-
Views
-
Cite
Cite
C. B. Wasike, M. Rolf, N. C. D. Silva, R. Puchala, T. Sahlu, A. L. Goetsch, T. A. Gipson, 1683 Genome-wide association analysis of residual feed intake and milk yield in dairy goats, Journal of Animal Science, Volume 94, Issue suppl_5, October 2016, Page 820, https://doi.org/10.2527/jam2016-1683
Close - Share Icon Share
Abstract
Interest in both dairy and meat goat production in the US has been increasing, and there is tremendous opportunity for genetic progress in traits that are easy to measure (e.g., milk yield) and those that are more difficult (e.g., residual feed intake, RFI). However, there is little research or infrastructure within the goat industry for implementation of large-scale genetic evaluation. The objective of this study was to conduct a genome-wide association study (GWAS) for RFI and total milk yield in dairy goats. Forty-eight Alpine females (56.4 ± 7.15 kg BW; 423 ± 146.1 kg milk; 225 ± 20.9 d in milk; 16 primiparous) were used. Data in mid- to late lactation were used to calculate RFI. Milk yield and components were collected over a 12-wk period in mid- to late lactation and were used to calculate energy-corrected milk yield (ECMY). ECMY DMI, and BW from the same period were used to calculate RFI, which ranged from −794 to 594 g. DNA was collected via venipuncture and stored on Whatman FTA cards. Genotypes were assayed using the Illumina 52K goat SNP chip. SNPs with a minor allelic frequency < .01 were removed, resulting in 48,632 SNPs available for analysis. Missing genotypes were imputed using BEAGLE and SNP effects were estimated using GenSel on the iPlant platform. For RFI, the posterior mean of the residual variance was 47,934 and the posterior mean of genetic variance was 14,428, giving an estimated heritability of 0.23. For total milk yield, the posterior mean of the residual variance was 10,141 and the posterior mean of genetic variance was 9826, giving an estimated heritability of 0.49. The 100 SNP with the greatest effects contributed 3.1% and 3.3% of the total genetic variance for RFI and total milk yield, respectively. Although the sample size in this study is very small and the ideal usage of genomic information would be to supplement large-scale genetic evaluation programs, it illustrates the potential of utilizing genomic selection with phenotypes on large populations of dairy goats to make genetic improvement. Genetic selection for RFI and milk yield in dairy goats may be expedited by selection programs that incorporate genomic information, particularly in the absence of large, nationwide breeding value prediction programs.
