Can greenhouse gases in breath be used to genetically improve feed efficiency of dairy cows?

This study was conducted to estimate the genetic parameters of different feeding pattern traits, including average daily feed intake (ADFI), average occupation time per day (AOTD), average occupation time per visit (AOTV), average daily feeding rate (ADFR), average feeding rate per feeding visit (AFRV), average feed intake per feeding visit (AFIV), and average number of visits per day (ANVD), and their genetic relationship to production traits, such as on-test average daily gain (ADG), backfat thickness (BFT), loin muscle area (LMA), lean percentage (LP), and feed efficiency traits, such as feed conversion ratio (FCR) and five measures of residual feed intake (RFI1 to RFI5), in Duroc pigs (DD). The non-heritable common spatial pen effect was also estimated in all studied traits. The feeding pattern traits used in this study were derived from filtered feeding visits of 602 DD pigs. Using three animal models and the REML method, the genetic parameters revealed low to moderate heritability for ADFI (0.19 to 0.32) and AFIV (0.18 to 0.33), moderate heritability for ANVD (0.28 to 0.35) and AOTV (0.21 to 0.31), and high heritability for AOTD (0.73), ADFR (0.62 to 0.64), and AFRV (0.59 to 0.63). The addition of a common spatial pen effect in models 2 and 3 had a substantial impact, ranging from 8% to 23%, on the total variability of most feeding pattern traits, with the exception of AOTD, which only had a percentage variance of 0.30% due to the pen effect. The genetic and phenotypic correlation revealed that ADFI had consistent moderate to high genetic and phenotypic correlation with production and feed efficiency (FE) traits. However, selection against ADFI would negatively affect on-test ADG. Interestingly, the AOTD had no genetic correlation with ADG (0.04), low to moderate positive genetic correlation with FCR (0.27) and all RFI measures (0.24 to 0.33), and moderate negative correlation with LP (−0.39), indicating that selection for DD pigs with lower AOTD would not influence on-test ADG but may increase LP and improve feed efficiency by lowering FCR and all RFI measures. However, the corresponding phenotypic correlation of AOTD with production and feed efficiency traits was mostly weak, which can be attributed to the low residual or environmental correlation between these correlated traits. At the genetic level, the feeding pattern traits showed potential in improving feed efficiency and production traits. However, further studies are needed to evaluate their impact at phenotypic level.

This study aimed at estimating genetic and phenotypic relationships among feed efficiency, immune and production traits measured pre- (9–20 weeks of age) and post- (12 weeks from on-set of lay) maturity. Production traits were average daily gain (ADG) and average daily feed-intake (ADFI1) in the pre-maturity period and age at first egg (AFE), average daily feed-intake (ADFI2) and average daily egg mass (EM) in the post-maturity period. Feed efficiency comprised of residual feed intake (RFI) estimated in both periods. Natural antibodies binding to keyhole limpet hemocyanin (KLH-IgM) and specific antibodies binding to Newcastle disease virus (NDV-IgG) measured at 16 and 28 weeks of age represented immune traits pre- and post-maturity, respectively. In the growing period, 1,820 records on ADG, KLH-IgM and NDV-IgG, and 1,559 records on ADFI1 and RFI were available for analyses. In the laying period, 1,340 records on AFE, EM, KLH-IgM and NDV-IgG, and 1,288 records on ADFI2 and RFI were used in the analyses. Bi-variate animal mixed model was fitted to estimate (co)variance components, heritability and correlations among the traits. The model constituted sex, population, generation, line and genotype as fixed effects, and animal and residual effects as random variables. During the growing period, moderate to high heritability (0.36–0.68) was estimated for the production traits and RFI while the antibody traits had low (0.10–0.22) heritability estimates. Post-maturity, the production traits and RFI were moderately (0.30–0.37) heritable while moderate to high (0.25–0.41) heritability was estimated for the antibody traits. Genetic correlations between feed efficiency and production traits in both periods showed that RFI had negative genetic correlations with ADG (−0.47) and EM (−0.56) but was positively correlated with ADFI1 (0.60), ADFI2 (0.74) and AFE (0.35). Among immune and production traits, KLH-IgM and NDV-IgG had negative genetic correlations with ADG (−0.22; −0.56), AFE (−0.39; −0.42) and EM (−0.35; −0.16) but were positively correlated with ADFI1 (0.41; 0.34) and ADFI2 (0.47; 0.52). Genetic correlations between RFI with KLH-IgM (0.62; 0.33) and NDV-IgG (0.58; 0.50) were positive in both production periods. Feed intake, RFI and antibody traits measured in both production periods were positively correlated with estimates ranging from 0.48 to 0.82. Results from this study indicate selection possibilities to improve production, feed efficiency and immune-competence in indigenous chicken. The genetic correlations suggest that improved feed efficiency would be associated with high growth rates, early maturing chicken, high egg mass and reduced feed intake. In contrast, improved general (KLH-IgM) and specific (NDV-IgG) immunity would result in lower growth rates and egg mass but associated with early sexual maturation and high feed intake. Unfavorable genetic correlations between feed efficiency and immune traits imply that chicken of higher productivity and antibody levels will consume more feed to support both functions. These associations indicate that selective breeding for feed efficiency and immune-competence may have genetic consequences on production traits and should therefore be accounted for in indigenous chicken improvement programs

