Ploidy and the Predictability of Evolution in Fisher’s Geometric Model
Sandeep Venkataram, Diamantis Sellis, Dmitri A Petrov

Predicting adaptive evolutionary trajectories is a primary goal of evolutionary biology. One can differentiate between forward and backward predictability, where forward predictability measures the likelihood of the same adaptive trajectory occurring in independent evolutions and backward predictability measures the likelihood of a particular adaptive path given the knowledge of starting and final states. Recent studies have attempted to measure both forward and backward predictability using experimental evolution in asexual haploid microorganisms. Similar experiments in diploid organisms have not been conducted. Here we simulate adaptive walks using Fisher’s Geometric Model in haploids and diploids and find that adaptive walks in diploids are less forward- and more backward-predictable than adaptive walks in haploids. We argue that the difference is due to the ability of diploids in our simulations to generate transiently stable polymorphisms and to allow adaptive mutations of larger phenotypic effect. As stable polymorphisms can be generated in both haploid and diploid natural populations through a number of mechanisms, we argue that inferences based on experiments in which adaptive walks proceed through succession of monomorphic states might miss many of the key features of adaptation.

The effect of linkage on establishment and survival of locally beneficial mutations
Simon Aeschbacher, Reinhard Buerger
(Submitted on 25 Nov 2013)

When organisms adapt to spatially heterogeneous environments, selection may drive divergence at multiple genes. If populations under divergent selection also exchange migrants, we expect genetic differentiation to be high at selected loci, relative to the baseline caused by migration and genetic drift. Indeed, empirical studies have found peaks of putatively adaptive differentiation. These are highly variable in length, some of them extending over several hundreds of thousands of base pairs. How can such `islands of differentiation’ be explained? Physical linkage produces elevated levels of differentiation at loci close to genes under selection. However, whether this is enough to account for the observed patterns of divergence is not well understood. Here, we investigate the fate of a locally beneficial mutation that arises in linkage to an existing migration-selection polymorphism and derive two important quantities: the probability that the mutation becomes established, and the expected time to its extinction. We find that intermediate levels of recombinations are sometimes favourable, and that physical linkage can lead to strongly elevated invasion probabilities and extinction times. We provide a rule of thumb for when this is the case. Moreover, we quantify the long-term effect of polygenic local adaptation on linked neutral variation.

Computational inference beyond Kingman’s coalescent
Jere Koskela, Paul Jenkins, Dario Spano
(Submitted on 22 Nov 2013)

Full likelihood inference under Kingman’s coalescent is a computationally challenging problem to which importance sampling (IS) and the product of approximate conditionals (PAC) method have been applied successfully. Both methods can be expressed in terms of families of intractable conditional sampling distributions (CSDs), and rely on principled approximations for accurate inference. Recently, more general Λ- and Ξ-coalescents have been observed to provide better modelling fits to some genetic data sets. We derive families of approximate CSDs for finite sites Λ- and Ξ-coalescents, and use them to obtain “approximately optimal” IS and PAC algorithms for Λ-coalescents, yielding substantial gains in efficiency over existing methods.