Browsing by Author "Bennewitz, J."
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Item Evaluation of Selection Strategies in Dual-Purpose and Specialized Breeding of Indigenous Chicken(Elsevier, 2024-08) Miyumo, S.; Wasike, C.B.; Ilatsia, E.D.; Bennewitz, J.; Chagunda, M.G.; University of Hohenheim ; Maseno University ; Kenya Agricultural and Livestock Research Organization ; University of HohenheimThis 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.Item Genetic and Non-Genetic Factors Influencing KLH Binding Natural Antibodies and Specific Antibody Response to Newcastle Disease in Kenyan Chicken Populations(John Wiley & Sons Ltd., 2022-09-07) Miyumo, S.; Wasike, C.B.; Ilatsia, E.D.; Bennewitz, J.; Chagunda, M.Z.; University of Hohenheim ; Maseno University ; Kenya Agricultural and Livestock Research OrganizationThis study aimed at investigating the influence of genetic and non‐genetic factors on immune traits to inform on possibilities of genetic improvement of disease resistance traits in local chicken of Kenya. Immune traits such as natural and specific antibodies are considered suitable indicators of an individual's health status and consequently, used as indicator traits of disease resistance. In this study, natural antibodies binding to Keyhole Limpet Hemocyanin (KLH‐NAbs) was used to measure general disease resistance. Specific antibodies binding to Newcastle disease virus (NDV‐IgG) post vaccination was used to measure specific disease resistance. Titers of KLH‐NAbs isotypes (KLH‐IgM, KLH‐IgG and KLH‐IgA) and NDV‐IgG were measured in 1,540 chickens of different ages ranging from 12 to 56 weeks. A general linear model was fitted to determine the effect of sex, generation, population type, phylogenetic cluster, line, genotype and age on the antibody traits. A multivariate animal mixed model was fitted to estimate heritability and genetic correlations among the antibody traits. The model constituted of non‐genetic factors found to have a significant influence on the antibody traits as fixed effects, and animal and residual effects as random variables. Overall mean (±SE) concentration levels for KLH‐IgM, KLH‐IgG, KLH‐IgA and NDV‐IgG were 10.33 ± 0.04, 9.08 ± 0.02, 6.00 ± 0.02 and 10.12 ± 0.03, respectively. Sex, generation and age (linear covariate) significantly (p < 0.05) influenced variation across all the antibody traits. Genotype effects (p < 0.05) were present in all antibody traits, apart from KLH‐IgA. Interaction between generation and line was significant (p < 0.05) in KLH‐IgM and NDV‐IgG while nesting phylogenetic cluster within population significantly (p < 0.05) influenced all antibody traits, apart from KLH‐IgA. Heritability estimates for KLH‐IgM, KLH‐IgG, KLH‐IgA and NDV‐IgG were 0.28 ± 0.08, 0.14 ± 0.06, 0.07 ± 0.04 and 0.31 ± 0.06, respectively. There were positive genetic correlations (0.40–0.61) among the KLH‐NAbs while negative genetic correlations (−0.26 to −0.98) were observed between the KLH‐NAbs and NDV‐IgG. Results from this study indicate that non‐genetic effects due to biological and environmental factors influence natural and specific antibodies and should be accounted for to reduce bias and improve accuracy when evaluating the traits. Subsequently, the moderate heritability estimates in KLH‐IgM and NDV‐IgG suggest selection possibilities for genetic improvement of general and specific immunity, respectively, and consequently disease resistance. However, the negative correlations between KLH‐NAbs and NDV‐IgG indicate the need to consider a suitable approach that can optimally combine both traits in a multiple trait selection strategies.
