Stephan Schiffels

Population Genetics – Computational Methods - Human History

Illingworth et al. 2011

Quantifying selection acting on a complex trait using allele frequency time-series data. Molecular Biology and Evolution. Published November 23, 2011.

Authors: Christopher J R Illingworth, Leopold Parts, Stephan Schiffels, Gianni Liti and Ville Mustonen

[Website] [PDF]

Abstract: When selection is acting on a large, genetically diverse population, beneficial alleles increase in frequency. This fact can be used to map quantitative trait loci by sequencing the pooled DNA from the population at consecutive time points, and observing allele frequency changes. Here we present a population genetic method to analyse time-series data of allele frequencies from such an experiment. Beginning with a range of proposed evolutionary scenarios, the method measures the consistency of each with the observed frequency changes. Evolutionary theory is utilized to formulate equations of motion for the allele frequencies, following which likelihoods for having observed the sequencing data under each scenario are derived. Comparison of these likelihoods gives an insight into the prevailing dynamics of the system under study. We illustrate the method by quantifying selective effects from an experiment in which two phenotypically different yeast strains were first crossed and then propagated under heat stress (Parts et al., Genome Res. 2011). From these data we discover that about 6\% of polymorphic sites evolve non-neutrally under heat stress condition, either because of their linkage to beneficial (driver) alleles or because they are drivers themselves. We further identify 44 genomic regions containing one or more candidate driver alleles, quantify their apparent selective advantage, obtain estimates of recombination rates within the regions, and show that the dynamics of the drivers display a strong signature of selection going beyond additive models. Our approach is applicable to study adaptation in a range of systems under different evolutionary pressures.