Omics & gene networks

Integrating genomics, transcriptomics, and mid-infrared spectra to decipher the genetic basis of nitrogen use efficiency in dairy cattle

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Abstract

Nitrogen use efficiency (NUE) is an essential trait for enhancing both the economic and environmental sustainability of dairy production. However, the genetic mechanisms underlying NUE remain insufficiently characterized, particularly when integrating multi-omics data. This study aimed to identify and prioritize candidate variants and genes for NUE by integrating large-scale genomics, transcriptomics, and mid-infrared spectra data. We analyzed 59,909 Belgian dairy cows with daily NUE records. NUE was predicted using standardized milk mid-infrared spectra, parity, and milk yield. Daily NUE was classified into early (5-50 days in milk (DIM)), mid (51-200 DIM), and late (201-365 DIM) lactation stages, resulting in 97,510 records. The pedigree included 170,550 animals, among which 7,199 were genotyped for 564,060 SNPs. Transcriptomic data from the CattleGTEx resource were incorporated, including genome-wide expression quantitative trait loci (eQTL) mapped across 23 tissues from more than 8,000 RNA-seq samples. Single step genome-wide association analyses (ssGWAS) characterized the genetic architecture of NUE across three lactation stages. Post-GWAS analyses included transcriptome-wide association studies (TWAS), colocalization (COLOC), and mendelian randomization (MR). TWAS combined eQTL with ssGWAS summary statistics to identify genes whose predicted expression is associated with NUE. COLOC tested whether ssGWAS and eQTL signals arose from the same causal variant. MR further assessed causal effects of gene expression on NUE. Together, these post-GWAS approaches refined GWAS signals and prioritized candidate genes for NUE. A global multi-omics score was developed to rank candidate genes based on their recurrence across three lactation stages, 23 tissues, and four analysis methods. ssGWAS identified 334 significant SNPs (P P DGAT1 received the highest score and was the only gene supported by all four analytical methods. In conclusion, integrating ssGWAS with independent post-GWAS analyses strengthened variant detection and improved the identification of robust candidate genes for NUE. The proposed multi-omics scoring framework provides a practical tool for prioritizing candidate variants and could be incorporated into weighted genomic selection models to enhance breeding strategies targeting NUE.

Keywords: 2026

How to Cite:

Chen, Y., Atashi, H., Fang, L., Grelet, C., Gengler, N. & Li, B., (2026) “Integrating genomics, transcriptomics, and mid-infrared spectra to decipher the genetic basis of nitrogen use efficiency in dairy cattle”, World Congress on Genetics Applied to Livestock Production Digital Archive 2026(1): 2284151. doi: https://doi.org/10.31274/wcgalp.23551

Rights: 1