Impact of epistasis effects on the accuracy of predicting phenotypic values of residual feed intake in U.S. Holstein cows

Authors: LIANG, ZUOXIANG - University Of Minnesota; PRAKAPENKA, DZIANIS - University Of Minnesota; PARKER GADDIS, KRISTEN - Council On Dairy Cattle Breeding; VANDEHAAR, MICHAEL - Michigan State University; WEIGEL, KENT - University Of Wisconsin; TEMPELMAN, ROBERT - Michigan State University; KOLTES, JAMES - Iowa State University; SANTOS, JOSE EDUARDO - University Of Florida; WHITE, HEATHER - University Of Wisconsin; PENAGARICANO, FRANCISCO - University Of Wisconsin; Baldwin, Ransom - Randy; DA, YANG - University Of Minnesota

Submitted to: Frontiers in Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/19/2022
Publication Date: 11/1/2022
Citation: Liang, Z., Prakapenka, D., Parker Gaddis, K.L., Vandehaar, M.J., Weigel, K.A., Tempelman, R.J., Koltes, J.E., Santos, J.P., White, H.M., Penagaricano, F., Baldwin, R.L., Da, Y. 2022. Impact of epistasis effects on the accuracy of predicting phenotypic values of residual feed intake in U.S. Holstein cows. Frontiers in Genetics. 13:1017490. https://doi.org/10.3389/fgene.2022.1017490.
DOI: https://doi.org/10.3389/fgene.2022.1017490

Interpretive Summary: Residual feed intake (RFI) has been used as a measure of feed efficiency in farm animals. For lactating dairy cattle it is determined by an equation that tests a standard prediction against the actual nutritional intake by the cow. In order to provide a more accurate prediction regarding a cows expected performance we studied factors that can affect the equations. The inclusion of additive-by-additive effects along with additive effects in a prediction model improved the accuracy of predicting the RFI phenotypic values in U.S. Holstein cows over the accuracy of the additive-only model. Given the high costs of RFI measurements, the large number of genomic evaluated females and the high culling rate of U.S. Holstein cows, the inclusion of additive × additive effects in the prediction model is expected to have a positive impact on the culling decisions with respect to potential RFI phenotypic performance of Holstein cows.

Technical Abstract: The impact of genomic epistasis effects on the accuracy of predicting the phenotypic values of residual feed intake (RFI) in U.S. Holstein cows was evaluated using 6215 Holstein cows and 79,060 SNPs. Two SNP models and seven epistasis models were initially evaluated. Heritability estimates and the accuracy of predicting the RFI phenotypic values from 10-fold cross-validation studies identified the model with SNP additive effects and additive × additive (A×A) epistasis effects (A+A×A model) to be the best prediction model. Under the A+A×A model, additive heritability was 0.141, and A×A heritability was 0.263 that consisted of 0.260 inter-chromosome A×A heritability and 0.003 intra-chromosome A×A heritability, showing that inter-chromosome A×A effects were responsible for the accuracy increases due to A×A. Under the SNP additive model (A-only model), the additive heritability was 0.171. In the 10 validation populations, the average accuracy for predicting the RFI phenotypic values was 0.246 under A+A×A model and was 0.231 under the A-only model, the range of the prediction accuracy was 0.197-0.333 for A+A×A model and was 0.188-0.319 for the A-only model, the average increase in the accuracy of predicting the RFI phenotypic values by the A+A×A model over the A-only model was 6.49%, and the range of the accuracy increases by the A+A×A model over the A-only model was 3.02-14.29%. Results in this study showed A×A epistasis effects had a positive impact on the accuracy of predicting the RFI phenotypic values when combined with additive effects in the prediction model.