The efficiency of producing salable products in the pork industry is largely determined by costs associated with feed and by the amount and quality of lean meat produced. The objectives of this paper were 1) to explore heritability and genetic correlations for growth, feed efficiency, and real-time ultrasound traits using both pedigree and marker information and 2) to assess accuracy of genomic prediction for those traits using Bayes A prediction models in a Duroc terminal sire population. Body weight at birth (BW at birth) and weaning (BW at weaning) and real-time ultrasound traits, including back fat thickness (BF), muscle depth (MD), and intramuscular fat content (IMF), were collected on the basis of farm protocol. Individual feed intake and serial BW records of 1,563 boars obtained from feed intake recording equipment (FIRE; Osborne Industries Inc., Osborne, KS) were edited to obtain growth, feed intake, and feed efficiency traits, including ADG, ADFI, feed conversion ratio (FCR), and residual feed intake (RFI). Correspondingly, 1,047 boars were genotyped using the Illumina PorcineSNP60 BeadChip. The remaining 516 boars, as an independent sample, were genotyped with a low-density GGP-Porcine BeadChip and imputed to 60K. Magnitudes of heritability from pedigree analysis were moderate for growth, feed intake, and ultrasound traits (ranging from 0.44 ± 0.11 for ADG to 0.58 ± 0.09 for BF); heritability estimates were 0.32 ± 0.09 for FCR but only 0.10 ± 0.05 for RFI. Comparatively, heritability estimates using marker information by Bayes A models were about half of those from pedigree analysis, suggesting "missing heritability." Moderate positive genetic correlations between growth and feed intake (0.32 ± 0.05) and back fat (0.22 ± 0.04), as well as negative genetic correlations between growth and feed efficiency traits (-0.21 ± 0.08, -0.05 ± 0.07), indicate selection solely on growth traits may lead to an undesirable increase in feed intake, back fat, and reduced feed efficiency. Genetic correlations among growth, feed intake, and FCR assessed by a multiple-trait Bayes A model resulted in increased genetic correlation between ADG and ADFI, a negative correlation between ADFI and FCR, and a positive correlation between ADG and FCR. Accuracies of genomic prediction for the traits investigated, ranging from 9.4% for RFI to 36.5% for BF, were reported that might provide new insight into pig breeding and future selection programs using genomic information.

