Thinking too positive? Revisiting current methods of population genetic selection inference

Abstract

In the age of next-generation sequencing, the availability of increasing amounts and improved quality of data at decreasing cost ought to allow for a better understanding of how natural selection is shaping the genome than ever before. However, alternative forces, such as demography and background selection (BGS), obscure the footprints of positive selection that we would like to identify. In this review, we illustrate recent developments in this area, and outline a roadmap for improved selection inference. We argue (i) that the development and obligatory use of advanced simulation tools is necessary for improved identification of selected loci, (ii) that genomic information from multiple time points will enhance the power of inference, and (iii) that results from experimental evolution should be utilized to better inform population genomic studies.

A simple, semi-deterministic approximation to the distribution of selective sweeps in large populations

“…This would provide an expected distribution of the observed valley (from the distribution ofp 0 ) for given parameters and for both soft and hard sweeps (Pennings and Hermisson, 2006). This explicit probabilistic description would thus allow using maximum likelihood, instead of relying on simulations and ABC methods as is typically done today (Bank et al, 2014). The latter methods can be fairly demanding in computer time, and may suffer from convergence issues.…”

The life aquatic: advances in marine vertebrate genomics

“…The length of time since the selected allele increased in frequency, the strength of the selection and the recombination rate in the region all play a part in the length of the haplotype around a selected allele. Site frequency spectra or linkage-disequilibrium-based data may be used to detect natural selection in a population; however, a recent review cautions that alternative forces, including background selection and demography, may obscure signatures of positive selection 110 . It is possible to detect local adaptation between two or more populations using genetic-environment association analyses 111 or differentiation outlier approaches 112 .…”

The Impact of Linked Selection in Chimpanzees: A Comparative Study

“…Although both processes are likely at play (see Hudson 1994), and strong arguments have been made for the relative importance of one over the other in particular organisms, the pattern itself has been demonstrated to be remarkably pervasive – having been observed across mammals (e.g., Nachman 1997; Lohmueller et al 2011), birds (e.g., Rao et al 2011), insects (e.g., Begun and Aquadro 1992; Stump et al 2005), fungi (e.g., Cutter and Moses 2011), plants (e.g., Dvorák et al 1998), and viruses (e.g., Renzette et al 2016). Although the observation is open to interpretation, an undeniable strength of BGS-based arguments is the fact that there is a far greater proportion of newly arising deleterious mutations compared to newly arising beneficial mutations across the genome, a notion already well appreciated in the early literature of the field (Timofeeff-Ressovsky 1940; Muller 1949, 1950; and see review of Bank et al 2014). Thus, the selective removal of such mutations is likely a very common process.…”