Landmarks in the history of selective sweeps

Abstract

Half a century ago, a seminal article on the hitchhiking effect by Smith and Haigh inaugurated the concept of the selection signature. Selective sweeps are characterised by the rapid spread of an advantageous genetic variant through a population and hence play an important role in shaping evolution and research on genetic diversity. The process by which a beneficial allele arises and becomes fixed in a population, leading to a increase in the frequency of other linked alleles, is known as genetic hitchhiking or genetic draft. Kimura's neutral theory and hitchhiking theory are complementary, with Kimura's neutral evolution as the ‘null model’ and positive selection as the ‘signal’. Both are widely accepted in evolution, especially with genomics enabling precise measurements. Significant advances in genomic technologies, such as next‐generation sequencing, high‐density SNP arrays and powerful bioinformatics tools, have made it possible to systematically investigate selection signatures in a variety of species. Although the history of selection signatures is relatively recent, progress has been made in the last two decades, owing to the increasing availability of large‐scale genomic data and the development of computational methods. In this review, we embark on a journey through the history of research on selective sweeps, ranging from early theoretical work to recent empirical studies that utilise genomic data.

iHDSel software: The Price equation and the population stability index to detect genomic patterns compatible with selective sweeps. An example with SARS-CoV-2.

“…There are many statistics for identifying regions of selective sweeps in genomes, see for example (Horscroft et al 2019; Stephan 2019; Horscroft et al 2020; Abondio et al 2022; Panigrahi et al 2023). The use of machine learning-based methods to detect selection patterns has been increasing due to their accuracy and ability to handle large amounts of complex data.…”

“…That is, if we aim to detect a selection pattern, we train the algorithm with data that we know contains that pattern and with other data without the pattern. Different types of algorithms have been applied: neural networks, extremely randomized trees, and boosting algorithms (Horscroft et al 2019; Panigrahi et al 2023). A major advantage of these methods is their power and flexibility, partly due to the ease of incorporating new statistics with minimal changes to the structure of the method.…”

“…Regarding the detection of the footprint left by selective sweeps in genomes, from the earliest methods that explored haplotype patterns, whether by studying homozygosity (Sabeti et al 2007), its diversity (Kimura et al 2007), or interpopulation differentiation (Chen et al 2010), among others, a great number of methods have been developed and continue to be developed. Significant advancements have been made, including the use of summary statistics, the combination of different statistical strategies, and the incorporation of artificial intelligence-based methods (Horscroft et al 2019; Stephan 2019; Abondio et al 2022; Arnab et al 2023; Panigrahi et al 2023; Whitehouse and Schrider 2023).…”

Whole‐genome resequencing deciphers patterns of genetic diversity, phylogeny, and evolutionary dynamics in Kashmir cattle

“…sweepfinder2 was used to identify selective sweeps through the computation of CLR from site frequency spectrum data in a 50 kb window and with a 20 kb step size (DeGiorgio et al., 2016) (command, “SweepFinder2 ‐lu GridFile FreqFile SpectFile OutFile”). This estimation of CLRs involves comparing the likelihood of the observed site frequency spectrum under a selective sweep model with the likelihood under a neutral model, with regions exhibiting high CLR values indicating potential selective sweeps (Panigrahi et al., 2023). Together, we computed empirical p ‐values for both θπ and CLR windows.…”