Recently, automatic feeders have become popular for collecting daily feed intake data in the pig industry, making it possible to evaluate genetic effects on feed efficiency and resilience traits, expressed as day-to-day fluctuations in feeding records. This study aimed to understand the influence of genetic factors on feed efficiency traits, including residual intake and BW gain (RIG), and resilience traits, as well as to compare the differences in genetic parameter estimates among three purebred pig breeds. A total of 6 103 pigs from three breeds (Large White: 1 193 pigs, Landrace: 3 010 pigs, and Duroc: 1 900 pigs) were raised in a specific pathogen-free environment. The growth and feed intake records during the testing period were obtained using automatic feeders, and the average daily gain (ADG) and average feed intake (AFI) were calculated. Feed conversion ratio (FCR), residual feed intake (RFI), residual gain, and RIG were calculated as feed efficiency traits, and the log-transformed variance of deviation for the daily feed intake (LnVar_FI), daily occupation time (LnVar_OC), and the daily number of visits to the feeder (LnVar_VT) was calculated as resilience traits. After estimating the genetic parameters for each breed, a meta-analysis was performed to obtain the weighted mean of heritability estimates (hm2) and genetic correlation estimates (GCm) for the three breeds. The hm2 were moderate and ranged from 0.31 to 0.39 for feed efficiency traits and 0.31 to 0.40 for resilience traits, and there were no significant differences in heritability estimates among the three breeds except for AFI, RFI, and RIG. For feed efficiency traits, the FCR and RIG showed favourably moderate GCm with AFI (0.29 and −0.33, respectively) and ADG (−0.39 and 0.31, respectively). For resilience traits, the LnVar_FI and LnVar_VT showed favourably low to moderate GCm with FCR (0.33 and 0.28, respectively) and RIG (−0.37 and 0.28, respectively), and there were no genetic relationships of LnVar_OC with FCR and RIG (the absolute value of GCm was 0.01). There was no significant difference in the genetic correlation estimates among the three breeds for feed efficiency and resilience traits. Our results suggest that feed efficiency and resilience traits were heritable, and resilience traits showed favourable or no genetic correlation with feed efficiency traits. In addition, the influence of genetic factors on feed efficiency and resilience traits could be the same among breeds.

Improving feed efficiency is of interest to French beef producers so as to increase their profitability. To enable this improvement through selection, genetic correlations with production traits need to be quantified. The objective of this study was to estimate the genetic parameters for growth, feed efficiency (FE), and slaughter performance of young beef bulls of the French Charolais breed. Three feed efficiency criteria were calculated: residual feed intake (RFI), residual gain (RG), and ratio of FE. Data on feed intake, growth, and FE were available for 4,675 Charolais bulls tested in performance test stations and fed with pelleted diet. Between 1985 and 1989, 60 among 510 of these bulls were selected to procreate one generation of 1,477 progeny bulls which received the same pelleted diet at the experimental farm in Bourges. In addition to feed intake, growth, and FE traits, these terminal bulls also had slaughter traits of carcass yield, carcass composition, and weight of visceral organs collected. Genetic parameters were estimated using linear mixed animal models. Between performance test bulls and terminal bulls, the genetic correlation of RFI was 0.80 ± 0.18; it was 0.70 ± 0.21 for RG and 0.46 ± 0.20 for FE. For carcass traits, RFI was negatively correlated with carcass yield (-0.18 ± 0.14) and muscle content (-0.47 ± 0.14) and positively with fat content (0.48 ± 0.13). Conversely, RG and FE were positively correlated with carcass yield and muscle content and negatively with fat content. For the three FE criteria, efficient animals had leaner carcass. For visceral organs (as a proportion of empty body weight), RFI was genetically correlated with the proportions of the 5th quarter (0.51 ± 0.17), internal fat (0.36 ± 0.14), abomasum (0.46 ± 0.20), intestines (0.38 ± 0.17), liver (0.36 ± 0.16), and kidneys (0.73 ± 0.11). Conversely, RG and FE were negatively associated with these traits. The high-energy expenditure associated with the high-protein turnover in visceral organs may explain this opposite relationship between FE and the proportion of visceral organs. Selection for final weight and RFI increased growth and FE in progeny, and also improved carcass yield and muscle content in the carcass. To conclude, determinations of growth and feed intake in performance test stations are effective to select bulls to improve their growth, FE, and muscle content in carcass.

