Genetic Diversity in the Interference Selection Limit


A central goal of evolutionary genetics is to understand how natural selection influences DNA sequence variability. Yet while empirical studies have uncovered significant evidence for selection in many natural populations, a rigorous characterization of these selection pressures has so far been difficult to achieve. The problem is that when selection acts on linked loci, it introduces correlations along the genome that are difficult to disentangle. These “interference” effects have been extensively studied in simulation, but theory still struggles to account for interference in predicted patterns of sequence variability, which limits the quantitative conclusions that can be drawn from modern sequence data. Here, we show that in spite of this complexity, simple behavior emerges in the limit that interference is common. Patterns of molecular evolution depend on the variance in fitness within the population, and are only weakly influenced by the fitness effects of individual mutations. We leverage this “emergent simplicity” to establish a new framework for predicting genetic diversity in these populations. Our results have important practical implications for the interpretation of natural sequence variability, particularly in regions of low recombination, and suggest an inherent “resolution limit” for the quantitative inference of selection pressures from sequence polymorphism data.


Vyšlo v časopise: Genetic Diversity in the Interference Selection Limit. PLoS Genet 10(3): e32767. doi:10.1371/journal.pgen.1004222
Kategorie: Research Article
prolekare.web.journal.doi_sk: 10.1371/journal.pgen.1004222

Souhrn

A central goal of evolutionary genetics is to understand how natural selection influences DNA sequence variability. Yet while empirical studies have uncovered significant evidence for selection in many natural populations, a rigorous characterization of these selection pressures has so far been difficult to achieve. The problem is that when selection acts on linked loci, it introduces correlations along the genome that are difficult to disentangle. These “interference” effects have been extensively studied in simulation, but theory still struggles to account for interference in predicted patterns of sequence variability, which limits the quantitative conclusions that can be drawn from modern sequence data. Here, we show that in spite of this complexity, simple behavior emerges in the limit that interference is common. Patterns of molecular evolution depend on the variance in fitness within the population, and are only weakly influenced by the fitness effects of individual mutations. We leverage this “emergent simplicity” to establish a new framework for predicting genetic diversity in these populations. Our results have important practical implications for the interpretation of natural sequence variability, particularly in regions of low recombination, and suggest an inherent “resolution limit” for the quantitative inference of selection pressures from sequence polymorphism data.


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