A Unified Unsupervised Framework for Genome-Wide Association Studies in Heterogeneous Populations

Abstract

Genome-wide association studies (GWAS) have greatly advanced the discovery of genetic variants underlying complex traits and diseases. Yet in heterogeneous populations, existing GWAS strategies typically either pool all individuals under an assumption of population homogeneity or perform meta-analysis across predefined subgroups, both of which are limited when latent genetic heterogeneity attenuates subgroup-specific effects and masks true associations or subgroup labels are imprecise. Here we present UCALM, a unified unsupervised framework that infers genetically homogeneous subgroups directly from the data and integrates subgroup-specific GWAS with a novel layered meta-analysis method to capture both shared and subgroup-specific association signals. Through extensive simulations and analyses of large-scale human and livestock cohorts, including the UK Biobank (n ≈ 487,000) and a heterogeneous pig cohort (n ≈ 85,000), we demonstrate that UCALM substantially alleviated the mean genomic inflation across 24 UK Biobank traits to 1.17 compared with 1.43 for GLM and 1.37 for LDAK-KVIK, and further revealed 74 loci in the pig cohort that were previously obscured by conventional approaches. Our results establish a robust and broadly applicable strategy for association mapping in structured populations and improve the resolution of genetic signals across diverse species.

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BibTeX RIS

BibTeX

@misc{shen2026unified,
  title = {{A Unified Unsupervised Framework for Genome-Wide Association Studies in Heterogeneous Populations}},
  author = {Xiong Shen and Zhenshuang Tang and Yong Liao and Zhiyuan Lin and Huajun Zhou and Haohao Zhang and Yangfan Liu and Dong Yin and Yue Wang and Yuan Quan and Zhuqing Zheng and Xiong Xiong and Yuhua Fu and Shuhong Zhao and Xinyun Li and Lilin Yin and Xiaolei Liu},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.04633},
  eprint = {2610.04633},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.04633},
  note = {CC BY-NC-SA 4.0},
}

RIS

TY  - UNPB
TI  - A Unified Unsupervised Framework for Genome-Wide Association Studies in Heterogeneous Populations
AU  - Xiong Shen
AU  - Zhenshuang Tang
AU  - Yong Liao
AU  - Zhiyuan Lin
AU  - Huajun Zhou
AU  - Haohao Zhang
AU  - Yangfan Liu
AU  - Dong Yin
AU  - Yue Wang
AU  - Yuan Quan
AU  - Zhuqing Zheng
AU  - Xiong Xiong
AU  - Yuhua Fu
AU  - Shuhong Zhao
AU  - Xinyun Li
AU  - Lilin Yin
AU  - Xiaolei Liu
PY  - 2026
DA  - 2026/10/03
PB  - arXiv
DO  - 10.48550/arxiv.2610.04633
UR  - https://arxiv.org/abs/2610.04633
AB  - Genome-wide association studies (GWAS) have greatly advanced the discovery of genetic variants underlying complex traits and diseases. Yet in heterogeneous populations, existing GWAS strategies typically either pool all individuals under an assumption of population homogeneity or perform meta-analysis across predefined subgroups, both of which are limited when latent genetic heterogeneity attenuates subgroup-specific effects and masks true associations or subgroup labels are imprecise. Here we present UCALM, a unified unsupervised framework that infers genetically homogeneous subgroups directly from the data and integrates subgroup-specific GWAS with a novel layered meta-analysis method to capture both shared and subgroup-specific association signals. Through extensive simulations and analyses of large-scale human and livestock cohorts, including the UK Biobank (n ≈ 487,000) and a heterogeneous pig cohort (n ≈ 85,000), we demonstrate that UCALM substantially alleviated the mean genomic inflation across 24 UK Biobank traits to 1.17 compared with 1.43 for GLM and 1.37 for LDAK-KVIK, and further revealed 74 loci in the pig cohort that were previously obscured by conventional approaches. Our results establish a robust and broadly applicable strategy for association mapping in structured populations and improve the resolution of genetic signals across diverse species.
ER  -

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