Genomic prediction ability for feed efficiency traits using different models and pseudo-phenotypes under several validation strategies in Nelore cattle

There is a growing interest to improve feed efficiency (FE) traits in cattle. The genomic selection was proposed to improve these traits since they are difficult and expensive to measure. Up to date, there are scarce studies about the implementation of genomic selection for FE traits in indicine cattle under different scenarios of pseudo-phenotypes, models, and validation strategies on a commercial large scale. Thus, the aim was to evaluate the feasibility of genomic selection implementation for FE traits in Nelore cattle applying different models and pseudo-phenotypes under validation strategies. Phenotypic and genotypic information from 4 329 and 3 467 animals were used, respectively, which were tested for residual feed intake, DM intake, feed efficiency, feed conversion ratio, residual BW gain, and residual intake and BW gain. Six prediction methods were used: single-step genomic best linear unbiased prediction, Bayes A, Bayes B, Bayes Cπ, Bayesian least absolute shrinkage and selection operator (BLASSO), and Bayes R. Phenotypes adjusted for fixed effects (Y*), estimated breeding value (EBV), and EBV deregressed (DEBV) were used as pseudo-phenotypes. The validation approaches used were: (1) random: the data was randomly divided into ten subsets and the validation was done in each subset at a time; (2) age: the partition into training and testing sets was based on year of birth and testing animals were born after 2016; and (3) EBV accuracy: the data was split into two groups, being animals with accuracy above 0.45 the training set; and below 0.45 the validation set. In the analyses that used the Y* as pseudo-phenotype, prediction ability (PA) was obtained by dividing the correlation between pseudo-phenotype and genomic EBV (GEBV) by the square root of the heritability of the trait. When EBV and DEBV were used as the pseudo-phenotype, the simple correlation of this quantity with the GEBV was considered as PA. The prediction methods show similar results for PA and bias. The random cross-validation presented higher PA (0.17) than EBV accuracy (0.14) and age (0.13). The PA was higher for Y* than for EBV and DEBV (30.0 and 34.3%, respectively). Random validation presented the highest PA, being indicated for use in populations composed mainly of young animals and traits with few generations of data recording. For high heritability traits, the validation can be done by age, enabling the prediction of the next-generation genetic merit. These results would support breeders to identify genomic approaches that are more viable for genomic prediction for FE-related traits.

Weighted single-step genome-wide association study and pathway analyses for feed efficiency traits in Nellore cattle

The aim was to conduct a weighted single-step genome-wide association study to detect genomic regions and putative candidate genes related to residual feed intake, dry matter intake, feed efficiency (FE), feed conversion ratio, residual body weight gain, residual intake and weight gain in Nellore cattle. Several protein-coding genes were identified within the genomic regions that explain more than 0.5% of the additive genetic variance for these traits. These genes were associated with insulin, leptin, glucose, protein and lipid metabolisms; energy balance; heat and oxidative stress; bile secretion; satiety; feed behaviour; salivation; digestion; and nutrient absorption. Enrichment analysis revealed functional pathways (p-value < .05) such as neuropeptide signalling (GO:0007218), negative regulation of canonical Wingless/Int-1 (Wnt) signalling (GO:0090090), bitter taste receptor activity (GO:0033038), neuropeptide hormone activity (GO:0005184), bile secretion (bta04976), taste transduction (bta0742) and glucagon signalling pathway (bta04922). The identification of these genes, pathways and their respective functions should contribute to a better understanding of the genetic and physiological mechanisms regulating Nellore FE-related traits.

