Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript.
Soumya Raychaudhuri and colleagues report a broadly applicable method that uses chromatin marks, specifically H3K4me3, to identify critical cell types to fine map complex trait variants.
Peter Donnelly and colleagues report an analysis considering the inclusion of non-confounding covariates within genome-wide association studies and provide software that can be used to assess the impact on power within a particular study. They find that, when the disease prevalence is low, including known covariates, such as sex or established genetic associations, can reduce the power to detect new associations.
Bogdan Pasaniuc, David Reich, Alkes Price and colleagues report analyses considering the potential of genome-wide association studies (GWAS) based on extremely low-coverage sequence data sets combined with imputation using data sets from the 1000 Genomes Project. They show with simulations and real exome-sequencing data that low-coverage sequencing can increase power for GWAS relative to genotyping arrays.
Eli Stahl, Robert Plenge and colleagues report the application of a polygenic analysis, using a Bayesian inference framework, to rheumatoid arthritis GWAS datasets. They find that polygenic risk scores are associated with rheumatoid arthritis case-control status and estimate the total variance explained by common variants in these GWAS. They show comparable estimates for applications to GWAS for celiac disease, myocardial infarction and coronary artery disease and type 2 diabetes.
Peter Visscher and colleagues report a new method for approximate conditional and joint association analysis that makes use of summary statistics from meta-analysis of GWAS. They apply this to meta-analysis summary data for height, body mass index and type 2 diabetes.
Naomi Wray, Peter Visscher and colleagues report analyses of the common variation that contributes to schizophrenia risk within three independent case-control datasets from the Psychiatric GWAS Consortium for schizophrenia. They estimate that 23% of the variation in liability to schizophrenia is captured by SNPs on current platforms.
Gil McVean and Iain Mathieson report an analysis of the differential effects of population stratification on rare and common variants within association studies. They find that rare variants may show stronger stratification in some situations and that this is not corrected for by current structure methods, suggesting the need for the development of new statistical methods.
