Unveiling the Genetic Landscape of Feed Efficiency in Holstein Dairy Cows: Insights into Heritability, Genetic Markers, and Pathways via Meta-Analysis

Improving the feeding efficiency of dairy cows is a key component to improve the utilization of land resources and meet the demand for high-quality protein. Advances in genomic methods and omics techniques have made it possible to breed more efficient dairy cows through genomic selection. The aim of this review is to obtain a comprehensive understanding of the biological background of feed efficiency (FE) complex traits in purebred Holstein dairy cows including heritability estimate, and genetic markers, genes, and pathways participating in FE regulation mechanism. Through a literature search, we systematically reviewed the heritability estimation, molecular genetic markers, genes, biomarkers, and pathways of traits related to feeding efficiency in Holstein dairy cows. A meta-analysis based on a random-effects model was performed to combine reported heritability estimates of FE complex. The heritability of residual feed intake, dry matter intake, and energy balance was 0.20, 0.34, and 0.22, respectively, which proved that it was reasonable to include the related traits in the selection breeding program. For molecular genetic markers, a total of 13 single-nucleotide polymorphisms and copy number variance loci, associated genes, and functions were reported to be significant across populations. A total of 169 reported candidate genes were summarized on a large scale, using a higher threshold (adjusted P value < 0.05). Then, the subsequent pathway enrichment of these genes was performed. The important genes reported in the articles were included in a gene list and the gene list was enriched by gene ontology (GO):biological process (BP), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis. Three GO:BP terms and four KEGG terms were statistically significant, which mainly focused on adenosine triphosphate (ATP) synthesis, electron transport chain, and OXPHOS pathway. Among these pathways, involved genes such as ATP5MC2, NDUFA, COX7A2, UQCR, and MMP are particularly important as they were previously reported. Twenty-nine reported biological mechanisms along with involved genes were explained mainly by four biological pathways (insulin-like growth factor axis, lipid metabolism, oxidative phosphorylation pathways, tryptophan metabolism). The information from this study will be useful for future studies of genomic selection breeding and genetic structures influencing animal FE. A better understanding of the underlying biological mechanisms would be beneficial, particularly as it might address genetic antagonism.

Unraveling the Genetic Basis of Feed Efficiency in Cattle through Integrated DNA Methylation and CattleGTEx Analysis

Feed costs can amount to 75 percent of the total overhead cost of raising cows for milk production. Meanwhile, the livestock industry is considered a significant contributor to global climate change due to the production of greenhouse gas emissions, such as methane. Indeed, the genetic basis of feed efficiency (FE) is of great interest to the animal research community. Here, we explore the epigenetic basis of FE to provide base knowledge for the development of genomic tools to improve FE in cattle. The methylation level of 37,554 CpG sites was quantified using a mammalian methylation array (HorvathMammalMethylChip40) for 48 Holstein cows with extreme residual feed intake (RFI). We identified 421 CpG sites related to 287 genes that were associated with RFI, several of which were previously associated with feeding or digestion issues. Activator of transcription and developmental regulation (AUTS2) is associated with digestive disorders in humans, while glycerol-3-phosphate dehydrogenase 2 (GPD2) encodes a protein on the inner mitochondrial membrane, which can regulate glucose utilization and fatty acid and triglyceride synthesis. The extensive expression and co-expression of these genes across diverse tissues indicate the complex regulation of FE in cattle. Our study provides insight into the epigenetic basis of RFI and gene targets to improve FE in dairy cattle.

Genomic evaluation of feed efficiency in US Holstein heifers

There is growing interest in improving feed efficiency traits in dairy cattle. The objectives of this study were to estimate the genetic parameters of residual feed intake (RFI) and its component traits [dry matter intake (DMI), metabolic body weight (MBW), and average daily gain (ADG)] in Holstein heifers, and to develop a system for genomic evaluation for RFI in Holstein dairy calves. The RFI data were collected from 6,563 growing Holstein heifers (initial body weight = 261 ± 52 kg; initial age = 266 ± 42 d) for 70 d, across 182 trials conducted between 2014 and 2022 at the STgenetics Ohio Heifer Center (South Charleston, OH) as part of the EcoFeed program, which aims to improve feed efficiency by genetic selection. The RFI was estimated as the difference between a heifer's actual feed intake and expected feed intake, which was determined by regression of DMI against midpoint MBW, age, and ADG across each trial. A total of 61,283 SNPs were used in genomic analyses. Animals with phenotypes and genotypes were used as training population, and 4 groups of prediction population, each with 2,000 animals, were selected from a pool of Holstein animals with genotypes, based on their relationship with the training population. All traits were analyzed using univariate animal model in DMU version 6 software. Pedigree information and genomic information were used to specify genetic relationships to estimate the variance components and genomic estimated breeding values (GEBV), respectively. Breeding values of the prediction population were estimated by using the 2-step approach: deriving the prediction equation of GEBV from the training population for estimation of GEBV of prediction population with only genotypes. Reliability of breeding values was obtained by approximation based on partitioning a function of the accuracy of training population GEBV and magnitudes of genomic relationships between individuals in the training and prediction population. Heifers had DMI (mean ± SD) of 8.11 ± 1.59 kg over the trial period, with growth rate of 1.08 ± 0.25 kg/d. The heritability estimates (mean ± SE) of RFI, MBW, DMI, and growth rate were 0.24 ± 0.02, 0.23 ± 0.02, 0.27 ± 0.02, and 0.19 ± 0.02, respectively. The range of genomic predicted transmitted abilities (gPTA) of the training population (-0.94 to 0.75) was higher compared with the range of gPTA (-0.82 to 0.73) of different groups of prediction population. Average reliability of breeding values from the training population was 58%, and that of prediction population was 39%. The genomic prediction of RFI provides new tools to select for feed efficiency of heifers. Future research should be directed to find the relationship between RFI of heifers and cows, to select individuals based on their lifetime production efficiencies.

Dry matter intake and feed efficiency of heifers from 4 dairy breed types grazing organic grass and grass-birdsfoot trefoil mixed pastures

Insufficient dry matter intake (DMI) of pasture by dairy cattle is a major factor limiting growth and milk production; however, it has been hypothesized that some dairy breeds may be more efficient grazers than others. This study was conducted to determine whether dairy breed types differ in DMI and feed efficiency when grazing either grass monoculture or grass-legume mixed pastures. The experiment compared 4 different dairy breed types (Jersey, Holstein, Holstein-Jersey crossbreds, and Montbéliarde-Swedish Red-Holstein 3-breed crossbreds) and 2 levels of pasture type [grass monoculture (MONO) and grass-birdsfoot trefoil (BFT) mixture (MX)] for a total of 8 treatments. Pastures were rotationally stocked with groups of 4 prepubertal heifers for 105 d for 3 yr, and DMI was determined from herbage disappearance. Feed conversion efficiency (FCE) and residual feed intake (RFI) were then derived from DMI, and heifer body weights (BW) and normalized to animal units (AU) as 40% metabolic mature BW of the corresponding dairy breed type to account for inherent differences in size and growth rates. We observed differences in DMI and feed efficiency among breed types and between pasture types. On average, Holsteins had the greatest overall DMI (4.4 kg/AU), followed by intermediate DMI by the crossbreds (4.0 kg/AU), and Jerseys had the least DMI (3.6 kg/AU). Heifers grazing MX pastures had on average 22% greater DMI than those grazing MONO, but heifers on grass monocultures were more efficient in converting DMI to BW gain (i.e., RFI/AU of 0.27 and -0.27, respectively; more negative RFI numbers indicate less DMI to achieve the expected gains). Overall, Jerseys had the most favorable feed efficiency; however, ranking of Holsteins and crossbreds depended upon the feed efficiency metric. This study is one of the first to compare the interaction of dairy breed and pasture quality on grazing efficiency. However, the lack of a breed type × pasture type interaction for DMI, FCE, or RFI indicated that none of these dairy breed types were better adapted than another breed type to pastures with contrasting levels of nutritive value.

Implementation of Feed Efficiency in Iranian Holstein Breeding Program

This study aimed to evaluate the economic impact of improving feed efficiency on breeding objectives for Iranian Holsteins. Production and economic data from seven dairy herds were used to estimate the economic values of different traits, and a meta-analysis was conducted to analyze the genetic relationships between feed efficiency and other traits. Economic weights were calculated for various traits, with mean values per cow and per year across herds estimated at USD 0.34/kg for milk yield, USD 6.93/kg for fat yield, USD 5.53/kg for protein yield, USD −1.68/kg for dry matter intake, USD −1.70/kg for residual feed intake, USD 0.47/month for productive life, and USD −2.71/day for days open. The Iranian selection index was revised to improve feed efficiency, and the feed efficiency sub-index (FE$) introduced by the Holstein Association of the United States of America was adopted to reflect Iran’s economic and production systems. However, there were discrepancies between Iranian and US genetic coefficients in the sub-index, which could be attributed to differences in genetic and phenotypic parameters, as well as the economic value of each trait. More accurate estimates of economic values for each trait in FE$ could be obtained by collecting dry matter intake from Iranian herds and conducting genetic evaluations for residual feed intake.

