The genetic and biological basis of feed efficiency in mid-lactation Holstein dairy cows

Abstract

The objective of this study was to identify genomic regions and candidate genes associated with feed efficiency in lactating Holstein cows. In total, 4,916 cows with actual or imputed genotypes for 60,671 single nucleotide polymorphisms having individual feed intake, milk yield, milk composition, and body weight records were used in this study. Cows were from research herds located in the United States, Canada, the Netherlands, and the United Kingdom. Feed efficiency, defined as residual feed intake (RFI), was calculated within location as the residual of the regression of dry matter intake (DMI) on milk energy (MilkE), metabolic body weight (MBW), change in body weight, and systematic effects. For RFI, DMI, MilkE, and MBW, bivariate analyses were performed considering each trait as a separate trait within parity group to estimate variance components and genetic correlations between them. Animal relationships were established using a genomic relationship matrix. Genome-wide association studies were performed separately by parity group for RFI, DMI, MilkE, and MBW using the Bayes B method with a prior assumption that 1% of single nucleotide polymorphisms have a nonzero effect. One-megabase windows with greatest percentage of the total genetic variation explained by the markers (TGVM) were identified, and adjacent windows with large proportion of the TGVM were combined and reanalyzed. Heritability estimates for RFI were 0.14 (±0.03; ±SE) in primiparous cows and 0.13 (±0.03) in multiparous cows. Genetic correlations between primiparous and multiparous cows were 0.76 for RFI, 0.78 for DMI, 0.92 for MBW, and 0.61 for MilkE. No single 1-Mb window explained a significant proportion of the TGVM for RFI; however, after combining windows, significance was met on Bos taurus autosome 27 in primiparous cows, and nearly reached on Bos taurus autosome 4 in multiparous cows. Among other genes, these regions contain β-3 adrenergic receptor and the physiological candidate gene, leptin, respectively. Between the 2 parity groups, 3 of the 10 windows with the largest effects on DMI neighbored windows affecting RFI, but were not in the top 10 regions for MilkE or MBW. This result suggests a genetic basis for feed intake that is unrelated to energy consumption required for milk production or expected maintenance as determined by MBW. In conclusion, feed efficiency measured as RFI is a polygenic trait exhibiting a dynamic genetic basis and genetic variation distinct from that underlying expected maintenance requirements and milk energy output.

Review: Selecting for improved feed efficiency and reduced methane emissions in dairy cattle

“…Recording windows may be shortened (to days or weeks) to enable more animals to be tested within a given facility each year. Thus, there is a need to select a timeframe likely to reflect the whole lactation-period or life-span efficiency, such as the more stable mid-lactation period (Hooven et al, 1972;VandeHaar et al, 2016;Hardie et al, 2017).…”

The genetic basis of feed efficiency in swine divergently selected for residual feed intake

“…Many genomic regions accounting for a small proportion of the genetic variation in RFI has been reported in other swine populations (Jiao et al, 2013;. RFI in pigs has been found to be polygenic, with many loci contributing small effects, which agrees with what has been reported in dairy cattle (Hardie et al, 2017), poultry (Wolc et al, 2013), and beef cattle (Saatchi et al, 2014). However, these small effects add up to a substantial genetic component for feed efficiency in livestock species.…”

“…Residual feed intake has been estimated to be moderately heritable, 0.13-0.45, in growing pigs , thus indicating genetic selection is a viable way to improve feed efficiency in pigs. Low to moderate heritability estimates for RFI have been reported for many livestock species including beef cattle (Crews, 2005), dairy cattle (Hardie et al, 2017), poultry (Willems et al, 2013), and sheep (Cammack et al, 2005).…”

Intégrer les changements d’échelle pour améliorer l’efficience des productions animales et réduire les rejets

“…On peut craindre que la sélection de la CMJR conduise à des vaches ayant moins de réserves corporelles ou les sollicitant plus, avec des risques potentiels sur la santé et la reproduction. Les travaux récents de Hardie et al (2017) montrent que lorsque seuls la production de lait et le poids sont introduits dans le modèle de la CMJR, il existe un lien entre l'efficience et des gènes candidats impliqués dans la gestion des réserves (leptine, récepteurs adrénergiques). On peut donc craindre de renforcer le caractère maigre des vaches laitières si on sélectionne l'efficience sans introduire ce caractère dans le modèle.…”