Heritability and genetic correlations of feed intake, body weight gain, residual gain, and residual feed intake of beef cattle as heifers and cows

The cow herd consumes approximately 70% of the annual feed resources. To date, most genetic evaluations of feed intake in beef cattle have been made in growing animals and little information is available for mature cows. Genetic evaluations in mature cows have predominately been confined to lactating dairy cows and the relationship between feed intake as growing heifers and mature cows has not been addressed. It was the purpose of this study to estimate the heritability of feed intake when measured as growing heifers and mature cows and determine the genetic correlation between these measurements. Individual feed intake and BW gain were measured on 687 heifers and 622 5-yr-old cows. The heritability of average daily DMI (ADDMI) estimated in heifers was 0.84 ± 0.12 and 0.53 ± 0.12 in cows. The heritability of ADG estimated in heifers was 0.53 ± 0.12 and 0.34 ± 0.11 in cows. The genetic correlation between heifer and cow ADDMI was 0.84 ± 0.09. The genetic correlation between heifer and cow ADG was 0.73 ± 019. Heritability of residual feed intake in heifers was 0.25 ± 0.11 and 0.16 ± 0.10 in cows. Heritability for residual gain in heifers was 0.21 ± 0.11 and 0.14 ± 0.10 in cows. Feed intake and ADG are heritable and genetically correlated between heifers and cows. Selection for decreased feed intake and ADG in growing animals will probably have the same directional effects on mature cows.

Genome-wide association analysis of feed intake and residual feed intake in Nellore cattle

Background

Feed intake plays an important economic role in beef cattle, and is related with feed efficiency, weight gain and carcass traits. However, the phenotypes collected for dry matter intake and feed efficiency are scarce when compared with other measures such as weight gain and carcass traits. The use of genomic information can improve the power of inference of studies on these measures, identifying genomic regions that affect these phenotypes. This work performed the genome-wide association study (GWAS) for dry matter intake (DMI) and residual feed intake (RFI) of 720 Nellore cattle (Bos taurus indicus).

Results

In general, no genomic region extremely associated with both phenotypic traits was observed, as expected for the variables that have their regulation controlled by many genes. Three SNPs surpassed the threshold for the Bonferroni multiple test for DMI and two SNPs for RFI. These markers are located on chromosomes 4, 8, 14 and 21 in regions near genes regulating appetite and ion transport and close to important QTL as previously reported to RFI and DMI, thus corroborating the literature that points these two processes as important in the physiological regulation of intake and feed efficiency.

Conclusions

This study showed the first GWAS of DMI to identify genomic regions associated with feed intake and efficiency in Nellore cattle. Some genes and QTLs previously described for DMI and RFI, in other subspecies (Bos taurus taurus), that influences these phenotypes are confirmed in this study.

Single nucleotide polymorphisms for feed efficiency and performance in crossbred beef cattle

Background

This study was conducted to: (1) identify new SNPs for residual feed intake (RFI) and performance traits within candidate genes identified in a genome wide association study (GWAS); (2) estimate the proportion of variation in RFI explained by the detected SNPs; (3) estimate the effects of detected SNPs on carcass traits to avoid undesirable correlated effects on these economically important traits when selecting for feed efficiency; and (4) map the genes to biological mechanisms and pathways. A total number of 339 SNPs corresponding to 180 genes were tested for association with phenotypes using a single locus regression (SLRM) and genotypic model on 726 and 990 crossbred animals for feed efficiency and carcass traits, respectively.

Results

Strong evidence of associations for RFI were located on chromosomes 8, 15, 16, 18, 19, 21, and 28. The strongest association with RFI (P = 0.0017) was found with a newly discovered SNP located on BTA 8 within the ELP3 gene. SNPs rs41820824 and rs41821600 on BTA 16 within the gene HMCN1 were strongly associated with RFI (P = 0.0064 and P = 0.0033, respectively). A SNP located on BTA 18 within the ZNF423 gene provided strong evidence for association with RFI (P = 0.0028). Genomic estimated breeding values (GEBV) from 98 significant SNPs were moderately correlated (0.47) to the estimated breeding values (EBVs) from a mixed animal model. The significant (P < 0.05) SNPs (98) explained 26% of the genetic variance for RFI. In silico functional analysis for the genes suggested 35 and 39 biological processes and pathways, respectively for feed efficiency traits.

Conclusions

This study identified several positional and functional candidate genes involved in important biological mechanisms associated with feed efficiency and performance. Significant SNPs should be validated in other populations to establish their potential utilization in genetic improvement programs.

