MPL resolves genetic linkage in fitness inference from complex evolutionary histories.
Muhammad Saqib SohailRaymond H Y LouieMatthew R McKayJohn P BartonPublished in: Nature biotechnology (2020)
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.
Keyphrases
- genome wide
- hiv testing
- dna methylation
- copy number
- men who have sex with men
- physical activity
- body composition
- electronic health record
- high resolution
- single cell
- gene expression
- hiv infected
- hepatitis c virus
- hiv positive
- big data
- human immunodeficiency virus
- hiv aids
- case report
- artificial intelligence
- case control