A Coalescent Model for a Sweep of a Unique Standing Variant

Abstract

The use of genetic polymorphism data to understand the dynamics of adaptation and identify the loci that are involved has become a major pursuit of modern evolutionary genetics. In addition to the classical "hard sweep" hitchhiking model, recent research has drawn attention to the fact that the dynamics of adaptation can play out in a variety of different ways and that the specific signatures left behind in population genetic data may depend somewhat strongly on these dynamics. One particular model for which a large number of empirical examples are already known is that in which a single derived mutation arises and drifts to some low frequency before an environmental change causes the allele to become beneficial and sweeps to fixation. Here, we pursue an analytical investigation of this model, bolstered and extended via simulation study. We use coalescent theory to develop an analytical approximation for the effect of a sweep from standing variation on the genealogy at the locus of the selected allele and sites tightly linked to it. We show that the distribution of haplotypes that the selected allele is present on at the time of the environmental change can be approximated by considering recombinant haplotypes as alleles in the infinite-alleles model. We show that this approximation can be leveraged to make accurate predictions regarding patterns of genetic polymorphism following such a sweep. We then use simulations to highlight which sources of haplotypic information are likely to be most useful in distinguishing this model from neutrality, as well as from other sweep models, such as the classic hard sweep and multiple-mutation soft sweeps. We find that in general, adaptation from a unique standing variant will likely be difficult to detect on the basis of genetic polymorphism data from a single population time point alone, and when it can be detected, it will be difficult to distinguish from other varieties of selective sweeps. Samples from multiple populations and/or time points have the potential to ease this difficulty.KEYWORDS coalescent theory; genetic hitchhiking; natural selection; soft sweep; standing variation I N recent decades, an understanding of how positive directional selection and the associated hitchhiking effect influence patterns of genetic variation has become a valuable tool for evolutionary geneticists. The reductions in genetic diversity and long extended haplotypes that are characteristic of a recent selective sweep can allow for both the identification of individual genes that have contributed to recent adaptation within a population (i.e., hitchhiking mapping) and understanding the rate and dynamics of adaptation at a genome-wide level (Wiehe and Stephan 1993;Andolfatto 2007;Eyre-Walker and Keightley 2009;Elyashiv et al. 2014).While the contribution of many different modes to the adaptive process has long been recognized, early work on the hitchhiking effect focused largely on the scenario where a single codominant mutation arose and was immediately beneficial, rapidly sweep...

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“…The relative contributions of de novo variants, standing variation, and the number of competing beneficial variants depend in part on the strength of selection, its spatial patterning, existing genetic diversity and the beneficial mutation rate. Although modes of evolution can be difficult to distinguish (23), our data are revealing. We observe signals of convergence and divergence.…”

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“…b). Berg & Coop () show that the number and frequencies of these early recombination haplotypes follow the Ewens sampling distribution (Ewens ). The net effect is a weakening of the sweep signal, especially close to the selected site (where the hard sweep signal is strongest): the core region is narrower and nearby flanking regions are not strongly dominated by low‐frequency variants.…”

“…Indeed, equivalent models have been used to describe recessive hard sweeps (Ewing et al . ) and single‐origin soft sweeps from SGV (Berg & Coop ). As stressed by Berg & Coop (), both scenarios can only be distinguished if additional biological information about dominance is available.…”

“…The notion of a 'soft sweep' as defined here following previous work (e.g. Hermisson & Pennings 2005;Messer & Petrov 2013;Jensen 2014;Berg & Coop 2015) is therefore not synonymous for 'adaptation footprint from SGV'. Sweep types refer to classes of patterns that result from characteristic coalescent genealogies, not to evolutionary processes.…”

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“…2B) (47). Although recent methods have increased the power to scan for soft sweeps (48, 49), their detection is still limited because of relatively weak genomic signals (50) and because regions flanking a hard sweep (the “shoulders”) can mimic a soft sweep (51, 52). Development of machine-learning methods, which can effectively “learn” the appropriate signals within complex data when given training examples, may be effective for detecting hard and soft selective sweeps (53).…”