Evidence map›Paper›PMID 40502059›Full record

ArticlebioRxiv : the preprint server for biology2025

The impact of background selection in mutation-selection-drift balance models of complex trait evolution.

Xinyi Li, Jeremy J Berg

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

The trial behind it

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3 · Its place in the literature

Who cites it

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No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Xinyi LiCommittee on Genetics, Genomics, & Systems Biology, University of Chicago, Chicago, IL, USA.
Jeremy J BergCommittee on Genetics, Genomics, & Systems Biology, University of Chicago, Chicago, IL, USA.

Funding

Theory, Methods, and Resources for Understanding and Leveraging Spatial Variation in Population Genetic DataR35GM149521 · NIGMS · UNIVERSITY OF CHICAGO · PI John Novembre · 2023 to 2026
$1.7M
Refining mutation rates and measures of purifying selection with an application to understanding the impact of non-coding variation on neuropsychiatric diseasesR01HG010773 · NHGRI · UNIVERSITY OF CHICAGO · PI HE, XIN, NOVEMBRE, JOHN · 2020 to 2023
$1.7M
Population genetic modeling of genetic variation for complex traits and diseasesR35GM151257 · NIGMS · UNIVERSITY OF CHICAGO · PI Jeremy Jackson Berg · 2023 to 2026
$1.6M
NHGRI NIH HHS R01 HG010773NIGMS NIH HHS R35 GM149521NIGMS NIH HHS R35 GM151257
6 · The paper itself

Abstract

Over the past three decades, theoretical population genetic models and empirical analyses have elaborated on how the elimination of deleterious variants reduces variation at linked neutral sites. This process is known as background selection. However, the impact of background selection has been studied almost exclusively without reference to its effect on phenotypic variation. Here, we use population genetic theory and simulation to investigate the consequences of background selection for linked variation impacting complex traits. We find the impact of background selection on a complex trait depends on the type of selection acting on the trait, the effect size distribution, and the background selection intensity. For instance, background selection impacts a much broader range of effect sizes for traits under directional selection via a liability threshold mechanism (i.e. when selection acts on individuals with phenotypes exceeding a threshold) than when fitness decays exponentially with trait value. We also find that background selection can increase the population prevalence of a liability threshold trait (e.g. a complex binary disease trait). Furthermore, while background selection can only reduce the genetic variance of a trait under directional selection (as might be expected from the classical population genetic theory for linked neutral variants) we find that background selection can increase the genetic variance of a trait under stabilizing selection. The results presented here use a synthesis of population genetic and quantitative genetic modeling, and overall, have relevance for understand the genetic architecture and evolution of complex traits, including complex diseases.

Identifiers

PMID40502059
PMCPMC12154738

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