TY - JOUR AB - Background Epigenetic clocks can track both chronological age (cAge) and biological age (bAge). The latter is typically defined by physiological biomarkers and risk of adverse health outcomes, including all-cause mortality. As cohort sample sizes increase, estimates of cAge and bAge become more precise. Here, we aim to develop accurate epigenetic predictors of cAge and bAge, whilst improving our understanding of their epigenomic architecture. Methods First, we perform large-scale (N = 18,413) epigenome-wide association studies (EWAS) of chronological age and all-cause mortality. Next, to create a cAge predictor, we use methylation data from 24,674 participants from the Generation Scotland study, the Lothian Birth Cohorts (LBC) of 1921 and 1936, and 8 other cohorts with publicly available data. In addition, we train a predictor of time to all-cause mortality as a proxy for bAge using the Generation Scotland cohort (1214 observed deaths). For this purpose, we use epigenetic surrogates (EpiScores) for 109 plasma proteins and the 8 component parts of GrimAge, one of the current best epigenetic predictors of survival. We test this bAge predictor in four external cohorts (LBC1921, LBC1936, the Framingham Heart Study and the Women’s Health Initiative study). Results Through the inclusion of linear and non-linear age-CpG associations from the EWAS, feature pre-selection in advance of elastic net regression, and a leave-one-cohort-out (LOCO) cross-validation framework, we obtain cAge prediction with a median absolute error equal to 2.3 years. Our bAge predictor was found to slightly outperform GrimAge in terms of the strength of its association to survival (HRGrimAge = 1.47 [1.40, 1.54] with p = 1.08 × 10−52, and HRbAge = 1.52 [1.44, 1.59] with p = 2.20 × 10−60). Finally, we introduce MethylBrowsR, an online tool to visualise epigenome-wide CpG-age associations. Conclusions The integration of multiple large datasets, EpiScores, non-linear DNAm effects, and new approaches to feature selection has facilitated improvements to the blood-based epigenetic prediction of biological and chronological age. AU - Bernabeu, Elena AU - Mccartney, Daniel L. AU - Gadd, Danni A. AU - Hillary, Robert F. AU - Lu, Ake T. AU - Murphy, Lee AU - Wrobel, Nicola AU - Campbell, Archie AU - Harris, Sarah E. AU - Liewald, David AU - Hayward, Caroline AU - Sudlow, Cathie AU - Cox, Simon R. AU - Evans, Kathryn L. AU - Horvath, Steve AU - Mcintosh, Andrew M. AU - Robinson, Matthew Richard AU - Vallejos, Catalina A. AU - Marioni, Riccardo E. ID - 12719 JF - Genome Medicine TI - Refining epigenetic prediction of chronological and biological age VL - 15 ER - TY - JOUR AB - There is currently little evidence that the genetic basis of human phenotype varies significantly across the lifespan. However, time-to-event phenotypes are understudied and can be thought of as reflecting an underlying hazard, which is unlikely to be constant through life when values take a broad range. Here, we find that 74% of 245 genome-wide significant genetic associations with age at natural menopause (ANM) in the UK Biobank show a form of age-specific effect. Nineteen of these replicated discoveries are identified only by our modeling framework, which determines the time dependency of DNA-variant age-at-onset associations without a significant multiple-testing burden. Across the range of early to late menopause, we find evidence for significantly different underlying biological pathways, changes in the signs of genetic correlations of ANM to health indicators and outcomes, and differences in inferred causal relationships. We find that DNA damage response processes only act to shape ovarian reserve and depletion for women of early ANM. Genetically mediated delays in ANM were associated with increased relative risk of breast cancer and leiomyoma at all ages and with high cholesterol and heart failure for late-ANM women. These findings suggest that a better understanding of the age dependency of genetic risk factor relationships among health indicators and outcomes is achievable through appropriate statistical modeling of large-scale biobank data. AU - Ojavee, Sven E. AU - Darrous, Liza AU - Patxot, Marion AU - Läll, Kristi AU - Fischer, Krista AU - Mägi, Reedik AU - Kutalik, Zoltan AU - Robinson, Matthew Richard ID - 14258 IS - 9 JF - American Journal of Human Genetics SN - 0002-9297 TI - Genetic insights into the age-specific biological mechanisms governing human ovarian aging VL - 110 ER - TY - JOUR AB - Background: Blood-based markers of cognitive functioning might provide an accessible way to track neurodegeneration years prior to clinical manifestation of cognitive impairment and dementia. Results: Using blood-based epigenome-wide analyses of general cognitive function, we show that individual differences in DNA methylation (DNAm) explain 35.0% of the variance in general cognitive function (g). A DNAm predictor explains ~4% of the variance, independently of a polygenic score, in two external cohorts. It also associates with circulating levels of neurology- and inflammation-related proteins, global brain imaging metrics, and regional cortical volumes. Conclusions: As sample sizes increase, the ability to assess cognitive function from DNAm data may be informative in settings where cognitive testing is unreliable or unavailable. AU - McCartney, Daniel L. AU - Hillary, Robert F. AU - Conole, Eleanor L.S. AU - Banos, Daniel Trejo AU - Gadd, Danni A. AU - Walker, Rosie M. AU - Nangle, Cliff AU - Flaig, Robin AU - Campbell, Archie AU - Murray, Alison D. AU - Maniega, Susana Muñoz AU - Valdés-Hernández, María Del C. AU - Harris, Mathew A. AU - Bastin, Mark E. AU - Wardlaw, Joanna M. AU - Harris, Sarah E. AU - Porteous, David J. AU - Tucker-Drob, Elliot M. AU - McIntosh, Andrew M. AU - Evans, Kathryn L. AU - Deary, Ian J. AU - Cox, Simon R. AU - Robinson, Matthew Richard AU - Marioni, Riccardo E. ID - 10702 IS - 1 JF - Genome Biology SN - 1474-7596 TI - Blood-based epigenome-wide analyses of cognitive abilities VL - 23 ER - TY - JOUR AB - Genetically informed, deep-phenotyped biobanks are an important research resource and it is imperative that the most powerful, versatile, and efficient analysis approaches are used. Here, we apply our recently developed Bayesian grouped mixture of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest genomic prediction accuracy reported to date across 21 heritable traits. When compared to other approaches, GMRM accuracy was greater than annotation prediction models run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%), respectively, and was 18% (SE 3%) greater than a baseline BayesR model without single-nucleotide polymorphism (SNP) markers grouped into minor allele frequency–linkage disequilibrium (MAF-LD) annotation categories. For height, the prediction accuracy R2 was 47% in a UK Biobank holdout sample, which was 76% of the estimated h2SNP. We then extend our GMRM prediction model to provide mixed-linear model association (MLMA) SNP marker estimates for genome-wide association (GWAS) discovery, which increased the independent loci detected to 16,162 in unrelated UK Biobank individuals, compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62 and 65% increase, respectively. The average χ2 value of the leading markers increased by 15.24 (SE 0.41) for every 1% increase in prediction accuracy gained over a baseline BayesR model across the traits. Thus, we show that modeling genetic associations accounting for MAF and LD differences among SNP markers, and incorporating prior knowledge of genomic function, is important for both genomic prediction and discovery in large-scale individual-level studies. AU - Orliac, Etienne J. AU - Trejo Banos, Daniel AU - Ojavee, Sven E. AU - Läll, Kristi AU - Mägi, Reedik AU - Visscher, Peter M. AU - Robinson, Matthew Richard ID - 11733 IS - 31 JF - Proceedings of the National Academy of Sciences of the United States of America TI - Improving GWAS discovery and genomic prediction accuracy in biobank data VL - 119 ER - TY - GEN AB - Genetically informed, deep-phenotyped biobanks are an important research resource and it is imperative that the most powerful, versatile, and efficient analysis approaches are used. Here, we apply our recently developed Bayesian grouped mixture of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest genomic prediction accuracy reported to date across 21 heritable traits. When compared to other approaches, GMRM accuracy was greater than annotation prediction models run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%), respectively, and was 18% (SE 3%) greater than a baseline BayesR model without single-nucleotide polymorphism (SNP) markers grouped into minor allele frequency–linkage disequilibrium (MAF-LD) annotation categories. For height, the prediction accuracy R 2 was 47% in a UK Biobank holdout sample, which was 76% of the estimated h SNP 2 . We then extend our GMRM prediction model to provide mixed-linear model association (MLMA) SNP marker estimates for genome-wide association (GWAS) discovery, which increased the independent loci detected to 16,162 in unrelated UK Biobank individuals, compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62 and 65% increase, respectively. The average χ2 value of the leading markers increased by 15.24 (SE 0.41) for every 1% increase in prediction accuracy gained over a baseline BayesR model across the traits. Thus, we show that modeling genetic associations accounting for MAF and LD differences among SNP markers, and incorporating prior knowledge of genomic function, is important for both genomic prediction and discovery in large-scale individual-level studies. AU - Orliac, Etienne AU - Trejo Banos, Daniel AU - Ojavee, Sven AU - Läll, Kristi AU - Mägi, Reedik AU - Visscher, Peter AU - Robinson, Matthew Richard ID - 13064 TI - Improving genome-wide association discovery and genomic prediction accuracy in biobank data ER - TY - JOUR AB - Theory for liability-scale models of the underlying genetic basis of complex disease provides an important way to interpret, compare, and understand results generated from biological studies. In particular, through estimation of the liability-scale heritability (LSH), liability models facilitate an understanding and comparison of the relative importance of genetic and environmental risk factors that shape different clinically important disease outcomes. Increasingly, large-scale biobank studies that link genetic information to electronic health records, containing hundreds of disease diagnosis indicators that mostly occur infrequently within the sample, are becoming available. Here, we propose an extension of the existing liability-scale model theory suitable for estimating LSH in biobank studies of low-prevalence disease. In a simulation study, we find that our derived expression yields lower mean square error (MSE) and is less sensitive to prevalence misspecification as compared to previous transformations for diseases with =< 2% population prevalence and LSH of =< 0.45, especially if the biobank sample prevalence is less than that of the wider population. Applying our expression to 13 diagnostic outcomes of =< 3% prevalence in the UK Biobank study revealed important differences in LSH obtained from the different theoretical expressions that impact the conclusions made when comparing LSH across disease outcomes. This demonstrates the importance of careful consideration for estimation and prediction of low-prevalence disease outcomes and facilitates improved inference of the underlying genetic basis of =< 2% population prevalence diseases, especially where biobank sample ascertainment results in a healthier sample population. AU - Ojavee, Sven E. AU - Kutalik, Zoltan AU - Robinson, Matthew Richard ID - 12142 IS - 11 JF - The American Journal of Human Genetics KW - Genetics (clinical) KW - Genetics SN - 0002-9297 TI - Liability-scale heritability estimation for biobank studies of low-prevalence disease VL - 109 ER - TY - JOUR AB - Background: About 800 women die every day worldwide from pregnancy-related complications, including excessive blood loss, infections and high-blood pressure (World Health