Motivation LD score regression is a reliable and efficient method of using genome-wide association study (GWAS) summary-level results data to estimate the SNP heritability of complex traits and diseases, partition this heritability into functional categories, and estimate the genetic correlation between different phenotypes. Because the method relies on summary level results data, LD score regression is computationally tractable even for very large sample sizes. However, publicly available GWAS summary-level data are typically stored in different databases and have different formats, making it difficult to apply LD score regression to estimate genetic correlations across many different traits simultaneously. Results In this manuscript, we describe LD Hub - a centralized database of summary-level GWAS results for 173 diseases/traits from different publicly available resources/consortia and a web interface that automates the LD score regression analysis pipeline. To demonstrate functionality and validate our software, we replicated previously reported LD score regression analyses of 49 traits/diseases using LD Hub; and estimated SNP heritability and the genetic correlation across the different phenotypes. We also present new results obtained by uploading a recent atopic dermatitis GWAS meta-analysis to examine the genetic correlation between the condition and other potentially related traits. In response to the growing availability of publicly accessible GWAS summary-level results data, our database and the accompanying web interface will ensure maximal uptake of the LD score regression methodology, provide a useful database for the public dissemination of GWAS results, and provide a method for easily screening hundreds of traits for overlapping genetic aetiologies. Availability and implementation The web interface and instructions for using LD Hub are available at http://ldsc.broadinstitute.org/

BackgroundThe influence of genetic variation on complex diseases is potentially mediated through a range of highly dynamic epigenetic processes exhibiting temporal variation during development and later life. Here we present a catalogue of the genetic influences on DNA methylation (methylation quantitative trait loci (mQTL)) at five different life stages in human blood: children at birth, childhood, adolescence and their mothers during pregnancy and middle age.ResultsWe show that genetic effects on methylation are highly stable across the life course and that developmental change in the genetic contribution to variation in methylation occurs primarily through increases in environmental or stochastic effects. Though we map a large proportion of the cis-acting genetic variation, a much larger component of genetic effects influencing methylation are acting in trans. However, only 7 % of discovered mQTL are trans-effects, suggesting that the trans component is highly polygenic. Finally, we estimate the contribution of mQTL to variation in complex traits and infer that methylation may have a causal role consistent with an infinitesimal model in which many methylation sites each have a small influence, amounting to a large overall contribution.ConclusionsDNA methylation contains a significant heritable component that remains consistent across the lifespan. Our results suggest that the genetic component of methylation may have a causal role in complex traits. The database of mQTL presented here provide a rich resource for those interested in investigating the role of methylation in disease.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-0926-z) contains supplementary material, which is available to authorized users.

Asthma, hay fever (or allergic rhinitis) and eczema (or atopic dermatitis) often coexist in the same individuals1, partly because of a shared genetic origin2–4. To identify shared risk variants, we performed a genome-wide association study (GWAS, n=360,838) of a broad allergic disease phenotype that considers the presence of any one of these three diseases. We identified 136 independent risk variants (P<3x10-8), including 73 not previously reported, which implicate 132 nearby genes in allergic disease pathophysiology. Disease-specific effects were detected for only six variants, confirming that most represent shared risk factors. Tissue-specific heritability and biological process enrichment analyses suggest that shared risk variants influence lymphocyte-mediated immunity. Six target genes provide an opportunity for drug repositioning, while for 36 genes CpG methylation was found to influence transcription independently of genetic effects. Asthma, hay fever and eczema partly coexist because they share many genetic risk variants that dysregulate the expression of immune-related genes.

Osteoporosis is a common disease diagnosed primarily by measurement of bone mineral density (BMD). We undertook a genome-wide association study in 142,487 individuals from the UK Biobank to identify loci associated with BMD estimated by quantitative ultrasound of the heel (“eBMD”). We identified 307 conditionally independent SNPs attaining genome-wide significance at 203 loci, explaining approximately 12% of the phenotypic variance. These included 153 novel loci, and several rare variants with large effect sizes. To investigate underlying mechanisms we undertook: 1) bioinformatic, functional genomic annotation and human osteoblast expression studies; 2) gene function prediction; 3) skeletal phenotyping of 120 knockout mice with deletions of genes adjacent to lead independent SNPs; and 4) analysis of gene expression in mouse osteoblasts, osteocytes and osteoclasts. These studies strongly implicate GPC6 as a novel determinant of BMD and also identify abnormal skeletal phenotypes in knockout mice for a further 100 prioritized genes.