An Improved Approximate-Bayesian Model-choice Method for Estimating Shared Evolutionary History

An Improved Approximate-Bayesian Model-choice Method for Estimating Shared Evolutionary History

Jamie R. Oaks
(Submitted on 25 Feb 2014)

To understand the processes that generate biodiversity, it is important to account for large-scale processes that affect the evolutionary history of groups of co-distributed populations of organisms. Such events predict temporally clustered divergences times, a pattern that can be estimated using genetic data from co-distributed species. I introduce a new approximate-Bayesian method for comparative phylogeographical model-choice that estimates the temporal distribution of divergences across taxa from multi-locus DNA sequence data. The model is an extension of that implemented in msBayes. By reparameterizing the model, introducing more flexible priors on demographic and divergence-time parameters, and implementing a non-parametric Dirichlet-process prior over divergence models, I improved the robustness, accuracy, and power of the method for estimating shared evolutionary history across taxa. The results demonstrate the improved performance of the new method is due to (1) more appropriate priors on divergence-time and demographic parameters that avoid prohibitively small marginal likelihoods for models with more divergence events, and (2) the Dirichlet-process providing a flexible prior on divergence histories that does not strongly disfavor models with intermediate numbers of divergence events. The new method yields more robust estimates of posterior uncertainty, and thus greatly reduces the tendency of the model to incorrectly estimate biogeographically interesting models with strong support.

Genetic drift opposes mutualism during spatial population expansion

Genetic drift opposes mutualism during spatial population expansion

Melanie JI Muller, Beverly I Neugeboren, David R Nelson, Andrew W Murray
(Submitted on 24 Feb 2014)

Mutualistic interactions benefit both partners, promoting coexistence and genetic diversity. Spatial structure can promote cooperation, but spatial expansions may also make it hard for mutualistic partners to stay together, since genetic drift at the expansion front creates regions of low genetic and species diversity. To explore the antagonism between mutualism and genetic drift, we grew cross-feeding strains of the budding yeast S. cerevisiae on agar surfaces as a model for mutualists undergoing spatial expansions. By supplying varying amounts of the exchanged nutrients, we tuned strength and symmetry of the mutualistic interaction. Strong mutualism suppresses genetic demixing during spatial expansions and thereby maintains diversity, but weak or asymmetric mutualism is overwhelmed by genetic drift even when mutualism is still beneficial, slowing growth and reducing diversity. Theoretical modeling using experimentally measured parameters predicts the size of demixed regions and how strong mutualism must be to survive a spatial expansion.

Strong selective sweeps associated with ampliconic regions in great ape X chromosomes

Strong selective sweeps associated with ampliconic regions in great ape X chromosomes

Kiwoong Nam, Kasper Munch, Asger Hobolth, Julien Y. Dutheil, Krishna Veeramah, August Woerner, Michael F. Hammer, Great Ape Genome Diversity Project, Thomas Mailund, Mikkel H. Schierup
(Submitted on 24 Feb 2014)

The unique inheritance pattern of X chromosomes makes them preferential targets of adaptive evolution. We here investigate natural selection on the X chromosome in all species of great apes. We find that diversity is more strongly reduced around genes on the X compared with autosomes, and that a higher proportion of substitutions results from positive selection. Strikingly, the X exhibits several megabase long regions where diversity is reduced more than five fold. These regions overlap significantly among species, and have a higher singleton proportion, population differentiation, and nonsynonymous to synonymous substitution ratio. We rule out background selection and soft selective sweeps as explanations for these observations, and conclude that several strong selective sweeps have occurred independently in similar regions in several species. Since these regions are strongly associated with ampliconic sequences we propose that intra-genomic conflict between the X and the Y chromosomes is a major driver of X chromosome evolution.

Author post: Genome scans for detecting footprints of local adaptation using a Bayesian factor model

This guest post is by Michael Blum, Eric Bazin, and Nicolas Duforet-Frebourg on their preprint Genome scans for detecting footprints of local adaptation using a Bayesian factor model, available from the arXiv here.

Finding genomic regions subject to local adaptation is a central part of population genomics, which is based on genotyping numerous molecular markers and looking for outlier loci. Most common approaches use measures of genetic differentiation such as Fst. There are many software implementing genome scans based on statistics related to Fst (BayeScan, DetSel, FDist2 , Lositan), and they contribute to the popularity of this approach in population genomics.

However, there are different statistical and computational problems that may arise with approaches based on Fst or related measures. The first problem arises because methods related to Fst assume the so-called F-model, which corresponds to a particular covariance structure for gene frequencies among populations (Bierne et al. 2013). When spatial structure departs from the assumption of the F-model, it can generate many false positives. A second potential problem concerns the computational burden of some Bayesian approaches, which can become an obstacle with large number of SNPs. The last problem is that individuals should be grouped into populations in advance whereas working at the scale of individuals is desirable because it avoids defining populations.

