Use of dry-matter intake recorded at multiple time periods during lactation increases the accuracy of genomic prediction for dry-matter intake and residual feed intake in dairy cattle
Sunduimijid Bolormaa
A * , Mekonnen Haile-Mariam A L , Leah C. Marett B C , Filippo Miglior D E , Christine F. Baes E F , Flavio S. Schenkel E , Erin E. Connor G H , Coralia I. V. Manzanilla-Pech I , Eileen Wall J , Mike P. Coffey J , Michael E. Goddard A K , Iona M. MacLeod A and Jennie E. Pryce A L
+ Author Affiliations
- Author Affiliations
A Agriculture Victoria Research, AgriBio Centre for AgriBioscience, Bundoora, Vic. 3083, Australia.
B Agriculture Victoria Research, Ellinbank, Vic. 3821, Australia.
C Centre for Agricultural Innovation, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, Vic. 3010, Australia.
D LACTANET, Sainte-Anne-de-Bellevue, QC H9X 3R4, Canada.
E CGIL, University of Guelph, Guelph, ON N1G 2W1, Canada.
F Institute of Genetics, Vetsuisse Faculty, University of Bern, Bern 3002, Switzerland.
G Animal Genomics and Improvement Laboratory, USDA, Agricultural Research Service, Beltsville Agricultural Research Center, Beltsville, MD 20705, USA.
H Department of Animal and Food Sciences, University of Delaware, Newark, DE 19716, USA.
I Center for Quantitative Genetics and Genomics, Aarhus University, C. F Møllers allé 3, Aarhus DK-8000, Denmark.
J Scotland’s Rural College, Roslin Institute, Midlothian EH25 9RG, UK.
K School of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, Vic. 3052, Australia.
L School of Applied Systems Biology, La Trobe University, Bundoora, Vic. 3083, Australia.
Animal Production Science 63(11) 1113-1125 https://doi.org/10.1071/AN23022
Submitted: 12 January 2023 Accepted: 8 May 2023 Published: 29 May 2023
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)
Abstract
Context: Feed is the largest expense on a dairy farm, therefore improving feed efficiency is important. Recording dry-matter intake (DMI) is a prerequisite for calculating feed efficiency. Genetic variation of feed intake and feed efficiency varies across lactation stages and parities. DMI is an expensive and difficult-to-measure trait. This raises the question of which time periods during lactation would be most appropriate to measure DMI.
Aims: The aim was to evaluate whether sequence variants selected from genome-wide association studies (GWAS) for DMI recorded at multiple lactation time periods and parities would increase the accuracy of genomic estimated breeding values (GEBVs) for DMI and residual feed intake (RFI).
Methods: Data of 2274 overseas lactating cows were used for the GWAS to select sequence variants. GWAS was performed using the average of the DMI phenotypes in a 30-day window of six different time periods across the lactation. The most significant sequence variants were selected from the GWAS at each time period for either first or later parities. GEBVs for DMI and RFI in Australian lactating cows were estimated using BayesRC with 50 k single nucleotide polymorphisms (SNPs) and selected GWAS sequence variants.
Key results: There were differences in DMI genomic correlations and heritabilities between first and later parities and within parity across lactation time periods. Compared with using 50 k single-nucleotide polymorphisms (SNPs) only, the accuracy of DMI GEBVs increased by up to 11% by using the 50 k SNPs plus the selected sequence variants. Compared with DMI, the increase in accuracy for RFI was lower (by 6%) likely because the sequence variants were selected from GWAS for DMI not RFI. The accuracies for DMI and RFI GEBVs were highest by using selected sequence variants from the DMI GWAS in the mid- to late-lactation periods in later parity.
Conclusions: Our results showed that DMI phenotypes in late lactation time periods could capture more genetic variation and increase genomic prediction accuracy through the use of custom genotype panels in genomic selection.
Implications: Collecting DMI at the optimal time period(s) of lactation may help develop more accurate and cost-effective breeding values for feed efficiency in dairy cattle.
Keywords: BayesRC, DMI, days in milk, disentangling phenotypes, genomic accuracy, genomic correlation, GWAS, hierarchical clustering, lactation time periods, RFI.
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