Analysis of biological networks and biological pathways associated with residual feed intake in beef cattle

In this study, biological networks were reconstructed from genes and metabolites significantly associated with residual feed intake (RFI) in beef cattle. The networks were then used to identify biological pathways associated with RFI. RFI is a measure of feed efficiency, which is independent of body size and growth; therefore selection for RFI is expected to result in cattle that consume less feed without adverse effects on growth rate and mature size. Although several studies have identified genes associated with RFI, the mechanisms of the biological processes are not well understood. In this study, we utilised the results obtained from two association studies, one using 24 genes and one using plasma metabolites to reconstruct biological networks associated with RFI using IPA software (Igenuity Systems). The results pointed to biological processes such as lipid and steroid biosynthesis, protein and carbohydrate metabolism and regulation of gene expression through DNA transcription, protein stability and degradation. The major canonical pathways included signaling of growth hormone, Oncostatin M, insulin-like growth factor and AMP activated protein kinase, and cholesterol biosynthesis. This study provides information on potential biological mechanisms, and genes and metabolites involved in feed efficiency in beef cattle.

Single nucleotide polymorphisms and haplotypes associated with feed efficiency in beef cattle

BACKGROUND

General, breed- and diet-dependent associations between feed efficiency in beef cattle and single nucleotide polymorphisms (SNPs) or haplotypes were identified on a population of 1321 steers using a 50 K SNP panel. Genomic associations with traditional two-step indicators of feed efficiency - residual feed intake (RFI), residual average daily gain (RADG), and residual intake gain (RIG) - were compared to associations with two complementary one-step indicators of feed efficiency: efficiency of intake (EI) and efficiency of gain (EG). Associations uncovered in a training data set were evaluated on independent validation data set. A multi-SNP model was developed to predict feed efficiency. Functional analysis of genes harboring SNPs significantly associated with feed efficiency and network visualization aided in the interpretation of the results.

RESULTS

For the five feed efficiency indicators, the numbers of general, breed-dependent, and diet-dependent associations with SNPs (P-value < 0.0001) were 31, 40, and 25, and with haplotypes were six, ten, and nine, respectively. Of these, 20 SNP and six haplotype associations overlapped between RFI and EI, and five SNP and one haplotype associations overlapped between RADG and EG. This result confirms the complementary value of the one and two-step indicators. The multi-SNP models included 89 SNPs and offered a precise prediction of the five feed efficiency indicators. The associations of 17 SNPs and 7 haplotypes with feed efficiency were confirmed on the validation data set. Nine clusters of Gene Ontology and KEGG pathway categories (mean P-value < 0.001) including, 9nucleotide binding; ion transport, phosphorous metabolic process, and the MAPK signaling pathway were overrepresented among the genes harboring the SNPs associated with feed efficiency.

CONCLUSIONS

The general SNP associations suggest that a single panel of genomic variants can be used regardless of breed and diet. The breed- and diet-dependent associations between SNPs and feed efficiency suggest that further refinement of variant panels require the consideration of the breed and management practices. The unique genomic variants associated with the one- and two-step indicators suggest that both types of indicators offer complementary description of feed efficiency that can be exploited for genome-enabled selection purposes.

Candidate genes and biological pathways associated with carcass quality traits in beef cattle

Karisa, B. K., Thomson, J., Wang, Z., Bruce, H. L., Plastow, G. S. and Moore, S. S. 2013. Candidate genes and biological pathways associated with carcass quality traits in beef cattle. Can. J. Anim. Sci. 93: 295–306. The objective of this study was to use the candidate gene approach to identify the genes associated with carcass quality traits in beef cattle steers at the University of Alberta Ranch at Kinsella, Canada. This approach involved identifying positional candidate genes and prioritizing them according to their functions into functional candidate genes before performing statistical association analysis. The positional candidate genes and single nucleotide polymorphisms (SNP) were identified from previously reported quantitative trait loci for component traits including body weight, average daily gain, metabolic weight, feed efficiency and energy balance. Positional candidate genes were then prioritized into functional candidate genes according to the associated gene ontology terms and their functions. A total of 116 genes were considered functional candidate genes and 117 functional SNPs were genotyped and used for multiple marker association analysis using ASReml®. Seven SNPs were significantly associated with various carcass quality traits (P≤0.005). The significant genes were associated with biological processes such as fat, glucose, protein and steroid metabolism, growth, energy utilization and DNA transcription and translation as inferred from the protein knowledgebase (UniprotKB). Gene network analysis indicated significant involvement of biological processes related to fat and steroid metabolism and regulation of transcription and translation of DNA.

