Santiago
Reinoso Castillo
SURF The genetic basis of inflorescence architecture in Arabidopsis thaliana: implications for adaptation and maladaptation Life Sciences
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Authors:
Santiago Reinoso Castillo
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Understanding the genetic basis of local adaptation is a key goal of evolutionary biology. Adaptation to one habitat requires trait values that confer high fitness (survivability and reproduction) and these values may reduce fitness in other habitats leading to fitness trade-offs across environments. Arabidopsis is a model for studying adaptation due to its well-characterized genome and short life cycle. Additionally, its wide geographical distribution ensures differences among populations creating locally adapted ecological traits. Here we aim to, 1) compare inflorescence architecture between two locally adapted populations of A. thaliana from Italy (IT) and Sweden (SW), 2) identify regions of the genome contributing to differences in inflorescence architecture and 3) test if maladaptive fecundity loci can be explained by differences in inflorescence architecture. To achieve this, a panel of Near-Isogenic Lines (NILs) with introgression segments of the SW genome tilling across the IT genetic background were used. These introgressions spanned both maladaptive loci and genes previously reported to be involved in inflorescence architecture. Results thus far indicate that IT produces 55% more flowers, 19% more cauline leaves and 28% more secondary branches than SW. This suggests that IT may achieve greater fecundity through greater branching and photosynthetic capacity of the inflorescence. Data currently being collected in the NILs will determine the genomic regions underlying these ecotypic differences. These results will provide greater understanding of how specific genomic regions controlling ecotypic differences in inflorescence architecture contribute to adaptation and maladaptation. Keywords: Inflorescence Architecture; Local Adaptation; Genetic Trade-Offs; Genetic Mapping; Arabidopsis thaliana
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Purdue University / 2024
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Santiago Reinoso Castillo