Identification of polymorphisms influencing feed intake and efficiency in beef cattle

Feed efficiency is an economically important trait in beef cattle. Net feed efficiency, measured as residual feed intake (RFI), is the difference between actual feed intake and the predicted feed intake required for maintenance and gain of the animal. SNPs that show associations with RFI may be useful quantitative trait nucleotides for marker-assisted selection. This study identified associations between SNPs underlying five RFI QTL on five bovine chromosomes (BTA2, 5, 10, 20 and 29) with measures of dry matter intake (DMI), RFI and feed conversion ratio (FCR) in beef cattle. Six SNPs were found to have effects on RFI (P < 0.05). The largest single SNP allele substitution effect for RFI was -0.25 kg/day located on BTA2. The combined effects of the SNPs found significant in this experiment explained 6.9% of the phenotypic variation of RFI. Not all the RFI SNPs showed associations with DMI and FCR even though these traits are highly correlated with RFI (r = 0.77 and r = 0.62 respectively). This shows that these SNPs may be affecting the underlying biological mechanisms of feed efficiency beyond feed intake control and weight gain efficiency. These SNPs can be used in marker-assisted selection but first it will be important to verify these effects in independent populations of cattle.     

Whole Genome Association Studies of Residual Feed Intake and Related Traits in the Pig

Background

Residual feed intake (RFI), a measure of feed efficiency, is the difference between observed feed intake and the expected feed requirement predicted from growth and maintenance. Pigs with low RFI have reduced feed costs without compromising their growth. Identification of genes or genetic markers associated with RFI will be useful for marker-assisted selection at an early age of animals with improved feed efficiency.

Methodology/Principal findings

Whole genome association studies (WGAS) for RFI, average daily feed intake (ADFI), average daily gain (ADG), back fat (BF) and loin muscle area (LMA) were performed on 1,400 pigs from the divergently selected ISU-RFI lines, using the Illumina PorcineSNP60 BeadChip. Various statistical methods were applied to find SNPs and genomic regions associated with the traits, including a Bayesian approach using GenSel software, and frequentist approaches such as allele frequency differences between lines, single SNP and haplotype analyses using PLINK software. Single SNP and haplotype analyses showed no significant associations (except for LMA) after genomic control and FDR. Bayesian analyses found at least 2 associations for each trait at a false positive probability of 0.5. At generation 8, the RFI selection lines mainly differed in allele frequencies for SNPs near (<0.05 Mb) genes that regulate insulin release and leptin functions. The Bayesian approach identified associations of genomic regions containing insulin release genes (e.g., GLP1R, CDKAL, SGMS1) with RFI and ADFI, of regions with energy homeostasis (e.g., MC4R, PGM1, GPR81) and muscle growth related genes (e.g., TGFB1) with ADG, and of fat metabolism genes (e.g., ACOXL, AEBP1) with BF. Specifically, a very highly significantly associated QTL for LMA on SSC7 with skeletal myogenesis genes (e.g., KLHL31) was identified for subsequent fine mapping.

Conclusions/significance

Important genomic regions associated with RFI related traits were identified for future validation studies prior to their incorporation in marker-assisted selection programs.     

Genomic selection for feed efficiency in dairy cattle

Feed is a major component of variable costs associated with dairy systems and is therefore an important consideration for breeding objectives. As a result, measures of feed efficiency are becoming popular traits for genetic analyses. Already, several countries account for feed efficiency in their breeding objectives by approximating the amount of energy required for milk production, maintenance, etc. However, variation in actual feed intake is currently not captured in dairy selection objectives, although this could be possible by evaluating traits such as residual feed intake (RFI), defined as the difference between actual and predicted feed (or energy) intake. As feed intake is expensive to accurately measure on large numbers of cows, phenotypes derived from it are obvious candidates for genomic selection provided that: (1) the trait is heritable; (2) the reliability of genomic predictions are acceptable to those using the breeding values; and (3) if breeding values are estimated for heifers, rather than cows then the heifer and cow traits need to be correlated. The accuracy of genomic prediction of dry matter intake (DMI) and RFI has been estimated to be around 0.4 in beef and dairy cattle studies. There are opportunities to increase the accuracy of prediction, for example, pooling data from three research herds (in Australia and Europe) has been shown to increase the accuracy of genomic prediction of DMI from 0.33 within country to 0.35 using a three-country reference population. Before including RFI as a selection objective, genetic correlations with other traits need to be estimated. Weak unfavourable genetic correlations between RFI and fertility have been published. This could be because RFI is mathematically similar to the calculation of energy balance and failure to account for mobilisation of body reserves correctly may result in selection for a trait that is similar to selecting for reduced (or negative) energy balance. So, if RFI is to become a selection objective, then including it in an overall multi-trait selection index where the breeding objective is net profit is sensible, as this would allow genetic correlations with other traits to be properly accounted for. If genetic parameters are accurately estimated then RFI is a logical breeding objective. If there is uncertainty in these, then DMI may be preferable.     

