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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #314937

Research Project: Improving Genetic Predictions in Dairy Animals Using Phenotypic and Genomic Information

Location: Animal Genomics and Improvement Laboratory

Title: Identification of gene networks underlying dystocia in dairy cattle

item Arceo, Maria - North Carolina State University
item Tiezzi, Francesco - North Carolina State University
item Cole, John
item Maltecca, Christian - North Carolina State University

Submitted to: Journal of Dairy Science
Publication Type: Abstract Only
Publication Acceptance Date: 3/17/2015
Publication Date: 7/12/2015
Citation: Arceo, M.E., Tiezzi, F., Cole, J.B., Maltecca, C. 2015. Identification of gene networks underlying dystocia in dairy cattle. Journal of Dairy Science. 98(Suppl. 2)/Journal of Animal Science 93(Suppl. 3):840(abstr. 749).

Interpretive Summary:

Technical Abstract: Dystocia is a trait with a high impact in the dairy industry. Among its risk factors are calf weight, gestation length, breed and conformation. Biological networks have been proposed to capture the genetic architecture of complex traits, where GWAS show limitations. The objective of this study was to identify gene networks in Holstein (HO), Brown Swiss (BS) and Jersey (JE) cattle related to dystocia. De-regressed PTA (dPTA) for calving ease (direct and maternal), gestation length, stature, strength and rump width of 8780 HO, 505 BS, and 1818 JE bulls were used in the analysis. A total of 45188 quality-controlled genotypes were available for all bulls. A single trait Bayes-B GWAS was performed within breed with pi = 0.9. The proportion of genetic variance (PVg) explained by each SNP was computed by scaling the allele substitution effect in additive genetic standard deviations and dividing by the summed effects for all markers. The SNP with VPg>=75th percentile of the sample were ruled significant. Relevant SNP (rSNP) were defined as: significant in all traits, significant in all functional traits, or significant in all type traits. An association weight matrix (AWM) was constructed with rSNP in rows and traits in columns. Cells of the AWM corresponded to the normalized rSNP effect size. The SNP were mapped to genes within 2500 bp, and rows in the AWM were indexed with them. Genes were used to search for enriched functional annotation (FDR <= 0.15 HO, JE; FDR <= 0.3 BS). The AWM row-wise partial correlations were computed. Significant correlations were interpreted as gene-gene interactions, resulting in a gene network. Networks included 1272 (HO), 1454 (BS) and 1455 (JE) genes. The number of connections ranged between 1 and 80 (HO), 15 (BS), 13 (JE). A total of 152 (HO), 13 (BS), 108 (JE) genes in the networks were within reported dystocia QTL. Top enriched terms were cell and biological adhesion (HO, JE), regulation of purine nucleotide metabolic process (BS). The most connected genes among the networks, enriching GO terms, and within dystocia QTL were: RASA1 (HO, 77 interactions), FLOT1 (BS, 9), and ADRBK2 (JE, 12). Integrating knowledge from available annotation tools to identify the functional biology of dystocia in dairy cattle can potentially improve genomic predictions which could result in increasing profitability of the dairy industry.