Organization, 2019). To improve screening for high-risk pregnancies, we set out to identify patterns of maternal hematological changes associated with future pregnancy complications. Methods: Using mixed effects models, we established changes in 14 complete blood count (CBC) parameters for 1710 healthy pregnancies and compared them to measurements from 98 pregnancy-induced hypertension, 106 gestational diabetes and 339 postpartum hemorrhage cases. Results: Results show interindividual variations, but good individual repeatability in CBC values during physiological pregnancies, allowing the identification of specific alterations in women with obstetric complications. For example, in women with uncomplicated pregnancies, haemoglobin count decreases of 0.12 g/L (95% CI −0.16, −0.09) significantly per gestation week (p value <.001). Interestingly, this decrease is three times more pronounced in women who will develop pregnancy-induced hypertension, with an additional decrease of 0.39 g/L (95% CI −0.51, −0.26). We also confirm that obstetric complications and white CBC predict the likelihood of giving birth earlier during pregnancy. Conclusion: We provide a comprehensive description of the associations between haematological changes through pregnancy and three major obstetric complications to support strategies for prevention, early-diagnosis and maternal care. AU - Patxot, Marion AU - Stojanov, Miloš AU - Ojavee, Sven Erik AU - Gobert, Rosanna Pescini AU - Kutalik, Zoltán AU - Gavillet, Mathilde AU - Baud, David AU - Robinson, Matthew Richard ID - 12235 IS - 5 JF - European Journal of Haematology KW - Hematology KW - General Medicine SN - 0902-4441 TI - Haematological changes from conception to childbirth: An indicator of major pregnancy complications VL - 109 ER - TY - GEN AB - CpGs and corresponding mean weights for DNAm-based prediction of cognitive abilities (6 traits) AU - McCartney, Daniel L AU - Hillary, Robert F AU - Conole, Eleanor LS AU - Trejo Banos, Daniel AU - Gadd, Danni A AU - Walker, Rosie M AU - Nangle, Cliff AU - Flaig, Robin AU - Campbell, Archie AU - Murray, Alison D AU - Munoz Maniega, Susana AU - del C Valdes-Hernandez, Maria AU - Harris, Mathew A AU - Bastin, Mark E AU - Wardlaw, Joanna M AU - Harris, Sarah E AU - Porteous, David J AU - Tucker-Drob, Elliot M AU - McIntosh, Andrew M AU - Evans, Kathryn L AU - Deary, Ian J AU - Cox, Simon R AU - Robinson, Matthew Richard AU - Marioni, Riccardo E ID - 13072 TI - Blood-based epigenome-wide analyses of cognitive abilities ER - TY - JOUR AB - While recent advancements in computation and modelling have improved the analysis of complex traits, our understanding of the genetic basis of the time at symptom onset remains limited. Here, we develop a Bayesian approach (BayesW) that provides probabilistic inference of the genetic architecture of age-at-onset phenotypes in a sampling scheme that facilitates biobank-scale time-to-event analyses. We show in extensive simulation work the benefits BayesW provides in terms of number of discoveries, model performance and genomic prediction. In the UK Biobank, we find many thousands of common genomic regions underlying the age-at-onset of high blood pressure (HBP), cardiac disease (CAD), and type-2 diabetes (T2D), and for the genetic basis of onset reflecting the underlying genetic liability to disease. Age-at-menopause and age-at-menarche are also highly polygenic, but with higher variance contributed by low frequency variants. Genomic prediction into the Estonian Biobank data shows that BayesW gives higher prediction accuracy than other approaches. AU - Ojavee, Sven E AU - Kousathanas, Athanasios AU - Trejo Banos, Daniel AU - Orliac, Etienne J AU - Patxot, Marion AU - Lall, Kristi AU - Magi, Reedik AU - Fischer, Krista AU - Kutalik, Zoltan AU - Robinson, Matthew Richard ID - 8430 IS - 1 JF - Nature Communications TI - Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis VL - 12 ER - TY - JOUR AB - The extent to which women differ in the course of blood cell counts throughout pregnancy, and the importance of these changes to pregnancy outcomes has not been well defined. Here, we develop a series of statistical analyses of repeated measures data to reveal the degree to which women differ in the course of pregnancy, predict the changes that occur, and determine the importance of these changes for post-partum hemorrhage (PPH) which is one of the leading causes of maternal mortality. We present a prospective cohort of 4082 births recorded at the University Hospital, Lausanne, Switzerland between 2009 and 2014 where full labour records could be obtained, along with complete blood count data taken at hospital admission. We find significant differences, at a [Formula: see text] level, among women in how blood count values change through pregnancy for mean corpuscular hemoglobin, mean corpuscular volume, mean platelet volume, platelet count and red cell distribution width. We find evidence that almost all complete blood count values show trimester-specific associations with PPH. For example, high platelet count (OR 1.20, 95% CI 1.01-1.53), high mean platelet volume (OR 1.58, 95% CI 1.04-2.08), and high erythrocyte levels (OR 1.36, 95% CI 1.01-1.57) in trimester 1 increased PPH, but high values in trimester 3 decreased PPH risk (OR 0.85, 0.79, 0.67 respectively). We show that differences among women in the course of blood cell counts throughout pregnancy have an important role in shaping pregnancy outcome and tracking blood count value changes through pregnancy improves identification of women at increased risk of postpartum hemorrhage. This study provides greater understanding of the complex changes in blood count values that occur through pregnancy and provides indicators to guide the stratification of patients into risk groups. AU - Robinson, Matthew Richard AU - Patxot, Marion AU - Stojanov, Miloš AU - Blum, Sabine AU - Baud, David ID - 10069 JF - Scientific Reports TI - Postpartum hemorrhage risk is driven by changes in blood composition through pregnancy VL - 11 ER - TY - JOUR AB - We develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability estimation, an alternative to marker discovery, and accurate genomic prediction, taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritability parameters in the UK Biobank. We find that only ≤10% of the genetic variation captured for height, body mass index, cardiovascular disease, and type 2 diabetes is attributable to proximal regulatory regions within 10kb upstream of genes, while 12-25% is attributed to coding regions, 32–44% to introns, and 22-28% to distal 10-500kb upstream regions. Up to 24% of all cis and coding regions of each chromosome are associated with each trait, with over 3,100 independent exonic and intronic regions and over 5,400 independent regulatory regions having ≥95% probability of contributing ≥0.001% to the genetic variance of these four traits. Our open-source software (GMRM) provides a scalable alternative to current approaches for biobank data. AU - Patxot, Marion AU - Trejo Banos, Daniel AU - Kousathanas, Athanasios AU - Orliac, Etienne J AU - Ojavee, Sven E AU - Moser, Gerhard AU - Sidorenko, Julia AU - Kutalik, Zoltan AU - Magi, Reedik AU - Visscher, Peter M AU - Ronnegard, Lars AU - Robinson, Matthew Richard ID - 8429 IS - 1 JF - Nature Communications TI - Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits VL - 12 ER - TY - GEN AB - We develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability estimation, an alternative to marker discovery, and accurate genomic prediction, taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritability parameters in the UK Biobank. We find that only $\leq$ 10\% of the genetic variation captured for height, body mass index, cardiovascular disease, and type 2 diabetes is attributable to proximal regulatory regions within 10kb upstream of genes, while 12-25% is attributed to coding regions, 32-44% to introns, and 22-28% to distal 10-500kb upstream regions. Up to 24% of all cis and coding regions of each chromosome are associated with each trait, with over 3,100 independent exonic and intronic regions and over 5,400 independent regulatory regions having >95% probability of contributing >0.001% to the genetic variance of these four traits. Our open-source software (GMRM) provides a scalable alternative to current approaches for biobank data. AU - Robinson, Matthew Richard ID - 13063 TI - Probabilistic inference of the genetic architecture of functional enrichment of complex traits ER - TY - JOUR AB - We conducted DNA methylation association analyses using Illumina 450K data from whole blood for an Australian amyotrophic lateral sclerosis (ALS) case–control cohort (782 cases and 613 controls). Analyses used mixed linear models as implemented in the OSCA software. We found a significantly higher proportion of neutrophils in cases compared to controls which replicated in an independent cohort from the Netherlands (1159 cases and 637 controls). The OSCA MOMENT linear mixed model has been shown in simulations to best account for confounders. When combined in a methylation profile score, the 25 most-associated probes identified by MOMENT significantly classified case–control status in the Netherlands sample (area under the curve, AUC = 0.65, CI95% = [0.62–0.68], p = 8.3 × 10−22). The maximum AUC achieved was 0.69 (CI95% = [0.66–0.71], p = 4.3 × 10−34) when cell-type proportion was included in the predictor. AU - Nabais, Marta F. AU - Lin, Tian AU - Benyamin, Beben AU - Williams, Kelly L. AU - Garton, Fleur C. AU - Vinkhuyzen, Anna A. E. AU - Zhang, Futao AU - Vallerga, Costanza L. AU - Restuadi, Restuadi AU - Freydenzon, Anna AU - Zwamborn, Ramona A. J. AU - Hop, Paul J. AU - Robinson, Matthew Richard AU - Gratten, Jacob AU - Visscher, Peter M. AU - Hannon, Eilis AU - Mill, Jonathan AU - Brown, Matthew A. AU - Laing, Nigel G. AU - Mather, Karen A. AU - Sachdev, Perminder S. AU - Ngo, Shyuan T. AU - Steyn, Frederik J. AU - Wallace, Leanne AU - Henders, Anjali K. AU - Needham, Merrilee AU - Veldink, Jan H. AU - Mathers, Susan AU - Nicholson, Garth AU - Rowe, Dominic B. AU - Henderson, Robert D. AU - McCombe, Pamela A. AU - Pamphlett, Roger AU - Yang, Jian AU - Blair, Ian P. AU - McRae, Allan F. AU - Wray, Naomi R. ID - 7708 JF - npj Genomic Medicine SN - 2056-7944 TI - Significant out-of-sample classification from methylation profile scoring for amyotrophic lateral sclerosis VL - 5 ER - TY - JOUR AB - The growing sample size of genome-wide association studies has facilitated the discovery of gene-environment interactions (GxE). Here we propose a maximum likelihood method to estimate the contribution of GxE to continuous traits taking into account all interacting environmental variables, without the need to measure any. Extensive simulations demonstrate that our method provides unbiased interaction estimates and excellent coverage. We also offer strategies to distinguish specific GxE from general scale effects. Applying our method to 32 traits in the UK Biobank reveals that while the genetic risk score (GRS) of 376 variants explains 5.2% of body mass index (BMI) variance, GRSxE explains an additional 1.9%. Nevertheless, this interaction holds for any variable with identical correlation to BMI as the GRS, hence may not be GRS-specific. Still, we observe that the global contribution of specific GRSxE to complex traits is substantial for nine obesity-related measures (including leg impedance and trunk fat-free mass). AU - Sulc, Jonathan AU - Mounier, Ninon AU - Günther, Felix AU - Winkler, Thomas AU - Wood, Andrew R. AU - Frayling, Timothy M. AU - Heid, Iris M. AU - Robinson, Matthew Richard AU - Kutalik, Zoltán ID - 7707 JF - Nature Communications SN - 2041-1723 TI - Quantification of the overall contribution of gene-environment interaction for obesity-related traits VL - 11 ER - TY - JOUR AB - Linking epigenetic marks to clinical outcomes improves insight into molecular processes, disease prediction, and therapeutic target identification. Here, a statistical approach is presented to infer the epigenetic architecture of complex disease, determine the variation captured by epigenetic effects, and estimate