Journal
G3-GENES GENOMES GENETICS
Volume 12, Issue 11, Pages -Publisher
OXFORD UNIV PRESS INC
DOI: 10.1093/g3journal/jkac236
Keywords
recombination; population genetics; gene flow; linkage disequilibrium; methods
Categories
Funding
- National Science Foundation [DEB-1545627, 1754022, 1754439]
- Natural Sciences and Engineering Research Council of Canada Postdoctoral Fellowship
- Direct For Biological Sciences
- Division Of Environmental Biology [1754439] Funding Source: National Science Foundation
- Division Of Environmental Biology
- Direct For Biological Sciences [1754022] Funding Source: National Science Foundation
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Accurate estimation of recombination rate is crucial for evolutionary research. Current linkage disequilibrium-based methods make simplifying assumptions and gene flow can affect the accuracy of recombination rate estimation, resulting in overestimation or underestimation of recombination rates, as well as incorrect detection of interpopulation differences in recombination rate.
Accurate estimates of the rate of recombination are key to understanding a host of evolutionary processes as well as the evolution of the recombination rate itself. Model-based population genetic methods that infer recombination rates from patterns of linkage disequilibrium in the genome have become a popular method to estimate rates of recombination. However, these linkage disequilibrium-based methods make a variety of simplifying assumptions about the populations of interest that are often not met in natural populations. One such assumption is the absence of gene flow from other populations. Here, we use forward-time population genetic simulations of isolation-with-migration scenarios to explore how gene flow affects the accuracy of linkage disequilibrium-based estimators of recombination rate. We find that moderate levels of gene flow can result in either the overestimation or underestimation of recombination rates by up to 20-50% depending on the timing of divergence. We also find that these biases can affect the detection of interpopulation differences in recombination rate, causing both false positives and false negatives depending on the scenario. We discuss future possibilities for mitigating these biases and recommend that investigators exercise caution and confirm that their study populations meet assumptions before deploying these methods.
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