Genetic parameters for feed efficiency in Romane rams and responses to single-generation selection

Feeding costs represent one of the highest expenditures in animal production systems. Breeding efficient animals that express their growth potential while eating less is therefore a major objective for breeders. We estimated the genetic parameters for feed intake, feed efficiency traits (residual feed intake (RFI) and feed conversion ratio (FCR)), growth and body composition traits in the Romane meat sheep breed. In these traits, selection responses to single-generation divergent selection on RFI were evaluated. From 2009 to 2016, a total of 951 male lambs were tested for 8 weeks starting from 3 months of age. They were weighed at the beginning and at the end of the testing period. Backfat thickness and muscle depth were recorded at the end of the testing period through ultrasound measurements. Feed intake was continuously recorded over the testing period using the automatic concentrate feeders. The heritability of RFI was estimated at 0.45 ± 0.08, which was higher than the heritability of FCR (0.30 ± 0.08). No significant genetic correlations were observed between RFI and growth traits. A favourable low negative genetic correlation was estimated between RFI and muscle depth (−0.30 ± 0.15), though additional data are needed to confirm these results. The selection of low RFI sires based on their breeding values led to the production of lambs eating significantly less concentrate (3% decrease in the average daily feed intake), but with the same growth as lambs from sires selected based on high RFI breeding values. We concluded that in meat sheep, RFI is a heritable trait that is genetically independent of post-weaning growth and body composition traits. A one-generation divergent selection based on RFI breeding values highlighted that substantial gains in feeding costs can be expected in selection schemes for meat sheep breeds.     

Variation in faecal digestibility values related to feed efficiency traits of grower-finisher pigs

Providing pigs a diet that matches their nutrient requirements involves optimizing the diet based on the nutrient digestibility values of the considered feed ingredients. Feeding the same quantity of a diet to pigs with similar BW but with different requirements, however, can result in a different average daily gain (ADG) and backfat thickness (BF) between pigs. Digestibility may contribute to this variation in efficiency. We investigated variation in feed efficiency traits in grower-finisher pigs associated with variation in faecal digestibility values, independent of feed intake at the time of measuring faecal digestibility. Considered traits were ADG, average daily feed intake (ADFI), feed conversion ratio (FCR), BF and residual feed intake (RFI). Feed intake, BW, and BF data of one hundred and sixty three-way crossbreed grower-finisher pigs (eighty female and eighty male) were collected during two phases, from day 0 of the experiment (mean BW 23 kg) till day 56 (mean BW 70 kg) and from day 56 to slaughter (mean BW 121 kg). Pigs were either fed a diet based on corn/soybean meal or a more fibrous diet based on wheat/barley/by-products, with titanium dioxide as indigestible marker. Faecal samples of one hundred and five pigs were collected on the day before slaughter and used to determine apparent faecal digestibility of DM, ash, organic matter (OM), CP, crude fat (CFat), crude fibre (CF), and to calculate the digestibility of nonstarch polysaccharides (NSPs) and energy (E). The effects of diet, sex and covariate feed intake at sampling (FIs) on faecal digestibility values were estimated and were significant for all except for CFat. Faecal digestibility values of each individual pig determined at the day before slaughter, corrected for diet, sex and FIs, were used to estimate their association with ADG, ADFI, FCR, BF, and RFI. In the first phase, a one percent unit increase in faecal digestibility of DM, ash, OM, E, CP, CFat, CF, NSP, and Ash individually was related to 0.01–0.03 unit reduction in FCR and 6–23 g/day reduction in RFI. A unit increase in CP digestibility was related to 0.1 mm increase in BF and 10 g/day increase in ADG. In the second phase, a one percent unit increase in faecal digestibility of DM, CP and Ash was related to a decrease of 16–20 g/day in RFI. In conclusion, the relationship between variation in feed efficiency traits and faecal digestibility values is different across the developmental stages of a pig.     

