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ARS Home » Plains Area » Miles City, Montana » Livestock and Range Research Laboratory » Research » Publications at this Location » Publication #343859

Title: Genomic prediction of continuous and binary fertility traits of females in a composite beef cattle breed

Author
item TOGHIANI, SAJJAD - University Of Georgia
item Hay, El Hamidi
item SUMREDDEE, PATTARAPOL - University Of Georgia
item Geary, Thomas
item REKAYA, ROMDHANE - University Of Georgia
item Roberts, Andrew - Andy

Submitted to: Journal of Animal Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/31/2017
Publication Date: 10/5/2017
Publication URL: http://handle.nal.usda.gov/10113/6471025
Citation: Toghiani, S., Hay, E.A., Sumreddee, P., Geary, T.W., Rekaya, R., Roberts, A.J. 2017. Genomic prediction of continuous and binary fertility traits of females in a composite beef cattle breed. Journal of Animal Science. 95:4787-4795. doi:10.2527/jas2017.1944.

Interpretive Summary: Reproductive efficiency is considered the most important factor of commercial beef prodction. With the advent of genomic technology, this new promising tool may improve the genetic progress of reproductive traits. In this study, a joint analysis of age at puberty (APU), age at first calving (AFC) and the binary pregnacy status (PS) was carried out. Data used in this study consisted of records from 1,365 Composite Gene Combination (CGC; 50% Red Angus, 25% Charolais, 25% Tarentaise) first parity females born between 2002 and 2011. Estimates of heritabilities using univariate and multivariate analyses based on pedigree relationships ranged between 0.03 (for age at first calving) to 0.2 (for age at puberty). Heritability of pregnancy status was 0.15 and 0.09. Inclusion of genomic informaiton increased the genetic prediction of pregnancy status by 26 to 29%. Further research on improving genetic progress of fertility traits is warranted.

Technical Abstract: Reproduction efficiency is a major factor in the profitability of the beef cattle industry. Genomic selection (GS) is a promising tool that may improve the predictive accuracy and genetic gain of fertility traits. There is a wide range of traits used to measure fertility in dairy and beef cattle including continuous (days open), discrete (pregnancy status), and count (number of inseminations) responses. In this study, a joint analysis of age of puberty (AOP), age at first calving (AOC) and the heifer pregnancy status (HPS) was carried out. Data used in this study consisted of records from 1,365 Composite Gene Combination (CGC; 50% Red Angus, 25% Charolais, 25% Tarentaise) first parity females born between 2002 and 2011. The pedigree file included 5374 animals. A total of 3,902 animals were genotyped with different density SNP chips (3K to 50K SNPs). Animals genotyped with low density panels were imputed to higher density (BovineSNP50 BeadChip) using FImpute. Data were analyzed using univariate and multivariate classical quantitative models (pedigree based) and univariate genomic approaches. For the latter, three different Bayesian methods (BayesA, BayesB, and BayesCp) were implemented and compared. Estimates of heritabilities using univariate and multivariate analyses based on pedigree relationships ranged between 0.03 (for age at first calving) to 0.2 (for age at puberty). Heritability of pregnancy status was 0.15 and 0.09 using the univariate and multivariate analyses, respectively. Genetic correlation between pregnancy status and the other two traits was low being 0.08 with age at puberty and -0.10 with age at first calving. Heritability estimates were slightly higher using genomic rather than average additive relationships. Accuracy of genomic prediction was similar across the three Bayesian methods with higher accuracies for age at puberty than age at first calving likely due to higher heritability of the former. Inclusion of genomic information increased the area under the curve for the prediction of the binary pregnancy status by 26 to 29% depending on the method. Due to the small size of the data, all estimates have large posterior standard deviations and results should be interpreted with caution.