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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.92574PMC PMC11031082

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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 3619 independent HFS–trait associations with a significance of p < 5 × 10 −8 . Fine-mapping revealed 2699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with single-nucleotide polymorphism (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 least absolute shrinkage and selection operator (LASSO) regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1–39.8% 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
Qingdao University (CN)
Corresponding author
  • Song, Weichen
  • Shi, Yongyong
  • Lin, Guan Ning
ORCID
Venue
eLife
Publisher
eLife Sciences Publications, Ltd
Published
2024-04-19
Volume
12
ISSN
2050-084X (ISSN-L)
DOI
10.7554/elife.92574
PMC
PMC11031082
OpenAlex
W4389334899
Cited by
0
References
5
FWCI
0.00
Type
journal article
Language
en
Open access
Licence
Locations
  • eLife (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
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
deep learning, SEI model, complex traits, polygenic risk score, fine mapping, pathway enrichment, arachidonic acid, chronotype, intelligence, UK Biobank
MeSH terms
Haplotypes, Humans, Phenotype, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Genome-Wide Association Study

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