2BAD: an application to estimate the parental contributions during two independent admixture events

Several approaches have been developed to calculate the relative contributions of parental populations in single admixture event scenarios, including Bayesian methods. In many breeds and populations, it may be more realistic to consider multiple admixture events. However, no approach has been developed to date to estimate admixture in such cases. This report describes a program application, 2BAD (for 2-event Bayesian ADmixture), which allows the consideration of up to two independent admixture events involving two or three parental populations and a single admixed population, depending on the number of populations sampled. For each of these models, it is possible to estimate several parameters (admixture, effective sizes, etc.) using an approximate Bayesian computation approach. In addition, the program allows comparing pairs of admixture models, determining which is the most likely given data. The application was tested through simulations and was found to provide good estimates for the contribution of the populations at the two admixture events. We were also able to determine whether an admixture model was more likely than a simple split model

Adaptive approximate Bayesian computation for complex models

Approximate Bayesian computation (ABC) is a family of computational techniques in Bayesian statistics. These techniques allow to fi t a model to data without relying on the computation of the model likelihood. They instead require to simulate a large number of times the model to be fi tted. A number of re finements to the original rejection-based ABC scheme have been proposed, including the sequential improvement of posterior distributions. This technique allows to de- crease the number of model simulations required, but it still presents several shortcomings which are particu- larly problematic for costly to simulate complex models. We here provide a new algorithm to perform adaptive approximate Bayesian computation, which is shown to perform better on both a toy example and a complex social model.Comment: 14 pages, 5 figure

Inferring introduction routes of invasive species using approximate Bayesian computation on microsatellite data

Determining the routes of introduction provides not only information about the history of an invasion process, but also information about the origin and construction of the genetic composition of the invading population. It remains difficult, however, to infer introduction routes from molecular data because of a lack of appropriate methods. We evaluate here the use of an approximate Bayesian computation (ABC) method for estimating the probabilities of introduction routes of invasive populations based on microsatellite data. We considered the crucial case of a single source population from which two invasive populations originated either serially from a single introduction event or from two independent introduction events. Using simulated datasets, we found that the method gave correct inferences and was robust to many erroneous beliefs. The method was also more efficient than traditional methods based on raw values of statistics such as assignment likelihood or pairwise F(ST). We illustrate some of the features of our ABC method, using real microsatellite datasets obtained for invasive populations of the western corn rootworm, Diabrotica virgifera virgifera. Most computations were performed with the DIYABC program (http://www1.montpellier.inra.fr/CBGP/diyabc/)

Genome scan of Diabrotica virgifera virgifera for genetic variation associated with crop rotation tolerance

Crop rotation has been a valuable technique for control of Diabrotica virgifera virgifera for almost a century. However, during the last two decades, crop rotation has ceased to be effective in an expanding area of the US corn belt. This failure appears to be due to a change in the insect's oviposition behaviour, which, in all probability, has an underlying genetic basis. A preliminary genome scan using 253 amplified fragment-length polymorphism (AFLP) markers sought to identify genetic variation associated with the circumvention of crop rotation. Samples of D. v. virgifera from east-central Illinois, where crop rotation is ineffective, were compared with samples from Iowa at locations that the behavioural variant has yet to reach. A single AFLP marker showed signs of having been influenced by selection for the circumvention of crop rotation. However, this marker was not diagnostic. The lack of markers strongly associated with the trait may be due to an insufficient density of marker coverage throughout the genome. A weak but significant general heterogeneity was observed between the Illinois and Iowa samples at microsatellite loci and AFLP markers. This has not been detected in previous population genetic studies of D. v. virgifera and may indicate a reduction in gene flow between variant and wild-type beetles

Noisy Monte Carlo: Convergence of Markov chains with approximate transition kernels