Candidate genes and single nucleotide polymorphisms associated with variation in residual feed intake in beef cattle

The candidate gene approach was used to identify genes associated with residual feed intake (RFI) in beef steers. The approach uses prior knowledge of gene functions to predict their biological role in the variation observed in a trait. It is suited to identify genes associated with complex traits where each gene has a relatively small effect. First, positional candidate genes were identified within the genomic positions of previously reported QTL associated with component traits related to RFI such as dry matter intake (DMI), growth, feed conversion ratio (FCR), average daily gain (ADG), and energy balance. Secondly, the positional candidate genes were prioritized into functional candidate genes according to their biological functions and their relationship with the biological processes associated with RFI including carbohydrate, fat and protein metabolism, thermoregulation, immunity and muscle activity. Single nucleotide polymorphisms (SNPs) located within the functional candidate genes were identified using mRNA sequences and prioritized into functional classes such as non-synonymous (nsSNP), synonymous (sSNP) or intronic SNP. A total of 117 nsSNP were considered as functional SNP and genotyped in steers at the University of Alberta ranch in Kinsella. Multiple marker association analysis in ASReml was performed using RFI data obtained from 531 beef steers. Twenty-five SNP were significantly associated with RFI (P < 0.05) accounting for 19.7% of the phenotypic variation. Using SIFT program to predict the effect of the SNP on the function of the corresponding protein, 3 of the 25 SNP were predicted to cause a significant effect on protein function (P < 0.05). One of the 3 SNP was located in the GHR gene and was also associated with a significant effect on the tertiary structure of the GHR protein (P < 0.05) as modeled using SWISSModel software. Least square means for each genotype were estimated and an over-dominance effect was observed for the SNP located in the GHR, CAST, ACAD11 and UGT3A1 genes. In addition, 2 other SNP showed a dominance effect and 3 genes had an additive effect. Gene network analysis performed in Ingenuity pathway analysis (IPA) software (Ingenuity Systems, www.ingenuity.com) indicated that the significant genes were involved in biological pathways such as lipid, protein and energy metabolism, electron transport and membrane signaling. The genes in this study, if validated in other beef cattle populations, may be useful for marker assisted selection for feed efficiency.

The relationship between feed efficiency traits and fertility in young beef bulls

Awda, B. J., Miller, S. P., Montanholi, Y. R., Vander Voort, G., Caldwell, T., Buhr, M. M. and Swanson, K. C. 2013. The relationship between feed efficiency traits and fertility in young beef bulls. Can. J. Anim. Sci. 93: 185–192. Great efforts have been taken in recent years for improving feed efficiency in beef cattle. Despite there being several studies on biological factors associated with this trait, little is known about the potential relationships between fertility and feed efficiency in beef cattle. The objective of this study was to examine the relationship between feed efficiency traits and bull fertility traits [sperm motility, viability and scrotal circumference (SC)]. From a total of 328 crossbred beef bulls that were subjected to a performance test (112 d in each of 6 yr), SC was measured and semen collected from 110 bulls using the electroejaculation method (average age±SE=417±2.5 d). Sperm were extended, cooled, and frozen in liquid nitrogen. Two residual feed intake (RFI) measures were considered with different prediction models for dry matter intake (DMI), RFIKoch included size and growth rate and RFIbkft included the additional adjustment for backfat thickness (BKFT). Sperm viability, motility and progressive motility of the 10 bulls with the greatest RFIKoch (Hi-RFIKoch) were greater than those of the 10 bulls with the lowest RFIKoch (Lo-RFIKoch; P<0.05, 0.01, and 0.05, respectively). Sperm motility (P<0.01), progressive motility and SC (P<0.05) of the 10 bulls with the greatest RFIbkft (Hi-RFIbkft) were greater than those of the 10 bulls with the lowest RFIbkft (Lo-RFIbkft). In summary, these data indicate that young beef bulls with greater feed efficiency have decreased sperm motility, sperm viability and SC, which is an undesirable effect of selection for improved feed efficiency that needs to be addressed through multiple trait selection.

