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 - JOUR AB - Creating a selective gel that filters particles based on their interactions is a major goal of nanotechnology, with far-reaching implications from drug delivery to controlling assembly pathways. However, this is particularly difficult when the particles are larger than the gel’s characteristic mesh size because such particles cannot passively pass through the gel. Thus, filtering requires the interacting particles to transiently reorganize the gel’s internal structure. While significant advances, e.g., in DNA engineering, have enabled the design of nano-materials with programmable interactions, it is not clear what physical principles such a designer gel could exploit to achieve selective permeability. We present an equilibrium mechanism where crosslink binding dynamics are affected by interacting particles such that particle diffusion is enhanced. In addition to revealing specific design rules for manufacturing selective gels, our results have the potential to explain the origin of selective permeability in certain biological materials, including the nuclear pore complex. AU - Goodrich, Carl Peter AU - Brenner, Michael P. AU - Ribbeck, Katharina ID - 7754 JF - Nature Communications SN - 2041-1723 TI - Enhanced diffusion by binding to the crosslinks of a polymer gel VL - 9 ER -