Monte Carlo algorithms often aim to draw from a distribution $\pi$ by simulating a Markov chain with transition kernel $P$ such that $\pi$ is invariant under $P$. However, there are many situations for which it is impractical or impossible to draw from the transition kernel $P$. For instance, this is the case with massive datasets, where is it prohibitively expensive to calculate the likelihood and is also the case for intractable likelihood models arising from, for example, Gibbs random fields, such as those found in spatial statistics and network analysis. A natural approach in these cases is to replace $P$ by an approximation $\hat{P}$. Using theory from the stability of Markov chains we explore a variety of situations where it is possible to quantify how 'close' the chain given by the transition kernel $\hat{P}$ is to the chain given by $P$. We apply these results to several examples from spatial statistics and network analysis.Comment: This version: results extended to non-uniformly ergodic Markov chain

A generative inference framework for analysing patterns of cultural change in sparse population data with evidence for fashion trends in LBK culture

Cultural change can be quantified by temporal changes in frequency of different cultural artefacts and it is a central question to identify what underlying cultural transmission processes could have caused the observed frequency changes. Observed changes, however, often describe the dynamics in samples of the population of artefacts, whereas transmission processes act on the whole population. Here we develop a modelling framework aimed at addressing this inference problem. To do so, we firstly generate population structures from which the observed sample could have been drawn randomly and then determine theoretical samples at a later time t2 produced under the assumption that changes in frequencies are caused by a specific transmission process. Thereby we also account for the potential effect of time-averaging processes in the generation of the observed sample. Subsequent statistical comparisons (e.g. using Bayesian inference) of the theoretical and observed samples at t2 can establish which processes could have produced the observed frequency data. In this way, we infer underlying transmission processes directly from available data without any equilibrium assumption. We apply this framework to a dataset describing pottery from settlements of some of the first farmers in Europe (the LBK culture) and conclude that the observed frequency dynamic of different types of decorated pottery is consistent with age-dependent selection, a preference for 'young' pottery types which is potentially indicative of fashion trends

Evidence for directional selection at a novel major histocompatibility class I marker in wild common frogs (Rana temporaria) exposed to a viral pathogen (Ranavirus).

(c) 2009 Teacher et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Whilst the Major Histocompatibility Complex (MHC) is well characterized in the anuran Xenopus, this region has not previously been studied in another popular model species, the common frog (Rana temporaria). Nor, to date, have there been any studies of MHC in wild amphibian host-pathogen systems. We characterise an MHC class I locus in the common frog, and present primers to amplify both the whole region, and specifically the antigen binding region. As no more than two expressed haplotypes were found in over 400 clones from 66 individuals, it is likely that there is a single class I locus in this species. This finding is consistent with the single class I locus in Xenopus, but contrasts with the multiple loci identified in axolotls, providing evidence that the diversification of MHC class I into multiple loci likely occurred after the Caudata/Anura divergence (approximately 350 million years ago) but before the Ranidae/Pipidae divergence (approximately 230 mya). We use this locus to compare wild populations of common frogs that have been infected with a viral pathogen (Ranavirus) with those that have no history of infection. We demonstrate that certain MHC supertypes are associated with infection status (even after accounting for shared ancestry), and that the diseased populations have more similar supertype frequencies (lower F(ST)) than the uninfected. These patterns were not seen in a suite of putatively neutral microsatellite loci. We interpret this pattern at the MHC locus to indicate that the disease has imposed selection for particular haplotypes, and hence that common frogs may be adapting to the presence of Ranavirus, which currently kills tens of thousands of amphibians in the UK each year

Red blood cell distribution width: Genetic evidence for aging pathways in 116,666 volunteers

