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Haplotype Function Score improves biological interpretation and cross-ancestry polygenic prediction of human complex traits

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Song, Weichen, Shi, Yongyong, Lin, Guan Ning·2024·eLife
DOI 10.7554/elife.92574.2

Preprint — published as 10.7554/elife.92574.3

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Abstract

Abstract We propose a new framework for human genetic association studies: at each locus, a deep learning model (in this study, Sei) is used to calculate the functional genomic activity score for two haplotypes per individual. This score, defined as the Haplotype Function Score (HFS), replaces the original genotype in association studies. Applying the HFS framework to 14 complex traits in the UK Biobank, we identified 3,619 independent HFS-trait associations with a significance of p<5×10−8. Fine-mapping revealed 2,699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with SNP-based analysis. HFS-based enrichment analysis uncovered 727 pathway-trait associations and 153 tissue-trait associations with strong biological interpretability, including “circadian pathway-chronotype” and “arachidonic acid-intelligence”. Lastly, we applied LASSO regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1% to 39.8% in cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding the genetic basis of human complex traits.

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Metadata

Authors
Song, W., Shi, Y., & Lin, G. N.
Affiliations
  • Shanghai Jiao Tong University (CN)
  • Qingdao University (CN)
ORCID
Venue
eLife
Publisher
eLife Sciences Publications, Ltd
Published
2024-03-21
ISSN
2050-084X (ISSN-L)
DOI
10.7554/elife.92574.2
OpenAlex
W4393057592
Cited by
0
References
5
Type
posted content
Language
en
Open access
Licence
Locations
eLife (acceptedVersion, cc-by)
Funders
  • Ministry of Science and Technology (2030 Science and Technology Innovation Key Program 2022ZD020910001)
  • National Natural Science Foundation of China (81971292, 82150610506)
  • Natural Science Foundation of Shanghai (21ZR1428600)
  • Medical-Engineering Cross Foundation of Shanghai Jiao Tong University (YG2022ZD026)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
Keywords
haplotype association, deep learning, SEI model, complex traits, fine mapping, chronotype, arachidonic acid, intelligence, polygenic risk score, biological interpretability

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