Sharing of either phenotypes or genetic variants can increase the accuracy of genomic prediction of feed efficiency

Background

Sharing individual phenotype and genotype data between countries is complex and fraught with potential errors, while sharing summary statistics of genome-wide association studies (GWAS) is relatively straightforward, and thus would be especially useful for traits that are expensive or difficult-to-measure, such as feed efficiency. Here we examined: (1) the sharing of individual cow data from international partners; and (2) the use of sequence variants selected from GWAS of international cow data to evaluate the accuracy of genomic estimated breeding values (GEBV) for residual feed intake (RFI) in Australian cows.

Results

GEBV for RFI were estimated using genomic best linear unbiased prediction (GBLUP) with 50k or high-density single nucleotide polymorphisms (SNPs), from a training population of 3797 individuals in univariate to trivariate analyses where the three traits were RFI phenotypes calculated using 584 Australian lactating cows (AUSc), 824 growing heifers (AUSh), and 2526 international lactating cows (OVE). Accuracies of GEBV in AUSc were evaluated by either cohort-by-birth-year or fourfold random cross-validations. GEBV of AUSc were also predicted using only the AUS training population with a weighted genomic relationship matrix constructed with SNPs from the 50k array and sequence variants selected from a meta-GWAS that included only international datasets. The genomic heritabilities estimated using the AUSc, OVE and AUSh datasets were moderate, ranging from 0.20 to 0.36. The genetic correlations (r_g) of traits between heifers and cows ranged from 0.30 to 0.95 but were associated with large standard errors. The mean accuracies of GEBV in Australian cows were up to 0.32 and almost doubled when either overseas cows, or both overseas cows and AUS heifers were included in the training population. They also increased when selected sequence variants were combined with 50k SNPs, but with a smaller relative increase.

Conclusions

The accuracy of RFI GEBV increased when international data were used or when selected sequence variants were combined with 50k SNP array data. This suggests that if direct sharing of data is not feasible, a meta-analysis of summary GWAS statistics could provide selected SNPs for custom panels to use in genomic selection programs. However, since this finding is based on a small cross-validation study, confirmation through a larger study is recommended.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-022-00749-z.

Genetic parameters for residual feed intake, methane emissions, and body composition in New Zealand maternal sheep

There is simultaneous interest in improving the feed efficiency of ruminant livestock and reducing methane (CH₄) emissions. The relationship (genetic and phenotypic) between feed efficiency (characterized as residual feed intake: RFI) and greenhouse gases [methane (CH₄) and carbon dioxide (CO₂)] traits in New Zealand (NZ) maternal sheep has not previously been investigated, nor has their relationship with detailed estimates of body composition. To investigate these relationships in NZ maternal sheep, a feed intake facility was established at AgResearch Invermay, Mosgiel, NZ in 2015, comprising automated feeders that record individual feeding events. Individual measures of feed intake, feeding behavior (length and duration of eating events), and gas emissions (estimated using portable accumulation chambers) were generated on 986 growing maternal ewe lambs sourced from three pedigree recorded flocks registered in the Sheep Improvement Limited database (www.sil.co.nz). Additional data were generated from a subset of 591 animals for body composition (estimated using ultrasound and computed tomography scanning). The heritability estimates for RFI, CH₄, and CH₄/(CH₄+CO₂) were 0.42 ± 0.09, 0.32 ± 0.08, and 0.29 ± 0.06, respectively. The heritability estimates for the body composition traits were high for carcass lean and fat traits; for example, the heritability for visceral fat (adjusted for body weight) was 0.93 ± 0.19. The relationship between RFI and CH₄ emissions was complex, and although less feed eaten will lead to a lowered absolute amount of CH₄ emitted, there was a negative phenotypic and genetic correlation between RFI and CH₄/(CH₄+CO₂) _of −0.13 ± 0.03 and −0.41 ± 0.15, respectively. There were also genetic correlations, that were different from zero, between both RFI and CH₄ traits with body composition including a negative correlation between the proportion of visceral fat in the body and RFI (−0.52 ± 0.16) and a positive correlation between the proportion of lean in the body and CH₄ (0.54 ± 0.12). Together the results provide the first accurate estimates of the genetic correlations between RFI, CH₄ emissions, and the body composition (lean and fat) in sheep. These correlations will need to be accounted for in genetic improvement programs.

Residual feed intake in peripartal dairy cows is associated with differences in milk fat yield, ruminal bacteria, biopolymer hydrolyzing enzymes, and circulating biomarkers of immunometabolism

Residual feed intake (RFI) measures feed efficiency independent of milk production level, and is typically calculated using data past peak lactation. In the current study, we retrospectively classified multiparous Holstein cows (n = 320) from 5 of our published studies into most feed-efficient (M-eff) or least feed-efficient (L-eff) groups using performance data collected during the peripartal period. Objectives were to assess differences in profiles of plasma biomarkers of immunometabolism, relative abundance of key ruminal bacteria, and activities of digestive enzymes in ruminal digesta between M-eff and L-eff cows. Individual data from cows with ad libitum access to a total mixed ration from d -28 to d +28 relative to calving were used. A linear regression model including dry matter intake (DMI), energy-corrected milk (ECM), changes in body weight (BW), and metabolic BW was used to classify cows based on RFI divergence into L-eff (n = 158) and M-eff (n = 162). Plasma collected from the coccygeal vessel at various times around parturition (L-eff = 60 cows; M-eff = 47 cows) was used for analyses of 30 biomarkers of immunometabolism. Ruminal digesta collected via esophageal tube (L-eff = 19 cows; M-eff = 29 cows) was used for DNA extraction and assessment of relative abundance (%) of 17 major bacteria using real-time PCR, as well as activity of cellulase, amylase, xylanase, and protease. The UNIVARIATE procedure of SAS 9.4 (SAS Institute Inc.) was used for analyses of RFI coefficients. The MIXED procedure of SAS was used for repeated measures analysis of performance, milk yield and composition, plasma immunometabolic biomarkers, ruminal bacteria, and enzyme activities. The M-eff cows consumed less DMI during the peripartal period compared with L-eff cows. In the larger cohort of cows, despite greater overall BW for M-eff cows especially in the prepartum (788 vs. 764 kg), no difference in body condition score was detected due to RFI or the interaction of RFI × time. Milk fat content (4.14 vs. 3.75 ± 0.06%) and milk fat yield (1.75 vs. 1.62 ± 0.04 kg) were greater in M-eff cows. Although cumulative ECM yield did not differ due to RFI (1,138 vs. 1,091 ± 21 kg), an RFI × time interaction due to greater ECM yield was found in M-eff cows. Among plasma biomarkers studied, concentrations of nonesterified fatty acids, β-hydroxybutyrate, bilirubin, ceruloplasmin, haptoglobin, myeloperoxidase, and reactive oxygen metabolites were overall greater, and glucose, paraoxonase, and IL-6 were lower in M-eff compared with L-eff cows. Among bacteria studied, abundance of Ruminobacter amylophilus and Prevotella ruminicola were more than 2-fold greater in M-eff cows. Despite lower ruminal activity of amylase in M-eff cows in the prepartum, regardless of RFI, we observed a marked linear increase after calving in amylase, cellulase, and xylanase activities. Protease activity did not differ due to RFI, time, or RFI × time. Despite greater concentrations of biomarkers reflective of negative energy balance and inflammation, higher feed efficiency measured as RFI in peripartal dairy cows might be associated with shifts in ruminal bacteria and amylase enzyme activity. Further studies could help address such factors, including the roles of the liver and the mammary gland.

