Impact of putatively beneficial genomic loci on gene expression in little brown bats (

Myotis lucifugus White‐nose syndrome disease eQTL evolution expression

Journal

Evolutionary applications
ISSN: 1752-4571
Titre abrégé: Evol Appl
Pays: England
ID NLM: 101461828

Informations de publication

Date de publication:
Sep 2024
Historique:
received: 06 08 2023
revised: 06 06 2024
accepted: 19 06 2024
medline: 23 9 2024
pubmed: 23 9 2024
entrez: 23 9 2024
Statut: epublish

Résumé

Genome-wide scans for selection have become a popular tool for investigating evolutionary responses in wildlife to emerging diseases. However, genome scans are susceptible to false positives and do little to demonstrate specific mechanisms by which loci impact survival. Linking putatively resistant genotypes to observable phenotypes increases confidence in genome scan results and provides evidence of survival mechanisms that can guide conservation and management efforts. Here we used an expression quantitative trait loci (eQTL) analysis to uncover relationships between gene expression and alleles associated with the survival of little brown bats (

Identifiants

pubmed: 39310794
doi: 10.1111/eva.13748
pii: EVA13748
pmc: PMC11413065
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e13748

Informations de copyright

© 2024 The Author(s). Evolutionary Applications published by John Wiley & Sons Ltd.

Déclaration de conflit d'intérêts

The authors of this manuscript have no conflicts of interest to report.

Auteurs

Robert Kwait (R)

Department of Ecology, Evolution and Natural Resources Rutgers, The State University of New Jersey New Brunswick New Jersey USA.

Malin L Pinsky (ML)

Department of Ecology, Evolution and Natural Resources Rutgers, The State University of New Jersey New Brunswick New Jersey USA.
Department of Ecology and Evolutionary Biology University of California Santa Cruz Santa Cruz California USA.

Sarah Gignoux-Wolfsohn (S)

Department of Biological Sciences University of Massachusetts Lowell Massachusetts USA.

Evan A Eskew (EA)

Institute for Interdisciplinary Data Sciences University of Idaho Moscow Idaho USA.

Kathleen Kerwin (K)

Department of Ecology, Evolution and Natural Resources Rutgers, The State University of New Jersey New Brunswick New Jersey USA.

Brooke Maslo (B)

Department of Ecology, Evolution and Natural Resources Rutgers, The State University of New Jersey New Brunswick New Jersey USA.

Classifications MeSH