Inferring phylogenetic trees from multiple sequence alignments often relies upon Markov chain Monte Carlo (MCMC) methods to generate tree samples from a posterior distribution. To give a rigorous approximation of the posterior expectation, one needs to compute the mean of the tree samples and therefore a sound definition of a mean and algorithms for its computation are highly demanded. To the best of our knowledge, no existing method of phylogenetic inference can handle the full set of sample trees, because such trees typically have different topologies. We develop a novel statistical model for the inference of phylogenetic trees based on the tree space due to Billera et al. [2001]. Since it is an Hadamard space, the mean and median are well defined, which we also motivate from a decision theoretic perspective. The actual approximation of the posterior expectation relies on some recent developments in Hadamard spaces (Ba\v{c}\’ak [2013a], Miller et al. [2012]) and the fast computation of geodesics in tree space (Owen and Provan [2011]), which altogether enable to compute medians and means of trees with different topologies. Our intention is to give a full self-contained description of the methods required to approximate posterior expectations. We demonstrate these methods on the small ribosomal subunit rRNA sequence alignment. The posterior expectations obtained on this data set are a meaningful summary of the posterior distribution and the uncertainty about the tree topology.

A protocol for the identification of ancestry informative markers (AIMs) from genome-wide single nucleotide polymorphism (SNP) data is proposed. The protocol consists of three main steps: (a) identification of potential positive selection regions via Fst extremity measurement, (b) SNP screening via two-stage attribute selection and (c) classification model construction using a naive Bayes classifier. The two-stage attribute selection is composed of a newly developed round robin symmetrical uncertainty ranking technique and a wrapper embedded with a naive Bayes classifier. The protocol has been applied to the HapMap Phase II data. Two AIM panels, which consist of 10 and 16 SNPs that lead to complete classification between CEU, CHB, JPT and YRI populations, are identified. Moreover, the panels are at least four times smaller than those reported in previous studies. The results suggest that the protocol could be useful in a scenario involving a larger number of populations.

One of the important challenges in modern phylogenetics is to identify data that can be used to resolve species relationships accurately. Whole-genome shotgun sequencing provides large amounts of data from which to identify phylogenetically informative sites; however, previous studies have required genome assembly or alignment to a reference genome, which is difficult when species are not closely related.
We have developed a pipeline to extract potentially informative sites directly from raw short-read sequence data. Reads are assembled into conserved genome fragments, reads are then aligned to these fragments, and informative sites are identified. This pipeline produced >14000 informative sites from reads for 12 species of Leishmania and a reference genome. When analyzed using standard phylogenetic methods, these data resulted in a fully bifurcating tree with strongly supported nodes.
Our procedure is implemented in the software SISRS (pronounced “scissors”) which is freely available at this https URL.

The vast majority of connections between complex disease and common genetic variants were identified through meta-analysis, a powerful approach that enables large samples sizes while protecting against common artifacts due to population structure, repeated small sample analyses, and/or limitations with sharing individual level data. As the focus of genetic association studies shifts to rare variants, genes and other functional units are becoming the unit of analysis. Here, we propose and evaluate new approaches for meta-analysis of rare variant association. We show that our approach retains useful features of single variant meta-analytic approaches and demonstrate its utility in a study of blood lipid levels in ~18,500 individuals genotyped with exome arrays.

Our view of the microbial world and its impact on human health is changing radically with the ability to sequence uncultured or unculturable microbes sampled directly from their habitats, ability made possible by fast and cheap next generation sequencing technologies. Such recent developments represents a paradigmatic shift in the analysis of habitat biodiversity, be it the human, soil or ocean microbiome. We review here some research examples and results that indicate the importance of the microbiome in our lives and then discus some of the challenges faced by metagenomic experiments and the subsequent analysis of the generated data. We then analyze the economic and social impact on genomic-medicine and research in both developing and developed countries. We support the idea that there are significant benefits in building capacities for developing high-level scientific research in metagenomics in developing countries. Indeed, the notion that developing countries should wait for developed countries to make advances in science and technology that they later import at great cost has recently been challenged.

