Journal
NATURE BIOTECHNOLOGY
Volume 39, Issue 4, Pages 472-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41587-020-0737-3
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Funding
- Hong Kong Research Grants Council [16201620]
- Australia's National Health and Medical Research Council [APP1121643]
- National Institute of General Medical Sciences of the National Institutes of Health [R35GM138233]
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Genetic linkage plays a significant role in determining the fate of new mutations and their effects on fitness in evolving populations. Resolving genetic linkage is crucial for accurately quantifying selection and understanding the impact of mutations on evolutionary histories.
Genetic linkage causes the fate of new mutations in a population to be contingent on the genetic background on which they appear. This makes it challenging to identify how individual mutations affect fitness. To overcome this challenge, we developed marginal path likelihood (MPL), a method to infer selection from evolutionary histories that resolves genetic linkage. Validation on real and simulated data sets shows that MPL is fast and accurate, outperforming existing inference approaches. We found that resolving linkage is crucial for accurately quantifying selection in complex evolving populations, which we demonstrate through a quantitative analysis of intrahost HIV-1 evolution using multiple patient data sets. Linkage effects generated by variants that sweep rapidly through the population are particularly strong, extending far across the genome. Taken together, our results argue for the importance of resolving linkage in studies of natural selection. A new method models the influence of genetic background on the fitness effects of mutations.
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