The objective of this study was to estimate economic returns and costs associated with 4 scenarios of genetic evaluation that combine genotypes, phenotypes, and pedigree information from a vertically integrated purebred (PB) and commercial (CM) beef cattle system. Inference was to a genetic evaluation for a production system producing Charolais terminal sires for 10,000 CM cows. The first genetic evaluation scenario, denoted PB_A, modeled a genetic evaluation in which pedigree information and phenotypes are available for PB seedstock animals. Scenario PB_H contained the same information as PB_A with the addition of 25K density (GeneSeek Genomic Profiler LD) single nucleotide polymorphism (SNP) genotypes from PB animals. Scenario PBCM_A contained pedigree records and phenotypes from PB and CM cattle. Scenario PBCM_H contained phenotypes, pedigree, and genotypes from the PB and CM animals. Estimates of prediction error variance, (co)variance, and selection index parameters were used to estimate accuracy of selection candidates (rTI) and genetic gain resulting from selection on an economic index in US dollars (ΔG). Annual costs and incomes were used to determine the 30-yr cumulative net present value (CNPV) per CM calf resulting from selection in these genetic evaluation scenarios. Adding genotypes and CM production phenotypes to genetic evaluation increased the rTI of selection candidates and ΔG across all 4 scenarios. Scenario PBCM_H produced the highest annual ΔG in the PB herd at US$11.91 per head. Including CM phenotypes and parentage testing in the genetic evaluation increased the time to breakeven from 12 yr in PB_A to 19 years in PBCM_A after accounting for the cost of that information. Adding CM phenotypes and genotypes increased the breakeven time from 12 yr in PB_H to 18 yr in PBCM_H. Scenario PB_H produced the highest 30-yr CNPV per slaughtered CM calf at US$371.16. These results using field data indicate that economically relevant rTI and ΔG can be realized by adding 25K SNP genotypes and CM phenotypes to genetic evaluation, but the additional cost of that data significantly delays the economic return to the enterprise.

Background

Recessive loss-of-function (LOF) alleles at genes which are essential for life, can result in early embryonic mortality. Cattle producers can use the LOF carrier status of individual animals to make selection and mate allocation decisions.

Methods

Two beef cattle breeding strategies i.e. (1) selection against LOF carriers as parents and (2) simultaneous selection and mate allocation to avoid the occurrence of homozygous offspring in three scenarios, which differed in number and frequency of LOF alleles were evaluated using the mate selection program, MateSel. Scenarios included (a) seven loci with high-frequency LOF alleles, (b) 76 loci with low-frequency LOF alleles, and (c) 50 loci with random high- and low-frequency LOF alleles. In addition, any savings resulting from the information obtained by varying the percentage (0-100%) of the herd genotyped, together with segregation analysis to cover ungenotyped animals, were calculated to determine (1) which percentage optimized net profit for a fixed cost of genotyping ($30/test), and (2) the breakeven cost for genotyping.

Results

With full knowledge of the LOF alleles carried by selection candidates, the most profitable breeding strategy was always simultaneous selection and mate allocation to avoid homozygous affected offspring (aa) as compared to indiscriminate selection against carrier parents (Aa). The breakeven value of genotyping depended on the number of loci modeled, the LOF allele frequencies, and the mating/selection strategies used. Genotyping was most valuable when it was used to avoid otherwise high levels of embryonic mortalities. As the number of essential loci with LOF alleles increased, especially when some were present at relatively high minor allele frequencies, embryonic losses increased, and profit was maximized by genotyping 10 to 20% of a herd and using that information to reduce these losses.

Conclusions

Genotyping 100% of the herd was never the most profitable outcome in any scenario; however, genotyping some proportion of the herd, together with segregation analysis to cover ungenotyped animals, maximized overall profit in scenarios with large numbers of loci with LOF alleles. As more LOF alleles are identified, such a mate selection software will likely be required to optimally select and allocate matings to balance the rate of genetic gain, embryonic losses, and inbreeding.