Improved feed efficiency is an essential goal for the sustainability of pig production in economic and environmental terms. Traits such as feed conversion rate (FCR), residual feed intake (RFI), residual body weight gain (RG) and feeding behaviour, such as duration (TPV) and feeding rate per visit (FR) can now be measured by automatic feeding systems. The aim of this study was to evaluate the benefits of incorporating feeding behaviour traits into a selection index to improve feed efficiency in a nucleus of purebred Pietrain pigs. Data on body weight, feed intake and duration were recorded at each visit in 1608 animals. The information contained in 843,605 visits was grouped by animal ID to obtain a set of feed efficiency and feeding behaviour traits. These traits were obtained in three periods (first, second and total period). Bayesian models were built to estimate the posterior marginal distribution of the variance components. The heritabilities were between 0.44 and 0.59 for feeding behaviour traits and between 0.31 and 0.49 for feed efficiency traits. The FCR and RFI showed a considerable genetic correlation with daily feed intake (~0.65). FCR showed a genetic correlation with feeding behaviour traits, such as feed intake per visit (FPV) (0.44) and FR (0.33). Furthermore, the fast-eating pigs were less efficient. This was due to the positive genetic correlation found between the FR and the FCR (0.33) and the RFI (0.23), and the negative correlation found with the RG (-0.28). On the other hand, the inclusion of the feeding behaviour traits into a selection index slightly increased the selection response for FCR (4%) and RFI (1.8%). However, there was an increase of up to 19% in the selection response for RG and an improvement in accuracy from 0.59 to 0.70. Therefore, we concluded that it would be interesting to include feeding behaviour traits in a selection index to improve the selection response and accuracy of feed efficiency traits.

In this study, we examined genetic parameters for feed efficiency, growth, and carcass traits in Japanese Shorthorn cattle, based on 714 performance tests and 15,790 field carcass records. Feed efficiency traits, including residual feed intake (RFI) and residual body weight gain (RG), were calculated. Single-trait and two-trait animal models were used to estimate heritability and genetic correlations. Heritability estimates for feed efficiency traits were found to be low to moderate (ranging from 0.03 to 0.36); notably, heritability was moderate for RG and low for RFI. Estimates for genetic correlations between feed efficiency traits and average daily gain (DG) were favorably moderate to high (absolute values of 0.43-0.85), and those with daily feed intake were low (absolute values of 0.00-0.32). We also estimated a high genetic correlation between RG and DG. The backfat thickness (BF) of bull calves showed favorable or no genetic correlation estimates with feed efficiency and growth traits, whereas RG and BF showed favorable or no genetic correlation estimates with carcass traits. Our findings indicate that genetic improvements in both feed utilization ability and carcass traits could be achieved by utilizing RG and BF in Japanese Shorthorn cattle.

This review article is devoted to the use of feed efficiency traits in dairy cattle breeding. An efficient cow is defined as the one that produces the same amount of milk and milk solids while consuming less feed and remaining healthy and fertile; thus, allowing to reduce costs without decrease in production. Improving feed efficiency is economically important due to the increasing price of fodder. 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. Nowadays genetic evaluation of dairy cattle for feed efficiency is routinely conducted in several countries, including Australia, USA, Canada, Netherlands, Denmark, Sweden, Finland, Norway and United Kingdom. Different countries use different measures of feed efficiency of dairy cows. The main feed efficiency traits are dry matter intake, gross feed efficiency, residual feed intake, energy balance and feed saved. Genome-wide association studies demonstrated that feed efficiency in polygenic trait. Nevertheless, several genes with large effects on feed efficiency were identified. Estimates of heritability of these traits vary from 0.07 to 0.49 and show the presence of considerable genetic variation of these traits and therefore, the possibility of their genetic improvement under the conditions of inclusion in breeding programs. Changes in diet and rumen microbiome substantially impact feed efficiency of dairy cows. Feed efficiency is related to methane emissions and excess nitrogen excretion. Genetic improvement of feed efficiency requires recording of individual data on feed intake in cows. Such data are limited. Two options exist to solve this problem: use of indirect predictors and genomic prediction. Accuracy of genomic prediction varies from 0.21 to 0.61 across countries. International cooperative pro­jects such as Efficient Dairy Genome Project in Canada were launched to establish large databases and to increase accuracy of feed efficiency traits genomic prediction. Future directions of research are the use of novel technologies: mid-infrared spectroscopy, artificial intelligence, holo-omics.

