Hierarchical Bayesian model of population structure reveals convergent adaptation to high altitude in human populations

Matthieu Foll, Oscar E. Gaggiotti, Josephine T. Daub, Laurent Excoffier
(Submitted on 18 Feb 2014)

Detecting genes involved in local adaptation is challenging and of fundamental importance in evolutionary, quantitative, and medical genetics. To this aim, a standard strategy is to perform genome scans in populations of different origins and environments, looking for genomic regions of high differentiation. Because shared population history or population sub-structure may lead to an excess of false positives, analyses are often done on multiple pairs of populations, which leads to i) a global loss of power as compared to a global analysis, and ii) the need for multiple tests corrections. In order to alleviate these problems, we introduce a new hierarchical Bayesian method to detect markers under selection that can deal with complex demographic histories, where sampled populations share part of their history. Simulations show that our approach is both more powerful and less prone to false positive loci than approaches based on separate analyses of pairs of populations or those ignoring existing complex structures. In addition, our method can identify selection occurring at different levels (i.e. population or region-specific adaptation), as well as convergent selection in different regions. We apply our approach to the analysis of a large SNP dataset from low- and high-altitude human populations from America and Asia. The simultaneous analysis of these two geographic areas allows us to identify several new candidate genome regions for altitudinal selection, and we show that convergent evolution among continents has been quite common. In addition to identifying several genes and biological processes involved in high altitude adaptation, we identify two specific biological pathways that could have evolved in both continents to counter toxic effects induced by hypoxia.

Estimate of Within Population Incremental Selection Through Branch Imbalance in Lineage Trees
Gilad Liberman, Jennifer Benichou, Lea Tsaban, yaakov maman, Jacob Glanville, yoram louzoun

Incremental selection within a population, defined as a limited fitness change following a mutation, is an important aspect of many evolutionary processes and can significantly affect a large number of mutations through the genome. Strongly advantageous or deleterious mutations are detected through the fixation of mutations in the population, using the synonymous to non-synonymous mutations ratio in sequences. There are currently to precise methods to estimate incremental selection occurring over limited periods. We here provide for the first time such a detailed method and show its precision and its applicability to the genomic analysis of selection. A special case of evolution is rapid, short term micro-evolution, where organism are under constant adaptation, occurring for example in viruses infecting a new host, B cells mutating during a germinal center reactions or mitochondria evolving within a given host. The proposed method is a novel mixed lineage tree/sequence based method to detect within population selection as defined by the effect of mutations on the average number of offspring. Specifically, we pro-pose to measure the log of the ratio between the number of leaves in lineage trees branches following synonymous and non-synonymous mutations. This method does not suffer from the need of a baseline model and is practically not affected by sampling biases. In order to show the wide applicability of this method, we apply it to multiple cases of micro-evolution, and show that it can detect genes and inter-genic regions using the selection rate and detect selection pressures in viral proteins and in the immune response to pathogens.

Response to a population bottleneck can be used to infer recessive selection
Daniel J. Balick, Ron Do, David Reich, Shamil R. Sunyaev
(Submitted on 11 Dec 2013)

Here we present the first genome wide statistical test for recessive selection. This test uses explicitly non-equilibrium demographic differences between populations to infer the mode of selection. By analyzing the transient response to a population bottleneck and subsequent re-expansion, we qualitatively distinguish between alleles under additive and recessive selection. We analyze the response of the average number of deleterious mutations per haploid individual and describe time dependence of this quantity. We introduce a statistic, BR, to compare the number of mutations in different populations and detail its functional dependence on the strength of selection and the intensity of the population bottleneck. This test can be used to detect the predominant mode of selection on the genome wide or regional level, as well as among a sufficiently large set of medically or functionally relevant alleles.

Evaluating the use of ABBA-BABA statistics to locate introgressed loci
Simon Henry Martin, John William Davey, Chris D Jiggins

Several methods have been proposed to test for introgression across genomes. One method identifies an excess of shared derived alleles between taxa using Patterson’s D statistic, but does not establish which loci show such an excess or whether the excess is due to introgression or ancestral population structure. Smith and Kronforst (2013) propose that, at loci identified as outliers for the D statistic, introgression is indicated by a reduction in absolute genetic divergence (dXY) between taxa with shared ancestry, whereas ancestral structure produces no reduction in dXY at these loci. Here, we use simulations and Heliconius butterfly data to investigate the behavior of D when applied to small genomic regions. We find that D imperfectly identifies loci with shared ancestry in many scenarios due to a bias in regions with few segregating sites. A related statistic, f, is mostly robust to this bias but becomes less accurate as gene flow becomes more ancient. Although reduced dXY does indicate introgression when loci with shared ancestry can be accurately detected, both D and f systematically identify regions of lower dXY in the presence of both gene flow and ancestral structure, so detecting a reduction in dXY at D or f outliers is not sufficient to infer introgression. However, models including gene flow produced a larger reduction in dXY than models including ancestral structure in almost all cases, so this reduction may be suggestive, but not conclusive, evidence for introgression.

Probabilistic models of genetic variation in structured populations applied to global human studies
Wei Hao, Minsun Song, John D. Storey
(Submitted on 7 Dec 2013)

Modern population genetics studies typically involve genome-wide genotyping of individuals from a diverse network of ancestries. An important, unsolved problem is how to formulate and estimate probabilistic models of observed genotypes that allow for complex population structure. We formulate two general probabilistic models, and we propose computationally efficient algorithms to estimate them. First, we show how principal component analysis (PCA) can be utilized to estimate a general model that includes the well-known Pritchard-Stephens-Donnelly mixed-membership model as a special case. Noting some drawbacks of this approach, we introduce a new “logistic factor analysis” (LFA) framework that seeks to directly model the logit transformation of probabilities underlying observed genotypes in terms of latent variables that capture population structure. We demonstrate these advances on data from the human genome diversity panel and 1000 genomes project, where we are able to identify SNPs that are highly differentiated with respect to structure while making minimal modeling assumptions.