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 -