Genomic prediction for residual feed intake and its component traits based on 50K and imputed 7.8 million whole genome sequence SNPs in multiple breed populations of Canadian beef cattle

Genomic prediction has the potential to accelerate
the genetic improvement rate for feed efficiency traits
in beef cattle. In this study, we evaluated genomic prediction
accuracies for residual feed intake (RFI) and
its component traits dry matter intake (DMI), average
daily gain (ADG), and metabolic body weight (MWT)
based on genotyped 50K and imputed 7.8 million
whole genome sequence SNPs in multiple Canadian
beef cattle populations. The populations included purebred
Angus (N=1,162), purebred Charolais (N=717),
Kinsella (N=1,506), Elora (N=775), PG1 (N=1,911),
and TX (N=1,502). Animals from the six populations were combined into a single reference population and
genomic prediction was conducted using GBLUP based
on 50K (50K-GBLUP) and 7.8 million imputed SNPs
(seqGBLUP) with 5-fold cross validation of each population.
In addition, a weighted GBLUP (w-seqGBLUP)
was performed for the 7.8 million imputed SNPs using
a G matrix constructed by weighting SNPs of nine
functional classes with their weighting factors obtained
based on the average square of estimated marker effects
of each functional class from GWAS. The results showed
that both seqGBLUP and w-seqGBLUP yielded similar
accuracies for all the traits of all breed populations. For
crossbred populations, seqGBLUP and w-seqGBLUP
improved the prediction accuracy by 4.1%, or from the
realized genomic prediction accuracy of 0.363 to 0.378
for RFI of the Kinsella population, to 16.4% or from
0.311 to 0.362 for ADG of the Elora population in comparison
to the 50K-GBLUP. However, both seqGBLUP
and w-seqGBLUP had a 6.6% to 11.6% lower prediction
accuracy than that of 50K-GBLUP for purebred
Angus. A reduction of 1.3% and 1.5% on genomic prediction
accuracy was also observed for MWT and RFI,
respectively, for purebred Charolais. On-going studies
are being undertaken to further improve genomic prediction
accuracies for feed efficiency traits in Canadian
beef cattle.