Effect of predictor traits on accuracy of genomic breeding values for feed intake based on a limited cow reference population

Abstract

The genomic breeding value accuracy of scarcely recorded traits is low because of the limited number of phenotypic observations. One solution to increase the breeding value accuracy is to use predictor traits. This study investigated the impact of recording additional phenotypic observations for predictor traits on reference and evaluated animals on the genomic breeding value accuracy for a scarcely recorded trait. The scarcely recorded trait was dry matter intake (DMI, n 5 869) and the predictor traits were fat-protein-corrected milk (FPCM, n 5 1520) and live weight (LW, n 5 1309). All phenotyped animals were genotyped and originated from research farms in Ireland, the United Kingdom and the Netherlands. Multi-trait REML was used to simultaneously estimate variance components and breeding values for DMI using available predictors. In addition, analyses using only pedigree relationships were performed. Breeding value accuracy was assessed through cross-validation (CV) and prediction error variance (PEV). CV groups (n 5 7) were defined by splitting animals across genetic lines and management groups within country. With no additional traits recorded for the evaluated animals, both CV-and PEV-based accuracies for DMI were substantially higher for genomic than for pedigree analyses (CV: max. 0.26 for pedigree and 0.33 for genomic analyses; PEV: max. 0.45 and 0.52, respectively). With additional traits available, the differences between pedigree and genomic accuracies diminished. With additional recording for FPCM, pedigree accuracies increased from 0.26 to 0.47 for CV and from 0.45 to 0.48 for PEV. Genomic accuracies increased from 0.33 to 0.50 for CV and from 0.52 to 0.53 for PEV. With additional recording for LW instead of FPCM, pedigree accuracies increased to 0.54 for CV and to 0.61 for PEV. Genomic accuracies increased to 0.57 for CV and to 0.60 for PEV. With both FPCM and LW available for evaluated animals, accuracy was highest (0.62 for CV and 0.61 for PEV in pedigree, and 0.63 for CV and 0.61 for PEV in genomic analyses). Recording predictor traits for only the reference population did not increase DMI breeding value accuracy. Recording predictor traits for both reference and evaluated animals significantly increased DMI breeding value accuracy and removed the bias observed when only reference animals had records. The benefit of using genomic instead of pedigree relationships was reduced when more predictor traits were used. Using predictor traits may be an inexpensive way to significantly increase the accuracy and remove the bias of (genomic) breeding values of scarcely recorded traits such as feed intake.

Hot topic: Definition and implementation of a breeding value for feed efficiency in dairy cows

“…However, although the data sets for difficult or expensive-to-measure traits are growing, international collaboration is likely to be the short-to medium-term solution to obtain acceptable reliabilities for novel traits that are only collected in females Egger-Danner et al, 2015). Predictor traits can also improve the accuracy of predicting DMI; for example, Pszczola et al (2013) showed an increase in accuracy from adding ECM and live weight, whereas Manzanilla Pech et al (2014) realized greater accuracies from conformation data.…”

Review: Opportunities and challenges for small populations of dairy cattle in the era of genomics

“…Problems also arise whenever traits are hard to measure, for example if they arise later in life, are visible in one sex only or no routine recording exist yet. Several studies on the use of cow reference populations exist to investigate the effect of indicator traits for scarcely recorded traits (de Haas et al, 2011;Pszczola et al, 2013), expensive to measure traits like residual feed intake (Pryce et al, 2012b) or progesterone-based fertility traits (Berry et al, 2012), and newly established traits (e.g. direct health traits, Egger-Danner et al, 2014).…”

“…Egger-Danner et al (2014) demonstrated that genomic estimated breeding values for direct health traits become available when cows with reliable phenotypes are genotyped. Generally speaking, in case of limited resources, for example, due to small population size or rare phenotypes, it is more efficient to genotype females instead of males only (Buch et al, 2012;Pszczola et al, 2013;Gonzalez-Recio et al, 2014). Thomasen et al (2014b) demonstrated that the inclusion of cows into reference populations is a solution to increase the competitiveness of small dairy populations.…”

An experimental validation of genomic selection in octoploid strawberry

“…In a broad context, increases in selection response via GS are related to: (i) prediction of GEBV of untested individuals that do not have phenotypic records; (ii) shortening the breeding cycle; 5,8 and (iii) improving the precision of estimates of genetic effects. 4 However, other potential benefits can be obtained with the incorporation of GS into the breeding strategy, such as: (i) increasing selection intensity by having larger populations for prediction; (ii) reducing testing effort by eliminating partially or completely the establishment of some field experiments; (iii) better planning of crosses by more effective control of inbreeding and relatedness; 9 (iv) predicting hard-to-measure traits using correlated traits as predictors; 10 and (v) controlled reduction of genetic diversity in the short term. 9 …”