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Identifying loci under natural selection from genomic surveys is of great interest in different research areas. Commonly used methods to separate neutral effects from adaptive effects are based on locus-specific population differentiation coefficients to identify outliers. Here we extend such an approach to estimate directly the probability that each locus is subject to selection using a Bayesian method. We also extend it to allow the use of dominant markers like AFLPs. It has been shown that this model is robust to complex demographic scenarios for neutral genetic differentiation. Here we show that the inclusion of isolated populations that underwent a strong bottleneck can lead to a high rate of false positives. Nevertheless, we demonstrate that it is possible to avoid them by carefully choosing the populations that should be included in the analysis. We analyze two previously published data sets: a human data set of codominant markers and a Littorina saxatilis data set of dominant markers. We also perform a detailed sensitivity study to compare the power of the method using amplified fragment length polymorphism (AFLP), SNP, and microsatellite markers. The method has been implemented in a new software available at our website (http://www-leca.ujf-grenoble.fr/logiciels.htm).

We introduce a flexible and robust simulation-based framework to infer demographic parameters from the site frequency spectrum (SFS) computed on large genomic datasets. We show that our composite-likelihood approach allows one to study evolutionary models of arbitrary complexity, which cannot be tackled by other current likelihood-based methods. For simple scenarios, our approach compares favorably in terms of accuracy and speed with ∂a∂i, the current reference in the field, while showing better convergence properties for complex models. We first apply our methodology to non-coding genomic SNP data from four human populations. To infer their demographic history, we compare neutral evolutionary models of increasing complexity, including unsampled populations. We further show the versatility of our framework by extending it to the inference of demographic parameters from SNP chips with known ascertainment, such as that recently released by Affymetrix to study human origins. Whereas previous ways of handling ascertained SNPs were either restricted to a single population or only allowed the inference of divergence time between a pair of populations, our framework can correctly infer parameters of more complex models including the divergence of several populations, bottlenecks and migration. We apply this approach to the reconstruction of African demography using two distinct ascertained human SNP panels studied under two evolutionary models. The two SNP panels lead to globally very similar estimates and confidence intervals, and suggest an ancient divergence (>110 Ky) between Yoruba and San populations. Our methodology appears well suited to the study of complex scenarios from large genomic data sets.

Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models, an analytical formula might be elusive or the likelihood function might be computationally very costly to evaluate. ABC methods bypass the evaluation of the likelihood function. In this way, ABC methods widen the realm of models for which statistical inference can be considered. ABC methods are mathematically well-founded, but they inevitably make assumptions and approximations whose impact needs to be carefully assessed. Furthermore, the wider application domain of ABC exacerbates the challenges of parameter estimation and model selection. ABC has rapidly gained popularity over the last years and in particular for the analysis of complex problems arising in biological sciences (e.g., in population genetics, ecology, epidemiology, and systems biology).

The diagnosis and classification of malignant pleural mesothelioma (MPM) is extremely challenging; obtaining an accurate histopathological diagnosis of the different types and subtypes requires expert assessment and suitable biopsies that are not always available, which can leave doctors uncertain about the patient’s diagnosis, sometimes resulting in a delay in the start of treatment. In this review, we discuss recent major advances in the molecular characterisation of MPM and their implications for histological classification. We detail what is known of the molecular landscape of MPM at the genomic, transcriptomic, and epigenomic levels, describe the similarities and dissimilarities of the multiple molecular classifications that have been proposed, and provide an overview of the current state of knowledge regarding inter- and intra-tumour heterogeneity. We also highlight the current gaps in knowledge and how addressing them would benefit classification, as well as the patients in general.

Although range expansions have occurred recurrently in the history of most species, their genetic consequences have been little investigated. Theoretical studies show that range expansions are quite different from pure demographic expansions and that the extent of recent gene flow conditions expected patterns of molecular diversity within and between populations. Spatially explicit simulation studies have led to unexpected and fascinating results about genetic patterns emerging after a range expansion. For instance, spatial expansions can generate allele frequency gradients, promote the surfing of rare variants into newly occupied territories, induce the structuring of newly colonized areas into distinct sectors of low genetic diversity, or lead to massive introgression of local genes into the genome of an invading species. Interestingly, most of these patterns had been previously attributed to distinct selective processes, showing that taking into account the dynamic nature of a species range can lead to a paradigm shift in our perception of evolutionary processes.

Neuroendocrine tumors (NETs) are a diverse group of malignancies that can occur in various organs, with a notable prevalence in the lungs and gastrointestinal tract, which are the focus of this Review. Although NETs are rare in individual organs, their incidence has increased over recent decades, highlighting the urgent need for current classification systems to evolve by incorporating recent advances in the understanding of NET biology. Several omics studies have revealed molecular subtypes, which, when integrated into existing classification frameworks, may provide more clinically relevant insights for patients with NETs. This Review examines recent progress in elucidating the biology of NETs, with a particular emphasis on the tumor microenvironment and cells of origin. The existence of different cells of origin, which may contribute to distinct molecular groups, along with profiles of immune infiltration - despite being generally low - could explain the emergence of more aggressive cases and the potential for metastatic progression. Given the molecular heterogeneity of NETs and the diversity of their microenvironments and different cells of origin, there is an urgent need to develop morphomolecular classification systems. Such systems would make it possible to better characterize tumor progression, identify new therapeutic targets, and, ultimately, guide the development of personalized therapies.