Estimates of genetic parameters for feeding behavior traits and their associations with feed efficiency in Holstein cows

Residual feed intake (RFI) is commonly used to measure feed efficiency but individual intake recording systems are needed. Feeding behavior may be used as an indicator trait for feed efficiency using less expensive precision livestock farming technologies. Our goal was to estimate genetic parameters for feeding behavior and the genetic correlations with feed efficiency in Holstein cows. Data consisted of 75,877 daily feeding behavior records of 1,328 mid-lactation Holstein cows in 31 experiments conducted from 2009 to 2020 with an automated intake recording system. Feeding behavior traits included number of feeder visits per day, number of meals per day, duration of each feeder visit, duration of each meal, total duration of feeder visits, intake per visit, intake per meal [kg of dry matter (DM)], feeding rate per visit, and feeding rate per meal (kg of DM per min). The meal criterion was estimated as 26.4 min, which means that any pair of feeder visits separated by less than 26.4 min were considered part of the same meal. The statistical model included lactation and days in milk as fixed effects, and experiment-treatment, animal, and permanent environment as random effects. Genetic parameters for feeding behavior traits were estimated using daily records and weekly averages. Estimates of heritability for daily feeding behavior traits ranged from 0.09 ± 0.02 (number of meals; mean ± standard error) to 0.23 ± 0.03 (feeding rate per meal), with repeatability estimates ranging from 0.23 ± 0.01 (number of meals) to 0.52 ± 0.02 (number of feeder visits). Estimates of heritability for weekly averages of feeding behavior traits ranged from 0.19 ± 0.04 (number of meals) to 0.32 ± 0.04 (feeding rate per visit), with repeatability estimates ranging from 0.46 ± 0.02 (duration of each meal) to 0.62 ± 0.02 (feeding rate per visit and per meal). Most of the feeding behavior measures were strongly genetically correlated, showing that with more visits or meals per day, cows spend less time in each feeder visit or meal with lower intake per visit or meal. Weekly averages for feeding behavior traits were analyzed jointly with RFI and its components. Number of meals was genetically correlated with milk energy (0.48), metabolic body weight (-0.27), and RFI (0.19). Duration of each feeder visit and meal were genetically correlated with milk energy (0.43 and 0.44, respectively). Total duration of feeder visits per day was genetically correlated with DM intake (0.29), milk energy (0.62), metabolic body weight (-0.37), and RFI (0.20). Intake per visit and meal were genetically correlated with DM intake (0.63 and 0.87), milk energy (0.47 and 0.69), metabolic body weight (0.47 and 0.68), and RFI (0.31 and 0.65). Feeding rate was genetically correlated with DM intake (0.69), metabolic body weight (0.67), RFI (0.47), and milk energy (0.21). We conclude that measures of feeding behavior could be useful indicators of dairy cow feed efficiency, and individual cows that eat at a slower rate may be more feed efficient.

Evaluation of updated Feed Saved breeding values developed in Australian Holstein dairy cattle

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The Feed Saved (FS) estimated breeding value (EBV) was updated by doubling the number of Australian and overseas cows.

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The reliability of the residual feed intake component of FS has increased from 11% (2015 model) to 20% (current model).

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The mean reliability of FS EBV in Holstein bulls that were born in the last 10 years has improved by 10%.

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The genetic trend of FS EBV has been stabilizing since 2015.

Although selection for increased milk production traits has led to a genetic increase in body weight (BW), the genetic gain in milk production has exceeded the gain in BW, so gross feed efficiency has improved. Nonetheless, greater gains may be possible by directly selecting for a measure of feed efficiency. Australia first introduced Feed Saved (FS) estimated breeding value (EBV) in 2015. Feed Saved combines residual feed intake (RFI) genomic EBV and maintenance requirements calculated from mature BW EBV. The FS EBV was designed to enable the selection of cows for reduced energy requirements with similar milk production. In this study, we used a reference population of 3,711 animals in a multivariate analysis including Australian heifers (AUSh), Australian cows (AUSc), and overseas cows (OVEc) to update the Australian EBV for lifetime RFI (i.e., a breeding value that incorporated RFI in growing and lactating cows) and to recalculate the FS EBV in Australian Holstein bulls (AUSb). The estimates of genomic heritabilities using univariate (only AUSc or AUSh) to trivariate (including the OVEc) analyses were similar. Genomic heritabilities for RFI were estimated as 0.18 for AUSc, 0.27 for OVEc, and 0.36 for AUSh. The genomic correlation for RFI between AUSc and AUSh was 0.47 and that between AUSc and OVEc was 0.94, but these estimates were associated with large standard errors (range: 0.18–0.28). The reliability of lifetime RFI (a component of FS) in the trivariate analysis (i.e., including OVEc) increased from 11% to 20% compared with the 2015 model and was greater, by 12%, than in a bivariate analysis in which the reference population included only AUSc and AUSh. By applying the prediction equation of the 2020 model, the average reliability of the FS EBV in 20,816 AUSb that were born between 2010 and 2020 improved from 33% to 43%. Previous selection strategies—that is, using the predecessor of the Balanced Performance Index (Australian Profit Ranking index) that did not include FS—have resulted in an unfavorable genetic trend in FS. However, this unfavorable trend has stabilized since 2015, when FS was included in the Balanced Performance Index, and is expected to move in a favorable direction with selection on Balanced Performance Index or the Health Weighted Index. Doubling the reference population, particularly by incorporating international data for feed efficiency, has improved the reliability of the FS EBV. This could lead to increased genetic gain for feed efficiency in the Australian industry.

Effect of diet energy level and genomic residual feed intake on bred Holstein dairy heifer growth and feed efficiency

The objective of this study was to determine growth, feed intake, and feed efficiency of postbred dairy heifers with different genomic residual feed intake (RFI) predicted as a lactating cow when offered diets differing in energy density. Postbred Holstein heifers (n = 128, ages 14-20 mo) were blocked by initial weight (high, medium-high, medium-low, and low) with 32 heifers per block. Each weight block was sorted by RFI (high or low) to obtain 2 pens of heifers with high and low genomically predicted RFI within each block (8 heifers per pen). Low RFI heifers were expected to have greater feed efficiency than high RFI heifers. Dietary treatments consisted of a higher energy control diet based on corn silage and alfalfa haylage [HE; 62.7% total digestible nutrients, 11.8% crude protein, and 45.6% neutral detergent fiber; dry matter (DM) basis], and a lower energy diet diluted with straw (LE; 57.0% total digestible nutrients, 11.7% crude protein, and 50.1% neutral detergent fiber; DM basis). Each pen within a block was randomly allocated a diet treatment to obtain a 2 × 2 factorial arrangement (2 RFI levels and 2 dietary energy levels). Diets were offered in a 120-d trial. Dry matter intake by heifers was affected by diet (11.0 vs. 10.0 kg/d for HE and LE, respectively) but not by RFI or the interaction of RFI and diet. Daily gain was affected by the interaction of RFI and diet, with low RFI heifers gaining more than high RFI heifers when fed LE (0.94 vs. 0.85 kg/d for low and high RFI, respectively), but no difference for RFI groups when fed HE (1.16 vs. 1.19 kg/d for low and high RFI, respectively). Respective feed efficiencies were improved for low RFI compared with high RFI heifers when fed LE (10.6 vs. 11.8 kg of feed DM/kg of gain), but no effect of RFI was found when fed HE (9.4 vs. 9.5 kg of DM/kg of gain for high and low RFI, respectively). No effect of RFI or diet on first-lactation performance through 150 DIM was observed. Based on these results, the feed efficiency of heifers having different genomic RFI may be dependent on diet energy level, whereby low RFI heifers utilized the LE diet more efficiently. The higher fiber straw (LE) diet controlled intake and maintained more desirable heifer weight gains. This suggests that selection for improved RFI in lactating cows may improve feed efficiency in growing heifers when fed to meet growth goals of 0.9 to 1.0 kg of gain/d.

Estimating methane coefficients to predict the environmental impact of traits in the Australian dairy breeding program

The dairy industry has been scrutinized for the environmental impact associated with rearing and maintaining cattle for dairy production. There are 3 possible opportunities to reduce emissions through genetic selection: (1) a direct methane trait, (2) a reduction in replacements, and (3) an increase in productivity. Our aim was to estimate the independent effects of traits in the Australian National Breeding Objective on the gross methane production and methane intensity (EI) of the Australian dairy herd of average genetic potential. Based on similar published research, the traits determined to have an effect on emissions include production, fertility, survival, health, and feed efficiency. The independent effect of each trait on the gross emissions produced per animal due to genetic improvement and change in EI due to genetic improvement (intensity value, IV) were estimated and compared. Based on an average Australian dairy herd, the gross emissions emitted per cow per year were 4,297.86 kg of carbon dioxide equivalents (CO₂-eq). The annual product output, expressed in protein equivalents (protein-eq), and EI per cow were 339.39 kg of protein-eq and 12.67 kg of CO₂-eq/kg of protein-eq, respectively. Of the traits included in the National Breeding Objective, genetic progress in survival and feed saved were consistently shown to result in a favorable environmental impact. Conversely, production traits had an unfavorable environmental impact when considering gross emissions, and favorable when considering EI. Fertility had minimal impact as its effects were primarily accounted for through survival. Mastitis resistance only affected IV coefficients and to a very limited extent. These coefficients may be used in selection indexes to apply emphasis on traits based on their environmental impact, as well as applied by governments and stakeholders to track trends in industry emissions. Although initiatives are underway to develop breeding values to reduce methane by combining small methane data sets internationally, alternative options to reduce emissions by utilizing selection indexes should be further explored.

