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

Author

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  • Md. Moksedul Momin

    (University of South Australia
    University of South Australia
    Chattogram Veterinary and Animal Sciences University (CVASU)
    University of South Australia)

  • Jisu Shin

    (University of South Australia
    University of South Australia
    University of Virginia
    University of Virginia)

  • Soohyun Lee

    (National Institute of Animal Science (NIAS))

  • Buu Truong

    (University of South Australia)

  • Beben Benyamin

    (University of South Australia
    University of South Australia
    University of South Australia)

  • S. Hong Lee

    (University of South Australia
    University of South Australia
    University of South Australia)

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.

Suggested Citation

  • Md. Moksedul Momin & Jisu Shin & Soohyun Lee & Buu Truong & Beben Benyamin & S. Hong Lee, 2023. "A method for an unbiased estimate of cross-ancestry genetic correlation using individual-level data," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36281-x
    DOI: 10.1038/s41467-023-36281-x
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