The interplay of gene flow, genetic drift, and local selective pressure is a dynamic process that has been well studied from a theoretical perspective over the last century. Wright and Haldane laid the foundation for expectations under an island-continent model, demonstrating that an island-specific beneficial allele may be maintained locally if the selection coefficient is larger than the rate of migration of the ancestral allele from the continent. Subsequent extensions of this model have provided considerably more insight. Yet, connecting theoretical results with empirical data has proven challenging, owing to a lack of information on the relationship between genotype, phenotype, and fitness. Here, we examine the demographic and selective history of deer mice in and around the Nebraska Sand Hills, a system in which variation at the Agouti locus affects cryptic coloration that in turn affects the survival of mice in their local habitat. We first genotyped 250 individuals from 11 sites along a transect spanning the Sand Hills at 660,000 single nucleotide polymorphisms across the genome. Using these genomic data, we found that deer mice first colonized the Sand Hills following the last glacial period. Subsequent high rates of gene flow have served to homogenize the majority of the genome between populations on and off the Sand Hills, with the exception of the Agouti pigmentation locus. Furthermore, mutations at this locus are strongly associated with the pigment traits that are strongly correlated with local soil coloration and thus responsible for cryptic coloration.

Helicobacter pylori is an important human pathogen associated with serious gastric diseases. Owing to its medical importance and close relationship with its human host, understanding genomic patterns of global and local adaptation in H. pylori may be of particular significance for both clinical and evolutionary studies. Here we present the first such whole genome analysis of 60 globally distributed strains, from which we inferred worldwide population structure and demographic history and shed light on interesting global and local events of positive selection, with particular emphasis on the evolution of San-associated lineages. Our results indicate a more ancient origin for the association of humans and H. pylori than previously thought. We identify several important perspectives for future clinical research on candidate selected regions that include both previously characterized genes (e.g., transcription elongation factor NusA and tumor necrosis factor alpha-inducing protein Tipα) and hitherto unknown functional genes.

Background Levels of differentiation among populations depend both on demographic and selective factors: genetic drift and local adaptation increase population differentiation, which is eroded by gene flow and balancing selection. We describe here the genomic distribution and the properties of genomic regions with unusually high and low levels of population differentiation in humans to assess the influence of selective and neutral processes on human genetic structure. Methods Individual SNPs of the Human Genome Diversity Panel (HGDP) showing significantly high or low levels of population differentiation were detected under a hierarchical-island model (HIM). A Hidden Markov Model allowed us to detect genomic regions or islands of high or low population differentiation. Results Under the HIM, only 1.5% of all SNPs are significant at the 1% level, but their genomic spatial distribution is significantly non-random. We find evidence that local adaptation shaped high-differentiation islands, as they are enriched for non-synonymous SNPs and overlap with previously identified candidate regions for positive selection. Moreover there is a negative relationship between the size of islands and recombination rate, which is stronger for islands overlapping with genes. Gene ontology analysis supports the role of diet as a major selective pressure in those highly differentiated islands. Low-differentiation islands are also enriched for non-synonymous SNPs, and contain an overly high proportion of genes belonging to the 'Oncogenesis' biological process. Conclusions Even though selection seems to be acting in shaping islands of high population differentiation, neutral demographic processes might have promoted the appearance of some genomic islands since i) as much as 20% of islands are in non-genic regions ii) these non-genic islands are on average two times shorter than genic islands, suggesting a more rapid erosion by recombination, and iii) most loci are strongly differentiated between Africans and non-Africans, a result consistent with known human demographic history.

The evolutionary history of the human pygmy phenotype (small body size), a characteristic of African and Southeast Asian rainforest hunter-gatherers, is largely unknown. Here we use a genome-wide admixture mapping analysis to identify 16 genomic regions that are significantly associated with the pygmy phenotype in the Batwa, a rainforest hunter-gatherer population from Uganda (east central Africa). The identified genomic regions have multiple attributes that provide supporting evidence of genuine association with the pygmy phenotype, including enrichments for SNPs previously associated with stature variation in Europeans and for genes with growth hormone receptor and regulation functions. To test adaptive evolutionary hypotheses, we computed the haplotype-based integrated haplotype score (iHS) statistic and the level of population differentiation (F ST) between the Batwa and their agricultural neighbors, the Bakiga, for each genomic SNP. Both |iHS| and F ST values were significantly higher for SNPs within the Batwa pygmy phenotype-associated regions than the remainder of the genome, a signature of polygenic adaptation. In contrast, when we expanded our analysis to include Baka rainforest hunter-gatherers from Cameroon and Gabon (west central Africa) and Nzebi and Nzime neighboring agriculturalists, we did not observe elevated |iHS| or F ST values in these genomic regions. Together, these results suggest adaptive and at least partially convergent origins of the pygmy phenotype even within Africa, supporting the hypothesis that small body size confers a selective advantage for tropical rainforest hunter-gatherers but raising questions about the antiquity of this behavior.