Using a Bayesian factor model, we address the three aforementioned problems. Factor models capture population structure by inferring latent variables called factors. Factor models have already been proposed to ascertain population structure (Engelhardt and Stephens 2010). Here we extend the framework of factor model in order to identify outlier loci in addition to the ascertainment of population structure. Our approach is not the first one to account for deviations to the assumptions of the F-model (Bonhomme et al. 2010, Günther and Coop 2013) but it does not require to define populations by contrast to the previous approaches. Using simulations, we show that factor model can achieve a 2-fold or more reduction of false discovery rate compared to the Fst-related approaches. We also analyze the HGDP human dataset to provide an example of how factor models can be used to detect local adaptation with a large number of SNPs. The Bayesian factor model is implemented in the PCAdapt software and we would be happy to answer to comments or questions regarding the software.

To explain why the factor model generates less false discoveries, we can introduce the notions of mechanistic and phenomenological models. Mechanistic models aim to mimic the biological processes that are thought to have given rise to the data whereas phenomenological models seek only to best describe the data using a statistical model. In the spectrum between mechanistic and phenomenological model, the F-model would stand close to mechanistic models whereas factor models would be closer to the phenomenological ones. Mechanistic models are appealing because they provide quantitative measures that can be related to biologically meaningful parameters. For instance, the parameters of the F-model measures genetic drift that can be related to migration rates, divergence times or population sizes. By contrast, phenomenological models work with mathematical abstractions such as latent factors that can be difficult to interpret biologically. The downside of mechanistic models is that violation of the modeling assumption can invalidate the proposed framework and generate many false discoveries in the context of selection scan. The F-model assumes a particular covariance matrix between populations which is found with star-like population trees for instance. However, more complex models of population structure can arise for various reasons including non-instantaneous divergence or isolation-by-distance, and they will violate the mechanistic assumptions and make phenomenological models preferable.

Michael Blum, Eric Bazin, and Nicolas Duforet-Frebourg

Genetic Analysis of Transformed Phenotypes

Genetic Analysis of Transformed Phenotypes

Nicolo Fusi, Christoph Lippert, Neil D. Lawrence, Oliver Stegle
(Submitted on 21 Feb 2014)

Linear mixed models (LMMs) are a powerful and established tool for studying the genetics of phenotypic variation. A limiting assumption of LMMs is that the phenotype is Gaussian distributed under the model, a requirement that rarely holds in practice. Since violations of this assumption can lead to false conclusions and losses in power, it’s common practice to pre-process the phenotypic values, for instance by applying logarithmic transformations. Unfortunately, these are not appropriate in every situation, and choosing a “good” transformation is in general challenging and subjective. Here, we present an extension of the LMM that estimates an optimal transformation from the data. We show in extensive simulations and real data from human, mouse and yeast that application of these optimal transformations leads to increased power in genome-wide association studies and higher accuracy in heritability estimates and phenotype predictions.

Extensive translation of small ORFs revealed by polysomal ribo-Seq

Extensive translation of small ORFs revealed by polysomal ribo-Seq

Julie L Aspden, Ying Chen Eyre-Walker, Rose J. Phillips, Michele Brocard, Unum Amin, Juan Couso

Thousands of small Open Reading Frames (smORFs) encoding small peptides of fewer than 100 amino acids exist in our genomes. Examples of functional smORFs have been characterised in a few species but the actual number of translated smORFs, and their molecular, functional and evolutionary features are not known. Here we present a genome-wide assessment of smORF translation by ribosomal profiling of polysomal fractions. This ‘polysomal ribo-Seq’ suggests that smORFs are translated at the same level and in the same relative numbers (80%) as normal proteins. The smORF peptides appear widely conserved, show activity in cells, and display a putative amino acid signature. These findings reinforce the idea that smORFs are an abundant and fundamental genome component, displaying features usually attributed to canonical proteins, including high translation levels, biological function, amino acid sequence specificity and cross-species conservation.

Genome scans for detecting footprints of local adaptation using a Bayesian factor model

Genome scans for detecting footprints of local adaptation using a Bayesian factor model

N. Duforet-Frebourg, E. Bazin, M.G.B. Blum
(Submitted on 21 Feb 2014)

A central part of population genomics consists of finding genomic regions implicated in local adaptation. Population genomic analyses are based on genotyping numerous molecular markers and looking for outlier loci in terms of patterns of genetic differentiation. One of the most common approach for selection scan is based on statistics that measure population differentiation such as FST. However they are important caveats with approaches related to FST because they require grouping individuals into populations and they additionally assume a particular model of population structure. Here we implement a more flexible individual-based approach based on Bayesian factor models. Using hierarchical Bayesian modeling, we both infer population structure and identify outlier loci that are candidates for local adaptation. Factor models are strongly related to principal components analysis (PCA) and they model population structure with latent variables called factors. The hierarchical factor model considers that outlier loci are atypically explained by one of the factors. In a model of population divergence, we show that it can achieve a 2-fold or more reduction of false discovery rate compared to the software BayeScan or compared to a FST approach. We show that our software can handle large SNP datasets by analyzing the HGDP SNP dataset. The Bayesian factor model is implemented in the command-line PCAdapt software.