Meta-Analysis Based Approach for Analyzing Bovine RNA-Seq Data

Despite increment of supply and demand on milk in the market, the gravity of research on milk production of cattle was somewhat underestimated; only a few studies have attempted to identify milk-synthesizing genes using RNA-seq data. Most of studies focused solely on detecting for the genes associated with milk yield rather than milk components such as protein, fat, and solid, using transcriptional analyses. In this study, we performed RNA-seq analysis using samples harvested from 21 cows, along with group information about diverse milk production abilities; milk yield; and protein, fat, and solid contents, to identify the genes which can induce milk yielding and milk nutrients synthesis. Then, Meta-analysis was employed to characterize its genes. We identified 271 genes (FDR adjusted P-value < 0.1) as milk production related DEGs. Among these genes, five of them, including ALB, NOS3, RNPC3, SECTM1 and SPTSSB were selected and validated using qRT-PCR for the technical verification. According to analyses, biological functions of the detected genes, are highly enriched in the circulatory system and mammary gland. We concluded that identified 271 genes would be strong candidates for determining milk production such as CAV1, M-SAA3.2, LAP, CATHL4, and CATHL6, which were also significantly associated with milk yielding in other species.