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

Zheng Ning, Yudi Pawitan, Xia Shen

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish
JournalNature Genetics
DOIs
Publication statusPublished - 29 Jun 2020

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