phenotype-epigenetic probe associations jointly. Implicitly adjusting for probe correlations, data structure (cell-count or relatedness), and single-nucleotide polymorphism (SNP) marker effects, improves association estimates and in 9,448 individuals, 75.7% (95% CI 71.70–79.3) of body mass index (BMI) variation and 45.6% (95% CI 37.3–51.9) of cigarette consumption variation was captured by whole blood methylation array data. Pathway-linked probes of blood cholesterol, lipid transport and sterol metabolism for BMI, and xenobiotic stimuli response for smoking, showed >1.5 times larger associations with >95% posterior inclusion probability. Prediction accuracy improved by 28.7% for BMI and 10.2% for smoking over a LASSO model, with age-, and tissue-specificity, implying associations are a phenotypic consequence rather than causal. AU - Trejo Banos, D AU - McCartney, DL AU - Patxot, M AU - Anchieri, L AU - Battram, T AU - Christiansen, C AU - Costeira, R AU - Walker, RM AU - Morris, SW AU - Campbell, A AU - Zhang, Q AU - Porteous, DJ AU - McRae, AF AU - Wray, NR AU - Visscher, PM AU - Haley, CS AU - Evans, KL AU - Deary, IJ AU - McIntosh, AM AU - Hemani, G AU - Bell, JT AU - Marioni, RE AU - Robinson, Matthew Richard ID - 7999 JF - Nature Communications SN - 2041-1723 TI - Bayesian reassessment of the epigenetic architecture of complex traits VL - 11 ER - TY - JOUR AB - The molecular factors which control circulating levels of inflammatory proteins are not well understood. Furthermore, association studies between molecular probes and human traits are often performed by linear model-based methods which may fail to account for complex structure and interrelationships within molecular datasets.In this study, we perform genome- and epigenome-wide association studies (GWAS/EWAS) on the levels of 70 plasma-derived inflammatory protein biomarkers in healthy older adults (Lothian Birth Cohort 1936; n = 876; Olink® inflammation panel). We employ a Bayesian framework (BayesR+) which can account for issues pertaining to data structure and unknown confounding variables (with sensitivity analyses using ordinary least squares- (OLS) and mixed model-based approaches). We identified 13 SNPs associated with 13 proteins (n = 1 SNP each) concordant across OLS and Bayesian methods. We identified 3 CpG sites spread across 3 proteins (n = 1 CpG each) that were concordant across OLS, mixed-model and Bayesian analyses. Tagged genetic variants accounted for up to 45% of variance in protein levels (for MCP2, 36% of variance alone attributable to 1 polymorphism). Methylation data accounted for up to 46% of variation in protein levels (for CXCL10). Up to 66% of variation in protein levels (for VEGFA) was explained using genetic and epigenetic data combined. We demonstrated putative causal relationships between CD6 and IL18R1 with inflammatory bowel disease and between IL12B and Crohn’s disease. Our data may aid understanding of the molecular regulation of the circulating inflammatory proteome as well as causal relationships between inflammatory mediators and disease. AU - Hillary, Robert F. AU - Trejo-Banos, Daniel AU - Kousathanas, Athanasios AU - Mccartney, Daniel L. AU - Harris, Sarah E. AU - Stevenson, Anna J. AU - Patxot, Marion AU - Ojavee, Sven Erik AU - Zhang, Qian AU - Liewald, David C. AU - Ritchie, Craig W. AU - Evans, Kathryn L. AU - Tucker-Drob, Elliot M. AU - Wray, Naomi R. AU - Mcrae, Allan F. AU - Visscher, Peter M. AU - Deary, Ian J. AU - Robinson, Matthew Richard AU - Marioni, Riccardo E. ID - 8133 IS - 1 JF - Genome Medicine TI - Multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults VL - 12 ER - TY - GEN AB - Additional file 2: Supplementary Tables. The association of pre-adjusted protein levels with biological and technical covariates. Protein levels were adjusted for age, sex, array plate and four genetic principal components (population structure) prior to analyses. Significant associations are emboldened. (Table S1). pQTLs associated with inflammatory biomarker levels from Bayesian penalised regression model (Posterior Inclusion Probability > 95%). (Table S2). All pQTLs associated with inflammatory biomarker levels from ordinary least squares regression model (P < 7.14 × 10− 10). (Table S3). Summary of lambda values relating to ordinary least squares GWAS and EWAS performed on inflammatory protein levels (n = 70) in Lothian Birth Cohort 1936 study. (Table S4). Conditionally significant pQTLs associated with inflammatory biomarker levels from ordinary least squares regression model (P < 7.14 × 10− 10). (Table S5). Comparison of variance explained by ordinary least squares and Bayesian penalised regression models for concordantly identified SNPs. (Table S6). Estimate of heritability for blood protein levels as well as proportion of variance explained attributable to different prior mixtures. (Table S7). Comparison of heritability estimates from Ahsan et al. (maximum likelihood) and Hillary et al. (Bayesian penalised regression). (Table S8). List of concordant SNPs identified by linear model and Bayesian penalised regression and whether they have been previously identified as eQTLs. (Table S9). Bayesian tests of colocalisation for cis pQTLs and cis eQTLs. (Table S10). Sherlock algorithm: Genes whose expression are putatively associated with circulating inflammatory proteins that harbour pQTLs. (Table S11). CpGs associated with inflammatory protein biomarkers as identified by Bayesian model (Bayesian model; Posterior Inclusion Probability > 95%). (Table S12). CpGs associated with inflammatory protein biomarkers as identified by linear model (limma) at P < 5.14 × 10− 10. (Table S13). CpGs associated with inflammatory protein biomarkers as identified by mixed linear model (OSCA) at P < 5.14 × 10− 10. (Table S14). Estimate of variance explained for blood protein levels by DNA methylation as well as proportion of explained attributable to different prior mixtures - BayesR+. (Table S15). Comparison of variance in protein levels explained by genome-wide DNA methylation data by mixed linear model (OSCA) and Bayesian penalised regression model (BayesR+). (Table S16). Variance in circulating inflammatory protein biomarker levels explained by common genetic and methylation data (joint and conditional estimates from BayesR+). Ordered by combined variance explained by genetic and epigenetic data - smallest to largest. Significant results from t-tests comparing distributions for variance explained by methylation or genetics alone versus combined estimate are emboldened. (Table S17). Genetic and epigenetic factors identified by BayesR+ when conditioning on all SNPs and CpGs together. (Table S18). Mendelian Randomisation analyses to assess whether proteins with concordantly identified genetic signals are causally associated with Alzheimer’s disease risk. (Table S19). AU - Hillary, Robert F. AU - Trejo-Banos, Daniel AU - Kousathanas, Athanasios AU - McCartney, Daniel L. AU - Harris, Sarah E. AU - Stevenson, Anna J. AU - Patxot, Marion AU - Ojavee, Sven Erik AU - Zhang, Qian AU - Liewald, David C. AU - Ritchie, Craig W. AU - Evans, Kathryn L. AU - Tucker-Drob, Elliot M. AU - Wray, Naomi R. AU - McRae, Allan F. AU - Visscher, Peter M. AU - Deary, Ian J. AU - Robinson, Matthew Richard AU - Marioni, Riccardo E. ID - 9706 TI - Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults ER - TY - JOUR AB - The number of human genomes being genotyped or sequenced increases exponentially and efficient haplotype estimation methods able to handle this amount of data are now required. Here we present a method, SHAPEIT4, which substantially improves upon other methods to process large genotype and high coverage sequencing datasets. It notably exhibits sub-linear running times with sample size, provides highly accurate haplotypes and allows integrating external phasing information such as large reference panels of haplotypes, collections of pre-phased variants and long sequencing reads. We provide SHAPEIT4 in an open source format and demonstrate its performance in terms of accuracy and running times on two gold standard datasets: the UK Biobank data and the Genome In A Bottle. AU - Delaneau, Olivier AU - Zagury, Jean-François AU - Robinson, Matthew Richard AU - Marchini, Jonathan L. AU - Dermitzakis, Emmanouil T. ID - 7710 JF - Nature Communications SN - 2041-1723 TI - Accurate, scalable and integrative haplotype estimation VL - 10 ER - TY - JOUR AB - The nature and extent of mitochondrial DNA variation in a population and how it affects traits is poorly understood. Here we resequence the mitochondrial genomes of 169 Drosophila Genetic Reference Panel lines, identifying 231 variants that stratify along 12 mitochondrial haplotypes. We identify 1,845 cases of mitonuclear allelic imbalances, thus implying that mitochondrial haplotypes are reflected in the nuclear genome. However, no major fitness effects are associated with mitonuclear imbalance, suggesting that such imbalances reflect population structure at the mitochondrial level rather than genomic incompatibilities. Although mitochondrial haplotypes have no direct impact on mitochondrial respiration, some haplotypes are associated with stress- and metabolism-related phenotypes, including food intake in males. Finally, through reciprocal swapping of mitochondrial genomes, we demonstrate that a mitochondrial haplotype associated with high food intake can rescue a low food intake phenotype. Together, our findings provide new insight into population structure at the mitochondrial level and point to the importance of incorporating mitochondrial haplotypes in genotype–phenotype relationship studies. AU - Bevers, Roel P. J. AU - Litovchenko, Maria AU - Kapopoulou, Adamandia AU - Braman, Virginie S. AU - Robinson, Matthew Richard AU - Auwerx, Johan AU - Hollis, Brian AU - Deplancke, Bart ID - 7711 IS - 12 JF - Nature Metabolism SN - 2522-5812 TI - Mitochondrial haplotypes affect metabolic phenotypes in the Drosophila Genetic Reference Panel VL - 1 ER - TY - GEN AB - As genome-wide association studies (GWAS) increased in size, numerous gene-environment interactions (GxE) have been discovered, many of which however explore only one environment at a time and may suffer from statistical artefacts leading to biased interaction estimates. Here we propose a maximum likelihood method to estimate the contribution of GxE to complex traits taking into account all interacting environmental variables at the same time, without the need to measure any. This is possible because GxE induces fluctuations in the conditional trait variance, the extent of which depends on the strength of GxE. The approach can be applied to continuous outcomes and for single SNPs or genetic risk scores (GRS). Extensive simulations demonstrated that our method yields unbiased interaction estimates and excellent confidence interval coverage. We also offer a strategy to distinguish specific GxE from general heteroscedasticity (scale effects). Applying our method to 32 complex traits in the UK Biobank reveals that for body mass index (BMI) the GRSxE explains an additional 1.9% variance on top of the 5.2% GRS contribution. However, this interaction is not specific to the GRS and holds for any variable similarly correlated with BMI. On the contrary, the GRSxE interaction effect for leg impedance Embedded Image is significantly (P < 10−56) larger than it would be expected for a similarly correlated variable Embedded Image. We showed that our method could robustly detect the global contribution of GxE to complex traits, which turned out to be substantial for certain obesity measures. AU - Sulc, Jonathan AU - Mounier, Ninon AU - Günther, Felix AU - Winkler, Thomas AU - Wood, Andrew R. AU - Frayling, Timothy M. AU - Heid, Iris M. AU - Robinson, Matthew Richard AU - Kutalik, Zoltán ID - 7782 T2 - bioRxiv TI - Maximum likelihood method quantifies the overall contribution of gene-environment interaction to continuous traits: An application to complex traits in the UK Biobank ER - TY - JOUR AB - Background: DNA methylation levels change along with age, but few studies have examined the variation in the rate of such changes between individuals. Methods: We performed a longitudinal