bioRxivEvolutionary Biology
Fine-scale Population Structure of North American Arabidopsis thaliana Reveals Multiple Sources of Introduction from Across Eurasia
G. Shirsekar, J. Devos, S. M. Latorre, A. Blaha, M. Queiroz Dias, A. Gonzalez Hernando, D. S. Lundberg, H. A. Burbano, C. B. Fenster, D. Weigel
Abstract
Large-scale movement of organisms across their habitable range, or migration, is an important evolutionary process that can contribute to observed patterns of genetic diversity and our understanding of the adaptive spread of alleles. While human migrations have been studied in great detail with modern and ancient genomes, recent anthropogenic influence on reducing the biogeographical constraints on the migration of non-native species has presented opportunities in several study systems to ask the questions about how repeated introductions shape genetic diversity in the introduced range. We present here the most comprehensive view of population structure of North American Arabidopsis thaliana by studying a set of 500 (whole-genome sequenced) and over 2800 (RAD-seq genotyped) individuals in the context of global diversity represented by Afro-Eurasian genomes. We use haplotype-sharing, phylogenetic modeling and rare-allele sharing based methods to identify putative sources of introductions of extant N. American A. thaliana from the native range of Afro-Eurasia. We find evidence of admixture among the introduced lineages that has resulted in the increased haplotype diversity and reduced mutational load. Further, we also present signals of selection in the immune-system related genes that impart qualitative disease resistance to pathogens of bacterial and oomycete origins. Thus, multiple introductions to a non-native range can quickly increase adaptive potential of a colonizing species by increasing haplotypic diversity through admixture. The results presented here lay the foundation for further investigations into the functional significance of admixture.
- organism: Arabidopsis
- assay: whole-genome sequencing
- research focus: fine-mapping
- approach: statistical model
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