A survey of methods and tools to detect recent and strong positive selection

Abstract

Positive selection occurs when an allele is favored by natural selection. The frequency of the favored allele increases in the population and due to genetic hitchhiking the neighboring linked variation diminishes, creating so-called selective sweeps. Detecting traces of positive selection in genomes is achieved by searching for signatures introduced by selective sweeps, such as regions of reduced variation, a specific shift of the site frequency spectrum, and particular LD patterns in the region. A variety of methods and tools can be used for detecting sweeps, ranging from simple implementations that compute summary statistics such as Tajima’s D, to more advanced statistical approaches that use combinations of statistics, maximum likelihood, machine learning etc. In this survey, we present and discuss summary statistics and software tools, and classify them based on the selective sweep signature they detect, i.e., SFS-based vs. LD-based, as well as their capacity to analyze whole genomes or just subgenomic regions. Additionally, we summarize the results of comparisons among four open-source software releases (SweeD, SweepFinder, SweepFinder2, and OmegaPlus) regarding sensitivity, specificity, and execution times. In equilibrium neutral models or mild bottlenecks, both SFS- and LD-based methods are able to detect selective sweeps accurately. Methods and tools that rely on LD exhibit higher true positive rates than SFS-based ones under the model of a single sweep or recurrent hitchhiking. However, their false positive rate is elevated when a misspecified demographic model is used to represent the null hypothesis. When the correct (or similar to the correct) demographic model is used instead, the false positive rates are considerably reduced. The accuracy of detecting the true target of selection is decreased in bottleneck scenarios. In terms of execution time, LD-based methods are typically faster than SFS-based methods, due to the nature of required arithmetic.

Host and geography together drive early adaptive radiation of Hawaiian planthoppers

“…We first investigated the shape of the site frequency spectrum (SFS), calculating the folded SFS using realSFS (part of the angsd package). In the absence of strong demographic changes or selection, we would expect the SFS to follow an exponential decrease (see e.g., Li et al, ; Pavlidis & Alachiotis, ) and indeed that was what we found. Furthermore, the shape of the SFS can indicate whether the amount of SNP data will be sufficient and whether the data show any signatures of selection or demographic effects (e.g., based on the amount of SNPs with medium allele frequencies).…”

Artificial selection causes significant linkage disequilibrium among multiple unlinked genes in Australian wheat

“…The simulations were performed using the PolySim software, which takes into account the organism's polyploidy and self-pollination. Simulation can offer a more precise detection for the significance threshold than the commonly used outlier approaches (Pavlidis & Alachiotis, 2017).…”

How well do we understand the basis of classic selective sweeps in humans?

“…These characteristics are illustrated in Fig. and have been extensively reviewed elsewhere , so we do not devote space to considering their basis here, but do discuss the evidence for selection for each gold standard.…”

“…More recently, the availability of population‐scale genomic data and appropriate statistical tests have allowed the signatures of classic sweeps to be detected in the genome independently of any understanding of the corresponding phenotype or selective force. Thus, there are now vast catalogues of candidate classic sweeps requiring association with their underlying biology .…”