This is the final version of the article. Available from Public Library of Science via the DOI in this record.INTRODUCTION: Variability in red blood cell volumes (distribution width, RDW) increases with age and is strongly predictive of mortality, incident coronary heart disease and cancer. We investigated inherited genetic variation associated with RDW in 116,666 UK Biobank human volunteers. RESULTS: A large proportion RDW is explained by genetic variants (29%), especially in the older group (60+ year olds, 33.8%, <50 year olds, 28.4%). RDW was associated with 194 independent genetic signals; 71 are known for conditions including autoimmune disease, certain cancers, BMI, Alzheimer's disease, longevity, age at menopause, bone density, myositis, Parkinson's disease, and age-related macular degeneration. Exclusion of anemic participants did not affect the overall findings. Pathways analysis showed enrichment for telomere maintenance, ribosomal RNA, and apoptosis. The majority of RDW-associated signals were intronic (119 of 194), including SNP rs6602909 located in an intron of oncogene GAS6, an eQTL in whole blood. CONCLUSIONS: Although increased RDW is predictive of cardiovascular outcomes, this was not explained by known CVD or related lipid genetic risks, and a RDW genetic score was not predictive of incident disease. The predictive value of RDW for a range of negative health outcomes may in part be due to variants influencing fundamental pathways of aging.This work was supported by an award to DM, TF, AM and LH by the UK Medical Research Council (grant number MR/M023095/1). SEJ is funded by the Medical Research Council (grant: MR/M005070/1). JT is funded by a Diabetes Research and Wellness Foundation Fellowship. RB is funded by the Wellcome Trust and Royal Society grant: 104150/Z/14/Z. MAT, MNW and AM are supported by the Wellcome Trust Institutional Strategic Support Award (WT097835MF). ARW, HY, and TF are supported by the European Research Council grant: 323195:GLUCOSEGENES-FP7-IDEAS-ERC. LF is supported by the Intramural Research Program of the National Institute on Aging, U.S. National Institutes of Health. Input from MD, CLK and GK was supported by the University of Connecticut Health Center. This research has been conducted using the UK Biobank Resource under Application Number 14631. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Amount of Information Needed for Model Choice in Approximate Bayesian Computation

Approximate Bayesian Computation (ABC) has become a popular technique in evolutionary genetics for elucidating population structure and history due to its flexibility. The statistical inference framework has benefited from significant progress in recent years. In population genetics, however, its outcome depends heavily on the amount of information in the dataset, whether that be the level of genetic variation or the number of samples and loci. Here we look at the power to reject a simple constant population size coalescent model in favor of a bottleneck model in datasets of varying quality. Not only is this power dependent on the number of samples and loci, but it also depends strongly on the level of nucleotide diversity in the observed dataset. Whilst overall model choice in an ABC setting is fairly powerful and quite conservative with regard to false positives, detecting weaker bottlenecks is problematic in smaller or less genetically diverse datasets and limits the inferences possible in non-model organism where the amount of information regarding the two models is often limited. Our results show it is important to consider these limitations when performing an ABC analysis and that studies should perform simulations based on the size and nature of the dataset in order to fully assess the power of the study

Anaerobic Carbon Monoxide Dehydrogenase Diversity in the Homoacetogenic Hindgut Microbial Communities of Lower Termites and the Wood Roach

Anaerobic carbon monoxide dehydrogenase (CODH) is a key enzyme in the Wood-Ljungdahl (acetyl-CoA) pathway for acetogenesis performed by homoacetogenic bacteria. Acetate generated by gut bacteria via the acetyl-CoA pathway provides considerable nutrition to wood-feeding dictyopteran insects making CODH important to the obligate mutualism occurring between termites and their hindgut microbiota. To investigate CODH diversity in insect gut communities, we developed the first degenerate primers designed to amplify cooS genes, which encode the catalytic (β) subunit of anaerobic CODH enzyme complexes. These primers target over 68 million combinations of potential forward and reverse cooS primer-binding sequences. We used the primers to identify cooS genes in bacterial isolates from the hindgut of a phylogenetically lower termite and to sample cooS diversity present in a variety of insect hindgut microbial communities including those of three phylogenetically-lower termites, Zootermopsis nevadensis, Reticulitermes hesperus, and Incisitermes minor, a wood-feeding cockroach, Cryptocercus punctulatus, and an omnivorous cockroach, Periplaneta americana. In total, we sequenced and analyzed 151 different cooS genes. These genes encode proteins that group within one of three highly divergent CODH phylogenetic clades. Each insect gut community contained CODH variants from all three of these clades. The patterns of CODH diversity in these communities likely reflect differences in enzyme or physiological function, and suggest that a diversity of microbial species participate in homoacetogenesis in these communities