LD Hub a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis, bioRxiv, 2016-05-04

AbstractMotivationLD 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.ResultsIn this manuscript, we describe LD Hub – a centralized database of summary-level GWAS results for 177 diseasestraits from different publicly available resourcesconsortia 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 traitsdiseases 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 implementationThe web interface and instructions for using LD Hub are available at <jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpldsc.broadinstitute.org>httpldsc.broadinstitute.org<jatsext-link>

biorxiv bioinformatics 0-100-users 2016

Topologically associated domains are ancient features that coincide with Metazoan clusters of extreme noncoding conservation, bioRxiv, 2016-03-16

AbstractIn vertebrates and other Metazoa, developmental genes are found surrounded by dense clusters of highly conserved noncoding elements (CNEs). CNEs exhibit extreme levels of sequence conservation of unexplained origin, with many acting as long-range enhancers during development. Clusters of CNEs, termed genomic regulatory blocks (GRBs), define the span of regulatory interactions for many important developmental regulators. The function and genomic distribution of these elements close to important regulatory genes raises the question of how they relate to the 3D conformation of these loci. We show that GRBs, defined using clusters of CNEs, coincide strongly with the patterns of topological organisation in metazoan genomes, predicting the boundaries of topologically associating domains (TADs) at hundreds of loci. The set of TADs that are associated with high levels of non-coding conservation exhibit distinct properties compared to TADs called in chromosomal regions devoid of extreme non-coding conservation. The correspondence between GRBs and TADs suggests that TADs around developmental genes are ancient, slowly evolving genomic structures, many of which have had conserved spans for hundreds of millions of years. This relationship also explains the difference in TAD numbers and sizes between genomes. While the close correspondence between extreme conservation and the boundaries of this subset of TADs does not reveal the mechanism leading to the conservation of these elements, it provides a functional framework for studying the role of TADs in long-range transcriptional regulation.

biorxiv genomics 0-100-users 2016

 

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