Assessing feed efficiency in early and mid lactation and its associations with performance and health in Holstein cows

Objectives were to evaluate the associations between residual dry matter (DM) intake (RFI) and residual N intake (RNI) in early lactation, from 1 to 5 wk postpartum, and in mid lactation, from 9 to 15 wk postpartum, and assess production performance and risk of diseases in cows according to RFI in mid lactation. Data from 4 experiments including 399 Holsteins cows were used in this study. Intakes of DM and N, yields of milk components, body weight, and body condition were evaluated daily or weekly for the first 105 d postpartum. Milk yield by 305 d postpartum was also measured. Incidence of disease was evaluated for the first 90 d postpartum and survival up to 300 d postpartum. Residual DM and N intake were calculated in early and mid lactation as the observed minus the predicted values, which were based on linear models that accounted for major energy or N sinks, including daily milk energy or N output, metabolic body weight, and daily body energy or N changes, and adjusting for parity, season of calving, and treatment within experiment. Cows were ranked by RFI and RNI in mid lactation and categorized into quartiles (Q1 = smallest RFI, to Q4 = largest RFI). Increasing efficiency in mid lactation resulted in linear decreases in RFI (depicted from Q1 to Q4; -0.93, -0.05, -0.04, and 0.98 kg/d), DMI (16.0, 16.9, 17.3, and 18.4 kg/d), net energy for lactation (NE_L) intake (26.8, 28.4, 29.0, and 30.8 Mcal/d), and NE_L balance (-9.0, -8.1, -8.2, and -5.5 Mcal/d) during early lactation, but no differences were observed in body NE_L or N changes or yield of energy-corrected milk in the first 5 wk of lactation. Residual DM intake in mid lactation was associated with RFI (Pearson r = 0.43, and Spearman ρ = 0.32) and RNI (r = 0.44, ρ = 0.36) in early lactation, and with RNI in mid lactation (r = 0.91, ρ = 0.84). Similarly, RNI in mid lactation was associated with RNI in early lactation (r = 0.42, ρ = 0.35). During the first 15 wk postpartum, more efficient cows in mid lactation consumed 3.5 kg/d less DM (Q1 = 19.3 vs. Q4 = 22.8 kg/d) and were more N efficient (Q1 = 31.6 vs. Q4 = 25.8%), at the same time that yields of milk (Q1 = 39.0 vs. Q4 = 39.4 kg/d), energy-corrected milk (Q1 = 38.6 vs. Q4 = 39.3 kg/d), and milk components did not differ compared with the quartile of least efficient cows. Furthermore, RFI in mid lactation was not associated with 305-d milk yield, incidence of diseases in the first 90 d postpartum, or survival by 300 d postpartum. Collectively, rankings of RFI and RNI are associated and repeatable across lactation stages. The most feed-efficient cows were also more N efficient in early and mid lactation. Phenotypic selection of RFI based on measurements in mid lactation is associated with improved efficiency without affecting production or health in dairy cows.

Comparative analyses of enteric methane emissions, dry matter intake, and milk somatic cell count in different residual feed intake categories of dairy cows

This study compared the different residual feed intake (RFI) categories of lactating Holsteins with respect to methane (CH4) emissions, dry matter intake (DMI, kg), milk somatic cell count (SCC, 103∙mL−1), and β-hydroxybutyrate (BHB, mmol∙L−1). The RFI was calculated in 131 lactating Holstein cows that were then categorized into −RFI (RFI < 0) vs. +RFI (RFI > 0) and low- [RFI < −0.5 standard deviation (SD)] vs. high-RFI (RFI > 0.5 SD) groups. Milk traits were recorded in 131 cows, whereas CH4 and carbon dioxide were measured in 83. Comparisons of −RFI vs. +RFI and low- vs. high-RFI showed 7.9% (22.3 ± 0.40 vs. 24.2 ± 0.39) and 12.8% (21.1 ± 0.40 vs. 24.2 ± 0.45) decrease (P < 0.05) in DMI of −RFI and low-RFI groups, respectively. Similarly, −RFI and low-RFI cows had lower (P < 0.05) CH4 (g∙d−1) by 9.7% (343.5 ± 11.1 vs. 380.4 ± 10.9) and 15.5% (332.5 ± 12.9 vs. 393.5 ± 12.6), respectively. Milk yield was not different (P > 0.05) in −RFI vs. +RFI and low vs. high comparisons. The −RFI and low-RFI cows had lower (P < 0.05) SCC in −RFI vs. +RFI and low-RFI vs. high-RFI comparisons. The BHB was lower (P < 0.05) in low-RFI compared with the high-RFI group. Low-RFI dairy cows consumed less feed, emitted less CH4 (g∙d−1), and had lower milk SCC and BHB without differing in milk yield.

Genetic mechanisms underlying feed utilization and implementation of genomic selection for improved feed efficiency in dairy cattle

The economic importance of genetically improving feed efficiency has been recognized by cattle producers worldwide. It has the potential to considerably reduce costs, minimize environmental impact, optimize land and resource use efficiency, and improve the overall cattle industry’s profitability. Feed efficiency is a genetically complex trait that can be described as units of product output (e.g., milk yield) per unit of feed input. The main objective of this review paper is to present an overview of the main genetic and physiological mechanisms underlying feed utilization in ruminants and the process towards implementation of genomic selection for feed efficiency in dairy cattle. In summary, feed efficiency can be improved via numerous metabolic pathways and biological mechanisms through genetic selection. Various studies have indicated that feed efficiency is heritable, and genomic selection can be successfully implemented in dairy cattle with a large enough training population. In this context, some organizations have worked collaboratively to do research and develop training populations for successful implementation of joint international genomic evaluations. The integration of “-omics” technologies, further investments in high-throughput phenotyping, and identification of novel indicator traits will also be paramount in maximizing the rates of genetic progress for feed efficiency in dairy cattle worldwide.

Residual Feed Intake in Dairy Ewes: An Evidence of Intraflock Variability

Simple Summary

Few, if any, reference is available in residual feed intake in dairy sheep. In this study, carried out during more than two months with French Lacaune dairy ewes in mid-lactation, we demonstrated an intraflock variability in feed efficiency determined by, beyond litter size and daily milking frequency, evident differences between the individuals in their efficiency of using the available total mixed rations.

Abstract

This study examined the intraflock variability of feed efficiency in dairy ewes, through monitoring residual feed intakes (RFI). Primiparous lactating ewes (n = 43; 57.7 ± 0.91 kg body weight [BW] at lambing), representative of a French Lacaune dairy flock, were allocated in an equilibrated 2 × 2 factorial design experiment, lasting for 63 days during mid-lactation and combining 2 litter sizes (singletons, SING or twins, TWIN) and 2 daily milking frequencies (once, ONE or twice, TWO). Weaning occurred, and milking started, at 35 days after lambing (DIM). Ewes were individually fed a diet based on ryegrass silage, local hay, and supplements. Individual dry matter intake (DMI) was recorded daily and further used to evaluate (and compare) differences in RFI between ewes at 42, 49, 56, 63, 70, 77, 84, 91, and 98. Average individual RFI were calculated weekly since the first week (i.e., 35–42 DIM). Total (BW) and metabolic (BW^0.75) body weight, body condition score BCS, milk yield, and plasma non-esterified fatty acids NEFA were monitored weekly. Differences in DMI were mainly due to the lactation stage and litter size and were 11% higher in ewes with TWIN compared to SING. This was positively correlated to milk yield and consistent with differences in RFI which varied due to litter size and to the milking frequency × lactation stage interaction. Ewes that lambed SING showed higher feed efficiency (−0.08 ± 0.018 vs. 0.13 ± 0.014 kg DM/ewe/d of RFI in SING vs. TWIN, respectively), whereas there were no differences in BW or BCS. Milking frequency did not affect DMI but milk yields were higher in TWO, which was related to a higher feed efficiency in this group (0.115 ± 0.016 vs. −0.07 ± 0.016 kg DM/ewe/d of RFI in ONE vs. TWO, respectively). Average RFI was affected (p < 0.0001) by the ewe, thus allowing a ranking among individuals to be established. High (n = 22) or low (n = 21) feed efficiency ewes averaged −0.17 ± 0.09 or 0.18 ± 0.09 kg DM/d RFI, respectively. Estimates of RFI were not correlated to the individual milk production potential. Even if no differences in BW, BW^0.75, or BCS were detected, high-efficiency ewes mobilized 1.5 times their body reserves (0.30 vs. 0.20 mmol NEFA/L of plasma) when compared to the low-efficiency group. The observed intraflock variability in feed efficiency of this dairy ewes’ flock was affected by litter size and milking frequency but also by evident differences between individuals’ physiologies.

