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Analytical and computational solution for the estimation of SNP-heritability in biobank-scale and distributed datasets

Author

Listed:
  • Guo-An Qi
  • Qi-Xin Zhang
  • Jingyu Kang
  • Tianyuan Li
  • Xiyun Xu
  • Zhe Zhang
  • Zhe Fan
  • Siyang Liu
  • Guo-Bo Chen

Abstract

Author summary: For a complex trait, heritability (h2) gives the genetic determination of its variation. Given the emergence of biobank-scale data, a more powerful method is needed to estimate h2. Based on the framework of Haseman-Elston regression (RHE-reg), we integrate a fast randomization algorithm to estimate h2, and RHE-reg can tackle biobank-scale data, such as UK Biobank (UKB), very efficiently. Furthermore, we present an analytical solution that balances computational cost and precision of the estimation, a property that is important in dealing with biobank-scale data. We investigated the performance of the RHE-reg in simulated data and also applied it for 81 UKB quantitative traits; as tested in UKB data of nearly 300,000 unrelated individuals, it took on average about 4.5 hours to complete an estimation when used 10 CPUs. We extended the application of RHE-reg into distributed datasets when privacy is not compromised. As shown in UKB and simulated data the performance of RHE-reg was accurate in estimating h2. The software for estimating SNP-heritability for biobank-scale data is released.

Suggested Citation

  • Guo-An Qi & Qi-Xin Zhang & Jingyu Kang & Tianyuan Li & Xiyun Xu & Zhe Zhang & Zhe Fan & Siyang Liu & Guo-Bo Chen, 2025. "Analytical and computational solution for the estimation of SNP-heritability in biobank-scale and distributed datasets," PLOS Computational Biology, Public Library of Science, vol. 21(10), pages 1-20, October.
    • Handle: RePEc:plo:pcbi00:1013568
    DOI: 10.1371/journal.pcbi.1013568
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