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

Abstract

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/

Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection

“…We selected three phenotypes for which (i) the genome-wide summary statistic data were publicly available, (ii) the sample size was larger than 50,000 individuals, (iii) the phenotype has minimal impact on fecundity 99–101 (and hence the traits behave as neutral or approximately neutral to selection) and (iv) summary statistics were considered adequate for LD Score analysis based on baseline z scores >4 26,102 (Supplementary Table 8). The phenotypes chosen were Alzheimer’s disease 103 , neu roticism 104 and type 2 diabetes 105 .…”

The new genetics of intelligence

“…In the same way, an atheoretical approach can be used in analyses of multiple GPS by incorporating as many GPS as possible rather than selecting a few candidate GPS. For example, the first study of this sort, mentioned above35, included a total of 81 GPS from well-powered GWAS of cognitive, medical and anthropometric traits available in LD Hub92 that together predicted 4.8% of the variance in intelligence. Although EA2, IQ and income GPS drove most of the predictive power of this multiple-GPS analysis, significant independent contributions to the prediction of intelligence were also found for major depressive disorder GPS and autism spectrum disorder GPS.…”

“…This problem has been solved with LD Hub, a centralized database and web interface that provides an automated pipeline for entering and using GWAS summary-level data92.…”

Cognitive genomics: Searching for the genetic roots of neuropsychological functioning.

“…These results would suggest genetic pleiotropy between cognition and many aspects of human health, both mental and physical. Resources such as LD Hub - a centralized database of summary-level GWAS results for hundreds of diseases/traits from different publicly available resources - have been recently developed, greatly facilitating analysis of hundreds of traits that potentially share common genetic etiologies (Zheng et al, 2017). …”