For our next guest post Philipp Messer and Dmitri Petrov write about their paper
The McDonald-Kreitman Test and its Extensions under Frequent Adaptation: Problems and Solutions, arXived here

The McDonald-Kreitman (MK) test is the basis of most modern approaches to measure the rate of adaptation from population genomic data. This test was used to argue that in some organisms, such as Drosophila, the rate of adaptation is surprisingly high. However, the MK test, and in fact most of the current machinery of population genetics, relies on the assumption that adaptation is rare so that the effects of selective sweeps on linked variation can be neglected. We test this assumption using a powerful forward simulation and show that the MK test is severely biased even when the rate of adaptation is only moderate. The biases arise from the complex linkage effects between slightly deleterious and strongly advantageous mutations. In order to deal with these biases, we suggest a new robust approach based on a simple asymptotic extension of the MK test.

We further show that already under very moderate amounts of adaptation, linkage effects from recurrent selective sweeps can profoundly affect key population genetic parameters, such as the fixation probabilities of deleterious mutations and the frequency distributions of polymorphisms. In synonymous polymorphism data, these linkage effects leave signatures that can easily be mistaken for the signatures of recent, severe population expansion.

The bigger claim of our paper is that the effects of linked selection cannot be simply swept under the rug by introducing effective parameters, such as effective population size or effective strength of selection, and then using these effective parameters in formulae derived from the diffusion approximation under the assumption of free recombination. Given that most of our estimates of the key evolutionary parameters are still obtained from methods based on this paradigm, we argue that it is crucial to verify whether they are robust to linkage effects.

Philipp Messer and Dmitri Petrov

Inference of Admixture Parameters in Human Populations Using Weighted Linkage Disequilibrium

Po-Ru Loh, Mark Lipson, Nick Patterson, Priya Moorjani, Joseph K Pickrell, David Reich, Bonnie Berger
(Submitted on 1 Nov 2012)

Long-range migrations and the resulting admixture between populations have been an important force shaping human genetic diversity. Most existing methods for detecting and reconstructing historical admixture events are based on allele frequency divergences or patterns of ancestry segments in chromosomes of admixed individuals. An emerging new approach harnesses the exponential decay of admixture-induced linkage disequilibrium (LD) as a function of genetic distance. Here, we comprehensively develop LD-based inference into a versatile tool for investigating admixture. We present a new weighted LD statistic that can be used to infer mixture proportions as well as dates with fewer constraints on reference populations than previous methods. We define an LD-based three-population test for admixture and identify scenarios in which it can detect admixture that previous formal tests cannot. We further show that we can discover phylogenetic relationships between populations by comparing weighted LD curves obtained using a suite of references. Finally, we describe several improvements to the computation and fitting of weighted LD curves that greatly increase the robustness and speed of the computation. We implement all of these advances in a software package, ALDER, which we validate in simulations and apply to test for admixture among all populations from the Human Genome Diversity Project (HGDP), highlighting insights into the admixture history of Central African Pygmies, Sardinians, and Japanese.

Using haplotype differentiation among hierarchically structured populations for the detection of selection signatures

Marìa Inès Fariello, Simon Boitard, Hugo Naya, Magali SanCristobal, Bertrand Servin
(Submitted on 29 Oct 2012)

The detection of molecular signatures of selection is one of the major concerns of modern population genetics. A widely used strategy in this context is to compare samples from several populations, and to look for genomic regions with outstanding genetic differentiation between these populations. Genetic differentiation is generally based on allele frequency differences between populations, which are measured by Fst or related statistics. Here we introduce a new statistic, denoted hapFLK, which focuses instead on the differences of haplotype frequencies between populations. In contrast to most existing statistics, hapFLK accounts for the hierarchical structure of the sampled populations. Using computer simulations, we show that each of these two features – the use of haplotype information and of the hierarchical structure of populations – significantly improves the detection power of selected loci, and that combining them in the hapFLK statistic provides even greater power. We also show that hapFLK is robust with respect to bottlenecks and migration and improves over existing approaches in many situations. Finally, we apply hapFLK to a set of six sheep breeds from Northern Europe, and identify seven regions under selection, which include already reported regions but also several new ones. We propose a method to help identifying the population(s) under selection in a detected region, which reveals that in many of these regions selection most likely occurred in more than one population. Furthermore, several of the detected regions correspond to incomplete sweeps, where the favourable haplotype is only at intermediate frequency in the population(s) under selection.