analysis to quantify the variation in the rate of change of DNA methylation between individuals using whole blood DNA methylation array profiles collected at 2–4 time points (N = 2894) in 954 individuals (67–90 years). Results: After stringent quality control, we identified 1507 DNA methylation CpG sites (rsCpGs) with statistically significant variation in the rate of change (random slope) of DNA methylation among individuals in a mixed linear model analysis. Genes in the vicinity of these rsCpGs were found to be enriched in Homeobox transcription factors and the Wnt signalling pathway, both of which are related to ageing processes. Furthermore, we investigated the SNP effect on the random slope. We found that 4 out of 1507 rsCpGs had one significant (P < 5 × 10−8/1507) SNP effect and 343 rsCpGs had at least one SNP effect (436 SNP-probe pairs) reaching genome-wide significance (P < 5 × 10−8). Ninety-five percent of the significant (P < 5 × 10−8) SNPs are on different chromosomes from their corresponding probes. Conclusions: We identified CpG sites that have variability in the rate of change of DNA methylation between individuals, and our results suggest a genetic basis of this variation. Genes around these CpG sites have been reported to be involved in the ageing process. AU - Zhang, Qian AU - Marioni, Riccardo E AU - Robinson, Matthew Richard AU - Higham, Jon AU - Sproul, Duncan AU - Wray, Naomi R AU - Deary, Ian J AU - McRae, Allan F AU - Visscher, Peter M ID - 7717 IS - 1 JF - Genome Medicine SN - 1756-994X TI - Genotype effects contribute to variation in longitudinal methylome patterns in older people VL - 10 ER - TY - JOUR AB - Flores Island, Indonesia, was inhabited by the small-bodied hominin species Homo floresiensis, which has an unknown evolutionary relationship to modern humans. This island is also home to an extant human pygmy population. Here we describe genome-scale single-nucleotide polymorphism data and whole-genome sequences from a contemporary human pygmy population living on Flores near the cave where H. floresiensis was found. The genomes of Flores pygmies reveal a complex history of admixture with Denisovans and Neanderthals but no evidence for gene flow with other archaic hominins. Modern individuals bear the signatures of recent positive selection encompassing the FADS (fatty acid desaturase) gene cluster, likely related to diet, and polygenic selection acting on standing variation that contributed to their short-stature phenotype. Thus, multiple independent instances of hominin insular dwarfism occurred on Flores. AU - Tucci, Serena AU - Vohr, Samuel H. AU - McCoy, Rajiv C. AU - Vernot, Benjamin AU - Robinson, Matthew Richard AU - Barbieri, Chiara AU - Nelson, Brad J. AU - Fu, Wenqing AU - Purnomo, Gludhug A. AU - Sudoyo, Herawati AU - Eichler, Evan E. AU - Barbujani, Guido AU - Visscher, Peter M. AU - Akey, Joshua M. AU - Green, Richard E. ID - 7718 IS - 6401 JF - Science SN - 0036-8075 TI - Evolutionary history and adaptation of a human pygmy population of Flores Island, Indonesia VL - 361 ER - TY - JOUR AB - Male pattern baldness (MPB) is a sex-limited, age-related, complex trait. We study MPB genetics in 205,327 European males from the UK Biobank. Here we show that MPB is strongly heritable and polygenic, with pedigree-heritability of 0.62 (SE = 0.03) estimated from close relatives, and SNP-heritability of 0.39 (SE = 0.01) from conventionally-unrelated males. We detect 624 near-independent genome-wide loci, contributing SNP-heritability of 0.25 (SE = 0.01), of which 26 X-chromosome loci explain 11.6%. Autosomal genetic variance is enriched for common variants and regions of lower linkage disequilibrium. We identify plausible genetic correlations between MPB and multiple sex-limited markers of earlier puberty, increased bone mineral density (rg = 0.15) and pancreatic β-cell function (rg = 0.12). Correlations with reproductive traits imply an effect on fitness, consistent with an estimated linear selection gradient of -0.018 per MPB standard deviation. Overall, we provide genetic insights into MPB: a phenotype of interest in its own right, with value as a model sex-limited, complex trait. AU - Yap, Chloe X. AU - Sidorenko, Julia AU - Wu, Yang AU - Kemper, Kathryn E. AU - Yang, Jian AU - Wray, Naomi R. AU - Robinson, Matthew Richard AU - Visscher, Peter M. ID - 7712 JF - Nature Communications SN - 2041-1723 TI - Dissection of genetic variation and evidence for pleiotropy in male pattern baldness VL - 9 ER - TY - JOUR AB - Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7% for height to 47% for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait. AU - Maier, Robert M. AU - Zhu, Zhihong AU - Lee, Sang Hong AU - Trzaskowski, Maciej AU - Ruderfer, Douglas M. AU - Stahl, Eli A. AU - Ripke, Stephan AU - Wray, Naomi R. AU - Yang, Jian AU - Visscher, Peter M. AU - Robinson, Matthew Richard ID - 7716 JF - Nature Communications SN - 2041-1723 TI - Improving genetic prediction by leveraging genetic correlations among human diseases and traits VL - 9 ER - TY - JOUR AB - Preference for mates with similar phenotypes; that is, assortative mating, is widely observed in humans1,2,3,4,5 and has evolutionary consequences6,7,8. Under Fisher's classical theory6, assortative mating is predicted to induce a signature in the genome at trait-associated loci that can be detected and quantified. Here, we develop and apply a method to quantify assortative mating on a specific trait by estimating the correlation (θ) between genetic predictors of the trait from single nucleotide polymorphisms on odd- versus even-numbered chromosomes. We show by theory and simulation that the effect of assortative mating can be quantified in the presence of population stratification. We applied this approach to 32 complex traits and diseases using single nucleotide polymorphism data from ~400,000 unrelated individuals of European ancestry. We found significant evidence of assortative mating for height (θ = 3.2%) and educational attainment (θ = 2.7%), both of which were consistent with theoretical predictions. Overall, our results imply that assortative mating involves multiple traits and affects the genomic architecture of loci that are associated with these traits, and that the consequence of mate choice can be detected from a random sample of genomes. AU - Yengo, Loic AU - Robinson, Matthew Richard AU - Keller, Matthew C. AU - Kemper, Kathryn E. AU - Yang, Yuanhao AU - Trzaskowski, Maciej AU - Gratten, Jacob AU - Turley, Patrick AU - Cesarini, David AU - Benjamin, Daniel J. AU - Wray, Naomi R. AU - Goddard, Michael E. AU - Yang, Jian AU - Visscher, Peter M. ID - 7715 IS - 12 JF - Nature Human Behaviour SN - 2397-3374 TI - Imprint of assortative mating on the human genome VL - 2 ER - TY - JOUR AB - Health risk factors such as body mass index (BMI) and serum cholesterol are associated with many common diseases. It often remains unclear whether the risk factors are cause or consequence of disease, or whether the associations are the result of confounding. We develop and apply a method (called GSMR) that performs a multi-SNP Mendelian randomization analysis using summary-level data from genome-wide association studies to test the causal associations of BMI, waist-to-hip ratio, serum cholesterols, blood pressures, height, and years of schooling (EduYears) with common diseases (sample sizes of up to 405,072). We identify a number of causal associations including a protective effect of LDL-cholesterol against type-2 diabetes (T2D) that might explain the side effects of statins on T2D, a protective effect of EduYears against Alzheimer’s disease, and bidirectional associations with opposite effects (e.g., higher BMI increases the risk of T2D but the effect of T2D on BMI is negative). AU - Zhu, Zhihong AU - Zheng, Zhili AU - Zhang, Futao AU - Wu, Yang AU - Trzaskowski, Maciej AU - Maier, Robert AU - Robinson, Matthew Richard AU - McGrath, John J. AU - Visscher, Peter M. AU - Wray, Naomi R. AU - Yang, Jian ID - 7714 JF - Nature Communications SN - 2041-1723 TI - Causal associations between risk factors and common diseases inferred from GWAS summary data VL - 9 ER - TY - JOUR AB - There are mean differences in complex traits among global human populations. We hypothesize that part of the phenotypic differentiation is due to natural selection. To address this hypothesis, we assess the differentiation in allele frequencies of trait-associated SNPs among African, Eastern Asian, and European populations for ten complex traits using data of large sample size (up to ~405,000). We show that SNPs associated with height (P=2.46×10−5), waist-to-hip ratio (P=2.77×10−4), and schizophrenia (P=3.96×10−5) are significantly more differentiated among populations than matched “control” SNPs, suggesting that these trait-associated SNPs have undergone natural selection. We further find that SNPs associated with height (P=2.01×10−6) and schizophrenia (P=5.16×10−18) show significantly higher variance in linkage disequilibrium (LD) scores across populations than control SNPs. Our results support the hypothesis that natural selection has shaped the genetic differentiation of complex traits, such as height and schizophrenia, among worldwide populations. AU - Guo, Jing AU - Wu, Yang AU - Zhu, Zhihong AU - Zheng, Zhili AU - Trzaskowski, Maciej AU - Zeng, Jian AU - Robinson, Matthew Richard AU - Visscher, Peter M. AU - Yang, Jian ID - 7713 JF - Nature Communications SN - 2041-1723 TI - Global genetic differentiation of complex traits shaped by natural selection in humans VL - 9 ER - TY - JOUR AB - The availability of genome-wide genetic data on hundreds of thousands of people has led to an equally rapid growth in methodologies available to analyse these data. While the motivation for undertaking genome-wide association studies (GWAS) is identification of genetic markers associated with complex traits, once generated these data can be used for many other analyses. GWAS have demonstrated that complex traits exhibit a highly polygenic genetic architecture, often with shared genetic risk factors across traits. New methods to analyse data from GWAS are increasingly being used to address a diverse set of questions about the aetiology of complex traits and diseases, including psychiatric disorders. Here, we give an overview of some of these methods and present examples of how they have contributed to our understanding of psychiatric disorders. We consider: (i) estimation of the extent of genetic influence on traits, (ii) uncovering of shared genetic control between traits, (iii) predictions of genetic risk for individuals, (iv) uncovering of causal relationships between traits, (v) identifying causal single-nucleotide polymorphisms and genes or (vi) the detection of genetic heterogeneity. This classification helps organise the large number of recently developed methods, although some could be placed in more than one category. While some methods require GWAS data on individual people, others simply use GWAS summary statistics data, allowing novel well-powered analyses to be conducted at a low computational burden. AU - Maier, R. M. AU - Visscher, P. M. AU - Robinson, Matthew Richard AU - Wray, N. R. ID - 7721 IS - 7 JF - Psychological Medicine SN - 0033-2917 TI - Embracing polygenicity: A review of methods and tools for psychiatric genetics research VL - 48 ER - TY - JOUR AB - Genome-wide association studies (GWAS) have identified thousands of loci that are robustly associated with complex diseases. The use of linear mixed model (LMM) methodology for GWAS is becoming more prevalent due to its ability to control for population structure and cryptic relatedness and to increase power. The odds ratio (OR) is a common measure of the association of a disease with an exposure (e.g., a genetic variant) and is readably available from logistic regression. However, when the LMM is applied to all-or-none traits it provides estimates of genetic effects on the observed 0–1 scale, a different scale to that in logistic regression. This limits the comparability of results across studies, for example in a meta-analysis, and makes the interpretation of the magnitude of an effect from an LMM GWAS