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Connecting theory and data to understand recombination rate evolution.

Amy L DapperBret A Payseur
Published in: Philosophical transactions of the Royal Society of London. Series B, Biological sciences (2018)
Meiotic recombination is necessary for successful gametogenesis in most sexually reproducing organisms and is a fundamental genomic parameter, influencing the efficacy of selection and the fate of new mutations. The molecular and evolutionary functions of recombination should impose strong selective constraints on the range of recombination rates. Yet, variation in recombination rate is observed on a variety of genomic and evolutionary scales. In the past decade, empirical studies have described variation in recombination rate within genomes, between individuals, between sexes, between populations and between species. At the same time, theoretical work has provided an increasingly detailed picture of the evolutionary advantages to recombination. Perhaps surprisingly, the causes of natural variation in recombination rate remain poorly understood. We argue that empirical and theoretical approaches to understand the evolution of recombination have proceeded largely independently of each other. Most models that address the evolution of recombination rate were created to explain the evolutionary advantage of recombination rather than quantitative differences in rate among individuals. Conversely, most empirical studies aim to describe variation in recombination rate, rather than to test evolutionary hypotheses. In this Perspective, we argue that efforts to integrate the rich bodies of empirical and theoretical work on recombination rate are crucial to moving this field forward. We provide new directions for the development of theory and the production of data that will jointly close this gap.This article is part of the themed issue 'Evolutionary causes and consequences of recombination rate variation in sexual organisms'.
Keyphrases
  • dna repair
  • dna damage
  • genome wide
  • oxidative stress
  • high resolution
  • dna methylation
  • electronic health record
  • machine learning
  • big data
  • multidrug resistant
  • gram negative
  • deep learning