Small intestine histomorphometry of beef cattle with divergent feed efficiency

BACKGROUND

The provision of feed is a major cost in beef production. Therefore, the improvement of feed efficiency is warranted. The direct assessment of feed efficiency has limitations and alternatives are needed. Small intestine micro-architecture is associated with function and may be related to feed efficiency. The objective was to verify the potential histomorphological differences in the small intestine of animals with divergent feed efficiency.

METHODS

From a population of 45 feedlot steers, 12 were selected with low-RFI (superior feed efficiency) and 12 with high-RFI (inferior feed efficiency) at the end of the finishing period. The animals were processed at 13.79 ± 1.21 months of age. Within 1.5 h of slaughter the gastrointestinal tract was collected and segments from duodenum and ileum were harvested. Tissue fragments were processed, sectioned and stained with hematoxylin and eosin. Photomicroscopy images were taken under 1000x magnification. For each animal 100 intestinal crypts were imaged, in a cross section view, from each of the two intestinal segments. Images were analyzed using the software ImageJ(®). The measurements taken were: crypt area, crypt perimeter, crypt lumen area, nuclei number and the cell size was indirectly calculated. Data were analyzed using general linear model and correlation procedures of SAS(®).

RESULTS

Efficient beef steers (low-RFI) have a greater cellularity (indicated by nuclei number) in the small intestinal crypts, both in duodenum and ileum, than less efficient beef steers (high-RFI) (P < 0.05). The mean values for the nuclei number of the low-RFI and high-RFI groups were 33.16 and 30.30 in the duodenum and 37.21 and 33.65 in the ileum, respectively. The average size of the cells did not differ between feed efficiency groups in both segments (P ≥ 0.10). A trend was observed (P ≤ 0.10) for greater crypt area and crypt perimeter in the ileum for cattle with improved feed efficiency.

CONCLUSION

Improved feed efficiency is associated with greater cellularity and no differences on average cell size in the crypts of the small intestine in the bovine. These observations are likely to lead to an increase in the energy demand by the small intestine regardless of the more desirable feed efficiency.

Whole-genome regression and prediction methods applied to plant and animal breeding

Genomic-enabled prediction is becoming increasingly important in animal and plant breeding and is also receiving attention in human genetics. Deriving accurate predictions of complex traits requires implementing whole-genome regression (WGR) models where phenotypes are regressed on thousands of markers concurrently. Methods exist that allow implementing these large-p with small-n regressions, and genome-enabled selection (GS) is being implemented in several plant and animal breeding programs. The list of available methods is long, and the relationships between them have not been fully addressed. In this article we provide an overview of available methods for implementing parametric WGR models, discuss selected topics that emerge in applications, and present a general discussion of lessons learned from simulation and empirical data analysis in the last decade.

The future of livestock breeding: genomic selection for efficiency, reduced emissions intensity, and adaptation

As the global population and global wealth both continue to increase, so will the demand for livestock products, especially those that are highly nutritious. However, competition with other uses for land and water resources will also intensify, necessitating more efficient livestock production. In addition, as climate change escalates, reduced methane emissions from cattle and sheep will be a critical goal. Application of new technologies, including genomic selection and advanced reproductive technologies, will play an important role in meeting these challenges. Genomic selection, which enables prediction of the genetic merit of animals from genome-wide SNP markers, has already been adopted by dairy industries worldwide and is expected to double genetic gains for milk production and other traits. Here, we review these gains. We also discuss how the use of whole-genome sequence data should both accelerate the rate of gain and enable rapid discovery and elimination of genetic defects from livestock populations.