Technical note: validation of an automated feeding system for measuring individual animal feed intake in sheep housed in groups

The development of feeding systems that can individually measure and control feed intake in a group-housed environment would allow a greater understanding of sheep intake without compromising animal welfare and behavior through the removal of social interactions between sheep. This study validated an automated feeding system for measuring feed intake of individual sheep when housed in groups. Validation of the feeding system was conducted during three separate experiments. The validation sampling involved the activation of four individual “feed events,” whereby four separate samples weighing approximately 50, 100, 200, and 400 g were removed from each feeder, with each feed event being linked to a specific radio frequency identification (RFID) tag. The feeder validation experiments evaluated the ability of the feeding system to 1) create a unique feed event every time a sample of pellets was collected from the feeder, 2) link the feed event to the correct RFID, and 3) accurately record the weight of feed that was manually removed. All feed events were initiated and logged in the feeding system with 100% of the events being linked to the correct test RFID. Concordance correlation coefficients between the feeding system-recorded feed weight and the manually removed weight were 0.99 within all three experiments. There was also no overall and little level-dependent bias between the weights measured by the feeding system and weights measured on the external scales. These results indicate the stability of the feeding system over time and consistency between the feeders within and across the three experiments. In conclusion, the automated feeding system developed for measuring individual animal feed intake was able to detect and record the unique electronic RFID associated with unique feed events and accurately capture the weight of feed removed. Furthermore, there was no change in the accuracy of the system from the start to the end of experimental periods, and the amount of feed removed in the feed event (or meal size) did not impact the accuracy of the results.

Relationship of residual feed intake and protein efficiency in lactating cows fed high- or low-protein diets

Our objectives were to determine the repeatability of residual feed intake (RFI) across dietary protein levels and to determine the association between RFI and protein efficiency in lactating cows. Holstein cows (n = 166; 92 primiparous, 74 multiparous) with initial milk yield 41.3 ± 9.8 kg/d were fed diets with high or low protein in peak lactation. Experiments were conducted as crossovers with 2 treatment periods of 28 to 35 d. Production of 69 of the 166 cows (42 primiparous, 27 multiparous) was also measured in late lactation. Low-protein diets were 14% crude protein (CP) in peak lactation and 13% CP in late lactation and were formulated to contain adequate rumen-degradable protein to maintain rumen function. High-protein diets were 18% CP in peak lactation and 16% CP in late lactation and contained extra expeller soybean meal to increase absorbed protein. Cows were milked twice daily; DMI and milk yield were recorded daily. Milk composition was measured over 4 consecutive milkings weekly, and body weight (BW) was measured 3 times weekly. Fixed effects of diet, parity, and treatment period, interaction of parity and diet, and random effects of experiment and cow nested within experiment were included in the model to compare intake and production performance between cows fed different levels of CP. The RFI value was calculated for each cow on each treatment based on the actual intake, milk energy output, metabolic BW, and body energy (calculated from BW change and body condition score over the treatment period) change. Ranking of cows for RFI was moderately repeatable across dietary protein in peak lactation (r = 0.59) but less repeatable in late lactation (r = 0.41). Negative correlation was observed between RFI and protein efficiency values (dietary protein captured in milk) for cows in both peak lactation (r = -0.42) and late lactation (r = -0.24), which suggested that cows with higher energy efficiency had greater protein efficiency. In conclusion, RFI was repeatable across dietary protein levels within lactation stage, and cows with lower RFI values utilized protein more efficiently.

Symposium review: Decomposing efficiency of milk production and maximizing profit

The dairy industry has focused on maximizing milk yield, as it is believed that this maximizes profit mainly through dilution of maintenance costs. Efficiency of milk production has received, until recently, considerably less attention. The most common method to determine biological efficiency of milk production is feed efficiency (FE), which is defined as the amount of milk produced relative to the amount of nutrients consumed. Economic efficiency is best measured as income over feed cost or gross margin obtained from feed investments. Feed efficiency is affected by a myriad of factors, but overall they could be clustered as follows: (1) physiological status of the cow (e.g., age, state of lactation, health, level of production, environmental conditions), (2) digestive function (e.g., feeding behavior, passage rate, rumen fermentation, rumen and hindgut microbiome), (3) metabolic partitioning (e.g., homeorhesis, insulin sensitivity, hormonal profile), (4) genetics (ultimately dictating the 2 previous aspects), and (5) nutrition (e.g., ration formulation, nutrient balance). Over the years, energy requirements for maintenance seem to have progressively increased, but efficiency of overall nutrient use for milk production has also increased due to dilution of nutrient requirements for maintenance. However, empirical evidence from the literature suggests that marginal increases in milk require progressively greater marginal increases in nutrient supply. Thus, the dilution of maintenance requirements associated with increases in production is partially overcome by a progressive diminishing marginal biological response to incremental energy and protein supplies. Because FE follows the law of diminishing returns, and because marginal feed costs increase progressively with milk production, profits associated with improving milk yield might, in some cases, be considerably lower than expected.

Nitrogen partitioning and microbial protein synthesis in lactating dairy cows with different phenotypic residual feed intake

Background

Residual feed intake (RFI) is an inheritable measure of feed efficiency that is independent on level of production. However, physiological and metabolic mechanisms underlying divergent RFI are not fully elucidated. This study was conducted to investigate dietary nitrogen (N) partitioning and microbial protein synthesis in lactating dairy cows divergent in phenotypic RFI.

Results

Thirty Holstein dairy cows (milk yield = 35.3 ± 4.71 kg/d; milk protein yield = 1.18 ± 0.13 kg/d; mean ± standard deviation) were selected for the experiment to derive RFI. After the RFI measurement period of 50 d, the 10 lowest RFI cows and 8 highest RFI cows were selected. The low RFI cows had lower dry matter intake (DMI, P < 0.05) than the high RFI cows, but they produced similar energy-corrected milk. The ratios of milk to DMI (1.41 vs. 1.24, P < 0.01) and energy-corrected milk to DMI (1.48 vs. 1.36, P < 0.01) were greater in low RFI cows than those in the high RFI cows. The low RFI cows had lower milk urea nitrogen than that in the high RFI cows (P = 0.05). Apparent digestibility of nutrients did not differ between two groups (P > 0.10). Compared with high RFI animals, the low RFI cows had a lower retention of N (5.72 vs. 51.4 g/d, P < 0.05) and a higher partition of feed N to milk N (29.7% vs. 26.5%, P < 0.05).

Conclusions

The results suggest that differences in N partition, synthesis of microbial protein, and utilization of metabolizable protein could be part of the mechanisms associated with variance in the RFI.

Effect of diet energy density and genomic residual feed intake on prebred dairy heifer feed efficiency, growth, and manure excretion

The objective of this study was to determine the growth, feed efficiency, and manure excretion of prebred dairy heifers with differing predicted genomic residual feed intakes (RFI) when offered diets differing in energy density. Prebred Holstein heifers (n = 128, ages 4 to 8 mo) were blocked by weight (low, medium-low, medium-high, or high) with 32 heifers per block. Heifers in each weight block were grouped by RFI and randomly assigned to obtain 2 pens of high (HRFI) and 2 pens of low RFI (LRFI) heifers within each block (8 heifers/pen). Heifers with LRFI were hypothesized to have greater feed efficiency than HRFI heifers. Dietary treatments were a high-energy diet (HE; 66.6% total digestible nutrients, 14.0% crude protein, and 36.3% neutral detergent fiber, dry matter basis) and a low-energy diet (LE; 63.8% total digestible nutrients, 13.5% crude protein, and 41.2% neutral detergent fiber, dry matter basis). Each pen of heifers was randomly assigned to a treatment to obtain a 2 × 2 factorial arrangement (2 RFI levels × 2 diet energy densities). Diets were offered in a 120-d trial. Dry matter intake was not affected by diet, RFI, or their interaction. Average daily gain (ADG) was affected by diet, with heifers fed HE having greater ADG than heifers fed LE. In addition, RFI affected ADG, with LRFI heifers having greater ADG than HRFI heifers, whereas the interaction of RFI and diet was not significant. Feed efficiency was improved for heifers fed the HE diet, but it was not affected by RFI or the interaction of RFI and diet. Overall, feed efficiency of prebred heifers was not dependent on predicted genomic RFI, because the greater ADG of LRFI heifers was accompanied by slightly higher dry matter intake. Feed efficiency of heifers was reduced when heifers were fed the LE diet, but this resulted in more optimal ADG compared with the HE diet fed for ad libitum intake.

