Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #317497

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

Location: Animal Genomics and Improvement Laboratory

Title: Including gene networks to predict calving ease in Holstein, Brown Swiss and Jersey cattle

Author
item TIEZZI, FRANCESCO - North Carolina State University
item ARCEO, MARIA - North Carolina State University
item Cole, John
item MALTECCA, CHRISTIAN - North Carolina State University

Submitted to: BioMed Central (BMC) Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/14/2018
Publication Date: 10/1/2018
Citation: Tiezzi, F., Arceo, M.E., Cole, J.B., Maltecca, C. 2018. Including gene networks to predict calving ease in Holstein, Brown Swiss and Jersey cattle. BioMed Central (BMC) Genetics. 19:20. https://doi.org/10.1186/s12863-018-0606-y.
DOI: https://doi.org/10.1186/s12863-018-0606-y

Interpretive Summary: Calving difficulty has a substantial economic impact on the US dairy industry. Different dairy cattle breeds have different incidence of calving difficulty, with Holstein having the highest dystocia rates and Jersey the lowest. This research identified gene networks associated with dystocia in the Holstein, Brown Swiss, and Jersey breeds. An across-breed network also was constructed using genes common to all of the individual breed networks. A total of 256 genes in the Holstein network, 275 genes in the Brown Swiss network, and 253 genes in the Jersey network were associated with previously reported genomic regions associated with dystocia. The across-breed network included 80 genes, 9 of which were previously associated with dystocia. Several physiological pathways included a greater-than-expected number of genes from the 4 networks, including pathways related to growth and development. The approach used in this paper can be extended to other economically important phenotypes, such as disease resistance.

Technical Abstract: Background Calving difficulty or dystocia has a great economic impact in the US dairy industry. Reported risk factors associated with calving difficulty are feto-pelvic disproportion, gestation length and conformation. Different dairy cattle breeds have different incidence of calving difficulty, with Holstein having the highest dystocia rates and Jersey the lowest. Genomic selection becomes important especially for complex traits with low heritability, where the accuracy of conventional selection is lower. However, for complex traits where a large number of genes influence the phenotype, genome-wide association studies showed limitations. Biological networks overcome these limitations and better capture the genetic architecture of complex traits. In this paper, we identify and characterize Holstein, Brown Swiss and Jersey breed-specific dystocia networks. We also characterize an across-breed network including all genes located at the intersection of the 3 breed-specific networks. Results Genome-wide association analysis identified single nucleotide polymorphisms explaining the largest average proportion of genetic variance on BTA18 in Holstein, BTA25 in Brown Swiss, and BTA15 in Jersey. Gene networks derived from the genome-wide association included 1272 genes in Holstein, 1454 genes in Brown Swiss, and 1455 genes in Jersey. Furthermore, 256 genes in the Holstein network, 275 genes in the Brown Swiss network, and 253 genes in the Jersey network were within previously reported dystocia quantitative trait loci. The across-breed network included 80 genes, with 9 genes being within previously reported dystocia quantitative trait loci. The gene-gene interactions in this network differed in the different breeds. Gene ontology enrichment analysis of genes in the networks showed regulation of ARF GTPase was very significant (FDR <= 0.0098) on Holstein. Neuron morphogenesis and differentiation was the term most enriched (FDR <= 0.0539) on the across-breed network. Pathway enrichment analysis results indicated most significant overrepresented pathways (FDR <= 0.35) were cell cycle on the Holstein network, small cell lung cancer on the Jersey network and Shigellosis on the across-breed network. Conclusion Regions identified in the genome were in the proximity of previously described quantitative trait loci that would most likely affect calving ease by altering the feto-pelvic proportion. This paper provides a further insight into the biology of calving difficulty in Holstein, Brown Swiss and Jersey cattle. Our approach of evaluating dystocia in three different breeds also allowed for the identification of an across-breed network that could potentially explain common mechanisms underlying dystocia in dairy cattle. The approach used in this paper could be extended to any instance with asymmetric distribution of phenotypes, for example, resistance to disease data.