Genomic prediction of dry matter intake in dairy cattle from an international data set consisting of research herds in Europe, North America, and Australasia

Abstract

With the aim of increasing the accuracy of genomic estimated breeding values for dry matter intake (DMI) in Holstein-Friesian dairy cattle, data from 10 research herds in Europe, North America, and Australasia were combined. The DMI records were available on 10,701 parity 1 to 5 records from 6,953 cows, as well as on 1,784 growing heifers. Predicted DMI at 70 d in milk was used as the phenotype for the lactating animals, and the average DMI measured during a 60-to 70-d test period at approximately 200 d of age was used as the phenotype for the growing heifers. After editing, there were 583,375 genetic markers obtained from either actual high-density single nucleotide polymorphism (SNP) genotypes or imputed from 54,001 marker SNP genotypes. Genetic correlations between the populations were estimated using genomic REML. The accuracy of genomic prediction was evaluated for the following scenarios: (1) within-country only, by fixing the correlations among populations to zero, (2) using near-unity correlations among populations and assuming the same trait in each population, and (3) a sharing data scenario using estimated genetic correlations among populations. For these 3 scenarios, the data set was divided into 10 sub-populations stratified by progeny group of sires; 9 of these sub-populations were used (in turn) for the genomic prediction and the tenth was used for calculation of the accuracy (correlation adjusted for heritability). A fourth scenario to quantify the benefit for countries that do not record DMI was investigated (i.e., having an entire country as the validation population and excluding this country in the development of the genomic predictions). The optimal scenario, which was sharing data, resulted in a mean prediction accuracy of 0.44, ranging from 0.37 (Denmark) to 0.54 (the Netherlands). Assuming nearunity among-country genetic correlations, the mean accuracy of prediction dropped to 0.40, and the mean within-country accuracy was 0.30. If no records were available in a country, the accuracy based on the other populations ranged from 0.23 to 0.53 for the milking cows, but were only 0.03 and 0.19 for Australian and New Zealand heifers, respectively; the overall mean prediction accuracy was 0.37. Therefore, there is a benefit in collaboration, because phenotypic information for DMI from other countries can be used to augment the accuracy of genomic evaluations of individual countries.

Harnessing the genetics of the modern dairy cow to continue improvements in feed efficiency

“…Another approach is to select against DMI. The heritability of DMI is ~0.37 (de Haas et al, 2015), but it has a positive genetic correlation with several important traits, including BW and milk energy output. Thus, DMI could be added to the selection index, but it would have a large effect on the selection emphasis placed on production and size traits.…”

Development and implementation of genomic predictions in beef cattle

“…Collation of data from multiple sources is, however, not without its own complications such as differences in trait definitions, differences in breed representation, and genotype by environment interactions. de Haas et al (2015) documented an improvement in accuracy of genomic prediction for dry matter intake in Holstein-Friesian dairy cow populations by exploiting phenotypic and genomic information from other populations of Holstein-Friesian cows; the within-country accuracy of genomic predictions when information from all eight countries was included in the genomic predictions was 1.04 to 1.35 (median of 1.13) that when only genomic information for the country itself was considered. International genetic connectedness allowed estimation of genetic correlations between countries; to aid in improving genetic connectedness in beef populations, an initiative to share germplasm between countries would be a prerequisite.…”

Genetic parameters for dry matter intake in primiparous Holstein, Nordic Red, and Jersey cows in the first half of lactation

“…One solution could be to join DMI data from multiple countries or breeds. Some international collaborative studies have been completed using feed intake in Holsteins, which made it possible to develop genetic and genomic evaluations of feed intake in Holstein through joining multiple-country data (Berry et al, 2014;de Haas et al, 2015;Manzanilla Pech et al, 2016). Feed intake information from multiple breeds, however, has not been used in previous studies.…”