Relationships between feeding behaviors and performance traits in slow-growing yellow broilers

This study was conducted to investigate the feeding behaviors of slow-growing yellow broilers and the relationships of feeding behaviors with performance traits. With the help of automatic recording systems in floor houses, feeding events from a pure line of slow-growing yellow broilers were recorded from 57 to 77 d of age. After data quality control, a total of 116,477 feeding records from 319 birds were used for analyses. Feeding behaviors including number of visits per day (18.74), feeding duration per day (71.17 min/d), feeding duration per visit (262.00 s/visit), feeding rate (2.19 g/min), and feed intake per visit (8.52 g/visit) were calculated according to feeding records. Correlation analyses and comparisons between divergent efficiency groups were performed to examine the relationships between feed efficiency and feeding behaviors. Absolute correlations between residual feed intake (RFI) and feeding behaviors (except for feed intake per visit) were significant but weak (r = 0.18 to 0.34, P < 0.05), whereas feed conversion ratio (FCR) was not significantly correlated with any feeding behaviors. All of the weight-associated traits were positively correlated with feeding rate and feed intake per visit (r = 0.19 to 0.25, P < 0.05). Compared with the inefficient birds with the 20% highest RFI or FCR (HRFI or HFCR), the efficient ones with the 20% lowest RFI or FCR (LRFI or LFCR) ate faster (P < 0.05), spent shorter eating duration (P < 0.05) and had similar feed intakes per visit (P > 0.05). However, number of visits per day and the feeding duration per day were lower in the LRFI group than in the HRFI group (P < 0.05) but were not significantly different between the LFCR and HFCR groups (P > 0.05). In summary, this study shows the feeding behaviors of group-housed slow-growing yellow broilers and observed that RFI has closer relationships with feeding behaviors than FCR does, and the selection for birds with improved RFI may result in fewer visits, shorter duration and faster feeding rate.

Gene co-expression networks from RNA sequencing of dairy cattle identifies genes and pathways affecting feed efficiency

Background

Selection for feed efficiency is crucial for overall profitability and sustainability in dairy cattle production. Key regulator genes and genetic markers derived from co-expression networks underlying feed efficiency could be included in the genomic selection of the best cows. The present study identified co-expression networks associated with high and low feed efficiency and their regulator genes in Danish Holstein and Jersey cows.

RNA-sequencing data from Holstein and Jersey cows with high and low residual feed intake (RFI) and treated with two diets (low and high concentrate) were used. Approximately 26 million and 25 million pair reads were mapped to bovine reference genome for Jersey and Holstein breed, respectively. Subsequently, the gene count expressions data were analysed using a Weighted Gene Co-expression Network Analysis (WGCNA) approach. Functional enrichment analysis from Ingenuity® Pathway Analysis (IPA®), ClueGO application and STRING of these modules was performed to identify relevant biological pathways and regulatory genes.

Results

WGCNA identified two groups of co-expressed genes (modules) significantly associated with RFI and one module significantly associated with diet. In Holstein cows, the salmon module with module trait relationship (MTR) = 0.7 and the top upstream regulators ATP7B were involved in cholesterol biosynthesis, steroid biosynthesis, lipid biosynthesis and fatty acid metabolism. The magenta module has been significantly associated (MTR = 0.51) with the treatment diet involved in the triglyceride homeostasis. In Jersey cows, the lightsteelblue1 (MTR = − 0.57) module controlled by IFNG and IL10RA was involved in the positive regulation of interferon-gamma production, lymphocyte differentiation, natural killer cell-mediated cytotoxicity and primary immunodeficiency.

Conclusion

The present study provides new information on the biological functions in liver that are potentially involved in controlling feed efficiency. The hub genes and upstream regulators (ATP7b, IFNG and IL10RA) involved in these functions are potential candidate genes for the development of new biomarkers. However, the hub genes, upstream regulators and pathways involved in the co-expressed networks were different in both breeds. Hence, additional studies are required to investigate and confirm these findings prior to their use as candidate genes.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2553-z) contains supplementary material, which is available to authorized users.

A 100-Year Review: Identification and genetic selection of economically important traits in dairy cattle

Over the past 100 yr, the range of traits considered for genetic selection in dairy cattle populations has progressed to meet the demands of both industry and society. At the turn of the 20th century, dairy farmers were interested in increasing milk production; however, a systematic strategy for selection was not available. Organized milk performance recording took shape, followed quickly by conformation scoring. Methodological advances in both genetic theory and statistics around the middle of the century, together with technological innovations in computing, paved the way for powerful multitrait analyses. As more sophisticated analytical techniques for traits were developed and incorporated into selection programs, production began to increase rapidly, and the wheels of genetic progress began to turn. By the end of the century, the focus of selection had moved away from being purely production oriented toward a more balanced breeding goal. This shift occurred partly due to increasing health and fertility issues and partly due to societal pressure and welfare concerns. Traits encompassing longevity, fertility, calving, health, and workability have now been integrated into selection indices. Current research focuses on fitness, health, welfare, milk quality, and environmental sustainability, underlying the concentrated emphasis on a more comprehensive breeding goal. In the future, on-farm sensors, data loggers, precision measurement techniques, and other technological aids will provide even more data for use in selection, and the difficulty will lie not in measuring phenotypes but rather in choosing which traits to select for.

Feed conversion ratio, residual feed intake and cholecystokinin type A receptor gene polymorphisms are associated with feed intake and average daily gain in a Chinese local chicken population

Background

The feed conversion ratio (FCR) and residual feed intake (RFI) are common indexes in measuring feed efficiency for livestock. RFI is a feed intake adjusted for requirements for maintenance and production so these two traits are related. Similarly, FCR is related to feed intake and weight gain because it is their ratio. Cholecystokinin type A receptor (CCKAR) plays an important role in animal digestive process. We examined the interplay of these three parameters in a local Chinese chicken population.

Results

The feed intake (FI) and body weights (BW) of 1,841 individuals were monitored on a daily basis from 56 to 105 d of age. There was a strong correlation between RFI and average daily feed intake (ADFI) and a negative correlation between the FCR and daily gain (r_g = − 0.710). Furthermore, we identified 51 single nucleotide polymorphisms (SNPs) in the CCKAR and 4 of these resulted in amino acid mutations. The C334A mutation was specifically associated with FI and the expected feed intake (EFI) (P <  0.01) and significantly associated with the average daily gain (ADG) (P <  0.05). G1290A was significantly associated with FI and EFI (P < 0.05).

Conclusion

FCR is apply to weight selecting, and RFI is more appropriate if the breeding focus is feed intake. And C334A and G1290A of the CCKAR gene can be deemed as candidate markers for feed intake and weight gain.

Electronic supplementary material

The online version of this article (10.1186/s40104-018-0261-1) contains supplementary material, which is available to authorized users.

Gene expression analysis of blood, liver, and muscle in cattle divergently selected for high and low residual feed intake

Improving feed efficiency in cattle is important because it increases profitability by reducing costs, and it also shrinks the environmental footprint of cattle production by decreasing manure and greenhouse gas emissions. Residual feed intake (RFI) is 1 measurement of feed efficiency and is the difference between actual and predicted feed intake. Residual feed intake is a complex trait with moderate heritability, but the genes and biological processes associated with its variation still need to be found. We explored the variation in expression of genes using RNA sequencing to find genes whose expression was associated with RFI and then investigated the pathways that are enriched for these genes. In this study, we used samples from growing Angus bulls (muscle and liver tissues) and lactating Holstein cows (liver tissue and white blood cells) divergently selected for low and high RFI. Within each breed-tissue combination, the correlation between the expression of genes and RFI phenotypes, as well as GEBV, was calculated to determine the genes whose expression was correlated with RFI. There were 16,039 genes expressed in more than 25% of samples in 1 or more tissues. The expression of 6,143 genes was significantly associated with RFI phenotypes, and expression of 2,343 genes was significantly associated with GEBV for RFI ( < 0.05) in at least 1 tissue. The genes whose expression was correlated with RFI phenotype (or GEBV) within each breed-tissue combination were enriched for 158 (78) biological processes (Fisher Exact Statistics for gene-enrichment analysis, EASE score < 0.1) and associated with 13 (13) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways ( < 0.05 and fold enrichment > 2). These biological processes were related to regulation of transcription, translation, energy generation, cell cycling, apoptosis, and proteolysis. However, the direction of the correlation between RFI and gene expression in some cases reversed between tissues. For instance, low levels of proteolysis in muscle were associated with high efficiency in growing bulls, but high levels of proteolysis in white blood cells were associated with efficiency of milk production in lactating cows.