Grazed grass herbage intake and performance of beef heifers with predetermined phenotypic residual feed intake classification

Data were collected on 85 Simmental and Simmental × Holstein–Friesian heifers. During the indoor winter period, they were offered grass silage ad libitum and 2 kg of concentrate daily, and individual dry matter intake (DMI) and growth was recorded over 84 days. Individual grass herbage DMI was determined at pasture over a 6-day period, using the n-alkane technique. Body condition score, skeletal measurements, ultrasonic fat and muscle depth, visual muscularity score, total tract digestibility, blood hormones, metabolites and haematology variables and activity behaviour were measured for all heifers. Phenotypic residual feed intake (RFI) was calculated for each animal as the difference between actual DMI and expected DMI during the indoor winter period. Expected DMI was calculated for each animal by regressing average daily DMI on mid-test live weight (LW)^0.75 and average daily gain (ADG) over an 84-day period. Standard deviations above and below the mean were used to group animals into high (>0.5 s.d.), medium (±0.5 s.d.) and low (<0.5 s.d.) RFI. Overall mean (s.d.) values for DMI (kg/day), ADG (kg), feed conversion ratio (FCR) kg DMI/kg ADG and RFI (kg dry matter/day) were 5.82 (0.73), 0.53 (0.18), 12.24 (4.60), 0.00 (0.43), respectively, during the RFI measurement period. Mean DMI (kg/day) and ADG (kg) during the grazing season was 9.77 (1.77) and 0.77 (0.14), respectively. The RFI groups did not differ (P > 0.05) in LW, ADG or FCR at any stage of measurement. RFI was positively correlated (r = 0.59; P < 0.001) with DMI during the RFI measurement period but not with grazed grass herbage DMI (r = 0.06; P = 0.57). Low RFI heifers had 0.07 greater (P < 0.05) concentration of plasma creatinine than high RFI heifers and, during the grazed herbage intake period, spent less time standing and more time lying (P < 0.05) than high RFI heifers. However, low and high RFI groups did not differ (P > 0.05) in ultrasonic backfat thickness or muscle depth, visual muscle scores, skeletal size, total tract digestibility or blood hormone and haematology variables at any stage of the experiment. Despite a sizeable difference in intake of grass silage between low and high RFI heifers during the indoor winter period, there were no detectable differences between RFI groupings for any economically important performance traits measured when animals were offered ensiled or grazed grass herbage.     

Using quantile regression methodology to evaluate changes in the shape of growth curves in pigs selected for increased feed efficiency based on residual feed intake

Growth rate is a major component of feed efficiency when estimating residual feed intake (RFI). Quantile regression (QR) methodology can be used to identify animals with different growth trajectories. The objective of this study was to evaluate the use of QR to identify phenotypic and genetic differences in pigs selected for low RFI. Using performance data on 750 Yorkshire pigs selected for low RFI, individual average daily gain (ADG), average daily feed intake (ADFI), RFI and Gompertz growth curve parameters (asymptotic weight (a), inflection point (b) and decay parameter (c)) were estimated for each pig. Using QR methodology, three Gompertz growth curves were estimated for the whole population for three quantiles (0.1, 0.5 and 0.9) of the BW data. Each animal was classified into one of the quantile regression groups (QRG) based on their overall Euclidian distance between each observed and estimated BW from the quantile growth curves. These three curves were also estimated using only part of the data (generations −1 to 3, and −1 to 4) in order to evaluate the agreement classification rate of animals from later generations into QRGs. We evaluated the effect of QRG on growth parameters and performance traits. Genetic parameters were estimated for these traits, as well as for QRG. In addition, genetic trends for each QRG were estimated. Three distinct growth curves were observed for animals classified into either quantiles 0.1 (QRG_0.1), 0.5 (QRG_0.5) or 0.9 (QRG_0.9). When only part of the data was used to estimate quantile growth curves, all animals from QRG_0.1 were correctly classified in their group. Animals in QRG_0.1 had significantly lower ADFI, ADG and RFI, and greater a, b and c than animals in the other groups. Quantile regression groups analysed as a trait was highly heritable (0.41) and had high (0.8) and moderate (0.46) genetic correlations with ADG and RFI, respectively. Selection for reduced RFI increased the number of animals classified as QRG_0.1 in the population. Overall, downward genetic trends were observed for all traits as a function of selection for reduced RFI. However, QRG_0.1 was the only group that had a positive genetic trend for ADG. Altogether, these results indicate that selection for reduced RFI changes the shape of growth curves in Yorkshire in pigs, and that QR methodology was able to identify animals having different genetic potential for feed efficiency, bringing a new opportunity to improve selection for reduced RFI.     

Using a system of differential equations that models cattle growth to uncover the genetic basis of complex traits

