|Templeman, R - Michigan State University|
|Spurlock, D - Iowa State University|
|Coffey, M - Scottish Agricultural College|
|Veerkamp, R - Wageningen Agricultural University|
|Armentano, L - University Of Wisconsin|
|Weigel, K - University Of Wisconsin|
|De Haas, Y - Wageningen Agricultural University|
|Staples, C - University Of Florida|
|Hanigan, M - Virginia Tech|
|Lu, Y - Michigan State University|
|Vandeharr, M - Michigan State University|
Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/17/2014
Publication Date: 1/9/2015
Citation: Templeman, R.J., Spurlock, D.M., Coffey, M., Veerkamp, R.F., Armentano, L.E., Weigel, K.A., De Haas, Y., Staples, C.R., Connor, E.E., Hanigan, M.D., Lu, Y.F., Vandeharr, M.J. 2015. Heterogeneity in genetic variation and energy sink relationships for residual feed intake across research stations and countries. Journal of Dairy Science. 98(3):2013-2026.
Interpretive Summary: Our long-term goal is to develop genomic breeding strategies for feed efficiency in dairy cattle. As a step toward that goal, we pooled data from several research stations across three countries to address a number of key research questions pertinent to a quantitative genetic analysis of a feed efficiency measurement called residual feed intake or RFI. Feed efficiency is a difficult trait to characterize because of the large costs of phenotype recording and the need to properly characterize relationships between feed intake and various energy sinks, including milk energy, body weight, and body weight change. We characterized feed efficiency as RFI and inferred relationships between feed intake and energy sinks as being generally heterogeneous across 12 research stations in three countries. Heritability of residual feed intake averaged 15% based on analysis of all data. This study represents an important step in the development of breeding strategies to improve feed efficiency of dairy cattle populations.
Technical Abstract: Our long-term objective is to develop genomic prediction 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 non-genetic 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 Mcals (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 was fitted as a function of MilkE, BW0.75, and change in BW ('BW), along with parity, a 5th order polynomial on days in milk (DIM), and the interaction between this polynomial and parity using data ranging from 50 to 200 DIM in a first stage model. The residuals from this analysis were considered to be a phenotypic measure of RFI. Partial regression coefficients of DMI on MilkE and on BW0.75 were generally important, ranging from 0.29-0.47 kg/Mcal for MilkE across research stations, whereas statistically important partial regression coefficients on BW.75 ranged from 0.06 to 0.16kg/kg.75. Partial regression coefficients on 'BW ranged from 0.06 to 0.39 kg/kg/day 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 across all research stations and all DIM was 0.15±0.02 whereas an alternative analysis based on combining the two models as one led to an overall heritability 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 heterogeneous variance components and partial relationships between DMI and other energy sink traits across environments when determining RFI.