Genetic and phenotypic parameters for feed efficiency in indigenous chicken in Kenya

Evaluation of selection strategies in dual-purpose and specialized breeding of indigenous chicken

This study aimed to evaluate various selection strategies for adoption in dual-purpose (ICD), meat (ICM) and layer (ICL) breeding goals in indigenous chicken breeding programs. The ICM goal aimed to improve live weight (LW12), daily gain (ADG) and egg weight (EW12) or together with feed efficiency and antibody response. For the ICL goal, age at first egg (AFE) and egg number (EN12) or together with feed efficiency and antibody response were targeted. In the ICD goal, the objective was to improve LW12, ADG, AFE and EN12 or together with feed efficiency and antibody response. Highest total index responses of US$ 49.83, US$ 65.71, and US$ 37.90 were estimated in indices targeting only production traits in the ICD, ICM and ICL goals, respectively. Highest index accuracy estimates of 0.77 and 0.70 were observed in indices that considered production and feed-related traits in the ICD and ICL goals, respectively, while in the ICM goal, the highest estimate of 0.96 was observed in an index targeting only production traits. Inbreeding levels ranged from 0.60 to 1.14% across the various indices considered in the breeding goals. Targeting only production traits in the ICD, ICM and ICL goals required the least number of generations of selection of 7.46, 5.50, and 8.52, respectively, to achieve predefined gains. Generally, a strategy targeting only production traits in a goal was the most optimal but resulted to unfavorable correlated responses in feed efficiency and antibody response. Addition of feed efficiency or/and antibody response in a goal was, however, not attractive due to the decline in total index response and accuracy and increase in inbreeding levels and number of generations of selection. Considering the feed availability and disease challenges in the tropics, choice of including feed efficiency or/and antibody response in the ICD, ICM and ICL goals should depend on targeted production system, resource availability to support breeding activities and magnitude of correlated responses on these traits when not included in the goals.

Genetic and phenotypic correlations among feed efficiency, immune and production traits in indigenous chicken of Kenya

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.

Multi-transcriptomics reveals RLMF axis-mediated signaling molecules associated with bovine feed efficiency

The regulatory axis plays a vital role in interpreting the information exchange and interactions among mammal organs. In this study on feed efficiency, it was hypothesized that a rumen-liver-muscle-fat (RLMF) regulatory axis exists and scrutinized the flow of energy along the RLMF axis employing consensus network analysis from a spatial transcriptomic standpoint. Based on enrichment analysis and protein-protein interaction analysis of the consensus network and tissue-specific genes, it was discovered that carbohydrate metabolism, energy metabolism, immune and inflammatory responses were likely to be the biological processes that contribute most to feed efficiency variation on the RLMF regulatory axis. In addition, clusters of genes related to the electron respiratory chain, including ND (2,3,4,4L,5,6), NDUF (A13, A7, S6, B3, B6), COX (1,3), CYTB, UQCR11, ATP (6,8), clusters of genes related to fatty acid metabolism including APO (A1, A2, A4, B, C3), ALB, FG (A, G), as well as clusters of the ribosomal-related gene including RPL (8,18A,18,15,13, P1), the RPS (23,27A,3A,4X), and the PSM (A1-A7, B6, C1, C3, D2-D4, D8 D9, E1) could be the primary effector genes responsible for feed efficiency variation. The findings demonstrate that high feed efficiency cattle, through the synergistic action of the regulatory axis RLMF, may improve the efficiency of biological processes (carbohydrate metabolism, protein ubiquitination, and energy metabolism). Meanwhile, high feed efficiency cattle might enhance the ability to respond to immunity and inflammation, allowing nutrients to be efficiently distributed across these organs associated with digestion and absorption, energy-producing, and energy-storing organs. Elucidating the distribution of nutrients on the RLMF regulatory axis could facilitate an understanding of feed efficiency variation and achieve the study on its molecular regulation.

