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.

Resistance is futile: A CRISPR homing gene drive targeting a haplolethal gene

“…With these parameters, the effective population size (set to be the average of the census size of the two generations in each generational transition) of the cage was inferred to be 5.6% of the census population size, with a 95% confidence interval of 1.6% to 13.8%. This estimate was similar to previous cages 26 , but the wider confidence interval was likely due to our inability to distinguish between drive homozygotes and heterozygotes. Our estimate of the fitness parameter was 1.1, indicating a higher fitness than wild-type individuals.…”

“…To assess the fitness of the drive allele, we adapted a maximum likelihood approach we developed previously 26 for gene drive inheritance and a haplolethal target. Drive performance for both males and females with two copies of Cas9 was estimated as above.…”

Predicting the Genomic Resolution of Bulk Segregant Analysis

“…In practice, the value of N e will typically be smaller than the actual number of individuals present in the interbreeding population, especially when there is high variance in o↵spring number among individuals [5]. While various methods have been developed for inferring N e of experimental populations [2,16,19,36], such inference may be non-trivial, and it may thus be unclear how to choose the appropriate value for this parameter; at a minimum, however, the population size of the interbreeding population constitutes an upper bound for N e . Our analysis suggests that N e should generally be kept as large as possible throughout the experiment to optimize mapping resolution.…”

The complete chloroplast genome sequence and phylogenetic analysis of Ilex ‘Beryl’, a hybrid of Ilex cornuta × Ilex latifolia (Aquifoliaceae)

“…To determine the phylogenetic status of I. 'Beryl', 15 other Ilex and one Helwingia chloroplast genomes from Aquifoliaceae (Yao et al 2016;Cascales et al 2017;Park et al 2019;Su et al 2020) were used for constructing maximum likelihood (bootstrap repeat is 1,000) phylogenetic trees using PhyML v3.0 (http://www.atgc-montpellier.fr/phyml/) (Liu et al 2019). The phylogenetic tree show that 'Beryl' is clustered into the Ilex section, and has more closely related to hybrid lines of I.…”