SARS-CoV is believed to originate from civets and was thought to have been eliminated as a threat after the 2003 outbreak. Here, we show that human SARS-CoV (huSARS-CoV) originated directly from bats, rather than civets, by a cross-species jump in 1991, and formed a human-adapted strain in 1998. Since then huSARS-CoV has evolved further into highly virulent strains with genotype T and a 29-nt deletion mutation, and weakly virulent strains with genotype C but without the 29-nt deletion. The former can cause pneumonia in humans and could be the major causative pathogen of the SARS outbreak, whereas the latter might not cause pneumonia in humans, but evolved the ability to co-utilize civet ACE2 as an entry receptor, leading to interspecies transmission between humans and civets. Three crucial time points – 1991, for the cross-species jump from bats to humans; 1998, for the formation of the human-adapted SARS-CoV; and 2003, when there was an outbreak of SARS in humans – were found to associate with anomalously low annual precipitation and high temperatures in Guangdong. Anti-SARS-CoV sero-positivity was detected in 20% of all the samples tested from Guangzhou children who were born after 2005, suggesting that weakly virulent huSARS-CoVs might still exist in humans. These existing but undetected SARS-CoVs have a large potential to evolve into highly virulent strains when favorable climate conditions occur, highlighting a potential risk for the reemergence of SARS.

Human populations have undergone dramatic changes in population size in the past 100,000 years, including a severe bottleneck of non-African populations and recent explosive population growth. There is currently great interest in how these demographic events may have affected the burden of deleterious mutations in individuals and the allele frequency spectrum of disease mutations in populations. Here we use population genetic models to show that–contrary to previous conjectures–recent human demography has likely had very little impact on the average burden of deleterious mutations carried by individuals. This prediction is supported by exome sequence data showing that African American and European American individuals carry very similar burdens of damaging mutations. We next consider whether recent population growth has increased the importance of very rare mutations in complex traits. Our analysis predicts that for most classes of disease variants, rare alleles are unlikely to contribute a large fraction of the total genetic variance, and that the impact of recent growth is likely to be modest. However, for diseases that have a direct impact on fitness, strongly deleterious rare mutations likely do play important roles, and the impact of very rare mutations will be far greater as a result of recent growth. In summary, demographic history has dramatically impacted patterns of variation in different human populations, but these changes have likely had little impact on either genetic load or on the importance of rare variants for most complex traits.

Random walks on multidimensional nonlinear landscapes are of interest in many areas of science and engineering. In particular, properties of adaptive trajectories on fitness landscapes determine population fates and thus play a central role in evolutionary theory. The topography of fitness landscapes and its effect on evolutionary dynamics have been extensively studied in the literature. We will survey the current research knowledge in this field, focusing on a recently developed systematic approach to characterizing path lengths, mean first-passage times, and other statistics of the path ensemble. This approach, based on general techniques from statistical physics, is applicable to landscapes of arbitrary complexity and structure. It is especially well-suited to quantifying the diversity of stochastic trajectories and repeatability of evolutionary events. We demonstrate this methodology using a biophysical model of protein evolution that describes how proteins maintain stability while evolving new functions.

In a letter published in Molecular Biology Evolution [10], Chen and Zhang argue that the variation of the mutation rate along the Escherichia coli genome that we recently reported [3] cannot be evolutionarily optimised. To support this claim they first attempt to calculate the selective advantage of a local reduction in the mutation rate and conclude that it is not strong enough to be favoured by selection. Second, they analyse the distribution of 166 mutations from a wild-type E. coli K12 MG1655 strain and 1,346 mutations from a repair-deficient strain, and claim to find a positive association between transcription and mutation rate rather than the negative association that we reported. Here we respond to this communication. Briefly, we explain how the long-standing theory of mutation-modifier alleles supports the evolution of local mutation rates within a genome by mechanisms acting on sufficiently large regions of a genome, which is consistent with our original observations [3,4]. We then explain why caution must be exercised when comparing mutations from repair deficient strains to data from wild-type strains, as different mutational processes dominate these conditions. Finally, a reanalysis of the data used by Zhang and Chen with an alternative expression dataset reveals that their conclussions are unreliable.

We propose a new algorithm to do posterior sampling of Kingman’s coalescent, based upon the Particle Markov Chain Monte Carlo methodology. Specifically, the algorithm is an instantiation of the Particle Gibbs Sampling method, which alternately samples coalescent times conditioned on coalescent tree structures, and tree structures conditioned on coalescent times via the conditional Sequential Monte Carlo procedure. We implement our algorithm as a C++ package, and demonstrate its utility via a parameter estimation task in population genetics on both single- and multiple-locus data. The experiment results show that the proposed algorithm performs comparable to or better than several well-developed methods.