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Updated: Jan 16, 2026

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Bridging GWAS to genes: an integrative multi-omics approach using cattle data.

Mohammad Ghoreishifar1,2, Iona M Macleod3,4, Tuan Nguyen3

  • 1Agriculture Victoria Research, AgriBio Centre for AgriBioscience, Bundoora, VIC, 3083, Australia. mohammad.ghoreishifar@agriculture.vic.gov.au.

BMC Genomics
|January 15, 2026
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Summary

Integrating multi-omics data, this study identifies 20 likely causal genes for milk lactose percentage in cows. These genes, identified through genome-wide association studies (GWAS) and gene expression analysis, are crucial for mammary gland function.

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Area of Science:

  • Animal Genomics
  • Quantitative Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) identify genetic loci for complex traits but struggle to pinpoint causal variants and target genes.
  • Multi-omics data integration offers a powerful strategy to overcome these challenges.

Purpose of the Study:

  • To identify causal genes for milk lactose percentage (LP) in dairy cattle using a multi-breed dataset and multi-omics approach.
  • To leverage genomic and transcriptomic data to link genetic variants to gene expression and phenotypic traits.

Main Methods:

  • Utilized a large multi-breed dataset (>81,000 cows) with milk LP phenotypes and imputed sequence genotypes.
  • Applied BayesR for SNP effect estimation and predicted local genomic breeding values (GEBVs).
  • Employed genetic score omics regression (GSOR) and a window-based colocalization test with GWAS summary statistics.

Main Results:

  • Identified 711 significant genes (FDR ≤ 0.1) associated with local GEBVs in mammary tissue using GSOR.
  • Found 30 significant colocalization windows between GWAS signals and GSOR-identified genes, implicating 34 candidate genes.
  • Highlighted 20 genes enriched in 'transmembrane transport' GO terms, relevant to lactose production physiology.

Conclusions:

  • The 20 identified genes are strong candidates for causal genes underlying milk lactose percentage, supported by mammary expression, GEBV association, GWAS colocalization, and functional enrichment.
  • Demonstrated the effectiveness of integrating GWAS, gene expression, and functional data for causal gene discovery in complex traits.