A method for an unbiased estimate of cross-ancestry genetic correlation using individual-level data

Md. Moksedul Momin, Jisu Shin, Soohyun Lee, Buu Truong, Beben Benyamin and S. Hong Lee ()
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Md. Moksedul Momin: University of South Australia
Jisu Shin: University of South Australia
Soohyun Lee: National Institute of Animal Science (NIAS)
Buu Truong: University of South Australia
Beben Benyamin: University of South Australia
S. Hong Lee: University of South Australia

Nature Communications, 2023, vol. 14, issue 1, 1-13

Abstract: Abstract Cross-ancestry genetic correlation is an important parameter to understand the genetic relationship between two ancestry groups. However, existing methods cannot properly account for ancestry-specific genetic architecture, which is diverse across ancestries, producing biased estimates of cross-ancestry genetic correlation. Here, we present a method to construct a genomic relationship matrix (GRM) that can correctly account for the relationship between ancestry-specific allele frequencies and ancestry-specific allelic effects. Through comprehensive simulations, we show that the proposed method outperforms existing methods in the estimations of SNP-based heritability and cross-ancestry genetic correlation. The proposed method is further applied to anthropometric and other complex traits from the UK Biobank data across ancestry groups. For obesity, the estimated genetic correlation between African and European ancestry cohorts is significantly different from unity, suggesting that obesity is genetically heterogenous between these two ancestries.

Date: 2023
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DOI: 10.1038/s41467-023-36281-x

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