Variance components due to direct genetic, maternal genetic and permanent environmental effect for growth and feed-efficiency traits in young male Japanese Black cattle

Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle

BACKGROUND

Growth rate is an important component of feed conversion efficiency in cattle and varies across the different stages of the finishing period. The metabolic effect of the rumen microbiome is essential for cattle growth, and investigating the genomic and microbial factors that underlie this temporal variation can help maximize feed conversion efficiency at each growth stage.

RESULTS

By analysing longitudinal body weights during the finishing period and genomic and metagenomic data from 359 beef cattle, our study demonstrates that the influence of the host genome on the functional rumen microbiome contributes to the temporal variation in average daily gain (ADG) in different months (ADG1, ADG2, ADG3, ADG4). Five hundred and thirty-three additive log-ratio transformed microbial genes (alr-MG) had non-zero genomic correlations (rg) with at least one ADG-trait (ranging from |0.21| to |0.42|). Only a few alr-MG correlated with more than one ADG-trait, which suggests that a differential host-microbiome determinism underlies ADG at different stages. These alr-MG were involved in ribosomal biosynthesis, energy processes, sulphur and aminoacid metabolism and transport, or lipopolysaccharide signalling, among others. We selected two alternative subsets of 32 alr-MG that had a non-uniform or a uniform rg sign with all the ADG-traits, regardless of the rg magnitude, and used them to develop a microbiome-driven breeding strategy based on alr-MG only, or combined with ADG-traits, which was aimed at shaping the rumen microbiome towards increased ADG at all finishing stages. Combining alr-MG information with ADG records increased prediction accuracy of genomic estimated breeding values (GEBV) by 11 to 22% relative to the direct breeding strategy (using ADG-traits only), whereas using microbiome information, only, achieved lower accuracies (from 7 to 41%). Predicted selection responses varied consistently with accuracies. Restricting alr-MG based on their rg sign (uniform subset) did not yield a gain in the predicted response compared to the non-uniform subset, which is explained by the absence of alr-MG showing non-zero rg at least with more than one of the ADG-traits.

CONCLUSIONS

Our work sheds light on the role of the microbial metabolism in the growth trajectory of beef cattle at the genomic level and provides insights into the potential benefits of using microbiome information in future genomic breeding programs to accurately estimate GEBV and increase ADG at each finishing stage in beef cattle.

Genetic and phenotypic associations of feed efficiency with growth and carcass traits in Australian Angus cattle

Genetic and phenotypic parameters for feed efficiency, growth, and carcass traits for Australian Angus beef cattle were estimated. Growth traits included birth weight (BWT), 200-d weight (200dWT), 400-d weight (400dWT), and 600-d weight (600dWT). Traits associated with feed efficiency were average daily weight gain (ADG), metabolic midweight, average of daily feed intake (FI), feed conversion ratio (FCR), residual feed intake (RFI), and residual gain (RG). Carcass traits involved were carcass eye muscle area (CEMA), carcass intramuscular fat (IMF), subcutaneous fat depths at the 12th/13th rib (CRIB), rump P8 fat depth (P8FAT), and carcass weight (CWT). For growth traits, heritability estimates ranged from 0.14 ± 0.03 for 200dWT to 0.48 ± 0.06 for 600dWT. For feed efficiency traits, direct heritability estimates for FI, FCR, RFI, and RG were 0.55 ± 0.08, 0.20 ± 0.06, 0.40 ± 0.07, and 0.19 ± 0.06, respectively. High heritability estimates were observed for CEMA, IMF, P8FAT, and CWT of 0.52 ± 0.09, 0.61 ± 0.09, 0.55 ± 0.09, and 0.66 ± 0.09, respectively. Strong positive genetic correlations were found for FI with 200dWT, 400dWT, and 600dWT of 0.68 ± 0.09, 0.42 ± 0.11, and 0.61 ± 0.07, respectively. Weak genetic correlations were observed between RFI and growth traits. For carcass traits, genetic correlations between RFI and CEMA, IMF, CRIB, P8FAT, CWT were -0.19 ± 0.14, 0.31 ± 0.14, 0.18 ± 0.16, 0.24 ± 0.13, and 0.40 ± 0.12, respectively. There was a tendency for low to moderate unfavorable genetic associations between feed efficiency traits, evaluated as RFI and RG, with growth and carcass traits. This implies that selection for RFI would have slight negative impacts on growth and reduce carcass quality. To avoid this, it would be necessary to build selection indices to select feed efficient animals without compromising growth and meat quality.

Estimating direct genetic and maternal effects affecting rabbit growth and feed efficiency with a factorial design

The aim of this experiment was to evaluate the significance of neonatal environment on feed efficiency. For that purpose, rabbits from a line selected for residual feed intake (RFI) during 10 generations (G10 kits) were cross-fostered with non-selected control does (i.e., G0 line), and reciprocally. In parallel, sibs were fostered by mothers from their original line. Nine hundred animals were raised in individual (N = 456) or collective (N = 320) cages. Traits analysed in this study were body weight at 32 days and at 63 days, average daily gain (ADG), feed intake between weaning and 63 days (FI), feed conversion ratio (FCR) and RFI. The maternal environment offered by does from the line selected for RFI deteriorated the FCR of the kits, independently of their line of origin, during fattening (+0.08 ± 0.02) compared to FCR of kits nursed by G0 does. The line, the type of housing and the batch were significant effects for all the measured traits: G10 kits were lighter than their G0 counterparts at 32 days (-82.9 ± 9 g, p < 0.0001) and at 63 days (-161 ± 16 g, p < 0.0001). They also had a lower ADG (-2.36 ± 0.36 g/day, p < 0.0001), RFI (-521 ± 24 g/day, p < 0.0001) and a lower FI (-855 ± 31 g, p < 0.0001), resulting in a more desirable feed efficiency (FCR: -0.35 ± 0.02). There was no significant difference in the contrast of G10 and G0 performances between collective and individual/digestive cages (p > 0.22): -2.35 g/day versus 2.94 g/day for ADG, -0.39 versus -0.40 for FCR, -577 g versus -565 g for RFI and -879 g versus -859 g for FI, respectively). Thus, no genotype-by-environment (housing) interaction is expected at the commercial level, that is, no re-ranking of the animals due to collective housing.

