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ARS Home » Plains Area » Miles City, Montana » Livestock and Range Research Laboratory » Research » Publications at this Location » Publication #336630
Title: Genomic prediction and genome-wide association analysis of female longevity in a composite beef cattle breed

Author
item Hay, El Hamidi
item Roberts, Andrew

Submitted to: Journal of Animal Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/3/2017
Publication Date: 4/13/2017
Citation: Hay, E.A., Roberts, A.J. 2017. Genomic prediction and genome-wide association analysis of female longevity in a composite beef cattle breed. Journal of Animal Science. 95: 1467-1471. doi:10.2527/jas2016.1355.

Interpretive Summary: Longevity of beef cows is of great importance to the cattle industry. Improving longevity of a herd increases economic returns by reducing the proportion of replacement heifers. The objective of this study was to understand the genetics of this economically important trait. The data used in this study consisted of 547 Composite Gene Combination (CGC) cows (1/2 Red Angus, ¼ Charolais, ¼ Tarentaise) born from 2002 to 2011 genotyped with Illumina BovineSNP50 BeadChip. Genetic markers were used to predict longevity and identify regions on the genome that affect this trait. The prediction accuracy was low 0.28, 0.25 and 0.22 using three different statistical approaches. Additionally, four significant regions were identified in the bovine genome that are associated with longevity.

Technical Abstract: Longevity is a highly important trait to the efficiency of beef cattle production. The objective of this study was to evaluate the genomic prediction of longevity and identify genomic regions associated with this trait. The data used in this study consisted of 547 Composite Gene Combination (CGC) cows (1/2 Red Angus, ¼ Charolais, ¼ Tarentaise) born from 2002 to 2011 genotyped with Illumina BovineSNP50 BeadChip. Three models were used to assess genomic prediction: Bayes A, Bayes B and GBLUP. To identify genomic regions associated with longevity two approaches were adopted: single marker genome wide association and Bayesian approach using GenSel software. The genomic prediction accuracy was low 0.28, 0.25 and 0.22 for Bayes A, Bayes B and GBLUP respectively. For the genome wide association analyses, the single marker identified 5 loci with p-value less than 0.05 after false discovery correction: UA-IFASA-7571 on chromosome 19 (58.03 Mb), ARS-BFGL-BAC-15059 on BTA 1 (28.8 Mb), ARS-BFGL-NGS-104159 on BTA3 (29.4 Mb), ARS-BFGL-NGS-32882 on BTA9 (104.07 Mb) and ARS-BFGL-NGS-32883 on BTA25 (33.77 Mb). The Bayesian approach yielded four genomic regions overlapping with the single marker results. The region with the highest percentage (3.73%) was detected on chromosome 19. Both GWAS approaches adopted in this study showed evidence for association with various chromosomal locations.