Clear: Composition of Likelihoods for Evolve and Resequence Experiments

Abstract

The advent of next generation sequencing technologies has made whole-genome and whole-population sampling possible, even for eukaryotes with large genomes. With this development, experimental evolution studies can be designed to observe molecular evolution “in action” via evolve-and-resequence (E&R) experiments. Among other applications, E&R studies can be used to locate the genes and variants responsible for genetic adaptation. Most existing literature on time-series data analysis often assumes large population size, accurate allele frequency estimates, or wide time spans. These assumptions do not hold in many E&R studies. In this article, we propose a method—composition of likelihoods for evolve-and-resequence experiments (Clear)—to identify signatures of selection in small population E&R experiments. Clear takes whole-genome sequences of pools of individuals as input, and properly addresses heterogeneous ascertainment bias resulting from uneven coverage. Clear also provides unbiased estimates of model parameters, including population size, selection strength, and dominance, while being computationally efficient. Extensive simulations show that Clear achieves higher power in detecting and localizing selection over a wide range of parameters, and is robust to variation of coverage. We applied the Clear statistic to multiple E&R experiments, including data from a study of adaptation of Drosophila melanogaster to alternating temperatures and a study of outcrossing yeast populations, and identified multiple regions under selection with genome-wide significance.

Identifying consistent allele frequency differences in studies of stratified populations

“…(,), and also re‐analysed in Topa et al. () and Iranmehr, Akbari, Shlötterer, and Bafna (). Here, we compare the results from the original study and re‐analyse the raw data with some modifications.…”

“…These data are reanalysed using quasibinomial GLMs as above. The original data analysis is described in Orozco-terWengel et al (2012a,2012b, and also re-analysed in Topa et al (2015) and Iranmehr, Akbari, Shlötterer, and Bafna (2016). Here, we compare the results from the original study and re-analyse the raw data with some modifications.…”

Optimizing the power to identify the genetic basis of complex traits with Evolve and Resequence studies

“…Recently however several test-statistics became available that utilize time series data, i.e. allele frequencies estimates for multiple time points (> 2) during the experiment (Topa et al, 2015;Iranmehr et al, 2017;Spitzer et al, 2019). We were interested if our conclusion, that an increasing regime enhances the power to identify QTNs, also holds when a time-series based test-statistic is used.…”

Benchmarking software tools for detecting and quantifying selection in Evolve and Resequencing studies

“…Composition of Likelihoods for Evolve and Resequence experiments (CLEAR). To detect selected loci, CLEAR uses a Hidden Markov Model consisting of an underlying Wright-Fisher process and observed allele frequency counts from pool-sequenced organisms (Iranmehr et al, 2017). Besides estimating selection coefficients, CLEAR also provides estimates for N e and h. We evaluated the performance of the software tools with individual-based forward simulations with MimicrEE2 (Vlachos and Kofler, 2018).…”

“…The great potential of E&R studies in combination with the continuously growing data sets of powerful experiments has driven the development of a diverse set of methods to detect selected SNPs, which change in allele frequency more than expected under neutrality (Iranmehr et al, 2017;Spitzer et al, 2019;Kofler et al, 2011;Taus et al, 2017;Kelly and Hughes, 2019;Wiberg et al, 2017;Topa et al, 2015;Feder et al, 2014;Mathieson and McVean, 2013). Some of the published methods use this information to estimate the underlying selection coefficient and dominance (Iranmehr et al, 2017;Taus et al, 2017;Foll et al, 2015;Mathieson and McVean, 2013). While publications reporting new software tools typically include some comparisons to previously published ones, a systematic comparison of the currently available tools with standardized data sets is still missing.…”