Methods to identify signatures of selective sweeps in population genomics data have been actively developed, but mostly do not identify the specific mutation favored by selection. We present a method, iSAFE, that uses a statistic derived solely from population genetics signals to accurately pinpoint the favored mutation in a large region (~5 Mbp). iSAFE does not require any knowledge of demography, specific phenotype under selection, or functional annotations of mutations.

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.

No 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.

The Central Asian Kyrgyz highland population provides a unique opportunity to address genetic diversity and understand the genetic mechanisms underlying high-altitude pulmonary hypertension (HAPH). Although a significant fraction of the population is unaffected, there are susceptible individuals who display HAPH in the absence of any lung, cardiac or hematologic disease. We report herein the analysis of the whole-genome sequencing of healthy individuals compared with HAPH patients and other controls (total n = 33). Genome scans reveal selection signals in various regions, encompassing multiple genes from the first whole-genome sequences focusing on HAPH. We show here evidence of three candidate genes MTMR4, TMOD3 and VCAM1 that are functionally associated with well-known molecular and pathophysiological processes and which likely lead to HAPH in this population. These processes are (a) dysfunctional BMP signaling, (b) disrupted tissue repair processes and (c) abnormal endothelial cell function. Whole-genome sequence of well-characterized patients and controls and using multiple statistical tools uncovered novel candidate genes that belong to pathways central to the pathogenesis of HAPH. These studies on high-altitude human populations are pertinent to the understanding of sea level diseases involving hypoxia as a main element of their pathophysiology.

To detect the genomic mechanisms underlying evolutionary dynamics of adaptation in sexually reproducing organisms, we analyze multigenerational whole genome sequences of Drosophila melanogaster adapting to extreme O2 conditions over an experiment conducted for nearly two decades. We develop methods to analyze time-series genomics data and predict adaptive mechanisms. Here, we report a remarkable level of synchronicity in both hard and soft selective sweeps in replicate populations as well as the arrival of favorable de novo mutations that constitute a few asynchronized sweeps. We additionally make direct experimental observations of rare recombination events that combine multiple alleles on to a single, better-adapted haplotype. Based on the analyses of the genes in genomic intervals, we provide a deeper insight into the mechanisms of genome adaptation that allow complex organisms to survive harsh environments.

No abstract

Methods that scan population genomics data to identify signatures of selective sweep have been actively developed, but mostly do not identify the specific mutation favored by the selective sweep. We present a method, iSAFE that uses population genetics signals and a boosting approach to pinpoint the favored mutation even when the signature of selection extends to 5Mbp. iSAFE was tested extensively on simulated data and 22 known sweeps in human populations using the 1000 genome project data with some evidence for the favored mutation. iSAFE ranked the candidate mutation among the top 13 (out of ∼21,000 variants) in 14 of the 22 loci, did not show a strong signal in 3. We identified previously unreported mutations as being favored in the remaining 5 regions. For these pigmentation related genes, iSAFE identified identical selected mutations in multiple non-African populations suggesting an out-of-Africa onset of selection.