Adaptive phenotypic and genetic variation in chickens: a landscape genomics approach

Smallholder chicken production is an integral part of tropical farming systems and contributes significantly to sustainable livelihoods. Performance of chickens in these systems is too low to meet the growing demands for meat and eggs. Unavailability of productive and adaptive breeds is a major constraint. Knowledge on phenotypic and genetic variation among populations contributes to the design of sustainable chicken genetic improvement and development programmes. I follow a landscape genomics approach and integrate genetic, phenotypic, and environmental information in my study design and statistical analyses. In the first part of this thesis, I aim to identify the environmental drivers of local adaptation and detect genomic footprints of natural selection in indigenous chickens. I use species distribution models (SDMs) to identify environmental predictors associated with habitat suitability for chickens. Based on higher level of matching between the presence of distinct phenotypes and availability of unique environmental niches, I classify the Ethiopian indigenous chicken populations into three ecotypes. I perform selection signatures analyses and redundancy analyses (RDA) at different analytical layers (considering gradient and agroecology) to identify candidate loci and genomic regions linked mainly with local adaptation. I show that Ethiopian chicken populations differentiated the most between gradients but selection pressures leading to adaptive variation are stronger between agroecologies. I indicate that environmental and phenotypic predictors are useful to explain genomic variation in Ethiopian indigenous chickens. I show that phenotypic distribution models (PDMs) such as boosted generalized additive models (GAMs) are valuable tools in animal breeding to integrate environmental and phenotypic information and to predict phenotypic values. In the second part of the thesis, I evaluate the performance of improved chicken breeds introduced into smallholder systems. I show that that agroecologies defined by SDMs improve model fit in GxE predictions. I utilize the concept of phenotypic plasticity to compare yield stability of improved chicken breeds. I show that two approaches of multi-environment breed performance analysis (MEPA), namely, additive main effects and multiplicative interaction models (AMMI) and linear mixed-effects models (LMM) are applicable in chicken to identify and recommend more productive and stable breeds. Together, I demonstrate how adaptive phenotypic and genetic variation can be exploited to enhance performance of chickens in smallholder systems.