The interplay between dynamic models of biological systems and genomics is based on the assumption that genetic variation of the complex trait (i.e., outcome of model behavior) arises from component traits (i.e., model parameters) in lower hierarchical levels. In order to provide a proof of concept of this statement for a cattle growth model, we ask whether model parameters map genomic regions that harbor quantitative trait loci (QTLs) already described for the complex trait. We conducted a genome-wide association study (GWAS) with a Bayesian hierarchical LASSO method in two parameters of the Davis Growth Model, a system of three ordinary differential equations describing DNA accretion, protein synthesis and degradation, and fat synthesis. Phenotypic and genotypic data were available for 893 Nellore (Bos indicus) cattle. Computed values for parameter k₁ (DNA accretion rate) ranged from 0.005 ± 0.003 and for α (constant for energy for maintenance requirement) 0.134 ± 0.024. The expected biological interpretation of the parameters is confirmed by QTLs mapped for k₁ and α. QTLs within genomic regions mapped for k₁ are expected to be correlated with the DNA pool: body size and weight. Single nucleotide polymorphisms (SNPs) which were significant for α mapped QTLs that had already been associated with residual feed intake, feed conversion ratio, average daily gain (ADG), body weight, and also dry matter intake. SNPs identified for k₁ were able to additionally explain 2.2% of the phenotypic variability of the complex ADG, even when SNPs for k₁ did not match the genomic regions associated with ADG. Although improvements are needed, our findings suggest that genomic analysis on component traits may help to uncover the genetic basis of more complex traits, particularly when lower biological hierarchies are mechanistically described by mathematical simulation models.     

Nutrient utilisation,growth performance and blood metabolites in Murrah buffalo calves (Bubalus bubalis) divergently selected for residual feed intake

The aim of this study was to evaluate differences in efficiency of feed utilisation between buffalo calves with low and high residual feed intake (RFI) by comparing feed intake, nutrient digestibility, growth traits and blood metabolites. Eighteen male Murrah buffalo calves (aged 4–6 months; 70 ± 1.0 kg body weight) were fed ad libitum with a total mixed ration for 120 d. Based on linear regression models involving dry matter intake (DMI), average daily gain (ADG) and mid-test metabolic body size, calves were assigned into low and high RFI groups. The RFI varied from ?0.33 to +0.28 kg DM/d with an average RFI of ?0.14 and 0.14 kg DM/d in low and high RFI calves, respectively. Calves had a mean DMI of 1.9 and 2.4 kg/d and an ADG of 0.5 and 0.6 kg/d in low and high RFI groups, respectively. Low RFI calves ate 19.0% less DM each day and required significantly less metabolisable energy for maintenance compared with high RFI calves (12.5 vs. 16.7 MJ/d). Nutrient digestibility and nitrogen balance did not differ among low and high RFI calves. In more efficient animals (low RFI calves) higher (p < 0.05) plasma level of growth hormone, insulin-like growth factor-1 (IGF-1), triiodothyronine (T₃) and lower concentration of thyroxin hormone were detected. No significant differences in levels of insulin, hydroxyproline, plasma and urine creatinine, total protein and albumin between high and low RFI groups were found. Blood metabolites showed significant (p < 0.05) differences at initial and final stages of study in both groups. At final stage of study, RFI showed negative correlations with growth hormone, IGF-1, T₃, urine creatinine and albumin. Low RFI buffalo calves are more efficient in feed utilisation and the differences in blood metabolites are probably due to differences in feed intake and body metabolism.     

A genome-wide association study for feed efficiency-related traits in a crossbred pig population

Feed efficiency (FE) is one of the most important traits in pig production. However, it is difficult and costly to measure it, limiting the collection of large amount of data for an accurate selection for better FE. Therefore, the identification of single-nucleotide polymorphisms (SNPs) associated with FE-related traits to be used in the genetic evaluation is of great interest of pig breeding programs for increasing the prediction accuracy and the genetic progress of these traits. The objective of this study was to identify SNPs significantly associated with FE-related traits: average daily gain (ADG), average daily feed intake (ADFI) and feed conversion ratio (FCR). We also aimed to identify potential candidate genes for these traits. Phenotypic information recorded on a population of 2386 three-way crossbreed pigs that were genotyped for 51 468 SNPs was used. We identified three loci of quantitative trait (QTL) regions associated with ADG and three QTL regions associated with ADFI; however, no significant association was found for FCR. A false discovery rate (FDR) ≤ 0.005 was used as the threshold for declaring an association as significant. The QTL regions associated with ADG on Sus scrofa chromosome (SSC) 1 were located between 177.01 and 185.47 Mb, which overlaps with the QTL regions for ADFI on SSC1 (173.26 and 185.47 Mb). The other QTL region for ADG was located on SSC12 (2.87 and 3.22 Mb). The most significant SNPs in these QTL regions explained up to 3.26% of the phenotypic variance of these traits. The non-identification of genomic regions associated with FCR can be explained by the complexity of this trait, which is a ratio between ADG and ADFI. Finally, the genes CDH19, CDH7, RNF152, MC4R, PMAIP1, FEM1B and GAA were the candidate genes found in the 1 Mb window around the QTL regions identified in this study. Among them, the MC4R gene (SSC1) has a well-known function related to ADG and ADFI. In this study, we identified three QTL regions for ADG (SSC1 and SSC12) and three for ADFI (SSC1). These regions were previously described in purebred pig populations; however, to our knowledge, this is the first study to confirm the relevance of these QTL regions in a crossbred pig population. The potential use of the SNPs and genes identified in this study in prediction models that combine genomic selection and marker-assisted selection should be evaluated for increasing the prediction accuracy of these traits in this population.     