difficult. In this study, we derived transformations from the genetic effects estimated under the LMM to the OR that only rely on summary statistics. To test the proposed transformations, we used real genotypes from two large, publicly available data sets to simulate all-or-none phenotypes for a set of scenarios that differ in underlying model, disease prevalence, and heritability. Furthermore, we applied these transformations to GWAS summary statistics for type 2 diabetes generated from 108,042 individuals in the UK Biobank. In both simulation and real-data application, we observed very high concordance between the transformed OR from the LMM and either the simulated truth or estimates from logistic regression. The transformations derived and validated in this study improve the comparability of results from prospective and already performed LMM GWAS on complex diseases by providing a reliable transformation to a common comparative scale for the genetic effects. AU - Lloyd-Jones, Luke R. AU - Robinson, Matthew Richard AU - Yang, Jian AU - Visscher, Peter M. ID - 7723 IS - 4 JF - Genetics SN - 0016-6731 TI - Transformation of summary statistics from linear mixed model association on all-or-none traits to odds ratio VL - 208 ER - TY - JOUR AB - We develop a Bayesian mixed linear model that simultaneously estimates single-nucleotide polymorphism (SNP)-based heritability, polygenicity (proportion of SNPs with nonzero effects), and the relationship between SNP effect size and minor allele frequency for complex traits in conventionally unrelated individuals using genome-wide SNP data. We apply the method to 28 complex traits in the UK Biobank data (N = 126,752) and show that on average, 6% of SNPs have nonzero effects, which in total explain 22% of phenotypic variance. We detect significant (P < 0.05/28) signatures of natural selection in the genetic architecture of 23 traits, including reproductive, cardiovascular, and anthropometric traits, as well as educational attainment. The significant estimates of the relationship between effect size and minor allele frequency in complex traits are consistent with a model of negative (or purifying) selection, as confirmed by forward simulation. We conclude that negative selection acts pervasively on the genetic variants associated with human complex traits. AU - Zeng, Jian AU - de Vlaming, Ronald AU - Wu, Yang AU - Robinson, Matthew Richard AU - Lloyd-Jones, Luke R. AU - Yengo, Loic AU - Yap, Chloe X. AU - Xue, Angli AU - Sidorenko, Julia AU - McRae, Allan F. AU - Powell, Joseph E. AU - Montgomery, Grant W. AU - Metspalu, Andres AU - Esko, Tonu AU - Gibson, Greg AU - Wray, Naomi R. AU - Visscher, Peter M. AU - Yang, Jian ID - 7722 IS - 5 JF - Nature Genetics SN - 1061-4036 TI - Signatures of negative selection in the genetic architecture of human complex traits VL - 50 ER - TY - JOUR AB - Modern molecular genetic datasets, primarily collected to study the biology of human health and disease, can be used to directly measure the action of natural selection and reveal important features of contemporary human evolution. Here we leverage the UK Biobank data to test for the presence of linear and nonlinear natural selection in a contemporary population of the United Kingdom. We obtain phenotypic and genetic evidence consistent with the action of linear/directional selection. Phenotypic evidence suggests that stabilizing selection, which acts to reduce variance in the population without necessarily modifying the population mean, is widespread and relatively weak in comparison with estimates from other species. AU - Sanjak, Jaleal S. AU - Sidorenko, Julia AU - Robinson, Matthew Richard AU - Thornton, Kevin R. AU - Visscher, Peter M. ID - 7724 IS - 1 JF - Proceedings of the National Academy of Sciences SN - 0027-8424 TI - Evidence of directional and stabilizing selection in contemporary humans VL - 115 ER - TY - GEN AB - The Drosophila Genetic Reference Panel (DGRP) serves as a valuable resource to better understand the genetic landscapes underlying quantitative traits. However, such DGRP studies have so far only focused on nuclear genetic variants. To address this, we sequenced the mitochondrial genomes of >170 DGRP lines, identifying 229 variants including 21 indels and 7 frameshifts. We used our mitochondrial variation data to identify 12 genetically distinct mitochondrial haplotypes, thus revealing important population structure at the mitochondrial level. We further examined whether this population structure was reflected on the nuclear genome by screening for the presence of potential mito-nuclear genetic incompatibilities in the form of significant genotype ratio distortions (GRDs) between mitochondrial and nuclear variants. In total, we detected a remarkable 1,845 mito-nuclear GRDs, with the highest enrichment observed in a 40 kb region around the gene Sex-lethal (Sxl). Intriguingly, downstream phenotypic analyses did not uncover major fitness effects associated with these GRDs, suggesting that a large number of mito-nuclear GRDs may reflect population structure at the mitochondrial level rather than actual genomic incompatibilities. This is further supported by the GRD landscape showing particular large genomic regions associated with a single mitochondrial haplotype. Next, we explored the functional relevance of the detected mitochondrial haplotypes through an association analysis on a set of 259 assembled, non-correlating DGRP phenotypes. We found multiple significant associations with stress- and metabolism-related phenotypes, including food intake in males. We validated the latter observation by reciprocal swapping of mitochondrial genomes from high food intake DGRP lines to low food intake ones. In conclusion, our study uncovered important mitochondrial population structure and haplotype-specific metabolic variation in the DGRP, thus demonstrating the significance of incorporating mitochondrial haplotypes in geno-phenotype relationship studies. AU - Bevers, Roel P.J. AU - Litovchenko, Maria AU - Kapopoulou, Adamandia AU - Braman, Virginie S. AU - Robinson, Matthew Richard AU - Auwerx, Johan AU - Hollis, Brian AU - Deplancke, Bart ID - 7783 T2 - bioRxiv TI - Extensive mitochondrial population structure and haplotype-specific phenotypic variation in the Drosophila Genetic Reference Panel ER - TY - JOUR AB - Meta-analyses of genome-wide association studies, which dominate genetic discovery, are based on data from diverse historical time periods and populations. Genetic scores derived from genome-wide association studies explain only a fraction of the heritability estimates obtained from whole-genome studies on single populations, known as the ‘hidden heritability’ puzzle. Using seven sampling populations (n = 35,062), we test whether hidden heritability is attributed to heterogeneity across sampling populations and time, showing that estimates are substantially smaller across populations compared with within populations. We show that the hidden heritability varies substantially: from zero for height to 20% for body mass index, 37% for education, 40% for age at first birth and up to 75% for number of children. Simulations demonstrate that our results are more likely to reflect heterogeneity in phenotypic measurement or gene–environment interactions than genetic heterogeneity. These findings have substantial implications for genetic discovery, suggesting that large homogenous datasets are required for behavioural phenotypes and that gene–environment interaction may be a central challenge for genetic discovery. AU - Tropf, Felix C. AU - Lee, S. Hong AU - Verweij, Renske M. AU - Stulp, Gert AU - van der Most, Peter J. AU - de Vlaming, Ronald AU - Bakshi, Andrew AU - Briley, Daniel A. AU - Rahal, Charles AU - Hellpap, Robert AU - Iliadou, Anastasia N. AU - Esko, Tõnu AU - Metspalu, Andres AU - Medland, Sarah E. AU - Martin, Nicholas G. AU - Barban, Nicola AU - Snieder, Harold AU - Robinson, Matthew Richard AU - Mills, Melinda C. ID - 7728 IS - 10 JF - Nature Human Behaviour SN - 2397-3374 TI - Hidden heritability due to heterogeneity across seven populations VL - 1 ER - TY - JOUR AB - Genes of the major histocompatibility complex (MHC) have been shown to influence social signalling and mate preferences in many species, including humans. First observations suggest that MHC signalling may also affect female fertility. To test this hypothesis, we exposed 191 female horses (Equus caballus) to either an MHC-similar or an MHC-dissimilar stimulus male around the time of ovulation and conception. A within-subject experimental design controlled for non-MHC-linked male characteristics, and instrumental insemination with semen of other males (n = 106) controlled for potential confounding effects of semen or embryo characteristics. We found that females were more likely to become pregnant if exposed to an MHC-dissimilar than to an MHC-similar male, while overall genetic distance to the stimulus males (based on microsatellite markers on 20 chromosomes) had no effect. Our results demonstrate that early pregnancy failures can be due to maternal life-history decisions (cryptic female choice) influenced by MHC-linked social signalling. AU - Burger, D. AU - Thomas, S. AU - Aepli, H. AU - Dreyer, M. AU - Fabre, G. AU - Marti, E. AU - Sieme, H. AU - Robinson, Matthew Richard AU - Wedekind, C. ID - 7727 IS - 1868 JF - Proceedings of the Royal Society B: Biological Sciences SN - 0962-8452 TI - Major histocompatibility complex-linked social signalling affects female fertility VL - 284 ER - TY - JOUR AB - Quantifying the effects of inbreeding is critical to characterizing the genetic architecture of complex traits. This study highlights through theory and simulations the strengths and shortcomings of three SNP-based inbreeding measures commonly used to estimate inbreeding depression (ID). We demonstrate that heterogeneity in linkage disequilibrium (LD) between causal variants and SNPs biases ID estimates, and we develop an approach to correct this bias using LD and minor allele frequency stratified inference (LDMS). We quantified ID in 25 traits measured in ∼140,000 participants of the UK Biobank, using LDMS, and confirmed previously published ID for 4 traits. We find unique evidence of ID for handgrip strength, waist/hip ratio, and visual and auditory acuity (ID between −2.3 and −5.2 phenotypic SDs for complete inbreeding; P<0.001). Our results illustrate that a careful choice of the measure of inbreeding combined with LDMS stratification improves both detection and quantification of ID using SNP data. AU - Yengo, Loic AU - Zhu, Zhihong AU - Wray, Naomi R. AU - Weir, Bruce S. AU - Yang, Jian AU - Robinson, Matthew Richard AU - Visscher, Peter M. ID - 7729 IS - 32 JF - Proceedings of the National Academy of Sciences SN - 0027-8424 TI - Detection and quantification of inbreeding depression for complex traits from SNP data VL - 114 ER - TY - JOUR AB - Phenotypic plasticity is the ability of an individual genotype to alter aspects of its phenotype depending on the current environment. It is central to the persistence, resistance and resilience of populations facing variation in physical or biological factors. Genetic variation in plasticity is pervasive, which suggests its local adaptation is plausible. Existing studies on the adaptation of plasticity typically focus on single traits and a few populations, while theory about interactions among genes (for example, pleiotropy) suggests that a multi-trait, landscape scale (for example, multiple populations) perspective is required. We present data from a landscape scale, replicated, multi-trait experiment using a classic predator–prey system centred on the water flea Daphnia pulex. We find predator regime-driven differences in genetic variation of multivariate plasticity. These differences are associated with strong divergent selection linked to a predation regime. Our findings are evidence for local adaptation of plasticity, suggesting that responses of populations to environmental variation depend on the conditions in which they evolved in the past. AU - Reger, Julia AU - Lind, Martin I. AU - Robinson, Matthew Richard AU - Beckerman, Andrew P. ID - 7725 JF - Nature Ecology & Evolution SN - 2397-334X TI - Predation drives local adaptation of phenotypic plasticity VL - 2 ER - TY - JOUR AB - Sections PDFPDF Tools Share Abstract Background: Gene discovery has provided remarkable biological insights into amyotrophic lateral sclerosis (ALS). One challenge for clinical application of genetic testing is critical evaluation of the significance of reported variants. Methods: We use whole exome sequencing (WES) to develop a clinically relevant approach to identify a subset of ALS patients harboring likely pathogenic mutations. In parallel, we assess if DNA methylation can be used to screen for pathogenicity of novel variants since a methylation signature has been shown to associate with the pathogenic C9orf72 expansion, but has not been explored for other ALS mutations. Australian patients identified with ALS‐relevant variants were cross‐checked with population databases and case reports to critically assess whether they were “likely causal,” “uncertain significance,” or “unlikely causal.” Results: Published ALS variants were identified in >10% of patients; however, in only 3% of patients (4/120) could these be confidently considered pathogenic (in SOD1 and TARDBP). We found no evidence for a differential DNA methylation signature in these mutation carriers. Conclusions: The use of WES in a typical ALS clinic demonstrates a critical approach to variant assessment with the capability to combine cohorts to enhance the largely unknown genetic basis of ALS. AU - Garton, Fleur C. AU - Benyamin, Beben AU - Zhao, Qiongyi AU - Liu, Zhijun AU - Gratten, Jacob AU - Henders, Anjali K. AU - Zhang, Zong-Hong AU - Edson, Janette AU - Furlong, Sarah AU - Morgan, Sarah AU - Heggie, Susan AU - Thorpe, Kathryn AU - Pfluger, Casey AU - Mather, Karen A. AU - Sachdev, Perminder S. AU - McRae, Allan F. AU - Robinson, Matthew Richard AU - Shah, Sonia AU - Visscher, Peter M. AU - Mangelsdorf, Marie AU - Henderson, Robert D. AU - Wray, Naomi R. AU - McCombe, Pamela A. ID - 7733 IS - 4 JF - Molecular Genetics & Genomic Medicine SN - 2324-9269 TI - Whole exome sequencing and DNA methylation analysis in a clinical amyotrophic lateral sclerosis cohort VL - 5 ER - TY - JOUR AB - Genetic association studies in admixed populations are underrepresented in the genomics literature, with a key concern for researchers being the adequate control of spurious associations due to population structure. Linear mixed models (LMMs) are well suited for genome-wide association studies (GWAS) because they account for both population stratification and cryptic relatedness and achieve increased statistical power by jointly modeling all genotyped markers. Additionally, Bayesian LMMs allow for more flexible assumptions about the underlying distribution of genetic effects, and can concurrently estimate the proportion of phenotypic variance explained by genetic markers. Using three recently published Bayesian LMMs, Bayes R, BSLMM, and BOLT-LMM, we investigate an existing data set on eye (n = 625) and skin (n = 684) color from Cape Verde, an island nation off West Africa that is home to individuals with a broad range of phenotypic values for eye and skin color due to the mix of West African and European ancestry. We use simulations to demonstrate the utility of Bayesian LMMs for mapping loci and studying the genetic architecture of quantitative traits in admixed populations. The Bayesian LMMs provide evidence for two new pigmentation loci: one for eye color (AHRR) and one for skin color (DDB1). AU - Lloyd-Jones, Luke R. AU - Robinson, Matthew Richard AU - Moser, Gerhard AU - Zeng, Jian AU - Beleza, Sandra AU - Barsh, Gregory S. AU - Tang, Hua AU - Visscher, Peter M. ID - 7731 IS - 2 JF - Genetics SN - 0016-6731 TI - Inference on the genetic basis of eye and skin color in an admixed population via Bayesian linear mixed models VL - 206 ER - TY - JOUR AB - Importance: Considerable partner resemblances have been found for a wide range of psychiatric disorders, meaning that partners of affected individuals have an increased risk of being affected compared with partners of unaffected individuals. If this resemblance is reflected in genetic similarity between partners, genetic risk is anticipated to accumulate in offspring, but these potential consequences have not been quantified and have been left implicit. Observations: The anticipated consequences of partner resemblance on prevalence and heritability of psychiatric traits in the offspring generation were modeled for disorders with varying heritabilities, population prevalence (lifetime risk), and magnitudes of partner resemblance. These models facilitate interpretation for a wide range of psychiatric disorders, such as autism, schizophrenia, and depression. The genetic consequences of partner resemblance are most pronounced when attributable to phenotypic assortment (driven by the psychiatric trait). Phenotypic assortment results in increased genetic variance in the offspring generation, which may result in increased heritability and population prevalence. These consequences add generation after generation to a limit, but assortative mating is unlikely to balance the impact of reduced fecundity of patients with psychiatric disorders in the long term. This modeling suggests that the heritabilities of psychiatric disorders are unlikely to increase by more than 5% from 1 generation of assortative mating (maximally 13% across multiple generations). The population prevalence will increase most for less common disorders with high heritability; for example, the prevalence of autism might increase by 1.5-fold after 1 generation of assortative mating (≥2.4-fold in the long term) depending on several assumptions. Conclusions and Relevance: The considerable partner resemblances found for psychiatric disorders deserve more detailed interpretation than has been provided thus far. Although the limitations of modeling are emphasized, the anticipated consequences are at most modest for the heritability but may be considerable for the population prevalence of rare disorders with a high heritability. AU - Peyrot, Wouter J. AU - Robinson, Matthew Richard AU - Penninx, Brenda W. J. H. AU - Wray, Naomi R. ID - 7734 IS - 11 JF - JAMA Psychiatry SN - 2168-622X TI - Exploring boundaries for the genetic consequences of assortative mating for psychiatric traits VL - 73 ER - TY - JOUR AB - We develop a novel approach to identify regions of the genome underlying population genetic differentiation in any genetic data where the underlying population structure is unknown, or where the interest is assessing divergence along a gradient. By combining the statistical framework for genome-wide association studies (GWASs) with eigenvector decomposition (EigenGWAS), which is commonly used in population genetics to characterize the structure of genetic data, loci under selection can be identified without a requirement for discrete populations. We show through theory and simulation that our approach can identify regions under selection along gradients of ancestry, and in real data we confirm this by demonstrating LCT to be under selection between HapMap CEU–TSI cohorts, and we then validate this selection signal across European countries in the POPRES samples. HERC2 was also found to be differentiated between both the CEU–TSI cohort and within the POPRES sample, reflecting the likely anthropological differences in skin and hair colour between northern and southern European populations. Controlling for population stratification is of great importance in any quantitative genetic study and our approach also provides a simple, fast and accurate way of predicting principal components in independent samples. With ever increasing sample sizes across many fields, this approach is likely to be greatly utilized to gain individual-level eigenvectors avoiding the computational challenges associated with conducting singular value decomposition in large data sets. We have developed freely available software, Genetic Analysis Repository (GEAR), to facilitate the application of the methods. AU - Chen, G-B AU - Lee, S H AU - Zhu, Z-X AU - Benyamin, B AU - Robinson, Matthew Richard ID - 7736 JF - Heredity SN - 0018-067X TI - EigenGWAS: Finding loci under selection through genome-wide association studies of eigenvectors in structured populations VL - 117 ER - TY - JOUR AB - Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with human complex traits. However, the genes or functional DNA elements through which these variants exert their effects on the traits are often unknown. We propose a method (called SMR) that integrates summary-level data from GWAS with data from expression quantitative trait locus (eQTL) studies to identify genes whose expression levels are associated with a complex trait because of pleiotropy. We apply the method to five human complex traits using GWAS data on up to 339,224 individuals and eQTL data on 5,311 individuals, and we prioritize 126 genes (for example, TRAF1 and ANKRD55 for rheumatoid arthritis and SNX19 and NMRAL1 for schizophrenia), of which 25 genes are new candidates; 77 genes are not the nearest annotated gene to the top associated GWAS SNP. These genes provide important leads to design future functional studies to understand the mechanism whereby DNA variation leads to complex trait variation. AU - Zhu, Zhihong AU - Zhang, Futao AU - Hu, Han AU - Bakshi, Andrew AU - Robinson, Matthew Richard AU - Powell, Joseph E AU - Montgomery, Grant W AU - Goddard, Michael E AU - Wray, Naomi R AU - Visscher, Peter M AU - Yang, Jian ID - 7737 IS - 5 JF - Nature Genetics SN - 1061-4036 TI - Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets VL - 48 ER - TY - JOUR AB - Across-nation differences in the mean values for complex traits are common1,2,3,4,5,6,7,8, but the reasons for these differences are unknown. Here we find that many independent loci contribute to population genetic differences in height and body mass index (BMI) in 9,416 individuals across 14 European countries. Using discovery data on over 250,000 individuals and unbiased effect size estimates from 17,500 sibling pairs, we estimate that 24% (95% credible interval (CI) = 9%, 41%) and 8% (95% CI = 4%, 16%) of the captured additive genetic variance for height and BMI, respectively, reflect population genetic differences. Population genetic divergence differed significantly from that in a null model (height, P < 3.94 × 10−8; BMI, P < 5.95 × 10−4), and we find an among-population genetic correlation for tall and slender individuals (r = −0.80, 95% CI = −0.95, −0.60), consistent with correlated selection for both phenotypes. Observed differences in height among populations reflected the predicted genetic means (r = 0.51; P < 0.001), but environmental differences across Europe masked genetic differentiation for BMI (P < 0.58). AU - Robinson, Matthew Richard AU - Hemani, Gibran AU - Medina-Gomez, Carolina AU - Mezzavilla, Massimo AU - Esko, Tonu AU - Shakhbazov, Konstantin AU - Powell, Joseph E AU - Vinkhuyzen, Anna AU - Berndt, Sonja I AU - Gustafsson, Stefan AU - Justice, Anne E AU - Kahali, Bratati AU - Locke, Adam E AU - Pers, Tune H AU - Vedantam, Sailaja AU - Wood, Andrew R AU - van Rheenen, Wouter AU - Andreassen, Ole A AU - Gasparini, Paolo AU - Metspalu, Andres AU - Berg, Leonard H van den AU - Veldink, Jan H AU - Rivadeneira, Fernando AU - Werge, Thomas M AU - Abecasis, Goncalo R AU - Boomsma, Dorret I AU - Chasman, Daniel I AU - de Geus, Eco J C AU - Frayling, Timothy M AU - Hirschhorn, Joel N AU - Hottenga, Jouke Jan AU - Ingelsson, Erik AU - Loos, Ruth J F AU - Magnusson, Patrik K E AU - Martin, Nicholas G AU - Montgomery, Grant W AU - North, Kari E AU - Pedersen, Nancy L AU - Spector, Timothy D AU - Speliotes, Elizabeth K AU - Goddard, Michael E AU - Yang, Jian AU - Visscher, Peter M ID - 7742 IS - 11 JF - Nature Genetics SN - 1061-4036 TI - Population genetic differentiation of height and body mass index across Europe VL - 47 ER - TY - JOUR AB - Phenotypes expressed in a social context are not only a function of the individual, but can also be shaped by the phenotypes of social partners. These social effects may play a major role in the evolution of cooperative breeding