Modeling heterotic effects in beef cattle using genome-wide SNP-marker genotypes1

Abstract

An objective of commercial beef cattle crossbreeding programs is to simultaneously optimize use of additive (breed differences) and non-additive (heterosis) effects. A total of 6,794 multibreed and crossbred beef cattle with phenotype and Illumina BovineSNP50 genotype data were used to predict genomic heterosis for growth and carcass traits by applying two methods assumed to be linearly proportional to heterosis. The methods were as follows: 1) retained heterozygosity predicted from genomic breed fractions (HET1) and 2) deviation of adjusted crossbred phenotype from midparent value (HET2). Comparison of methods was based on prediction accuracy from cross-validation. Here, a mutually exclusive random sampling of all crossbred animals (n = 5,327) was performed to form five groups replicated five times with approximately 1,065 animals per group. In each run within a replicate, one group was assigned as a validation set, while the remaining four groups were combined to form the reference set. The phenotype of the animals in the validation set was assumed to be unknown; thus, it resulted in every animal having heterosis values that were predicted without using its own phenotype, allowing their adjusted phenotype to be used for validation. The same approach was used to test the impact of predicted heterosis on accuracy of genomic breeding values (GBV). The results showed positive heterotic effects for growth traits but not for carcass traits that reflect the importance of heterosis for growth traits in beef cattle. Heterosis predicted by HET1 method resulted in less variable estimates that were mostly within the range of estimates generated by HET2. Prediction accuracy was greater for HET2 (0.37-0.98) than HET1 (0.34-0.43). Proper consideration of heterosis in genomic evaluation models has debatable effects on accuracy of EBV predictions. However, opportunity exists for predicting heterosis, improving accuracy of genomic selection, and consequently optimizing crossbreeding programs in beef cattle.

Genome-wide association scan for heterotic quantitative trait loci in multi-breed and crossbred beef cattle

“…See Fig. 1 in Akanno et al [ 13 ] for the distribution of estimated genomic breed fractions in the whole dataset. The genomic breed fraction was used to designate animals as purebreds (n = 1467) based on having Angus, Hereford or Charolais breed fractions greater than 80% while the rest were designated as crossbreds (n = 5327).…”

“…In an earlier study [ 13 ], assuming that heterosis is due to dominance and over-dominance, we investigated the contribution of additive and dominance effects to the total phenotypic variation in purebred, crossbred, and combined data, which underpins the motivation for the current study. Here, a single-SNP GWAS for the joint association of additive and dominance effects was performed on the studied traits in purebred, crossbred and combined data using the ASReml software [ 22 ] based on the following linear mixed effect model: where is a vector of phenotypic observation; is the population mean and is a vector of ones; depending on the trait analysed, is the design matrix that relates the fixed effects to the observation and is a vector of fixed effects including linear covariates of dam age, weaning age, start age for feedlot test and genomic breed fractions, data source and contemporary groups based on herd, year, sex, and management groups.…”

“…Few studies have investigated the importance of non-additive gene effects and these concluded that accounting for these effects in animal genetic evaluation models can improve genomic prediction in comparison to additive models [ 9 – 13 ]. In beef cattle, thanks to the availability of genomic tools, attempts to estimate the proportion of the total phenotypic variation that is attributed to non-additive genetic effects for those traits that express heterosis [ 13 , 14 ] have been made. For example, Bolormaa et al [ 14 ] estimated that the proportion of the variance explained by dominance ranged from 0 to 42% for growth, carcass, and fertility traits in beef cattle.…”

“…For example, Bolormaa et al [ 14 ] estimated that the proportion of the variance explained by dominance ranged from 0 to 42% for growth, carcass, and fertility traits in beef cattle. In an earlier study, Akanno et al [ 13 ] reported estimates of the proportion of variance explained by dominance from 0 to 9% for growth and carcass traits in beef cattle. Both studies suggest that non-additive genetic effects may contribute to variation in beef cattle traits, which may be explained by specific loci across the bovine genome.…”

Avustralya Merinosu ve Çin Merinosu Hibritlerinde Yün Miktarı İle Mikrosatellit Arasındaki İlişkinin Araştırılması

“…At present, such as DNA fingerprinting, rapid DNA and protein polymorphism, have been used to explore the heterosis of animals [30,31] . The relationship of gene heterozygosity with heterosis has been studied in wildlife and livestock [33][34][35] . These studies are based on the group level, the use of heterosis is performed by specified male and female animals in practice.…”