This study aimed to estimate genetic parameters, including genomic data, for feeding behavior, feed efficiency, and growth traits in Nellore cattle. The following feeding behavior traits were studied (861 animals with records): time spent at the feed bunk (TF), duration of one feeding event (FD), frequency of visits to the bunk (FF), feeding rate (FR), and dry matter intake (DMI) per visit (DMIv). The feed efficiency traits (1,543 animals with records) included residual feed intake (RFI), residual weight gain (RWG), and feed conversion (FC). The growth traits studied were average daily gain (ADG, n = 1,543 animals) and selection (postweaning) weight (WSel, n = 9,549 animals). The (co)variance components were estimated by the maximum restricted likelihood method, fitting animal models that did (single-step genomic best linear unbiased prediction) or did not include (best linear unbiased prediction) genomic information in two-trait analyses. The direct responses to selection were calculated for the feed efficiency traits, ADG, and WSel, as well as the correlated responses in feed efficiency and growth by direct selection for shorter TF. The estimated heritabilities were 0.51 ± 0.06, 0.35 ± 0.06, 0.27 ± 0.07, 0.34 ± 0.06, and 0.33 ± 0.06 for TF, FD, FF, FR, and DMIv, respectively. In general, TF and FD showed positive genetic correlations with all feed efficiency traits (RFI, RWG, and FC), ADG, DMI, and WSel. Additionally, TF showed high and positive genetic and phenotypic correlations with RFI (0.71 ± 0.10 and 0.46 ± 0.02, respectively) and DMI (0.56 ± 0.09 and 0.48 ± 0.03), and medium to weak genetic correlations with growth (0.32 ± 0.11 with ADG and 0.14 ± 0.09 with WSel). The results suggest that TF is a strong indicator trait of feed efficiency, which exhibits high heritability and a weak positive genetic correlation with growth. In a context of a selection index, the inclusion of TF to select animals for shorter TF may accelerate the genetic gain in feed efficiency by reducing RFI but with zero or slightly negative genetic gain in growth traits.

Simple SummaryGenetic improvements in feed efficiency (FE) and related traits could considerably reduce pig production costs and energy consumption. Thus, we performed a genetic parameter estimation and genome-wide association study of four FE and FE-related traits, namely, average daily feed intake, average daily gain, the feed conversion ratio, and residual feed intake, of two pig breeds, Yorkshire and Duroc. The results demonstrate the genetic relationships of FE and FE-related traits with two growth traits, age and backfat thickness at 100 kg. We also identified many single-nucleotide polymorphisms (SNPs) and novel candidate genes related to these traits. In addition, we found many pathways significantly associated with FE and FE-related traits, and they are generally involved in digestive and metabolic processes. The results of this study are expected to provide a valuable reference for the genomic selection of FE and FE-related traits in pigs.Feed efficiency (FE) traits are key factors that can influence the economic benefits of pig production. However, little is known about the genetic architecture of FE and FE-related traits. This study aimed to identify SNPs and candidate genes associated with FE and FE-related traits, namely, average daily feed intake (ADFI), average daily gain (ADG), the feed conversion ratio (FCR), and residual feed intake (RFI). The phenotypes of 5823 boars with genotyped data (50 K BeadChip) from 1365 boars from a nucleus farm were used to perform a genome-wide association study (GWAS) of two breeds, Duroc and Yorkshire. Moreover, we performed a genetic parameter estimation for four FE and FE-related traits. The heritabilities of the FE and FE-related traits ranged from 0.13 to 0.36, and there were significant genetic correlations (−0.69 to 0.52) of the FE and FE-related traits with two growth traits (age at 100 kg and backfat thickness at 100 kg). A total of 61 significant SNPs located on eight different chromosomes associated with the four FE and FE-related traits were identified. We further identified four regions associated with FE and FE-related traits that have not been previously reported, and they may be potential novel QTLs for FE. Considering their biological functions, we finally identified 35 candidate genes relevant for FE and FE-related traits, such as the widely reported MC4R and INSR genes. A gene enrichment analysis showed that FE and FE-related traits were highly enriched in the biosynthesis, digestion, and metabolism of biomolecules. This study deepens our understanding of the genetic mechanisms of FE in pigs and provides valuable information for using marker-assisted selection in pigs to improve FE.

