PSVIII-B-13 Machine Learning-Based co-Expression Network Analysis Unravels Fertility-Related Genes in Beef Cattle. (21st September 2022)

• Record Type: Journal Article
• Authors: Diniz, Wellison; Banerjee, Priyanka; Rodning, Soren P P; Dyce, Paul W W
• Is Part Of: Journal of animal science. Volume 100(2022)Supplement 3
• Page Start: 314
• Page End: 315
• Publication Date: 2022-09-21
• Subjects: cow fertility -- data integration -- machine learning; Livestock -- Periodicals; Livestock; Electronic journals; Periodicals; 636.005
• DOI: 10.1093/jas/skac247.573
• Languages: English
• ISSNs: 0021-8812

Abstract

Abstract: Reproductive efficiency is a critical component of a sustainable cow-calf system. While several studies have identified factors underlying fertility, the genetic mechanisms contributing to this complex trait are still unclear. Our goal was to identify a set of predictive biomarker signatures from transcriptomics data associated with female cattle fertility. We implemented a multi-tiered approach using machine learning (ML) feature selection, gene co-expression network, and functional analysis. To this end, we retrieved public data from the Gene Expression Omnibus database (GEO GSE171577). The RNA-Seq data was generated from uterine luminal epithelial cells of recipient cows sampled on day four before embryo transfer. The data (n = 18 non-pregnant – NP and n = 25 pregnant – P) were analyzed using a standard pipeline based on FastQC, MultiQC, STAR, and DESeq2. Genes with expression values > 0.5 counts per million in 50% of the samples were filtered out. Feature and model selection was implemented through BioDiscML. Further, the PCIT algorithm was used to create gene co-expression networks from 15, 039 genes kept after quality control. Our ML approach identified nine genes as predictors of pregnancy status. The genes included: SERPINE3, MRTFA, MEF2B, NAA16, ARHGEF7, PDCD1, FNDC1, ENSBTAG00000054585, and ENSBTAG00000019474. The networks from P and NP cows resulted in five and four thousand significantly co-expressed gene pairs, respectively. We then kept 1, 837 pairs with a |r| > 0.7 and were co-expressed with the gene predictors from the ML analysis. Biological processes, such as vasculature development, oxidative phosphorylation, and focal adhesion were over-represented by genes from the P network. We identified immune system development, negative regulation of the biological process, and protein modification over-represented processes in the NP gene network. We have demonstrated the potential of combining different methods to identify fertility-related biomarkers and have provided insights into the complex genomic basis underlying pregnancy establishment in cattle.