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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #395832
Research Project: Enhancing Genetic Merit of Ruminants Through Improved Genome Assembly, Annotation, and Selection

Location: Animal Genomics and Improvement Laboratory

Title: Genetic score omics regression and multi-trait meta-analysis detect widespread cis-regulatory effects shaping bovine complex traits

Author
item XIANG, RUIDONG - University Of Melbourne
item FANG, LINGZHAO - University Of Edinburgh
item LIU, SHULI - Westlake University
item Liu, Ge
item TENESA, ALBERT - University Of Edinburgh
item GAO, AHUI - University Of Maryland
item MASON, BRETT - Agrobio
item CHAMBERLAIN, AMANDA - Agrobio
item GODDARD, MICHAEL - University Of Melbourne

Submitted to: Proceedings of the National Academy of Sciences-Nexus
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/29/2025
Publication Date: 7/2/2025
Citation: Xiang, R., Fang, L., Liu, S., Liu, G., Tenesa, A., Gao, A., Mason, B.A., Chamberlain, A.J., Goddard, M.L. 2025. Genetic score omics regression and multi-trait meta-analysis detect widespread cis-regulatory effects shaping bovine complex traits. Proceedings of the National Academy of Sciences-Nexus. https://doi.org/10.1093/pnasnexus/pgaf208.
DOI: https://doi.org/10.1093/pnasnexus/pgaf208

Interpretive Summary: Comprehensive analyses of transcriptomes will benefit our understanding of genetic bases for complex traits. We introduced new methods (Genetic Score Omics Regression and multi-trait meta-analysis of omics-associations) to link omics information and complex traits. Farmers, breeders, scientists, and policy planners who need to improve animal health and production based on genome-enabled animal selection will benefit from this study.

Technical Abstract: Transcriptome-wide association studies (TWAS) correlate genetically predicted gene expression with observed phenotypic measurements. However, the relatively small training population assayed with gene expression could limit the accuracy of TWAS. Here, we propose Genetic Score Omics Regression (GSOR) which correlates observed gene expression with genetically predicted phenotype, i.e., genetic score. The score, calculated using variants near the gene with assayed expression, provides a powerful test of association between cis-effects on gene expression and the trait. In extensively simulated and real data, GSOR outperforms TWAS in detecting causal/informative genes. Applying GSOR to transcriptomes of 16 tissues (N~5000) and 37 traits in ~120,000 cattle, multi-trait meta-analyses of omics-associations (MTAO) found that, on average, each significant gene expression and splicing mediates cis-genetic effects on 8~10 traits. Supported by Mendelian Randomisation, MTAO prioritised genes/splicing show increased evolutionary constraints. Many newly discovered regulatory genes and splicing regions underlie previously thought single-gene loci to influence multiple traits.