Short communication: Validation of genomic breeding value predictions for feed intake and feed efficiency trials

JE Pryce*, O Gonzalez-Recio, JB Thornhill, LC Marett, WJ Wales, MP Coffey, Y de Haas, RF Veerkamp, BJ Hayes

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)
28 Downloads (Pure)


Validating genomic prediction equations in independent populations is an important part of evaluating genomic selection. Published genomic predictions from 2 studies on (1) residual feed intake and (2) dry matter intake (DMI) were validated in a cohort of 78 multiparous Holsteins from Australia. The mean realized accuracy of genomic prediction for residual feed intake was 0.27 when the reference population included phenotypes from 939 New Zealand and 843 Australian growing heifers (aged 5-8 mo) genotyped on high density (770k) single nucleotide polymorphism chips. The 90% bootstrapped confidence interval of this estimate was between 0.16 and 0.36. The mean realized accuracy was slightly lower (0.25) when the reference population comprised only Australian growing heifers. Higher realized accuracies were achieved for DMI in the same validation population and using a multicountry model that included 958 lactating cows from the Netherlands and United Kingdom in addition to 843 growing heifers from Australia. The multicountry analysis for DMI generated 3 sets of genomic predictions for validation animals, one on each country scale. The highest mean accuracy (0.72) was obtained when the genomic breeding values were expressed on the Dutch scale. Although the validation population used in this study was small (n=78), the results illustrate that genomic selection for DMI and residual feed intake is feasible. Multicountry collaboration in the area of dairy cow feed efficiency is the evident pathway to achieving reasonable genomic prediction accuracies for these valuable traits.
Original languageEnglish
Pages (from-to)537-542
Number of pages6
JournalJournal of Dairy Science
Issue number1
Early online date13 Nov 2013
Publication statusPrint publication - Jan 2014

Bibliographical note



  • Feed efficiency
  • Genomic prediction
  • Residual feed intake
  • Validation


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