Production and feed intake data on 36 115 first lactation Holstein cows obtained from Quebec Dairy Herd Analysis Service were combined with conformation data from the Holstein Association of Canada to estimate genetic correlations among production, energy intake, and conformation traits. Traits considered were 305-d milk yield, 305-d grain energy and total energy intake, feed efficiency (fat corrected milk yield/total energy intake), body weight at calving, capacity, size, stature, rump width and final score. Genetic and phenotypic parameters were estimated using Restricted Maximum Likelihood based on two-trait animal mixed model analyses. The model contained fixed effects of herd-year, season of calving, age of calving, sire group and a random animal genetic effect. Estimates of heritability were within the published range for all traits. Of the conformation traits examined, capacity, size and stature had the highest correlations with body weight, with phenotypic correlations between 0.36 and 0.43, and genetic correlations between 0.61 and 0.79. Feed efficiency was negatively correlated to all body size measures, both phenotypically (−0.01 to −0.29) and genetically (−0.31 to −0.53), but most significantly with body weight, capacity, size, and stature. Fat-corrected milk yield showed negligible phenotypic and low to moderately negative genetic (−0.07 to −0.29) correlations with body weight and related type traits. Total energy intake was positively related to all measures of body size, most notably body weight, while grain energy intake had moderately negative genetic correlations (−0.20 to −0.40) with the same body size traits. Because of their detrimental relationships with feed efficiency, negative selection emphasis should be placed on body weight and the related type traits capacity, size and stature. Capacity, size and stature are of moderate utility when selecting indirectly for body weight, total energy intake and feed efficiency. Key words: Dairy cattle, genetics, production, conformation, feed efficiency

Records on 1,180 young Angus bulls and heifers involved in performance tests were used to estimate genetic and phenotypic parameters for feed intake, feed efficiency, and other postweaning traits. The mean age was 268 d at the start of the performance test, which comprised 21-d adjustment and 70-d test periods. Traits studied included 200-d weight, 400-d weight, scrotal circumference, ultrasonic measurements of rib and rump fat depths and longissimus muscle area, ADG, metabolic weight, daily feed intake, feed conversion ratio, and residual feed intake. For all traits except the last five, additional data from the Angus Society ofAustralia pedigree and performance database were included, which increased the number of animals to 27,229. Genetic (co)variances were estimated by REML using animal models. Direct heritability estimates for 200-d weight, 400-d weight, rib fat depth, ADG, feed conversion,and residual feed intake were 0.17 +/- 0.03, 0.27 +/- 0.03, 0.35 +/- 0.04, 0.28 +/- 0.04, 0.29 +/- 0.04, and 0.39 +/- 0.03, respectively. Feed conversion ratio was genetically (r(g) = 0.66 ) and phenotypically (r(p) = 0.53) correlated with residual feed intake. Feed conversion ratio was correlated (r(g) = -0.62, r(p) = -0.74) with ADG, whereas residual feed intake was not (rg = -0.04, r(p) = -0.06). Genetically, both residual feed intake and feed conversion ratio were negatively correlated with direct effects of 200-d weight (r(g) = -0.45 and -0.21) and 400-d weight (r(g) = -0.26 and -0.09). The correlations between the remaining traits and the feed efficiency traits were near zero, except between feed intake and feed conversion ratio (r(g) = 0.31, r(p) = 0.23), feed intake and residual feed intake (r(g) = 0.69, r(p) = 0.72), and rib fat depth and residual feed intake (r(g) = 0.17, r(p) = 0.14). These results indicate that genetic improvement in feed efficiency can be achieved through selection and, in general, correlated responses in growth and the other postweaning traits will be minimal.

