Coherent and incoherent inference in phylogeography and human evolution

Human genetics and molecular genomics of Chiari malformation type 1

Chiari malformation type 1 (CM1) is the most common structural brain disorder involving the craniocervical junction, characterized by caudal displacement of the cerebellar tonsils below the foramen magnum into the spinal canal. Despite the heterogeneity of CM1, its poorly understood patho-etiology has led to a 'one-size-fits-all' surgical approach, with predictably high rates of morbidity and treatment failure. In this review we present multiplex CM1 families, associated Mendelian syndromes, and candidate genes from recent whole exome sequencing (WES) and other genetic studies that suggest a significant genetic contribution from inherited and de novo germline variants impacting transcription regulation, craniovertebral osteogenesis, and embryonic developmental signaling. We suggest that more extensive WES may identify clinically relevant, genetically defined CM1 subtypes distinguished by unique neuroradiographic and neurophysiological endophenotypes.

Human mitochondrial DNA diversity is compatible with the multiregional continuity theory of the origin of Homo sapiens

Confidence intervals for estimates of human mtDNA sequence diversity, chimpanzee-human mtDNA sequence divergence, and the time of splitting of the pongid-hominid lineages are presented. Consistent with all the data used in estimating the coalescence time for human mitochondrial lineages to a common ancestral mitochondrion is a range of dates from less than 79,000 years ago to more than 1,139,000 years ago. Consequently, the hypothesis that a migration of modern humans (Homo sapiens) out of Africa in the range of 140,000 to 280,000 years ago resulted in the complete replacement, without genetic interchange, of earlier Eurasian hominid populations (Homo erectus) is but one of several possible interpretations of the mtDNA data. The data are also compatible with the hypothesis, suggested earlier and supported by fossil evidence, of a single, more ancient expansion of the range of Homo erectus from Africa, followed by a gradual transition to Homo sapiens in Europe, Asia, and Africa.

Dating human cultural capacity using phylogenetic principles

Humans have genetically based unique abilities making complex culture possible; an assemblage of traits which we term “cultural capacity”. The age of this capacity has for long been subject to controversy. We apply phylogenetic principles to date this capacity, integrating evidence from archaeology, genetics, paleoanthropology, and linguistics. We show that cultural capacity is older than the first split in the modern human lineage, and at least 170,000 years old, based on data on hyoid bone morphology, FOXP2 alleles, agreement between genetic and language trees, fire use, burials, and the early appearance of tools comparable to those of modern hunter-gatherers. We cannot exclude that Neanderthals had cultural capacity some 500,000 years ago. A capacity for complex culture, therefore, must have existed before complex culture itself. It may even originated long before. This seeming paradox is resolved by theoretical models suggesting that cultural evolution is exceedingly slow in its initial stages.

Approximation Bayesian Computation

Approximation Bayesian computation [ABC] is an analysis approach that has arisen in response to the recent trend to collect data that is of a magnitude far higher than has been historically the case. This has led to many existing methods become intractable because of difficulties in calculating the likelihood function. ABC circumvents this issue by replacing calculation of the likelihood with a simulation step in which it is estimated in one way or another. In this review we give an overview of the ABC approach, giving examples of some of the more popular specific forms of ABC. We then discuss some of the areas of most active research and application in the field, specifically, choice of low-dimensional summaries of complex datasets and metrics for measuring similarity between observed and simulated data. Next, we consider the question of how to do model selection in an ABC context. Finally, we discuss an area of growing prominence in the ABC world, use of ABC methods in genetic pathway inference.

Bayesian computation via empirical likelihood

Approximate Bayesian computation has become an essential tool for the analysis of complex stochastic models when the likelihood function is numerically unavailable. However, the well-established statistical method of empirical likelihood provides another route to such settings that bypasses simulations from the model and the choices of the approximate Bayesian computation parameters (summary statistics, distance, tolerance), while being convergent in the number of observations. Furthermore, bypassing model simulations may lead to significant time savings in complex models, for instance those found in population genetics. The Bayesian computation with empirical likelihood algorithm we develop in this paper also provides an evaluation of its own performance through an associated effective sample size. The method is illustrated using several examples, including estimation of standard distributions, time series, and population genetics models.

Approximate Bayesian computation

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).

A multidisciplinary reconstruction of Palaeolithic nutrition that holds promise for the prevention and treatment of diseases of civilisation

Evolutionary medicine acknowledges that many chronic degenerative diseases result from conflicts between our rapidly changing environment, our dietary habits included, and our genome, which has remained virtually unchanged since the Palaeolithic era. Reconstruction of the diet before the Agricultural and Industrial Revolutions is therefore indicated, but hampered by the ongoing debate on our ancestors' ecological niche. Arguments and their counterarguments regarding evolutionary medicine are updated and the evidence for the long-reigning hypothesis of human evolution on the arid savanna is weighed against the hypothesis that man evolved in the proximity of water. Evidence from various disciplines is discussed, including the study of palaeo-environments, comparative anatomy, biogeochemistry, archaeology, anthropology, (patho)physiology and epidemiology. Although our ancestors had much lower life expectancies, the current evidence does neither support the misconception that during the Palaeolithic there were no elderly nor that they had poor health. Rather than rejecting the possibility of 'healthy ageing', the default assumption should be that healthy ageing posed an evolutionary advantage for human survival. There is ample evidence that our ancestors lived in a land-water ecosystem and extracted a substantial part of their diets from both terrestrial and aquatic resources. Rather than rejecting this possibility by lack of evidence, the default assumption should be that hominins, living in coastal ecosystems with catchable aquatic resources, consumed these resources. Finally, the composition and merits of so-called 'Palaeolithic diets', based on different hominin niche-reconstructions, are evaluated. The benefits of these diets illustrate that it is time to incorporate this knowledge into dietary recommendations.