Recombinant bovine somatotropin on heifer’s biometric measures, bodyweight, blood metabolites, and dry matter intake predictions

This study aimed to determine the influence of sustained-release recombinant bovine somatotropin (rbST) injections on biometrics measures, bodyweight (BW), average BW gain, observed and predicted DM intake, accuracy of recent methods to estimate DM intake, blood metabolites, haematological profile and rectal temperature in dairy heifers. Thirty Holstein heifers (132 ± 27 kg BW and 6.2 ± 0.35 months of age) were used in a complete randomised design experiment. Heifers were assigned to treatments: (1) Control (CON), 250 mL of saline solution, or (2) rbST, 250 mg of sustained-release rbST every 15 days. Treatments were injected in the subcutaneous of ischiorectal fossa or subscapular region in a regular alternating manner (right and left side) every 15 days throughout a period of 90 days. Prediction of DM intake was calculated using either non-linear or linear models for heifers in tropical conditions. rbST injections increased the average values of thoracic perimeter, length, and rump width in heifers. rbST-treated heifers had higher average BW and BW gain than CON. Regardless of the model applied, both observed and predicted DM intake were higher for heifers rbST-treated in relation to CON. Non-linear model was accurate without significant bias. rbST injections elevated blood glucose and high-density lipoprotein cholesterol concentration in heifers. No differences were detected on haematological profile and rectal temperature of heifers. rbST injections every 15 days to growing heifers promoted animal performance by increasing biometrics measures and BW gain. In addition, non-linear model was accurate to predict DM intake of heifers.

Heritable Bovine Rumen Bacteria Are Phylogenetically Related and Correlated with the Cow's Capacity To Harvest Energy from Its Feed

Ruminants sustain a long-lasting obligatory relationship with their rumen microbiome dating back 50 million years. In this unique host-microbiome relationship, the host’s ability to digest its feed is completely dependent on its coevolved microbiome. This extraordinary alliance raises questions regarding the dependent relationship between ruminants’ genetics and physiology and the rumen microbiome structure, composition, and metabolism. To elucidate this relationship, we examined the association of host genetics with the phylogenetic and functional composition of the rumen microbiome. We accomplished this by studying a population of 78 Holstein-Friesian dairy cows, using a combination of rumen microbiota data and other phenotypes from each animal with genotypic data from a subset of 47 animals. We identified 22 operational taxonomic units (OTUs) whose abundances were associated with rumen metabolic traits and host physiological traits and which showed measurable heritability. The abundance patterns of these microbes can explain high proportions of variance in rumen metabolism and many of the host physiological attributes such as its energy-harvesting efficiency. Interestingly, these OTUs shared higher phylogenetic similarity between themselves than expected by chance, suggesting occupation of a specific ecological niche within the rumen ecosystem. The findings presented here suggest that ruminant genetics and physiology are correlated with microbiome structure and that host genetics may shape the microbiome landscape by enriching for phylogenetically related taxa that may occupy a unique niche.

Dairy cows are an essential nutritional source for the world’s population; as such, they are extensively farmed throughout our planet and subsequently impact our environment. The microbial communities that reside in the upper digestive tract of these animals in a compartment named the rumen degrade and ferment the plant biomass that the animal ingests. Our recent efforts, as well as those of others, have shown that this microbial community’s composition and functionality are tightly linked to the cow’s capacity to harvest energy from its feed, as well as to other physiological traits. In this study, we identified microbial groups that are heritable and also linked to the cow’s production parameters. This finding could potentially allow us to apply selection programs on specific rumen microbial components that are linked to the animal’s physiology and beneficial to production. Hence, it is a steppingstone toward microbiome manipulation for increasing food availability while lowering environmental impacts such as methane emission.

Genetic assessment of residual feed intake as a feed efficiency trait in the Pacific white shrimp Litopenaeus vannamei

BACKGROUND

Residual feed intake (RFI) was investigated as a measure of feed efficiency in a breeding population of Litopenaeus vannamei. Shrimp from 34 families were housed individually and feed efficiency and growth traits were recorded during two successive growth periods. The objectives of this study were (1) to estimate the heritability of RFI and related traits, including feed efficiency ratio (FER), average daily gain (ADG) and daily feed intake (DFI), (2) to determine the relationships between RFI and other traits, and (3) to evaluate the variation of these traits across two growth periods.

RESULTS

Shrimp displayed large inter-individual variation in RFI, FER, ADG and DFI during each growth period. Heritability estimates of all these traits during both periods reached high values (0.577 ± 0.232 to 0.707 ± 0.252). RFI showed weak and no genetic correlations with ADG during the two growth periods between days 1 to 21 (0.135 ± 0.204) and 22 to 42 (-0.018 ± 0.128), respectively, but high positive genetic correlations with DFI (>0.8). Weak and moderate negative genetic correlations were observed between RFI and FER during the two periods (-0.126 ± 0.208 and -0.387 ± 0.183). As evidenced by the high genetic correlations between the two periods for each trait (>0.6), trait performance of the shrimp tended to be consistent across periods.

CONCLUSIONS

For the first time, accurate measurement of individual feed efficiency on a large scale was achieved in shrimp. Although the estimated heritability reported here for RFI may be overestimated, it is a heritable trait in L. vannamei that can be improved by genetic improvement. For L. vannamei, the biggest potential advantage in using RFI as a measure of feed efficiency is that it is independent of growth rate, and thus genetic selection on RFI has the potential to improve feed efficiency and reduce feed intake, without compromising growth performance.

Strategies for implementing genomic selection for feed efficiency in dairy cattle breeding schemes

Alternative genomic selection and traditional BLUP breeding schemes were compared for the genetic improvement of feed efficiency in simulated Norwegian Red dairy cattle populations. The change in genetic gain over time and achievable selection accuracy were studied for milk yield and residual feed intake, as a measure of feed efficiency. When including feed efficiency in genomic BLUP schemes, it was possible to achieve high selection accuracies for genomic selection, and all genomic BLUP schemes gave better genetic gain for feed efficiency than BLUP using a pedigree relationship matrix. However, introducing a second trait in the breeding goal caused a reduction in the genetic gain for milk yield. When using contracted test herds with genotyped and feed efficiency recorded cows as a reference population, adding an additional 4,000 new heifers per year to the reference population gave accuracies that were comparable to a male reference population that used progeny testing with 250 daughters per sire. When the test herd consisted of 500 or 1,000 cows, lower genetic gain was found than using progeny test records to update the reference population. It was concluded that to improve difficult to record traits, the use of contracted test herds that had additional recording (e.g., measurements required to calculate feed efficiency) is a viable option, possibly through international collaborations.

Assessment of the genomic prediction accuracy for feed efficiency traits in meat-type chickens

Feed represents the major cost of chicken production. Selection for improving feed utilization is a feasible way to reduce feed cost and greenhouse gas emissions. The objectives of this study were to investigate the efficiency of genomic prediction for feed conversion ratio (FCR), residual feed intake (RFI), average daily gain (ADG) and average daily feed intake (ADFI) and to assess the impact of selection for feed efficiency traits FCR and RFI on eviscerating percentage (EP), breast muscle percentage (BMP) and leg muscle percentage (LMP) in meat-type chickens. Genomic prediction was assessed using a 4-fold cross-validation for two validation scenarios. The first scenario was a random family sampling validation (CVF), and the second scenario was a random individual sampling validation (CVR). Variance components were estimated based on the genomic relationship built with single nucleotide polymorphism markers. Genomic estimated breeding values (GEBV) were predicted using a genomic best linear unbiased prediction model. The accuracies of GEBV were evaluated in two ways: the correlation between GEBV and corrected phenotypic value divided by the square root of heritability, i.e., the correlation-based accuracy, and model-based theoretical accuracy. Breeding values were also predicted using a conventional pedigree-based best linear unbiased prediction model in order to compare accuracies of genomic and conventional predictions. The heritability estimates of FCR and RFI were 0.29 and 0.50, respectively. The heritability estimates of ADG, ADFI, EP, BMP and LMP ranged from 0.34 to 0.53. In the CVF scenario, the correlation-based accuracy and the theoretical accuracy of genomic prediction for FCR were slightly higher than those for RFI. The correlation-based accuracies for FCR, RFI, ADG and ADFI were 0.360, 0.284, 0.574 and 0.520, respectively, and the model-based theoretical accuracies were 0.420, 0.414, 0.401 and 0.382, respectively. In the CVR scenario, the correlation-based accuracy and the theoretical accuracy of genomic prediction for FCR was lower than RFI, which was different from the CVF scenario. The correlation-based accuracies for FCR, RFI, ADG and ADFI were 0.449, 0.593, 0.581 and 0.627, respectively, and the model-based theoretical accuracies were 0.577, 0.629, 0.631 and 0.638, respectively. The accuracies of genomic predictions were 0.371 and 0.322 higher than the conventional pedigree-based predictions for the CVF and CVR scenarios, respectively. The genetic correlations of FCR with EP, BMP and LMP were -0.427, -0.156 and -0.338, respectively. The correlations between RFI and the three carcass traits were -0.320, -0.404 and -0.353, respectively. These results indicate that RFI and FCR have a moderate accuracy of genomic prediction. Improving RFI and FCR could be favourable for EP, BMP and LMP. Compared with FCR, which can be improved by selection for ADG in typical meat-type chicken breeding programs, selection for RFI could lead to extra improvement in feed efficiency.

