Biased gene conversion constrains adaptation in Arabidopsis thaliana

Tuomas Hämälä, Peter Tiffin

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Reduction of fitness due to deleterious mutations imposes a limit to adaptive evolution. By characterizing features that influence this genetic load we may better understand constraints on responses to both natural and human-mediated selection. Here, using whole-genome, transcriptome, and methylome data from .600 Arabidopsis thaliana individuals, we set out to identify important features influencing selective constraint. Our analyses reveal that multiple factors underlie the accumulation of maladaptive mutations, including gene expression level, gene network connectivity, and gene-body methylation. We then focus on a feature with major effect, nucleotide composition. The ancestral vs. derived status of segregating alleles suggests that GC-biased gene conversion, a recombination-associated process that increases the frequency of G and C nucleotides regardless of their fitness effects, shapes sequence patterns in A. thaliana. Through estimation of mutational effects, we present evidence that biased gene conversion hinders the purging of deleterious mutations and contributes to a genome-wide signal of decreased efficacy of selection. By comparing these results to two outcrossing relatives, Arabidopsis lyrata and Capsella grandiflora, we find that protein evolution in A. thaliana is as strongly affected by biased gene conversion as in the outcrossing species. Last, we perform simulations to show that natural levels of outcrossing in A. thaliana are sufficient to facilitate biased gene conversion despite increased homozygosity due to selfing. Together, our results show that even predominantly selfing taxa are susceptible to biased gene conversion, suggesting that it may constitute an important constraint to adaptation among plant species.

Original languageEnglish (US)
Pages (from-to)831-846
Number of pages16
Issue number3
StatePublished - Jul 2020

Bibliographical note

Funding Information:
We thank the associate editor, S.I. Wright, and three anonymous reviewers for their comments on improving the manuscript. Computational resources were provided by the Minnesota Supercomputing Institute (MSI) at the University of Minnesota. This work was supported by the National Science Foundation (NSF) grant IOS-1546863. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.

Publisher Copyright:
Copyright © 2020 by the Genetics Society of America.


  • Arabidopsis
  • Biased gene conversion
  • DFE-alpha
  • Deleterious mutations
  • Evolutionary simulations
  • Machine-learning


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