GWAS & Selection signatures

Quantifying annotation-stratified pleiotropy and co-polygenicity between complex traits

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Abstract

Understanding the shared genetic architecture of complex traits is fundamental for advancing both precision medicine in humans and genetic selection in livestock. Genome-wide association studies (GWAS) have revealed extensive pleiotropy and functional enrichment in humans, and large-scale genomic analyses in livestock are now enabling parallel investigations of correlated traits of economic importance. However, analyses in animal populations must account for their distinct population structures and much longer-range linkage disequilibrium (LD) compared with humans, as these differences can substantially affect the estimation and interpretation of shared genetic variation between traits. To address these challenges, we developed a unified Bayesian framework applicable to both humans and animals for quantifying the extent and sources of shared genetic variation between traits. While sharing a common modeling foundation, the framework adopts species-specific implementations that reflect differences in genomic architecture and data availability. For human analyses, the framework models genome-wide SNPs across LD blocks to leverage hierarchical LD structure and uses GWAS summary statistics as input to comply with data privacy constraints. For livestock analyses, the framework models sequence-level variants using individual-level data, without partitioning LD blocks, which is well suited to the longer-range LD in animal genomes. The framework incorporates functional annotations to estimate annotation-stratified coheritability enrichment and the corresponding co-polygenicity (i.e., the fraction of variants contributing to shared genetic effects), thereby decomposing coheritability enrichment into components driven by widespread sharing across many variants versus more concentrated covariance arising from fewer, larger shared effects. Simulation and real-data analyses across both human and livestock species demonstrate improved accuracy and biological interpretability over existing approaches. In humans, the method identifies cell-type-specific mechanisms underlying disease comorbidities; for example, in the smoking-lung cancer analysis, lung and immune cell-type annotations show strong coheritability enrichment (of the 42 lung-derived cell-type annotations analyzed, 7 are significantly enriched, and 6 of these 7 correspond to lung-specific or immune cell types), with the enrichment attributable to either pleiotropic sharing or lung-cancer-specific genetic effects, consistent with the causal relationship between the two analyzed traits. In livestock, real-data analyses reveal annotation-dependent shared genetic architecture, with functional categories showing distinct patterns of coheritability enrichment and co-polygenicity. Together, this framework offers a unified statistical approach for humans and livestock to quantify and interpret annotation-stratified genetic correlation and coheritability enrichment, decomposing shared genetic architecture into pleiotropic and co-polygenic components across diverse populations.

Keywords: 2026

How to Cite:

Cheng, H., Liang, D., Qu, J., Zeng, J. & Zhao, T., (2026) “Quantifying annotation-stratified pleiotropy and co-polygenicity between complex traits”, World Congress on Genetics Applied to Livestock Production Digital Archive 2026(1): 2287228. doi: https://doi.org/10.31274/wcgalp.24241

Rights: 1