Evidence map›Paper›PMID 33552534›Full record

ArticleEvolution letters2021

Strongly deleterious mutations are a primary determinant of extinction risk due to inbreeding depression.

Christopher C Kyriazis, Robert K Wayne, Kirk E Lohmueller

Abstract read
In one paragraph

Article in Evolution letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 140 papers.

0numbers the graph read from it
0cells of the map it votes in
140citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

140 citing papers in PubMed.

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80 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Christopher C KyriazisDepartment of Ecology and Evolutionary Biology University of California Los Angeles California 90095.
Robert K WayneDepartment of Ecology and Evolutionary Biology University of California Los Angeles California 90095.
Kirk E LohmuellerDepartment of Ecology and Evolutionary Biology University of California Los Angeles California 90095.ORCID https://orcid.org/0000-0002-3874-369X

Funding

Population genomics of the selective effects of new mutationsR35GM119856 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI LOHMUELLER, KIRK · 2016 to 2025
$3.4M
NIGMS NIH HHS R35 GM119856
6 · The paper itself

Abstract

Human-driven habitat fragmentation and loss have led to a proliferation of small and isolated plant and animal populations with high risk of extinction. One of the main threats to extinction in these populations is inbreeding depression, which is primarily caused by recessive deleterious mutations becoming homozygous due to inbreeding. The typical approach for managing these populations is to maintain high genetic diversity, increasingly by translocating individuals from large populations to initiate a "genetic rescue." However, the limitations of this approach have recently been highlighted by the demise of the gray wolf population on Isle Royale, which declined to the brink of extinction soon after the arrival of a migrant from the large mainland wolf population. Here, we use a novel population genetic simulation framework to investigate the role of genetic diversity, deleterious variation, and demographic history in mediating extinction risk due to inbreeding depression in small populations. We show that, under realistic models of dominance, large populations harbor high levels of recessive strongly deleterious variation due to these mutations being hidden from selection in the heterozygous state. As a result, when large populations contract, they experience a substantially elevated risk of extinction after these strongly deleterious mutations are exposed by inbreeding. Moreover, we demonstrate that, although genetic rescue is broadly effective as a means to reduce extinction risk, its effectiveness can be greatly increased by drawing migrants from small or moderate-sized source populations rather than large source populations due to smaller populations harboring lower levels of recessive strongly deleterious variation. Our findings challenge the traditional conservation paradigm that focuses on maximizing genetic diversity in small populations in favor of a view that emphasizes minimizing strongly deleterious variation. These insights have important implications for managing small and isolated populations in the increasingly fragmented landscape of the Anthropocene.

Indexed as

Extinctiongene flowinbreedingpurifying selection

Identifiers

PMID33552534
PMCPMC7857301

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.