Use of genotype × environment interaction model to accommodate genetic heterogeneity for residual feed intake, dry matter intake, net energy in milk, and metabolic body weight in dairy cattle

Feed efficiency in dairy cattle has gained much attention recently. Due to the cost-prohibitive measurement of individual feed intakes, combining data from multiple countries is often necessary to ensure an adequate reference population. It may then be essential to model genetic heterogeneity when making inferences about feed efficiency or selecting efficient cattle using genomic information. In this study, we constructed a marker × environment interaction model that decomposed marker effects into main effects and interaction components that were specific to each environment. We compared environment-specific variance component estimates and prediction accuracies from the interaction model analyses, an across-environment analyses ignoring population stratification, and a within-environment analyses using an international feed efficiency data set. Phenotypes included residual feed intake, dry matter intake, net energy in milk, and metabolic body weight from 3,656 cows measured in 3 broadly defined environments: North America (NAM), the Netherlands (NLD), and Scotland (SAC). Genotypic data included 57,574 single nucleotide polymorphisms per animal. The interaction model gave the highest prediction accuracy for metabolic body weight, which had the largest estimated heritabilities ranging from 0.37 to 0.55. The within-environment model performed the best when predicting residual feed intake, which had the lowest estimated heritabilities ranging from 0.13 to 0.41. For traits (dry matter intake and net energy in milk) with intermediate estimated heritabilities (0.21 to 0.50 and 0.17 to 0.53, respectively), performance of the 3 models was comparable. Genomic correlations between environments also were computed using variance component estimates from the interaction model. Averaged across all traits, genomic correlations were highest between NAM and NLD, and lowest between NAM and SAC. In conclusion, the interaction model provided a novel way to evaluate traits measured in multiple environments in which genetic heterogeneity may exist. This model allowed estimation of environment-specific parameters and provided genomic predictions that approached or exceeded the accuracy of competing within- or across-environment models.

Partitioning of energy and nitrogen in lactating primiparous and multiparous Holstein–Friesian cows with divergent residual feed intake

Residual feed intake (RFI) is the difference between an animal’s actual and expected feed intake. Two experiments were conducted comparing energy and nitrogen partitioning in mid-lactation, in Holstein–Friesian cows selected for high or low RFI measured previously as growing calves. Each experiment used 16 cows (8 high-RFI and 8 low-RFI); the first used primiparous (PP) cows and the second used multiparous (MP) cows. Cows were housed individually for 4 days in metabolism stalls, then open-circuit respiration chambers for 3 days. Each cow was offered ad libitum lucerne hay cubes plus 6 kg DM per day of crushed wheat grain. Individual feed intake, milk yield, milk composition and faecal and urine output were measured. Methane and carbon dioxide output and oxygen consumption were measured in the chambers. In MP cows, a greater proportion of energy intake was partitioned to milk and less to heat in low-RFI than high-RFI cows. The proportion of gross-energy intake per kilogram metabolic bodyweight partitioned to milk production was greater and the proportion partitioned to methane and heat production was lower in MP than in PP cows. Energy from tissue mobilisation was not affected by RFI or parity. The amount of nitrogen consumed from feed was greater in MP than PP cows. As a percentage of N intake, N partitioned to milk was greater in PP than in MP cows, but there were no overall effects of RFI on N partitioning. However, there was a trend towards a positive association between N excreted in the urine and RFI, which could have environmental implications. Both RFI and parity were associated with variation in energy and nitrogen partitioning and should be examined in a larger subset of animals in future.

Divergence for residual feed intake of Holstein-Friesian cattle during growth did not affect production and reproduction during lactation

Residual feed intake (RFI) is the difference between actual and predicted dry matter intake (DMI) of individual animals. Recent studies with Holstein-Friesian calves have identified an ~20% difference in RFI during growth (calf RFI) and these groups remained divergent in RFI during lactation. The objective of the experiment described here was to determine if cows selected for divergent RFI as calves differed in milk production, reproduction or in the profiles of BW and body condition score (BCS) change during lactation, when grazing pasture. The cows used in the experiment (n=126) had an RFI of -0.88 and +0.75 kg DM intake/day for growth as calves (efficient and inefficient calf RFI groups, respectively) and were intensively grazed at four stocking rates (SR) of 2.2, 2.6, 3.1 and 3.6 cows/ha on self-contained farmlets, over 3 years. Each SR treatment had equal number of cows identified as low and high calf RFI, with 24, 28, 34 and 40/11 ha farmlet. The cows divergent for calf RFI were randomly allocated to each SR. Although SR affected production, calf RFI group (low or high) did not affect milk production, reproduction, BW, BCS or changes in these parameters throughout lactation. The most efficient animals (low calf RFI) lost similar BW and BCS as the least efficient (high calf RFI) immediately post-calving, and regained similar BW and BCS before their next calving. These results indicate that selection for RFI as calves to increase efficiency of feed utilisation did not negatively affect farm productivity variables (milk production, BCS, BW and reproduction) as adults when managed under an intensive pastoral grazing system.

Combination analysis of genome-wide association and transcriptome sequencing of residual feed intake in quality chickens

Background

Residual feed intake (RFI) is a powerful indicator for energy utilization efficiency and responds to selection. Low RFI selection enables a reduction in feed intake without affecting growth performance. However, the effective variants or major genes dedicated to phenotypic differences in RFI in quality chickens are unclear. Therefore, a genome-wide association study (GWAS) and RNA sequencing were performed on RFI to identify genetic variants and potential candidate genes associated with energy improvement.

Results

A lower average daily feed intake was found in low-RFI birds compared to high-RFI birds. The heritability of RFI measured from 44 to 83 d of age was 0.35. GWAS showed that 32 of the significant single nucleotide polymorphisms (SNPs) associated with the RFI (P < 10⁻⁴) accounted for 53.01 % of the additive genetic variance. More than half of the effective SNPs were located in a 1 Mb region (16.3–17.3 Mb) of chicken (Gallus gallus) chromosome (GGA) 12. Thus, focusing on this region should enable a deeper understanding of energy utilization. RNA sequencing was performed to profile the liver transcriptomes of four male chickens selected from the high and low tails of the RFI. One hundred and sixteen unique genes were identified as differentially expressed genes (DEGs). Some of these genes were relevant to appetite, cell activities, and fat metabolism, such as CCKAR, HSP90B1, and PCK1. Some potential genes within the 500 Kb flanking region of the significant RFI-related SNPs detected in GWAS (i.e., MGP, HIST1H110, HIST1H2A4L3, OC3, NR0B2, PER2, ST6GALNAC2, and G0S2) were also identified as DEGs in chickens with divergent RFIs.

Conclusions

The GWAS findings showed that the 1 Mb narrow region of GGA12 should be important because it contained genes involved in energy-consuming processes, such as lipogenesis, social behavior, and immunity. Similar results were obtained in the transcriptome sequencing experiments. In general, low-RFI birds seemed to optimize energy employment by reducing energy expenditure in cell activities, immune responses, and physical activity compared to eating.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2861-5) contains supplementary material, which is available to authorized users.

FABP4 is a leading candidate gene associated with residual feed intake in growing Holstein calves

Ecological and economic concerns drive the need to improve feed utilization by domestic animals. Residual feed intake (RFI) is one of the most acceptable measures for feed efficiency (FE). However, phenotyping RFI-related traits is complex and expensive and requires special equipment. Advances in marker technology allow the development of various DNA-based selection tools. To assimilate these technologies for the benefit of RFI-based selection, reliable phenotypic measures are prerequisite. In the current study, we identified single nucleotide polymorphisms (SNPs) associated with RFI phenotypic consistency across different ages and diets (named RFI 1-3), using DNA samples of high or low RFI ranked Holstein calves. Using targeted sequencing of chromosomal regions associated with FE- and RFI-related traits, we identified 48 top SNPs significantly associated with at least one of three defined RFIs. Eleven of these SNPs were harbored by the fatty acid binding protein 4 (FABP4). While 10 significant SNPs found in FABP4 were common for RFI 1 and RFI 3, one SNP (FABP4_5; A<G substitution), in the promoter region of the gene, was significantly associated with all three RFIs. As the three RFI classes reflect changing diets and ages with concomitant RFI phenotypic consistency, the above polymorphisms and in particular FABP4_5, might be considered possible markers for RFI-based selection for FE in the Holstein breed, following a larger-scale validation.