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. 2018 Oct;96(2):438-451.
doi: 10.1111/tpj.14042. Epub 2018 Sep 1.

Genome-wide scans of selection highlight the impact of biotic and abiotic constraints in natural populations of the model grass Brachypodium distachyon

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Genome-wide scans of selection highlight the impact of biotic and abiotic constraints in natural populations of the model grass Brachypodium distachyon

Yann Bourgeois et al. Plant J. 2018 Oct.
Free article

Abstract

Grasses are essential plants for ecosystem functioning. Quantifying the selective pressures that act on natural variation in grass species is therefore essential regarding biodiversity maintenance. In this study, we investigate the selection pressures that act on two distinct populations of the grass model Brachypodium distachyon without prior knowledge about the traits under selection. We took advantage of whole-genome sequencing data produced for 44 natural accessions of B. distachyon and used complementary genome-wide selection scans (GWSS) methods to detect genomic regions under balancing and positive selection. We show that selection is shaping genetic diversity at multiple temporal and spatial scales in this species, and affects different genomic regions across the two populations. Gene ontology annotation of candidate genes reveals that pathogens may constitute important factors of positive and balancing selection in B. distachyon. We eventually cross-validated our results with quantitative trait locus data available for leaf-rust resistance in this species and demonstrate that, when paired with classical trait mapping, GWSS can help pinpointing candidate genes for further molecular validation. Thanks to a near base-perfect reference genome and the large collection of freely available natural accessions collected across its natural range, B. distachyon appears as a prime system for studies in ecology, population genomics and evolutionary biology.

Keywords: Brachypodium distachyon; demographic history; genome scans of selection; grasses; host-pathogen interaction; local adaptation; machine learning.

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