Age2 ≠ 0) and reduced (h2 = 0) models, where h2 refers to the heritability estimate. The bivariate REML option from this software was used to estimate the genetic correlation between two traits .To estimate the variance of G×E effects , only SUP showed a significant genotype-area interaction (P = 0.000315). SUP was measured just above the iliac crest in the mid-axillary line [P < 2.2E-16). SUP was also higher in cohorts from the urban city than from rural areas .We estimated the proportions of variance that could be explained by genetic and interaction components for each of the 49 traits , and thesformed). The hisEven though there were some effects of gender and area on the expression of this trait, this does not necessarily contribute to G×E. Nevertheless, the statistically significant heritability estimate of genotype-area interaction from the SUP trait suggests a change in the direction or magnitude of the effect of a genotype in different areas. As such, the genotypes may have a greater or lesser effect on the risk of abdominal obesity in different environmental settings . Together with a very recent study that explored G×E for diabetes-related traits in a European-American population, the preG/VP) was also estimated from this G×E model  with or without G×E in the model. The discrepancy came mostly from the amount of VG×E, since the total variance was decomposed into one additional component for the former model, and this caused some difference in estimating the proportion of variance explained by the genetic component.The proportion of phenotypic variance due to additive genetic effects (VP = 0.5)  and environmental factors (E) of two areas. For the SBP trait Figure A, G had P = 2.74E-6) and rs189317 on chromosome 8 (P = 8.01E-06) for SUP and SBP, respectively. We confirmed that the associated genetic effect does not necessarily interact with the environment or parity. For example, Cornes et al. found that the fat mass- and obesity-associated common variant (rs9939609 of the FTO gene) showed no evidence for G×E[PG = 0.001719 and PG×E = 0.7766 for the SUP trait, where PG and PG×E represent the significance of the genotype and genotype-area interaction, respectively. For the SBP trait, rs17249754, known for its relationship with blood pressure[PG = 5.04E-09), but no evidence of genotype-area interaction (PG×E = 0.4735).We also performed a genome-wide association (GWA) analysis to test genome-wide SNPs for a difference in association between the two environments with SUP and SBP traits. This si for G×E. This sapressure, had theP = 1) and rs17740112 on chromosome 18 (P = 1) for SUP and SBP, respectively. Based on these SNPs, we performed a traditional locus-specific NoR analysis . These results support the polygenicity of complex traits, in which a few “major” genes together explain only a small fraction of the heritability. There may be a significant locus related to the G×E of a certain trait, but this single locus  do not characterize the trait itself. This reveals the limitation of locus-specific analysis for understanding the genetic predisposition of complex traits.We also identified the least significant SNPs to be rs6063997 on chromosome 20  between areas for the SUP trait, suggesting that the genetic factors for this trait in two areas are not positively correlated . Although the significance does not survive multiple testing correction, this result may imply that different genetic signals are associated with abdominal obesity in different areas.To determine if variances captured by SNPs differ between areas, we performed bivariate analysis, considering SUP (or SBP as control) in area 1 as one trait and SUP (SBP) in area 2 as the other trait . The prevalence of obesity was reported to be 25−42% in urban areas, compared with 10−22% in rural areas. The main causes are nutrition (increased consumption of saturated fats and sugars), a sedentary lifestyle, and mechanization. Therefore, rural and urban living might be sufficiently strong environmental exposures that demonstrate large interaction effects with genetic factors[We extracted a total of 1,793 genes from SNPs that exceeded the threshold of al genes. The genal genes. The celal genes. Our resal genes. The braal genes. This waal genes, was enr factors,34.G×E might not appear in heritability estimates due to the lack of statistical power, particularly if a small fraction of individuals experience adverse exposure, and population stratification in the opposite direction of the allelic effect. HoweverAlthough the ‘nature versus nurture’ debate forced the admission that both genetic and environmental factors contribute to phenotypic variation, scientists continue to consider their interaction. Most genetic epidemiology studies have not considered G×E effects, simply because of the difficulty in assessing these effects in quantitative genetic models and the lack of sufficient statistical power to provide sufficient proof. HoweverG×E: Genotype-environment interaction; NoR: Norm of reaction; SUP: Supra-iliac skinfold thickness; SBP: Systolic blood pressure; SNP: Single nucleotide polymorphism; KARE: Korean Association Resource; GCTA: Genome-wide Complex Trait Analysis.The authors declare no competing interests.JK designed the study, analyzed the data, and wrote the manuscript. TL performed the data analysis. HL and HK conceived and designed the analysis. All authors read, commented on, and approved the manuscript.GE across 49 traits for each environmental factor area (A), gender (B), and age (C). Figure S2. Histograms of the SUP trait before (A) and after age, gender, and area adjustment (B). Figure S3. The proportion of phenotypic variance due to the additive genetic effects (VG/VP) with or without G×E in the model. The environmental factor E was defined as area (A), gender (B), or age (C). Table S1. Summary of the number of samples used in the analyses. Table S2. Variance explained by the genotype-area interaction for 49 traits. Table S3. Variance explained by genotype-gender interaction for 49 traits. Table S4. Variance explained by genotype-age interaction for all 49 traits. Table S5. Abbreviations of the significant traits. Table S6. The genetic variances proportional to the total variances (h2) and the genetic correlation (rg) estimated from bivariate analyses using the GCTA tool.Histograms of the distribution of VClick here for file