Genetic markers on BTA14 predictive for residual feed intake in beef steers and their effects on carcass and meat quality traits

With the high cost of feed for animal production, genetic selection for animals that metabolize feed more efficiently could result in substantial cost savings for cattle producers. The purpose of this study was to identify DNA markers predictive for differences among cattle for traits associated with feed efficiency. Crossbred steers were fed a high‐corn diet for 140 days and average daily feed intake (ADFI), average daily gain (ADG), and residual feed intake (RFI) phenotypes were obtained. A region on chromosome 14 was previously associated with RFI in this population of animals. To develop markers with the highest utility for predicting an animal's genetic potential for RFI, we genotyped additional markers within this chromosomal region. These polymorphisms were genotyped on the same animals (n = 1066) and tested for association with ADFI, ADG and RFI. Six markers within this region were associated with RFI (P ≤ 0.05). After conservative correction for multiple testing, one marker at 25.09 Mb remained significant (P = 0.02) and is responsible for 3.6% of the RFI phenotypic variation in this population of animals. Several of these markers were also significant for ADG, although none were significant after correction. Marker alleles with positive effects on ADG corresponded to lower RFI, suggesting an effect increasing growth without increasing feed intake. All markers were also assessed for their effects on meat quality and carcass traits. All of the markers associated with RFI were associated with adjusted fat thickness (AFT, P ≤ 0.009) and three were also associated with hot carcass weight (HCW, P ≤ 0.003). Marker alleles associated with lower RFI were also associated with reduced AFT, and if they were associated for HCW, the effect was an increase in weight. These markers may be useful as prediction tools for animals that utilize feed more efficiently; however, validation with additional populations of cattle is required.     

Assessing feed efficiency in beef steers through feeding behavior, infrared thermography and glucocorticoids

A better understanding of the factors regulating feed efficiency and their potential as predictors of feed efficiency in cattle is needed. Therefore, the potential of three classes of traits, namely, feeding behavior characteristics: daily time at feeder (TF; min/day), time per meal (TM; min), meal size (MS; g DM), eating rate (ER; g DM/min), number of daily meals (NM) and daily visits to the feeder (VF); infrared (IR) thermography traits (°C): eye (EY), cheek (CK), snout (SN), ribs (RB) and hind area (HA); and glucocorticoid levels: fecal cortisol metabolites (FCM; ng/g) and plasma cortisol (PC; ng/ml) as predictors of efficiency were evaluated in 91 steers (436 ± 37 kg) over 2 years (Y1 = 46; Y2 = 45). Additionally, the individual traits of each of these three classes were combined to define three single traits. Individual daily feed intake of a corn silage and high-moisture corn-based diet was measured using an automated feeding system. Body weight and thermographs were taken every 28 days over a period of 140 days. Four productive performance traits were calculated: daily dry matter intake (DMI), average daily gain (ADG), feed to gain ratio (F : G) and residual feed intake (RFI). Steers were also classified into three RFI categories (low-, medium- and high-RFI). Among the feeding behavior characteristics, MS and ER were correlated with all efficiency traits (range: 0.26 to 0.75). Low-RFI (more efficient steers) had smaller MS, lower ER and fewer VF in comparison to high-RFI steers. Less efficient steers (high-RFI) performed more VF during the nocturnal period than more efficient steers. More efficient steers had lower CK and SN temperatures than less efficient steers (28.1°C v. 29.2°C and 30.0°C v. 31.2°C), indicating greater energetic efficiency for low-RFI steers. In terms of glucocorticoids, PC was not correlated with efficiency traits. In contrast, more efficient steers had higher FCM in comparison to less efficient steers (51.1 v. 31.2 ng/g), indicating that a higher cortisol baseline is related to better feed efficiency. The overall evaluation of the three classes of traits revealed that feeding behavior, IR thermography and glucocorticoids accounted for 18%, 59% and 7% of the total variation associated with RFI, respectively. These classes of traits have usefulness in the indirect assessment of feed efficiency in cattle. Among them, IR thermography was the most promising alternative to screen cattle for this feed efficiency. These findings might have application in selection programs and in the better understanding of the biological basis associated with productive performance.     

