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 quality of data at decreasing cost ought to allow for a better understanding of how natural selection is shaping the genome than ever before. Yet, alternative forces such as demography and background selection obscure the footprints of positive selection that we would like to identify. Here, we illustrate recent developments in this area, and outline a roadmap for improved selection inference. We argue (1) that the development and obligatory use of advanced simulation tools is necessary for improved identification of selected loci, (2) that genomic information from multipletime points will enhance the power of inference, and (3) that results from experimental evolution should be utilized to better inform population-genomic studies.. CC-BY-NC-ND 4.0 International license not peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was . http://dx.doi.org/10.1101/009654 doi: bioRxiv preprint first posted online Sep. 25, 2014; 3 Identification of beneficial mutations in the genome: an ongoing questThe identification of genetic variants that confer an advantage to an organism, and that have spread by forces other than chance, remains as an important question in evolutionary biology. Success in this regard will have broad implications not only for informing our view of the process of evolution itself, but also for evolutionary applications ranging from clinical to ecological. Despite the tremendous quantity of polymorphism data now at our fingertips, which in principle ought to allow for a better characterization of such adaptive genetic variants, it remains a challenge to unambiguously identify alleles under selection. This is primarily owing to the difficulty in disentangling the effects of positive selection from those of other factors that shape the composition of genomes, including both demography as well as other selective processes.Approaches to identify positively selected variants from genomic data can be broadly divided into two categories: those that make use of within-population polymorphism data, and those that make use of between-population/species data. While each approach has its respective merits, we here focus on recent developments in population-genetic inference from polymorphism data in both natural and experimental settings (see [1,2] for more general reviews, and [3,4] for recent and specific literature on divergence-based selection inference). For population-genetic inference from single-time point polymorphism data (as is most commonly the case) this includes not only sophisticated statistical methods, but also simulation programs that enable us to model expected genomic signatures under a wide variety of possible scenarios. Alternatively, data from multiple-time points -such as those recently afforded by ancient genomic data as well as many clinical and experimental datasetscan be used to greatly improve inference by catching a selective sweep...

A Coalescent Model for a Sweep of a Unique Standing Variant

“…If population size is variable, particularly over the course of the standing phase, then the ESF fails to accurately describe recombinations during this phase, just as it fails to accurately describe the infinite-alleles model with nonequilibrium demographics. Inference methods based on our analytical calculations would likely be inaccurate in these situations (Bank et al 2014). Nonetheless, the general insight remains that, holding demography equal, sweeps from standing variation will generate genealogies with longer internal branches than classic hard sweeps and will therefore be characterized by more intermediate-frequency haplotypes.…”

Inference Under a Wright-Fisher Model Using an Accurate Beta Approximation

“…In the recent years, there has been a great focus on inference of selection coefficients from time-series data under a Wright-Fisher model (Malaspinas et al 2012;Bank et al 2014;Steinrücken et al 2014;Foll et al 2015;Terhorst et al 2015). A newly developed statistical method aims at modeling the evolution of multilocus alleles under a Wright-Fisher model with selection (Terhorst et al 2015) by fitting a multivariate normal distribution from the first moments of the DAF.…”

Interpreting differentiation landscapes in the light of long-term linked selection

“…The past years have seen an unprecedented quest for such regions (Haasl & Payseur 2016) that assumed accentuated differentiation to evolve trough processes related to adaptation or speciation, in particular positive selection of beneficial variants (Maynard Smith & Haigh 1974;Kaplan et al 1989) or selection against gene flow (Turner et al 2005) in extant populations or species (in the following referred to as "extant lineages", see Glossary). However, recent research highlights that accentuated differentiation may evolve through processes other than positive selection (e. g., Bank et al 2014;Cruickshank & Hahn 2014;Haasl & Payseur 2016).…”