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Admixture mapping and selection scans identify genomic regions associated with stomatal patterning and disease resistance in hybrid poplars.
Fetter, Karl C; Keller, Stephen R.
Afiliación
  • Fetter KC; Department of Plant Biology University of Vermont Burlington Vermont USA.
  • Keller SR; Department of Ecology and Evolutionary Biology University of Connecticut Storrs Connecticut USA.
Ecol Evol ; 13(10): e10579, 2023 Oct.
Article en En | MEDLINE | ID: mdl-37881228
ABSTRACT
Variation in fitness components can be linked in some cases to variation in key traits. Metric traits that lie at the intersection of development, defense, and ecological interactions may be expected to experience environmental selection, informing our understanding of evolutionary and ecological processes. Here, we use quantitative genetic and population genomic methods to investigate disease dynamics in hybrid and non-hybrid populations. We focus our investigation on morphological and ecophysiological traits which inform our understanding of physiology, growth, and defense against a pathogen. In particular, we investigate stomata, microscopic pores on the surface of a leaf that regulate gas exchange during photosynthesis and are sites of entry for various plant pathogens. Stomatal patterning traits were highly predictive of disease risk. Admixture mapping identified a polygenic basis of disease resistance. Candidate genes for stomatal and disease resistance map to the same genomic regions and experienced positive selection. Genes with functions to guard cell homeostasis, the plant immune system, components of constitutive defenses, and growth-related transcription factors were identified. Our results indicate positive selection acted on candidate genes for stomatal patterning and disease resistance, potentially acting in concert to structure their variation in naturally formed backcrossing hybrid populations.
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Texto completo: 1 Bases de datos: MEDLINE Idioma: En Revista: Ecol Evol Año: 2023 Tipo del documento: Article

Texto completo: 1 Bases de datos: MEDLINE Idioma: En Revista: Ecol Evol Año: 2023 Tipo del documento: Article