Genomic prediction of direct genomic breeding values for fatty acid concentrations in subcutaneous adipose and longissimus lumborum muscle of beef cattle

L. Chen1,2 , M. Vinsky2, C. Ekine1, J. Basarab3, J. Aalhus2, M. E.R. Dugan2, J. Curtis1, C. Fitzsimmons1,2, P. Stothard1, C. Li1,2

1Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada, T6G 2P5; 2Lacombe Research Centre, Agriculture and Agri-Food Canada, 6000 C&E Trail, Lacombe, AB, Canada T4L 1W1; 3Lacombe Research Centre, Alberta Agriculture and Rural Development, 6000 C & E Trail, Lacombe, Alberta, Canada T4L 1W1

The accuracy of predicting direct genomic breeding values for concentrations of 25 major fatty acids in both subcutaneous adipose and longissimus lumborum muscle (striploin) was evaluated with 1,350 crossbred beef steers and heifers genotyped on the Illumina BovineSNP50 BeadChip. Ten-fold cross-validations were implemented with two genomic prediction methods including genomic best linear unbiased prediction (GBLUP) and Bayesian mixture model prediction. Accuracies of genomic prediction of the 25 major fatty acid traits ranged from 0.21 (9c-c16:1) to 0.45 (13c-c18:1) for subcutaneous adipose and from 0.05 (conjugated linoleic acid, CLA) to 0.61 (c14:0) for striploin. Averaged across all traits, the GBLUP and Bayesian methods achieved an accuracy of 0.33 and 0.39 for striploin and 0.29 and 0.31 for subcutaneous adipose, respectively. Specifically, for CLA, trans vaccenic acid (11t-C18:1) and polyunsaturated fatty acids (PUFA), which have potential benefits to human health, the accuracy of genomic prediction using GBLUP (Bayesian in parenthesis) method was 0.06 (0.05), 0.31 (0.29), and 0.45 (0.45), respectively, for striploin, and 0.34 (0.33), 0.33 (0.33), and 0.35 (0.36) for subcutaneous adipose. The results suggest that genomic prediction has the potential to select genetically superior beef cattle to further enhance the healthfulness of beef. However, more studies are needed to improve the accuracy of genomic prediction for fatty acid composition.

 

Genomic prediction for feed efficiency and carcass traits in Angus and Charolais beef cattle

L. Chen1,2 , M. Vinsky2, C. Li1,2

 

1Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada, T6G 2P5; 2Lacombe Research Centre, Agriculture and Agri-Food Canada, 6000 C&E Trail, Lacombe, AB, Canada T4L 1W1

 

Genomic prediction is an emerging method that uses a reference population with both phenotypic and DNA marker information to estimate the DNA markers effects and subsequently use them to predict genetic merits of selection candidates based on their marker genotypes alone (Meuwissen et al., 2001). Therefore, genomic prediction offers an opportunity to select genetically superior cattle at birth without having to measure any phenotypic traits, which will accelerate genetic improvement rates, especially for difficult or costly-to-measure traits such as feed efficiency. Genomic prediction can also help enhance genetic improvement progress for carcass quality as phenotypes of carcass merit traits are collected by sacrificing potential breeding candidates. For the past 15 years a purebred Angus herd of approximately 185 cows and a purebred Charolais herd of 125 cows have been maintained by Agriculture and Agri-Food Canada (AAFC). In these herds the feed efficiency and carcass merit traits were measured on approximately 1100 Angus and 910 Charolais steers. These animals were also successfully genotyped on the Illumina BovineSNP50 Beadchip containing 54,609 single nucleotide polymorphisms (SNP). This data was used to assess the predictability of the DNA markers on feed efficiency and carcass traits. The results showed that the accuracy of genomic prediction for residual feed intake ranged from 0.29 to 0.58 for Angus and from 0.38 to 0.62 for Charolais. The accuracy of genomic prediction for carcass merit traits were from 0.32 (lean meat yield) to 0.37 (carcass marbling score) in the Angus population and from 0.24 (rib-eye area) to 0.46 (carcass backfat thickness) in Charolais. The genomic prediction accuracy largely depends on the relatedness of selection candidates with the reference populations. The highest accuracy was achieved when the selection candidates were immediate offspring of the Kinsella Angus or Charolais population. Further research is underway to improve the genomic prediction accuracy for both purebred and crossbred beef cattle.

 

 

RFI and Nutrition of the Pregnant Heifer: Impacts on Bull Calf Scrotal Circumference and Body Weight
L. Wynnyk, F. Paradis, J. Kastelic, M. Colazo, L. McKeown, H. Block, C. Li, B. Yaremcio, J.A. Basarab, H. Bruce, J. Thundathil, C. Fitzsimmons

 

There is evidence indicating that improvements in feed efficiency may be antagonistic to reproductive traits in beef cattle, and should be carefully considered to avoid negative effects on fertility. Maternal nutrition during gestation can affect offspring growth and reproductive development. Our study investigated effects of maternal diet during gestation, and genetic potential for RFI, on growth and scrotal development in bulls. Pregnant purebred Angus heifers received a ration formulated to gain either 0.5 kg/d (L-diet), or 0.7 kg/d (H-diet), from 30 d until 150 d of gestation. Scrotal circumferences (SC) and weights of bull calves were measured once a month from the ages of 6 to 16 months. SC and weight data were analyzed using SAS 9.2 PROC MIXED for repeated measures, with RFI, maternal diet, time, and their interactions, as fixed effects, and bull age as a covariate. SC was significantly affected by the genetic potential for RFI; high RFI bulls had larger SC. Bull weights were significantly affected by a maternal diet*time interaction where low diet bulls tended to be heavier than high diet bulls as they matured. Therefore, selection for low RFI animals utilizing GrowSafe® tests for RFI, while animals are going through puberty, has the potential to also select for later maturing cattle. Depending upon the source, current breeding values used for RFI may need to be adjusted to remove this bias or a selection index including SC and RFI should be developed. As well, maternal diet during gestation can affect growth traits well into maturity.

 

 

 

 

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