Impact of inclusion rates of crossbred phenotypes and genotypes in nucleus selection programs

Abstract

Numerous methods have been suggested to incorporate crossbred (CB) phenotypes and genotypes into swine selection programs, yet little research has focused on the implicit trade-off decisions between generating data at the nucleus or commercial level. The aim of this study was to investigate the impact of altering the proportion of purebred (PB) and CB phenotypes and genotypes in genetic evaluations on the response to selection of CB performance. Assuming CB and PB performance with moderate heritabilities (h2=0.4), a three-breed swine crossbreeding scheme was simulated and selection was practiced for six generations, where the goal was to increase CB performance. Phenotypes, genotypes, and pedigrees for three PB breeds (25 and 175 mating males and females for each breed, respectively), F1 crosses (400 mating females), and terminal cross progeny (2,500) were simulated. The genome consisted of 18 chromosomes with 1,800 quantitative trait loci and 72k single nucleotide polymorphism (SNP) markers. Selection was performed in PB breeds using estimated breeding value for each phenotyping/genotyping strategy. Strategies investigated were: 1) increasing the proportion of CB with genotypes, phenotypes, and sire pedigree relationships, 2) decreasing the proportion of PB phenotypes and genotypes, and 3) altering the genetic correlation between PB and CB performance (rpc). Each unique rpc scenario and data collection strategy was replicated 10 times. Results showed that including CB data improved the CB performance regardless of rpc or data collection strategy compared with when no CB data were included. Compared with using only PB information, including 10% of CB progeny per generation with sire pedigrees and phenotypes increased the response in CB phenotype by 134%, 55%, 33%, 23%, and 21% when rpc was 0.1, 0.3, 0.5, 0.7, and 0.9, respectively. When the same 10% of CB progeny were also genotyped, CB performance increased by 243%, 54%, 38%, 23%, and 20% when the rpc was 0.1, 0.3, 0.5, 0.7, and 0.9, respectively, compared with when no CB data were utilized. Minimal change was observed in the average CB phenotype when PB phenotypes were included or proportionally removed when CB were genotyped. Removal of both PB phenotypes and genotypes when CB were genotyped greatly reduced the response in CB performance. In practice, the optimal inclusion rate of CB and PB data depends upon the genetic correlation between CB and PB animals and the expense of additional CB data collection compared with the economic benefit associated with increased CB performance.

Keywords: commercial data; genotyping strategy; phenotyping strategy; swine.