Visceral organ weights,digestion and carcass characteristics of beef bulls differing in residual feed intake offered a high concentrate diet

This study examined the relationship of residual feed intake (RFI) with digestion, body composition, carcass traits and visceral organ weights in beef bulls offered a high concentrate diet. Individual dry matter (DM) intake (DMI) and growth were measured in a total of 67 Simmental bulls (mean initial BW 431 kg (s.d.=63.7)) over 3 years. Bulls were offered concentrates (860 g/kg rolled barley, 60 g/kg soya bean meal, 60 g/kg molasses and 20 g/kg minerals per vitamins) ad libitum plus 0.8 kg grass silage DM daily for 105 days pre-slaughter. Ultrasonic muscle and fat depth, body condition score (BCS), muscularity score, skeletal measurements, blood metabolites, rumen fermentation and total tract digestibility (indigestible marker) were determined. After slaughter, carcasses and perinephric and retroperitoneal fat were weighed, carcasses were graded for conformation and fat score and weight of non-carcass organs, liver, heart, kidneys, lungs, gall bladder, spleen, reticulo-rumen full and empty and intestines full, were determined. The residuals of the regression of DMI on average daily gain (ADG), mid-test metabolic BW (BW^0.75) and the fixed effect of year, using all animals, were used to compute individual RFI coefficients. Animals were ranked on RFI and assigned to high (inefficient), medium or low groupings. Overall mean ADG and daily DMI were 1.6 kg (s.d.=0.36) and 9.4 kg (s.d.=1.16), respectively. High RFI bulls consumed 7 and 14% more DM than medium and low RFI bulls, respectively (P<0.001). No differences between high and low RFI bulls were detected (P>0.05) for ADG, BW, BCS, skeletal measurements, muscularity scores, ultrasonic measurements, carcass weight, perinephric and retroperitoneal fat weight, kill-out proportion and carcass conformation and fat score. However, regression analysis indicated that a 1 kg DM/day increase in RFI was associated with a decrease in kill-out proportion of 20 g/kg (P<0.05) and a decrease in carcass conformation of 0.74 units (P<0.05). Weight of non-carcass organs did not differ (P>0.05) between RFI groups except for the empty weight of reticulo-rumen, which was 8% lighter (P=0.05) in low RFI compared with high RFI bulls. Regression analysis indicated that a 1 kg DM/day increase in RFI was associated with a 1 kg increase in reticulo-rumen empty weight (P<0.05). Of the visceral organs measured, the reticulo-rumen may be a biologically significant contributory factor to variation in RFI in beef bulls finished on a high concentrate diet.     

Combined analysis of group recorded feed intake and individually recorded body weight and litter size in mink

In the mink industry, feed costs are the largest variable expense and breeding for feed efficient animals is warranted. Implementation of selection for feed efficiency must consider the relationships between feed efficiency and the current selection traits BW and litter size. Often, feed intake (FI) is recorded on a cage with a male and a female and there is sexual dimorphism that needs to be accounted for. Study aims were to (1) model group recorded FI accounting for sexual dimorphism, (2) derive genetic residual feed intake (RFI) as a measure of feed efficiency, (3) examine the relationship between feed efficiency and BW in males (BWM) and females (BWF) and litter size at day 21 after whelping (LS21) in Danish brown mink and (4) investigate direct and correlated response to selection on each trait of interest. Feed intake records from 9574 cages, BW records on 16 782 males and 16 875 females and LS21 records on 6446 yearling females were used for analysis. Genetic parameters for FI, BWM, BWF and LS21 were obtained using a multivariate animal model, yielding sex-specific additive genetic variances for FI and BW to account for sexual dimorphism. The analysis was performed in a Bayesian setting using Gibbs sampling, and genetic RFI was obtained from the conditional distribution of FI given BW using genetic regression coefficients. Responses to single trait selection were defined as the posterior distribution of genetic superiority of the top 10% of animals after conditioning on the genetic trends. The heritabilities ranged from 0.13 for RFI in females and LS21 to 0.59 for BWF. Genetic correlations between BW in both sexes and LS21 and FI in both sexes were unfavorable, and single trait selection on BW in either sex showed increased FI in both sexes and reduced litter size. Due to the definition of RFI and high genetic correlation between BWM and BWF, selection on RFI did not significantly alter BW. In addition, selection on RFI in either sex did not affect LS21. Genetic correlation between sexes for FI and BW was high but significantly lower than unity. The high correlations across sex allowed for selection on standardized averages of animals’ breeding values (BVs) for RFI, FI and BW, which yielded selection responses approximately equal to the responses obtained using the sex-specific BVs. The results illustrate the possibility of selecting against RFI in mink with no negative effects on BW and litter size.     