Extremely rare interbreeding events can explain neanderthal DNA in living humans

Considering the recent experimental discovery of Green et al that present-day non-Africans have 1 to of their nuclear DNA of Neanderthal origin, we propose here a model which is able to quantify the genetic interbreeding between two subpopulations with equal fitness, living in the same geographic region. The model consists of a solvable system of deterministic ordinary differential equations containing as a stochastic ingredient a realization of the neutral Wright-Fisher process. By simulating the stochastic part of the model we are able to apply it to the interbreeding ofthe African ancestors of Eurasians and Middle Eastern Neanderthal subpopulations and estimate the only parameter of the model, which is the number of individuals per generation exchanged between subpopulations. Our results indicate that the amount of Neanderthal DNA in living non-Africans can be explained with maximum probability by the exchange of a single pair of individuals between the subpopulations at each 77 generations, but larger exchange frequencies are also allowed with sizeable probability. The results are compatible with a long coexistence time of 130,000 years, a total interbreeding population of order individuals, and with all living humans being descendants of Africans both for mitochondrial DNA and Y chromosome.

A Bayesian approach to phylogeographic clustering

Phylogeographic methods have attracted a lot of attention in recent years, stressing the need to provide a solid statistical framework for many existing methodologies so as to draw statistically reliable inferences. Here, we take a flexible fully Bayesian approach by reducing the problem to a clustering framework, whereby the population distribution can be explained by a set of migrations, forming geographically stable population clusters. These clusters are such that they are consistent with a fixed number of migrations on the corresponding (unknown) subdivided coalescent tree. Our methods rely upon a clustered population distribution, and allow for inclusion of various covariates (such as phenotype or climate information) at little additional computational cost. We illustrate our methods with an example from weevil mitochondrial DNA sequences from the Iberian peninsula.

Lack of confidence in approximate Bayesian computation model choice

Approximate Bayesian computation (ABC) have become an essential tool for the analysis of complex stochastic models. Grelaud et al. [(2009) Bayesian Anal 3:427-442] advocated the use of ABC for model choice in the specific case of Gibbs random fields, relying on an intermodel sufficiency property to show that the approximation was legitimate. We implemented ABC model choice in a wide range of phylogenetic models in the Do It Yourself-ABC (DIY-ABC) software [Cornuet et al. (2008) Bioinformatics 24:2713-2719]. We now present arguments as to why the theoretical arguments for ABC model choice are missing, because the algorithm involves an unknown loss of information induced by the use of insufficient summary statistics. The approximation error of the posterior probabilities of the models under comparison may thus be unrelated with the computational effort spent in running an ABC algorithm. We then conclude that additional empirical verifications of the performances of the ABC procedure as those available in DIY-ABC are necessary to conduct model choice.

Three roads diverged? Routes to phylogeographic inference

Phylogeographic methods facilitate inference of the geographical history of genetic lineages. Recent examples explore human migration and the origins of viral pandemics. There is longstanding disagreement over the use and validity of certain phylogeographic inference methodologies. In this paper, we highlight three distinct frameworks for phylogeographic inference to give a taste of this disagreement. Each of the three approaches presents a different viewpoint on phylogeography, most fundamentally on how we view the relationship between the inferred history of a sample and the history of the population the sample is embedded in. Satisfactory resolution of this relationship between history of the tree and history of the population remains a challenge for all but the most trivial models of phylogeographic processes. Intriguingly, we believe that some recent methods that entirely avoid inference about the history of the population will eventually help to reach a resolution.

ABC as a flexible framework to estimate demography over space and time: some cons, many pros

The analysis of genetic variation to estimate demographic and historical parameters and to quantitatively compare alternative scenarios recently gained a powerful and flexible approach: the Approximate Bayesian Computation (ABC). The likelihood functions does not need to be theoretically specified, but posterior distributions can be approximated by simulation even assuming very complex population models including both natural and human-induced processes. Prior information can be easily incorporated and the quality of the results can be analysed with rather limited additional effort. ABC is not a statistical analysis per se, but rather a statistical framework and any specific application is a sort of hybrid between a simulation and a data-analysis study. Complete software packages performing the necessary steps under a set of models and for specific genetic markers are already available, but the flexibility of the method is better exploited combining different programs. Many questions relevant in ecology can be addressed using ABC, but adequate amount of time should be dedicated to decide among alternative options and to evaluate the results. In this paper we will describe and critically comment on the different steps of an ABC analysis, analyse some of the published applications of ABC and provide user guidelines.