Abstract
Evolve-and-resequence experiments leverage next-generation sequencing technology to track allele frequency dynamics of populations as they evolve. While previous work has shown that adaptive alleles can be detected by comparing frequency trajectories from many replicate populations, this power comes at the expense of high-coverage (>100x) sequencing of many pooled samples, which can be cost-prohibitive. Here we show that accurate estimates of allele frequencies can be achieved with very shallow sequencing depths (<5x) via inference of known founder haplotypes in small genomic windows. This technique can be used to efficiently estimate frequencies for any number of alleles in any model system. Using both experimentally-pooled and simulated samples of Drosophila melanogaster, we show that haplotype inference can improve allele frequency accuracy by orders of magnitude, and that high accuracy is maintained after up to 200 generations of recombination, even in the presence of missing data or incomplete founder knowledge. By reducing sequencing costs without sacrificing accuracy, our method enables analysis of samples from more timepoints and replicates, increasing the statistical power to detect adaptive alleles.