An intersection network based on combining SNP coassociation and RNA coexpression networks for feed utilization traits in Japanese Black cattle1. (12th May 2018)

• Record Type: Journal Article
• Authors: Okada, Daigo; Endo, Satoko; Matsuda, Hirokazu; Ogawa, Shinichiro; Taniguchi, Yukio; Katsuta, Tomohiro; Watanabe, Toshio; Iwaisaki, Hiroaki
• Is Part Of: Journal of animal science. Volume 96:Number 7(2018)
• Page Start: 2553
• Page End: 2566
• Publication Date: 2018-05-12
• Subjects: feed utilization efficiency -- gene network -- Japanese Black cattle -- RNA coexpression -- SNP coassociation; Livestock -- Periodicals; Livestock; Electronic journals; Periodicals; 636.005
• DOI: 10.1093/jas/sky170
• Languages: English
• ISSNs: 0021-8812

Abstract

Abstract: Genome-wide association studies (GWAS ) of quantitative traits have detected numerous genetic associations, but they encounter difficulties in pinpointing prominent candidate genes and inferring gene networks. The present study used a systems genetics approach integrating GWAS results with external RNA-expression data to detect candidate gene networks in feed utilization and growth traits of Japanese Black cattle, which are matters of concern. A SNP coassociation network was derived from significant correlations between SNPs with effects estimated by GWAS across 7 phenotypic traits. The resulting network genes contained significant numbers of annotations related to the traits. Using bovine transcriptome data from a public database, an RNA coexpression network was inferred based on the similarity of expression patterns across different tissues. An intersection network was then generated by superimposing the SNP and RNA networks and extracting shared interactions. This intersection network contained 4 tissue-specific modules: nervous system, reproductive system, muscular system, and glands. To characterize the structure (topographical properties) of the 3 networks, their scale-free properties were evaluated, which revealed that the intersection network was the most scale-free. In the subnetwork containing the most connected transcription factors ( URI1, ROCK2, and ETV6 ), most genes were widely expressed across tissues, and genes previously shown to be involved in the traits were found. Results indicated that the current approach might be used to construct a gene network that better reflects biological information, providing encouragement for the genetic dissection of economically important quantitative traits.