if social partners differ in the quality of care they provide and if individual carers adjust their effort in relation to that of other carers. When applying social effects models to wild study systems, it is also important to explore sources of individual plasticity that could masquerade as social effects. We studied offspring provisioning rates of parents and helpers in a wild population of long-tailed tits Aegithalos caudatus using a quantitative genetic framework to identify these social effects and partition them into genetic, permanent environment and current environment components. Controlling for other effects, individuals were consistent in their provisioning effort at a given nest, but adjusted their effort based on who was in their social group, indicating the presence of social effects. However, these social effects differed between years and social contexts, indicating a current environment effect, rather than indicating a genetic or permanent environment effect. While this study reveals the importance of examining environmental and genetic sources of social effects, the framework we present is entirely general, enabling a greater understanding of potentially important social effects within any ecological population. AU - Adams, Mark James AU - Robinson, Matthew Richard AU - Mannarelli, Maria-Elena AU - Hatchwell, Ben J. ID - 7741 IS - 1810 JF - Proceedings of the Royal Society B: Biological Sciences SN - 0962-8452 TI - Social genetic and social environment effects on parental and helper care in a cooperatively breeding bird VL - 282 ER - TY - JOUR AB - Currently, there is much debate on the genetic architecture of quantitative traits in wild populations. Is trait variation influenced by many genes of small effect or by a few genes of major effect? Where is additive genetic variation located in the genome? Do the same loci cause similar phenotypic variation in different populations? Great tits (Parus major) have been studied extensively in long‐term studies across Europe and consequently are considered an ecological ‘model organism’. Recently, genomic resources have been developed for the great tit, including a custom SNP chip and genetic linkage map. In this study, we used a suite of approaches to investigate the genetic architecture of eight quantitative traits in two long‐term study populations of great tits—one in the Netherlands and the other in the United Kingdom. Overall, we found little evidence for the presence of genes of large effects in either population. Instead, traits appeared to be influenced by many genes of small effect, with conservative estimates of the number of contributing loci ranging from 31 to 310. Despite concordance between population‐specific heritabilities, we found no evidence for the presence of loci having similar effects in both populations. While population‐specific genetic architectures are possible, an undetected shared architecture cannot be rejected because of limited power to map loci of small and moderate effects. This study is one of few examples of genetic architecture analysis in replicated wild populations and highlights some of the challenges and limitations researchers will face when attempting similar molecular quantitative genetic studies in free‐living populations. AU - Santure, Anna W. AU - Poissant, Jocelyn AU - De Cauwer, Isabelle AU - van Oers, Kees AU - Robinson, Matthew Richard AU - Quinn, John L. AU - Groenen, Martien A. M. AU - Visser, Marcel E. AU - Sheldon, Ben C. AU - Slate, Jon ID - 7739 JF - Molecular Ecology SN - 0962-1083 TI - Replicated analysis of the genetic architecture of quantitative traits in two wild great tit populations VL - 24 ER - TY - CHAP AB - Experimental studies have demonstrated that environmental variation can create genotype‐environment interactions (GEIs) in the traits involved in sexual selection. Understanding the genetic architecture of phenotype across environments will require statistical tests that can describe both changes in genetic variance and covariance across environments. This chapter outlines the theoretical framework for the processes of sexual selection in the wild, identifying key parameters in wild systems, and highlighting the potential effects of the environment. It describes the proposed approaches for the estimation of these key parameters in a quantitative genetic framework within naturally occurring pedigreed populations. The chapter provides a worked example for a range of analysis methods. It aims to provide an overview of the analytical methods that can be used to model GEIs for traits involved in sexual selection in naturally occurring pedigreed populations. AU - Robinson, Matthew Richard AU - Qvarnström, Anna ED - Hunt, John ED - Hosken, David ID - 7743 SN - 9780470671795 T2 - Genotype-by-Environment Interactions and Sexual Selection TI - Influence of the environment on the genetic architecture of traits involved in sexual selection within wild populations ER - TY - JOUR AU - Robinson, Matthew Richard AU - Wray, Naomi R. AU - Visscher, Peter M. ID - 7744 IS - 4 JF - Trends in Genetics SN - 0168-9525 TI - Explaining additional genetic variation in complex traits VL - 30 ER - TY - JOUR AB - The underlying basis of genetic variation in quantitative traits, in terms of the number of causal variants and the size of their effects, is largely unknown in natural populations. The expectation is that complex quantitative trait variation is attributable to many, possibly interacting, causal variants, whose effects may depend upon the sex, age and the environment in which they are expressed. A recently developed methodology in animal breeding derives a value of relatedness among individuals from high‐density genomic marker data, to estimate additive genetic variance within livestock populations. Here, we adapt and test the effectiveness of these methods to partition genetic variation for complex traits across genomic regions within ecological study populations where individuals have varying degrees of relatedness. We then apply this approach for the first time to a natural population and demonstrate that genetic variation in wing length in the great tit (Parus major) reflects contributions from multiple genomic regions. We show that a polygenic additive mode of gene action best describes the patterns observed, and we find no evidence of dosage compensation for the sex chromosome. Our results suggest that most of the genomic regions that influence wing length have the same effects in both sexes. We found a limited amount of genetic variance in males that is attributed to regions that have no effects in females, which could facilitate the sexual dimorphism observed for this trait. Although this exploratory work focuses on one complex trait, the methodology is generally applicable to any trait for any laboratory or wild population, paving the way for investigating sex‐, age‐ and environment‐specific genetic effects and thus the underlying genetic architecture of phenotype in biological study systems. AU - Robinson, Matthew Richard AU - Santure, Anna W. AU - DeCauwer, Isabelle AU - Sheldon, Ben C. AU - Slate, Jon ID - 7745 IS - 15 JF - Molecular Ecology SN - 0962-1083 TI - Partitioning of genetic variation across the genome using multimarker methods in a wild bird population VL - 22 ER - TY - JOUR AB - Clutch size and egg mass are life history traits that have been extensively studied in wild bird populations, as life history theory predicts a negative trade‐off between them, either at the phenotypic or at the genetic level. Here, we analyse the genomic architecture of these heritable traits in a wild great tit (Parus major) population, using three marker‐based approaches – chromosome partitioning, quantitative trait locus (QTL) mapping and a genome‐wide association study (GWAS). The variance explained by each great tit chromosome scales with predicted chromosome size, no location in the genome contains genome‐wide significant QTL, and no individual SNPs are associated with a large proportion of phenotypic variation, all of which may suggest that variation in both traits is due to many loci of small effect, located across the genome. There is no evidence that any regions of the genome contribute significantly to both traits, which combined with a small, nonsignificant negative genetic covariance between the traits, suggests the absence of genetic constraints on the independent evolution of these traits. Our findings support the hypothesis that variation in life history traits in natural populations is likely to be determined by many loci of small effect spread throughout the genome, which are subject to continued input of variation by mutation and migration, although we cannot exclude the possibility of an additional input of major effect genes influencing either trait. AU - Santure, Anna W. AU - De Cauwer, Isabelle AU - Robinson, Matthew Richard AU - Poissant, Jocelyn AU - Sheldon, Ben C. AU - Slate, Jon ID - 7746 IS - 15 JF - Molecular Ecology SN - 0962-1083 TI - Genomic dissection of variation in clutch size and egg mass in a wild great tit (Parus major) population VL - 22 ER - TY - JOUR AB - Acquisition and allocation of resources are central to life‐history theory. However, empirical work typically focuses only on allocation despite the fact that relationships between fitness components may be governed by differences in the ability of individuals to acquire resources across environments. Here, we outline a statistical framework to partition the genetic basis of multivariate plasticity into independent axes of genetic variation, and quantify for the first time, the extent to which specific traits drive multitrait genotype–environment interactions. Our framework generalises to analyses of plasticity, growth and ageing. We apply this approach to a unique, large‐scale, multivariate study of acquisition, allocation and plasticity in the life history of the cricket, Gryllus firmus. We demonstrate that resource acquisition and allocation are genetically correlated, and that plasticity in trade‐offs between allocation to components of fitness is 90% dependent on genetic variance for total resource acquisition. These results suggest that genotype–environment effects for resource acquisition can maintain variation in life‐history components that are typically observed in the wild. AU - Robinson, Matthew Richard AU - Beckerman, Andrew P. ID - 7747 IS - 3 JF - Ecology Letters SN - 1461-023X TI - Quantifying multivariate plasticity: Genetic variation in resource acquisition drives plasticity in resource allocation to components of life history VL - 16 ER - TY - JOUR AB - Although studies on laboratory species and natural populations of vertebrates have shown reproduction to impair later performance, little is known of the age‐specific associations between reproduction and survival, and how such findings apply to the ageing of large, long‐lived species. Herein we develop a framework to examine population‐level patterns of reproduction and survival across lifespan in long‐lived organisms, and decompose those changes into individual‐level effects, and the effects of age‐specific trade‐offs between fitness components. We apply this to an extensive longitudinal dataset on female semi‐captive Asian timber elephants (Elephas maximus) and report the first evidence of age‐specific fitness declines that are driven by age‐specific associations between fitness components in a long‐lived mammal. Associations between reproduction and survival are positive in early life, but negative in later life with up to 71% of later‐life survival declines associated with investing in the production of offspring within this population of this critically endangered species. AU - Robinson, Matthew Richard AU - Mar, Khyne U AU - Lummaa, Virpi ID - 7749 IS - 3 JF - Ecology Letters SN - 1461-023X TI - Senescence and age-specific trade-offs between reproduction and survival in female Asian elephants VL - 15 ER - TY - JOUR AB - Female mate choice acts as an important evolutionary force, yet the influence of the environment on both its expression and the selective pressures acting upon it remains unknown. We found consistent heritable differences between