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

By Jie Zheng, Mesut A. Erzurumluoglu, B. Elsworth, J. Kemp, L. Howe, Philip C. Haycock, Gibran Hemani, K. Tansey, Charles Laurin, Early Genetics and 11 more. bioRxiv, 2016

890 citations recorded by the source corpus.

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 177 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/

Subfields: Dermatology, Genetics, Physiology

View Paper: https://www.semanticscholar.org/paper/50cc3df4ff4469cbe9ee18d30dbd3c439e268d78

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Citations in the corpus

Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders (2019, methodology): “Using data for 25 brain-related traits selected from LDHub ( Zheng et al., 2017), we found signiﬁcant genetic correlations of the cross-disorder phenotype with seven traits (at a FDR-corrected p value threshold 0.002): never/ever smoking status, years of education, neuroticism, subjective…”
Descriptive, Predictive and Explanatory Personality Research: Different Goals, Different Approaches, but a Shared Need to Move beyond the Big Few Traits (2020, background): “…with informant-ratings: combining multiple pieces of information allows breaking correlations between variables into several components such as the association net of single-rater and occasion-specific biases, rater-specific effects and occasion-specific effects (e.g., Koch et al., 2017).”
An integrated framework for local genetic correlation analysis (2022, methodology): “As extensive pleiotropy should induce a correlation between the genetic signals for different phenotypes, genetic correlation ( r g ) analysis has been frequently employed to identify pairs of phenotypes that may be subject to widespread pleiotropy across the genome 13,16–18 .”
Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism (2019, methodology): “6 We used results from six GWAS datasets: ASD, schizophrenia (SCZ), major depressive disorder 7 (MDD), and attention deficit hyperactivity disorder (ADHD), which are all positively genetically 8 correlated with ASD and with each other; educational attainment (EA), which is positively 9 correlated wi”