Quantitative trait loci with effects on feed efficiency traits in Hereford × composite double backcross populations

Two half-sib families of backcross progeny were produced by mating F₁ Line 1 Hereford (L1) × composite gene combination (CGC) bulls with L1 and CGC cows. Feed intake and periodic weights were measured for 218 backcross progeny. These progenies were genotyped using 232 microsatellite markers that spanned the 29 BTA. Progeny from L1 and CGC females was analysed separately using composite interval mapping to find quantitative trait loci (QTL) affecting daily dry matter intake (DMI), average daily gain (ADG), feed conversion (FCR) and residual feed intake (RFI). Results from both backcrosses were pooled to find additional QTL. In the backcross to L1, QTL were detected for RFI and DMI on BTA11, FCR on BTA16, and ADG on BTA9. In the backcross to CGC, QTL were detected for RFI on BTA10, FCR on BTA12 and 16 and ADG on BTA15 and 17. After pooling, QTL were detected for RFI on BTA 2, 6, 7, 10, 11, 13 and 16; for FCR on BTA 9, 12, 16, 17 and 21; for ADG on BTA 9, 14, 15, 17; and for DMI on BTA 2, 5, 6, 9, 10, 11, 20 and 23.     

Genetic parameters for performance,feed efficiency,and carcass traits in Senepol heifers

Improving feed efficiency is a key breeding goal in the beef cattle industry. In this study, we estimated the genetic parameters for feed efficiency and carcass traits in Senepol cattle raised in tropical regions. Various indicators of feed efficiency [gain to feed ratio (G:F), feed conversion ratio (FCR), residual weight gain (RG), residual intake and body weight gain (RIG), and residual feed intake (RFI)] as well as growth [final BW, average daily gain (ADG), and DM intake (DMI)], and carcass [rib-eye area (REA), backfat thickness (BF), intramuscular fat score, and carcass conformation score] traits were included in the study. After data editing, records from 1 393 heifers obtained between 2009 and 2018 were used for the analyses. We fitted an animal model that included contemporary group (animals from the same farm that were evaluated in the same test season) as the fixed effect, and a linear effect of animal age at the beginning of the test as a covariate; in addition to random direct additive genetic and residual effects. The (co)variance components were estimated by Bayesian inference in uni- and bivariate analyses. Our results showed that feed efficiency indicators derived from residual variables such as RG, RIG, and RFI can be improved through genetic selection (h² = 0.14 ± 0.06, 0.13 ± 0.06, and 0.20 ± 0.08, respectively). Variables calculated as ratios such as G:F and FCR were more influenced by environmental factors (h² = 0.08 ± 0.05 and 0.09 ± 0.05), and were, therefore, less suitable for use in breeding programs. The traits with the greatest and impact on genetic progress in feed efficiency were ADG, REA, and BF. The traits with the greatest and least impact on growth and carcass traits were RG and RFI, respectively. Selection for feed efficiency will result in distinct overall effects on the growth and carcass traits of Senepol heifers. Direct selection for lower RFI may reduce DMI and increase carcass fatness at the finishing stage, but it might also result in reduced growth and muscle deposition. Residual BW gain is associated with the highest weight gain and zero impact on REA and BF, however, it is linked to higher feed consumption. Thus, the most suitable feed efficiency indicator was RIG, as it promoted the greatest decrease in feed intake concomitant with faster growth, with a similar impact on carcass traits when compared to the other feed efficiency indicators.     

Phenotypic and genetic relationships between growth and feed intake curves and feed efficiency and amino acid requirements in the growing pig

Improvement of feed efficiency in pigs has been achieved essentially by increasing lean growth rate, which resulted in lower feed intake (FI). The objective was to evaluate the impact of strategies for improving feed efficiency on the dynamics of FI and growth in growing pigs to revisit nutrient recommendations and strategies for feed efficiency improvement. In 2010, three BWs, at 35±2, 63±9 and 107±7 kg, and daily FI during this period were recorded in three French test stations on 379 Large White and 327 French Landrace from maternal pig populations and 215 Large White from a sire population. Individual growth and FI model parameters were obtained with the InraPorc^R software and individual nutrient requirements were computed. The model parameters were explored according to feed efficiency as measured by residual feed intake (RFI) or feed conversion ratio (FCR). Animals were separated in groups of better feed efficiency (RFI⁻ or FCR⁻), medium feed efficiency and poor feed efficiency. Second, genetic relationships between feed efficiency and model parameters were estimated. Despite similar average daily gains (ADG) during the test for all RFI groups, RFI⁻ pigs had a lower initial growth rate and a higher final growth rate compared with other pigs. The same initial growth rate was found for all FCR groups, but FCR⁻ pigs had significantly higher final growth rates than other pigs, resulting in significantly different ADG. Dynamic of FI also differed between RFI or FCR groups. The calculated digestible lysine requirements, expressed in g/MJ net energy (NE), showed the same trends for RFI or FCR groups: the average requirements for the 25% most efficient animals were 13% higher than that of the 25% least efficient animals during the whole test, reaching 0.90 to 0.95 g/MJ NE at the beginning of the test, which is slightly greater than usual feed recommendations for growing pigs. Model parameters were moderately heritable (0.30±0.13 to 0.56±0.13), except for the precocity of growth (0.06±0.08). The parameter representing the quantity of feed at 50 kg BW showed a relatively high genetic correlation with RFI (0.49±0.14), and average protein deposition between 35 and 110 kg had the highest correlation with FCR (−0.76±0.08). Thus, growth and FI dynamics may be envisaged as breeding tools to improve feed efficiency. Furthermore, improvement of feed efficiency should be envisaged jointly with new feeding strategies.     

