A Ganna

B Bulik-Sullivan

C Bycroft

D Speed

G Ni

J Yang

J Zheng

LM Evans

O Canela-Xandri

P-R Loh

S Purcell

SH Lee

English

Genetic correlation is a central parameter for understanding the shared genetic architecture between complex traits and diseases. Making use of summary-level genome-wide association study (GWAS) data resources, LD Score regression (LDSC) was developed for unbiased estimation of genetic correlation. Though easy to use, LDSC only uses a small part of all the linkage disequilibrium (LD) information in the modeling of summary association statistics. In contrast, by fully accounting for LD information across the human genome, we develop a High-Definition Likelihood (HDL) method to improve the precision in genetic correlation estimation. Compared to LDSC, HDL reduces the variance of a genetic correlation estimate by about 60%, which is equivalent to a 2.5-fold increase in sample size. We implement HDL and LDSC to estimate 435 genetic correlations amongst 30 behavioral and disease-related phenotypes measured in UK Biobank. In addition to 154 genetic correlations significant for both methods, HDL identifies another 57 significant genetic correlations compared to only another 2 by LDSC. In summary, HDL brings more power to genome-wide analyses and can better reveal the underlying connections across human complex traits

Ning, Zheng

Pawitan, Yudi

Shen, Xia

Edinburgh Research Explorer

High-definition likelihood inference of genetic correlations across human complex traits

Crossref

https://www.research.ed.ac.uk/portal/files/144684519/High_definition_likelihood_inference_of_genetic_correlations_across_human_complex_traits.pdf

High-definition likelihood inference of genetic correlations across human complex traits

Abstract

Similar works

Full text

Available Versions

Edinburgh Research Explorer

Crossref