Genome-wide association analyses for growth and feed efficiency traits in beef cattle

A genome-wide association study using the Illumina 50K BeadChip included 38,745 SNP on 29 BTA analyzed on 751 animals, including 33 purebreds and 718 crossbred cattle. Genotypes and 6 production traits: birth weight (BWT), weaning weight (WWT), ADG, DMI, midtest metabolic BW (MMWT), and residual feed intake (RFI), were used to estimate effects of individual SNP on the traits. At the genome-wide level false discovery rate (FDR < 10%), 41 and 5 SNP were found significantly associated with BWT and WWT, respectively. Thirty-three of them were located on BTA6. At a less stringent significance level (P < 0.001), 277 and 27 SNP were in association with single traits and multiple traits, respectively. Seventy-three SNP on BTA6 and were mostly associated with BW-related traits, and heavily located around 30 to 50Mb. Markers that significantly affected multiple traits appeared to impact them in same direction. In terms of the size of SNP effect, the significant SNP (P < 0.001) explained between 0.26 and 8.06% of the phenotypic variation in the traits. Pairs of traits with low genetic correlation, such as ADG vs. RFI or DMI vs. BWT, appeared to be controlled by 2 groups of SNP; 1 of them affected the traits in same direction, the other worked in opposite direction. This study provides useful information to further assist the identification of chromosome regions and subsequently genes affecting growth and feed efficiency traits in beef cattle.

Phenotypic and genetic parameters for different measures of feed efficiency in different breeds of Irish performance-tested beef bulls

No genetic parameters for performance and feed efficiency traits are available for Irish performance-tested bulls. The objective of this study was to determine the phenotypic and genetic variation for feed intake, BW, ADG, and measures of feed efficiency including feed conversion ratio (FCR), relative growth rate, Kleiber ratio, residual BW gain (RG), and residual feed intake (RFI). Observations were available on up to 2,605 bulls for each trait from one test station across 24 yr; breeds included in the analyses were Aberdeen Angus (AN), Charolais (CH), Hereford, Limousin (LI), and Simmental. The test period was at least 70 d. Bulls were individually offered concentrates ad libitum, with a restricted forage allowance. Differences in performance and feed efficiency existed among breeds. For example, AN, on average, ate 0.04 kg of DM/d more than CH but had ADG of 0.14 kg/d less over the 70-d test period. Results showed LI and CH were the most efficient breeds when efficiency was defined as FCR or RFI. When animals were partitioned into groups based on high, medium, or low RFI, the low RFI (i.e., most efficient) group were also the more efficient as defined by RG and FCR. The low RFI group had the same ADG as the medium group and a greater ADG (P < 0.01) than the high group (1.67 vs. 1.66 and 1.63 kg/d); yet they ate 0.67 kg of DM/d less (P < 0.001) than the medium RFI group and 1.22 kg of DM/d less (P < 0.001) than the high RFI (i.e., least efficient) group. Genetic parameters for all performance and efficiency measures were estimated across breeds using linear animal mixed models; heritability estimates for feed efficiency traits ranged from 0.28 +/- 0.06 (RG) to 0.45 +/- 0.06 (RFI). An additional series of analyses included a maternal component in the model; maternal heritability estimates for feed efficiency traits ranged from 0.05 +/- 0.03 (RG) to 0.11 +/- 0.05 (relative growth rate). Genetic correlations between most of the different feed efficiency measures were strong. Results from this study indicate significant genetic differences in performance and some measures of feed efficiency among performance-tested beef bulls.

Variation in direct and maternal genetic effects for meat production traits in Egyptian Zaraibi goats

Multi-trait analyses were carried out to quantify the (co)variation in meat production traits in Zaraibi goats. The data were obtained from a research station. There were birth weight records on 6610 kids, of which 5970 and 5237 had also pre-and postweaning gain record, respectively. The kids were progeny of 115 bucks and 1387 does, which had altogether 3603 litter size and milk yield records in different parities and which were daughters of 109 sires and 721 dams. Single-trait analyses were carried out as preliminary to a three-trait (litter size, birth weight, early growth) and five-trait (litter size, milk and growth traits) analyses. The analyses containing birth weight data required the highest number of iteration rounds in estimating the variance components using AI REML. The maternal genetic component was important for the genetic variation of birth weight and preweaning gain. In general, direct heritability was low (0.03-0.12) for growth traits, possibly due to the low-input environment. The estimates on genetic correlation between direct and maternal effects within these traits indicated mostly favourable relationship. Genetic antagonism was found between birth weight and early growth. Heritability (repeatability) for 90-day and total milk yield was 0.16-0.23 and 0.23-0.24 (0.28 and 0.39-0.40), respectively and 0.04-0.05 (0.10-0.11) for litter size. The genetic correlation between 90-day (total) milk yield and litter size was 0.45 (0.22). The correlation between the milk yield and the maternal genetic effects for the preweaning gain was very high (0.94). Selection schemes aiming to improve meat (litter size and growth) and milk production simultaneously are feasible. The increased milk production serves also for the acceleration of early growth in kids.