Identifying the key genes and functional enrichment pathways associated with feed efficiency in cattle

Residual feed intake (RFI) is a measurement of feed efficiency, and is inversely correlated with feed efficiency. The differentially expressed genes (DEGs) associated with RFI vary substantially among studies, posing great challenges in finding the RFI-related marker genes. This study attempted to resolve this issue by integrating and comparing the multiple transcriptome sequencing data associated with RFI in the cattle liver, using differential, functional enrichment, protein-protein interaction (PPI) network, weighted co-expression network (WGCNA), and gene set enrichment analyses (GSEA) to identify the candidate genes and functional enrichment pathways that are closely associated with RFI. Four candidate genes namely SHC1, GPX4, ACADL, and IGF1 were identified and validated as the marker genes for RFI. Four functional enrichment pathways, namely the fatty acid metabolism, sugar metabolism, energy metabolism, and protein ubiquitination were also found to be closely related to RFI. This study identified several genes and signaling pathways with shared characteristics, which will provide new insights into the molecular mechanisms related to the regulation of feed efficiency, and provide basis for molecular markers related to feed efficiency in beef cattle.

Modeling genetic components of hatch of fertile in broiler breeders

Reproductive efficiency such as fertility and hatch of fertile (HoF) are of economic importance and concern to breeding companies becaue of their effects on chick output. Similar to other traits of economic importance in poultry breeding, the rate of response for HoF is largely dependent on the use of an appropriate model for evaluating the trait. Therefore, the objectives of this study were to estimate genetic parameters from cumulative, repeatability, fixed regression, random regression, and multitrait models for HoF from a pure-line broiler breeder. The data available for this study consisted of weekly HoF records from 11,729 hens with a total pedigree record of 38,260. Estimates of heritability from the various models ranged from 0.04 to 0.22 with the highest estimate obtained from the cumulative model and the lowest from the repeatability model. Responses to selection estimated for the different models ranged from 0.03 to 0.08% gain per year of the phenotypic mean. In general, the cumulative and the repeatability models underestimated response to selection. The multitrait and random regression models gave similar results for response to selection at 0.08 percentage change in phenotypic mean. In conclusion, the cumulative model is not optimal for modeling HoF, and likewise, the repeatability model. The random regression and multitrait models should be considered instead as they offered a higher response to selection. However, if a multitrait analysis is to be considered, it is recommended to split up the production period in such a way as to avoid computational constraints due to overparameterization.

Invited review: Advances and applications of random regression models: From quantitative genetics to genomics

An important goal in animal breeding is to improve longitudinal traits; that is, traits recorded multiple times during an individual's lifetime or physiological cycle. Longitudinal traits were first genetically evaluated based on accumulated phenotypic expression, phenotypic expression at specific time points, or repeatability models. Until now, the genetic evaluation of longitudinal traits has mainly focused on using random regression models (RRM). Random regression models enable fitting random genetic and environmental effects over time, which results in higher accuracy of estimated breeding values compared with other statistical approaches. In addition, RRM provide insights about temporal variation of biological processes and the physiological implications underlying the studied traits. Despite the fact that genomic information has substantially contributed to increase the rates of genetic progress for a variety of economically important traits in several livestock species, less attention has been given to longitudinal traits in recent years. However, including genomic information to evaluate longitudinal traits using RRM is a feasible alternative to yield more accurate selection and culling decisions, because selection of young animals may be based on the complete pattern of the production curve with higher accuracy compared with the use of traditional parent average (i.e., without genomic information). Moreover, RRM can be used to estimate SNP effects over time in genome-wide association studies. Thus, by analyzing marker associations over time, regions with higher effects at specific points in time are more likely to be identified. Despite the advances in applications of RRM in genetic evaluations, more research is needed to successfully combine RRM and genomic information. Future research should provide a better understanding of the temporal variation of biological processes and their physiological implications underlying the longitudinal traits.