females in their choice of mate based on ornament size during a 25‐year study of a population of collared flycatchers. However, the fitness consequences of mate choice were dependent on environmental conditions experienced whilst breeding. Females breeding with highly ornamented males experienced high relative fitness during dry summer conditions, but low relative fitness during wetter years. Our results imply that sexual selection within a population can be highly variable and dependent upon the prevailing weather conditions experienced by individuals. AU - Robinson, Matthew Richard AU - Sander van Doorn, G. AU - Gustafsson, Lars AU - Qvarnström, Anna ID - 7748 IS - 6 JF - Ecology Letters SN - 1461-023X TI - Environment-dependent selection on mate choice in a natural population of birds VL - 15 ER - TY - JOUR AU - Robinson, Matthew Richard ID - 7750 IS - 6 JF - Behavioral Ecology SN - 1465-7279 TI - Understanding intrasexual competition and sexual selection requires an evolutionary ecology framework VL - 22 ER - TY - JOUR AB - This work demonstrates that environmental conditions experienced by individuals can shape their development and affect the stability of genetic associations. The implication of this observation is that the environmental response may influence the evolution of traits in the wild. Here, we examined how the genetic architecture of a suite of sexually dimorphic traits changed as a function of environmental conditions in an unmanaged population of Soay sheep (Ovis aries) on the island of Hirta, St. Kilda, northwest Scotland. We examined the stability of phenotypic, genetic, and environmental (residual) covariance in males during the first year of life between horn length, body weight, and parasite load in environments of different quality. We then examined the same covariance structures across environments within and between the adult sexes. We found significant genotype-by-environment interactions for lamb male body weight and parasite load, leading to a change in the genetic correlation among environments. Horn length was genetically correlated with body weight in males but not females and the genetic correlation among traits within and between the sexes was dependent upon the environmental conditions experienced during adulthood. Genetic correlations were smaller in more favorable environmental conditions, suggesting that in good environments, loci are expressed that have sex-specific effects. The reduction in genetic correlation between the sexes may allow independent evolutionary trajectories for each sex. This study demonstrates that the genetic architecture of traits is not stable under temporally varying environments and highlights the fact that evolutionary processes may depend largely upon ecological conditions. ENVIRONMENTAL heterogeneity has long been recognized as an important factor influencing the evolution of fitness-related traits in the wild (Roff 2002). The evolution of a trait depends upon the selection upon it, underlying genetic variation, and to a large degree the genetic relationships with other traits (Lynch and Walsh 1998). There is evidence that selection can vary considerably from year to year (Price et al. 1984; Robinson et al. 2008) and genetic variability in quantitative traits can change in response to environmental conditions (Hoffmann and Merilä 1999; Charmantier and Garant 2005). However, we know surprisingly little about the influence of environmental conditions on genetic correlations between traits in wild populations. Laboratory evidence suggests that the environment may influence genetic relationships between traits (Sgrò and Hoffmann 2004), but estimates obtained in a controlled or in an arbitrary range of conditions show a lack of concordance with those obtained in wild habitats (Conner et al. 2003). As a result, laboratory and environment-specific estimates of genetic correlations can make predictions for a trait's evolution, but these are valid only for the environment in which they were measured. Therefore, at present, it is difficult to generalize about the evolution of a trait that is expressed in populations that experience variable environmental conditions (Steppan et al. 2002). The influence of changing environmental conditions on the G matrix (the matrix of additive genetic variance and covariances corresponding to a set of traits) has been the focus of theoretical quantitative genetic studies (e.g., Jones et al. 2003). There is evidence of genotype-by-environment interaction for many traits expressed in wild populations (Charmantier and Garant 2005) and thus we may also expect that associations between traits may depend upon the environmental conditions encountered by an individual. Genetic correlations among traits may arise from pleiotropy, where a given locus affects more than one trait (Cheverud 1988; Lynch and Walsh 1998), which may limit the potential for those traits to evolve independently. There has recently been much interest in assessing genetic correlations between the sexes (Rice and Chippindale 2001; Foerster et al. 2007; Poissant et al. 2008), but all of these predictions have also been made in average environmental conditions. For sexually dimorphic traits, expectations of between-sex genetic correlations are unclear (Lande 1980; Badyaev 2002). We might expect that the genetic determination of a trait and the patterns of genetic covariance between traits may differ both within and between the sexes, producing the differences in trait growth that are commonly observed (Lande 1980; Badyaev 2002; Roff 2002), but so far evidence suggests that genetic expression in both sexes is influenced by the same developmental pathway (Roff 2002; Jensen et al. 2003; Parker and Garant 2005). However, to our knowledge, no study has yet determined whether genetic correlations, both within and between the sexes, vary across gradients of the environmental conditions encountered by individuals in the wild (Garant et al. 2008). This study aims to assess the stability of phenotypic, genetic, and environmental (residual) associations between traits, within and between the sexes, across a range of environmental conditions experienced by a wild population. We focus on the traits of horn length, body weight, and parasite load in a feral population of Soay sheep (Ovis aries) from the island of Hirta, St. Kilda, United Kingdom. Weather conditions, population density, and consequently resource availability fluctuate from year to year, providing substantial differences between individuals in the environments they experience and thus their survival rates (Clutton-Brock and Pemberton 2004). These varying conditions, combined with a large pedigree and extensive repeated morphological measures, provide an excellent opportunity to assess the potential effects of environmental heterogeneity on genetic architecture of traits. Previous studies on this population have shown additive genetic variance for many morphological traits (Milner et al. 2000; Coltman et al. 2001; Wilson et al. 2005), genetic correlations between traits (Coltman et al. 2001), and genotype-by-environment interactions for birth weight (Wilson et al. 2006). Here we apply a random regression animal model approach to assess the extent to which quantitative genetic parameters of a range of morphological traits measured during life vary as a function of environmental conditions. We then extend this methodology to the multivariate case, testing whether the phenotypic covariance structure, and the underlying G matrix, depends on the environmental conditions experienced. Since the traits considered here are known to be sexually dimorphic and there are differences in trait growth and survival across ages, we look at sex-specific traits in lambs and then across all ages. AU - Robinson, Matthew Richard AU - Wilson, Alastair J. AU - Pilkington, Jill G. AU - Clutton-Brock, Tim H. AU - Pemberton, Josephine M. AU - Kruuk, Loeske E. B. ID - 7751 IS - 4 JF - Genetics SN - 0016-6731 TI - The impact of environmental heterogeneity on genetic architecture in a wild population of soay sheep VL - 181 ER - TY - JOUR AU - Robinson, Matthew Richard AU - Pilkington, Jill G. AU - Clutton-Brock, Tim H. AU - Pemberton, Josephine M. AU - Kruuk, Loeske. E.B. ID - 7752 IS - 10 JF - Current Biology SN - 0960-9822 TI - Environmental heterogeneity generates fluctuating selection on a secondary sexual trait VL - 18 ER - TY - JOUR AB - In many species, females show reduced expression of a trait that is under sexual selection in males, and this expression is thought to be maintained through genetic associations with the male phenotype. However, there is also the potential for the female trait to convey an advantage in intrasexual conflicts over resources. We tested this hypothesis in a feral population of Soay sheep, in which males and females have a polymorphism for horn development, producing either full (normal horned), reduced (scurred) or no (polled, females only) horns. During the lambing period, females who possessed horns were more likely to initiate and win aggressive interactions, independent of age, weight and birthing status. The occurrence of aggression was also context dependent, decreasing over the lambing period and associated with local density. Our results demonstrate that a trait that confers benefits to males during intrasexual competition for mates may also be used by females in intrasexual competition over resources: males use weaponry to gain mates, whereas females use weaponry to gain food. AU - Robinson, Matthew Richard AU - Kruuk, Loeske E.B ID - 7753 IS - 6 JF - Biology Letters SN - 1744-9561 TI - Function of weaponry in females: The use of horns in intrasexual competition for resources in female Soay sheep VL - 3 ER - TY - JOUR AB - Males are predicted to compete for reproductive opportunities, with sexual selection driving the evolution of large body size and weaponry through the advantage they confer for access to females. Few studies have explored potential trade‐offs of investment in secondary sexual traits between different components of fitness or tested for sexually antagonistic selection pressures. These factors may provide explanations for observed polymorphisms in both form and quality of secondary sexual traits. We report here an analysis of selection on horn phenotype in a feral population of Soay sheep (Ovis aries) on the island of Hirta, St. Kilda, Scotland. Soay sheep display a phenotypic polymorphism for horn type with males growing either normal or reduced (scurred) horns, and females growing either normal, scurred, or no (polled) horns; further variation in size exists within horn morphs. We show that horn phenotype and the size of the trait displayed is subject to different selection pressures in males and females, generating sexually antagonistic selection. Furthermore, there was evidence of a trade‐off between breeding success and longevity in normal‐horned males, with both the normal horn type and larger horn size being associated with greater annual breeding success but reduced longevity. Therefore, selection through lifetime breeding success was not found to act upon horn phenotype in males. In females, a negative association of annual breeding success within the normal‐horned phenotype did not result in a significant difference in lifetime fitness when compared to scurred individuals, as no significant difference in longevity was found. However, increased horn size within this group was negatively associated with breeding success and longevity. Females without horns (polled) suffered reduced longevity and thus reduced lifetime breeding success relative the other horn morphs. Our results therefore suggest that trade‐offs between different components of fitness and antagonistic selection between the sexes may maintain genetic variation for secondary sexual traits within a population. AU - Robinson, Matthew Richard AU - Pilkington, Jill G. AU - Clutton-Brock, Tim H. AU - Pemberton, Josephine M. AU - Kruuk, Loeske E.B. ID - 7781 IS - 10 JF - Evolution SN - 0014-3820 TI - Live fast, die young: Trade-offs between fitness components and sexually antagonistic selection on weaponry in soay sheep VL - 60 ER -