Genetic and phenotypic correlations among feed efficiency, production and selected conformation traits in dairy cows

The objective of the present study was to comprehensively evaluate whether body measurement traits, including body weight and body size, could be used as indicators of genetic selection for feed efficiency and carcass traits in Japanese Black steers. First, we estimated the genetic parameters for body measurements, feed efficiency, and carcass traits. Second, we estimated the correlated responses in feed efficiency and carcass traits when selection was applied to one or multiple-body measurement traits. In total, 4,578 Japanese Black steers with phenotypic values of residual feed intake (RFI) and residual body weight gain (RG) as feed efficiency traits and carcass weight (CWT) and beef marbling standard (BMS) as carcass traits were used. Eleven body measurement traits were measured at the start and finish of the fattening periods (BMT1 and BMT2, respectively), and their growth during the fattening period (BMT3) was used for genetic analyses. The results of genetic parameters showed that the heritability estimates were low to moderate (0.10 to 0.66), and the genetic correlations among body measurement traits were also estimated to be positively moderate to high in each measuring point (0.23 to 0.99). The genetic correlations of body measurement traits with RFI and BMS were estimated to be low (-0.14 to 0.30 and -0.17 to 0.35, respectively), but those with CWT were positively low to high (0.12 to 0.97). The genetic correlation estimates between BMT3 and RG were moderate to high (0.38 to 0.78). Second, correlated responses were estimated under positive selection for body measurement traits. Positive selection for BMT2 and BMT3 increased CWT and RG; however, positive selection for body measurement traits resulted in no change in RFI and BMS. Favorable directions of genetic gains, which were positive for RG, CWT, and BMS and negative for RFI, were obtained by selection indices, including multiple traits in BMT1. Our results suggest that using only one-body measurement trait as an indicator of genetic selection for RFI is difficult. However, body measurement traits can be indirect indicators of improved RG. Our results also suggest that genetic improvement of both RFI and RG without reducing CWT and BMS could be achieved using selection indices that account for a balance of body conformation using multiple-body measurement traits in Japanese Black cattle.

Feed cost is the largest expense of mink production systems, and, therefore, improvement of feed efficiency (FE) through selection for high feed-efficient mink is a practical way to increase the mink industry's sustainability. In this study, we estimated the heritability, phenotypic, and genetic correlations for different FE measures and component traits, including harvest weight (HW), harvest length (HL), final body length (FBL), final body weight (FBW), average daily gain (ADG), daily feed intake (DFI), feed conversion ratio (FCR), residual feed intake (RFI), residual gain (RG), residual intake and gain (RIG), and Kleiber ratio (KR), using data from 2,288 American mink (for HW and HL), and 1,038 to 1,906 American mink (for other traits). Significance (P < 0.05) of fixed effects (farm, sex, and color type), a covariate (age of animal), and random effects (additive genetic, maternal, and common litter) were evaluated through univariate models implemented in ASReml-R version 4. Genetic parameters were estimated via fitting a set of bivariate models using ASReml-R version 4. Estimates of heritabilities (±SE) were 0.28 ± 0.06, 0.23 ± 0.06, 0.28 ± 0.10, 0.27 ± 0.11, 0.25 ± 0.09, 0.26 ± 0.09, 0.20 ± 0.09, 0.23 ± 0.09, 0.21 ± 0.10, 0.25 ± 0.10, and 0.26 ± 0.10 for HW, HL, FBL, FBW, ADG, DFI, FCR, RFI, RG, RIG, and KR, respectively. RIG had favorable genetic correlations with DFI (-0.62 ± 0.24) and ADG (0.58 ± 0.21), and nonsignificant (P > 0.05) genetic correlations with FBW (0.14 ± 0.31) and FBL (-0.15 ± 0.31). These results revealed that RIG might be a superior trait as it guarantees reduced feed intake with faster-growing mink yet with no negative impacts on body weight and length. In addition, the strong positive genetic correlations (±SE) between KR with component traits (0.88 ± 0.11 with FBW, 0.68 ± 0.17 with FBL, and 0.97 ± 0.02 with ADG) suggested KR as an applicable indirect measure of FE for improvement of component traits as it did not require the individual feed intake to be measured. Overall, our results confirmed the possibility of including FE traits in mink breeding programs to effectively select feed-efficient animals.