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 78,964 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 (with range 0.197–0.333) under A + A×A model and was 0.231 (with range of 0.188–0.319) under 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% (with range of 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.
Bibliographical noteFunding Information:
This research was supported by the National Institutes of Health’s National Human Genome Research Institute, grant R01HG012425 as part of the NSF/NIH Enabling Discovery through GEnomics (EDGE) Program; grant 2020-67015-31133 from the USDA National Institute of Food and Agriculture; and project MIN-16-124 of the Agricultural Experiment Station at the University of Minnesota.
Members of the Council on Dairy Cattle Breeding (CDCB) and the Cooperative Dairy DNA Repository (CDDR) are acknowledged for providing the dairy genomic evaluation data. The RFI data collection was partially supported by funding from the USDA National Institute of Food and Agriculture (Washington, DC; grant # 2011-68004-30340), the US Council on Dairy Cattle Breeding (Bowie, MD), and the US Foundation for Food and Agriculture Research (Washington, DC; grant # CA18-SS-0000000236). The Ceres and Atlas high performance computing systems of USDA-ARS were used for the data analysis and for the evaluation and testing of the EPIHAP computing package. Paul VanRaden and Steven Schroeder are acknowledged for help with the use of the CDCB data and USDA-ARS computing facilities. Paul VanRaden raised the issue of nonadditive inheritance. The use of the USDA-ARS computers by this research was supported by USDA-ARS projects 8042-31000-002-00-D and 8042-31000-001-00-D.
Copyright © 2022 Liang, Prakapenka, Parker Gaddis, VandeHaar, Weigel, Tempelman, Koltes, Santos, White, Peñagaricano, Baldwin VI and Da.
- genomic prediction
PubMed: MeSH publication types
- Journal Article