Maximum likelihood estimation of fitness components in experimental evolution

Abstract

Estimating fitness differences between allelic variants is a central goal of experimental evolution. Current methods for inferring selection from allele frequency time series typically assume that evolutionary dynamics at the locus of interest can be described by a fixed selection coefficient. However, fitness is an aggregate of several components including mating success, fecundity, and viability, and distinguishing between these components could be critical in many scenarios. Here we develop a flexible maximum likelihood framework that can disentangle different components of fitness and estimate them individually in males and females from genotype frequency data. As a proof-of-principle, we apply our method to experimentally-evolved cage populations of Drosophila melanogaster, in which we tracked the relative frequencies of a loss-of-function and wild-type allele of yellow. This X-linked gene produces a recessive yellow phenotype when disrupted and is involved in male courtship ability. We find that the fitness costs of the yellow phenotype take the form of substantially reduced mating preference of wild-type females for yellow males, together with a modest reduction in the viability of yellow males and females. Our framework should be generally applicable to situations where it is important to quantify fitness components of specific genetic variants, including quantitative characterization of the population dynamics of CRISPR gene drives.