Genomic dissection and prediction of feed intake and residual feed intake traits using a longitudinal model in F2 chickens

Feed efficiency traits (FETs) are important economic indicators in poultry production. Because feed intake (FI) is a time-dependent variable, longitudinal models can provide insights into the genetic basis of FET variation over time. It is expected that the application of longitudinal models as part of genome-wide association (GWA) and genomic selection (i.e. genome-wide selection (GS)) studies will lead to an increase in accuracy of selection. Thus, the objectives of this study were to evaluate the accuracy of estimated breeding values (EBVs) based on pedigree as well as high-density single nucleotide polymorphism (SNP) genotypes, and to conduct a GWA study on longitudinal FI and residual feed intake (RFI) in a total of 312 chickens with phenotype and genotype in the F₂ population. The GWA and GS studies reported in this paper were conducted using β-spline random regression models for FI and RFI traits in a chicken F₂ population, with FI and BW recorded for each bird weekly between 2 and 10 weeks of age. A single SNP regression approach was used on spline coefficients for weekly FI and RFI traits, with results showing that two significant SNPs for FI occur in the synuclein (SNCAIP) gene. Results also show that these regions are significantly associated with the spline coefficients (q₂) for 5- and 6-week-old birds, while GWA study results showed no SNP association with RFI in F₂ chickens. Estimated breeding value predictions obtained using a pedigree-based best linear unbiased prediction (ABLUP) model were then compared with predictions based on genomic best linear unbiased prediction (GBLUP). The accuracy was measured as correlation between genomic EBV and EBV with the phenotypic value corrected for fixed effects divided by the square root of heritability. The regression of observed on predicted values was used to estimate bias of methods. Results show that prediction accuracies using GBLUP and ABLUP for the FI measured from 2^nd to 10^th week were between 0.06 and 0.46 and 0.03 and 0.37, respectively. These results demonstrate that genomic methods are able to increase the accuracy of predicted breeding values at later ages on the basis of both traits, and indicate that use of a longitudinal model can improve selection accuracy for the trajectory of traits in F₂ chickens when compared with conventional methods.     

Impact of including growth,carcass and feed efficiency traits in the breeding goal for combined milk and beef production systems

Improving feed efficiency in dairy cattle could result in more profitable and environmentally sustainable dairy production through lowering feed costs and emissions from dairy farming. In addition, beef production based on dairy herds generates fewer greenhouse gas emissions per unit of meat output than beef production from suckler cow systems. Different scenarios were used to assess the profitability of adding traits, excluded from the current selection index for Finnish Ayrshire, to the breeding goal for combined dairy and beef production systems. The additional breeding goal traits were growth traits (average daily gain of animals in the fattening and rearing periods), carcass traits (fat covering, fleshiness and dressing percentage), mature live weight (LW) of cows and residual feed intake (RFI) traits. A breeding scheme was modeled for Finnish Ayrshire under the current market situation in Finland using the deterministic simulation software ZPLAN+. With the economic values derived for the current production system, the inclusion of growth and carcass traits, while preventing LW increase generated the highest improvement in the discounted profit of the breeding program (3.7%), followed by the scenario where all additional traits were included simultaneously (5.1%). The use of a selection index that included growth and carcass traits excluding LW, increased the profit (0.8%), but reduced the benefits resulted from breeding for beef traits together with LW. A moderate decrease in the profit of the breeding program was obtained when adding only LW to the breeding goal (−3.1%), whereas, adding only RFI traits to the breeding goal resulted in a minor increase in the profit (1.4%). Including beef traits with LW in the breeding goal showed to be the most potential option to improve the profitability of the combined dairy and beef production systems and would also enable a higher rate of self-sufficiency in beef. When considering feed efficiency related traits, the inclusion of LW traits in the breeding goal that includes growth and carcass traits could be more profitable than the inclusion of RFI, because the marginal costs of measuring LW can be expected to be lower than for RFI and it is readily available for selection. In addition, before RFI can be implemented as a breeding objective, the genetic correlations between RFI and other breeding goal traits estimated for the studied population as well as information on the most suitable indicator traits for RFI are needed to assess more carefully the consequences of selecting for RFI.