Heterogeneity in genetic and nongenetic variation and energy sink relationships for residual feed intake across research stations and countries

Abstract

Our long-term objective is to develop breeding strategies for improving feed efficiency in dairy cattle. In this study, phenotypic data were pooled across multiple research stations to facilitate investigation of the genetic and nongenetic components of feed efficiency in Holstein cattle. Specifically, the heritability of residual feed intake (RFI) was estimated and heterogeneous relationships between RFI and traits relating to energy utilization were characterized across research stations. Milk, fat, protein, and lactose production converted to megacalories (milk energy; MilkE), dry matter intakes (DMI), and body weights (BW) were collected on 6,824 lactations from 4,893 Holstein cows from research stations in Scotland, the Netherlands, and the United States. Weekly DMI, recorded between 50 to 200 d in milk, was fitted as a linear function of MilkE, BW0.75, and change in BW (ΔBW), along with parity, a fifth-order polynomial on days in milk (DIM), and the interaction between this polynomial and parity in a first-stage model. The residuals from this analysis were considered to be a phenotypic measure of RFI. Estimated partial regression coefficients of DMI on MilkE and on BW0.75 ranged from 0.29 to 0.47 kg/Mcal for MilkE across research stations, whereas estimated partial regression coefficients on BW0.75 ranged from 0.06 to 0.16 kg/kg0.75. Estimated partial regression coefficients on ΔBW ranged from 0.06 to 0.39 across stations. Heritabilities for country-specific RFI were based on fitting second-stage random regression models and ranged from 0.06 to 0.24 depending on DIM. The overall heritability estimate across all research stations and all DIM was 0.15±0.02, whereas an alternative analysis based on combining the first- and second-stage model as 1 model led to an overall heritability estimate of 0.18±0.02. Hence future genomic selection programs on feed efficiency appear to be promising; nevertheless, care should be taken to allow for potentially heterogeneous variance components and partial relationships between DMI and other energy sink traits across environments when determining RFI.

Hot topic: Definition and implementation of a breeding value for feed efficiency in dairy cows

“…As RFI is corrected for BW, this is an important distinction and the reason why feed saved (integrating RFI and maintenance costs associated with higher BW) is a more desirable trait for implementation compared with RFI. Tempelman et al (2015) combined data from research stations in the United States, United Kingdom, and the Netherlands and observed heterogeneous variation in RFI arising from multiple environments from which data were sourced. Therefore, multivariate analyses are preferable when the trait is measured in different environments or life stages (De Haas et al, 2012) as they can treat differences both in trait definitions and genotype by environment interactions appropriately.…”

“…The heritability of RFI in Australian cows was 0.20 and heritabilities for growing calves and a combined data set of Dutch and UK cows were both 0.35 (Table 2), both of which are in the range of previous stud-ies Tempelman et al, 2015). The genetic correlation between RFI in growing calves and lactating cows was 0.67 (±0.45).…”

“…Genomic predictions for RFI can be developed in small, well-recorded reference populations that have genotypes and phenotypes, and the prediction equation can then be applied to genotyped animals without phenotypes Gonzalez-Recio et al, 2014a). Although genomic selection is an obvious route to enabling breeding values to be estimated for RFI Tempelman et al, 2015), the size of the reference population and its relationship to the predicted population are limiting factors. For a trait with a heritability of 0.2, typical of RFI (Connor, 2015), more than 30,000 individuals are required for the reference population to achieve reliabilities of greater than 50% Gonzalez-Recio et al, 2014a).…”

Genetic parameters between feed-intake-related traits and conformation in 2 separate dairy populations—the Netherlands and United States

“…A full description of the data editing strategies can be found in Tempelman et al (2015). Milk energy output is expressed in megacalories and was calculated from weekly MY plus the fat % (FP), protein % (PP), and lactose % (LP), using the following formula according to National Research Dairy Council (2001) Kleiber (1932) determined MBW to be proportional to the basal metabolic rate of animals and to be representative of maintenance nutrient requirements for animals as a function of their BW.…”

Residual feed intake is repeatable for lactating Holstein dairy cows fed high and low starch diets

“…Because RFI is independent of production level, recent attention has been given to using RFI as a tool to assess feed efficiency in dairy cattle for purposes of genetic selection (Connor et al, 2013;Green et al, 2013;Macdonald et al, 2014). The heritability of RFI is 0.15 to 0.19 in lactating dairy cattle (van Arendonk et al, 1991;Tempelman et al, 2015) and 0.22 to 0.38 in growing dairy heifers (Williams et al, 2011;Pryce et al, 2012). If RFI is to be used in selection strategies, it is important to know if it is repeatable across the range of diets commonly used on commercial dairy farms.…”