Recent work has demonstrated that some functional categories of the genome contribute
disproportionately to the heritability of complex diseases. Here we analyze a broad set of …

We introduce an approach to identify disease-relevant tissues and cell types by analyzing
gene expression data together with genome-wide association study (GWAS) summary …

Identifying genetic correlations between complex traits and diseases can provide useful
etiological insights and help prioritize likely causal relationships. The major challenges …

Both polygenicity (many small genetic effects) and confounding biases, such as cryptic
relatedness and population stratification, can yield an inflated distribution of test statistics in …

Major depressive disorder (MDD) is a common illness accompanied by considerable
morbidity, mortality, costs, and heightened risk of suicide. We conducted a genome-wide …

Identifying interesting relationships between pairs of variables in large data sets is increasingly
important. Here, we present a measure of dependence for two-variable relationships: the …

Linear mixed models are a powerful statistical tool for identifying genetic associations and
avoiding confounding. However, existing methods are computationally intractable in large …

INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological
comorbidity, inciting debate about their etiologic overlap. However, detailed study of …

Polygenic risk scores have shown great promise in predicting complex disease risk and will
become more accurate as training sample sizes increase. The standard approach for …

Haplotype phasing is a fundamental problem in medical and population genetics. Phasing
is generally performed via statistical phasing in a genotyped cohort, an approach that can …