Approximate Bayesian computation for modular inference problems with many parameters: the example of migration rates

We propose a two‐step procedure for estimating multiple migration rates in an approximate Bayesian computation (ABC) framework, accounting for global nuisance parameters. The approach is not limited to migration, but generally of interest for inference problems with multiple parameters and a modular structure (e.g. independent sets of demes or loci). We condition on a known, but complex demographic model of a spatially subdivided population, motivated by the reintroduction of Alpine ibex (Capra ibex) into Switzerland. In the first step, the global parameters ancestral mutation rate and male mating skew have been estimated for the whole population in Aeschbacher et al. (Genetics 2012; 192 : 1027). In the second step, we estimate in this study the migration rates independently for clusters of demes putatively connected by migration. For large clusters (many migration rates), ABC faces the problem of too many summary statistics. We therefore assess by simulation if estimation per pair of demes is a valid alternative. We find that the trade‐off between reduced dimensionality for the pairwise estimation on the one hand and lower accuracy due to the assumption of pairwise independence on the other depends on the number of migration rates to be inferred: the accuracy of the pairwise approach increases with the number of parameters, relative to the joint estimation approach. To distinguish between low and zero migration, we perform ABC‐type model comparison between a model with migration and one without. Applying the approach to microsatellite data from Alpine ibex, we find no evidence for substantial gene flow via migration, except for one pair of demes in one direction.     

Examining Phylogenetic Relationships Among Gibbon Genera Using Whole Genome Sequence Data Using an Approximate Bayesian Computation Approach

Gibbons are believed to have diverged from the larger great apes ∼16.8 MYA and today reside in the rainforests of Southeast Asia. Based on their diploid chromosome number, the family Hylobatidae is divided into four genera, Nomascus, Symphalangus, Hoolock, and Hylobates. Genetic studies attempting to elucidate the phylogenetic relationships among gibbons using karyotypes, mitochondrial DNA (mtDNA), the Y chromosome, and short autosomal sequences have been inconclusive . To examine the relationships among gibbon genera in more depth, we performed second-generation whole genome sequencing (WGS) to a mean of ∼15× coverage in two individuals from each genus. We developed a coalescent-based approximate Bayesian computation (ABC) method incorporating a model of sequencing error generated by high coverage exome validation to infer the branching order, divergence times, and effective population sizes of gibbon taxa. Although Hoolock and Symphalangus are likely sister taxa, we could not confidently resolve a single bifurcating tree despite the large amount of data analyzed. Instead, our results support the hypothesis that all four gibbon genera diverged at approximately the same time. Assuming an autosomal mutation rate of 1 × 10⁻⁹/site/year this speciation process occurred ∼5 MYA during a period in the Early Pliocene characterized by climatic shifts and fragmentation of the Sunda shelf forests. Whole genome sequencing of additional individuals will be vital for inferring the extent of gene flow among species after the separation of the gibbon genera.     

Exploring Approximate Bayesian Computation for inferring recent demographic history with genomic markers in nonmodel species

Approximate Bayesian computation (ABC) is widely used to infer demographic history of populations and species using DNA markers. Genomic markers can now be developed for nonmodel species using reduced representation library (RRL) sequencing methods that select a fraction of the genome using targeted sequence capture or restriction enzymes (genotyping‐by‐sequencing, GBS). We explored the influence of marker number and length, knowledge of gametic phase, and tradeoffs between sample size and sequencing depth on the quality of demographic inferences performed with ABC. We focused on two‐population models of recent spatial expansion with varying numbers of unknown parameters. Performing ABC on simulated data sets with known parameter values, we found that the timing of a recent spatial expansion event could be precisely estimated in a three‐parameter model. Taking into account uncertainty in parameters such as initial population size and migration rate collectively decreased the precision of inferences dramatically. Phasing haplotypes did not improve results, regardless of sequence length. Numerous short sequences were as valuable as fewer, longer sequences, and performed best when a large sample size was sequenced at low individual depth, even when sequencing errors were added. ABC results were similar to results obtained with an alternative method based on the site frequency spectrum (SFS) when performed with unphased GBS‐type markers. We conclude that unphased GBS‐type data sets can be sufficient to precisely infer simple demographic models, and discuss possible improvements for the use of ABC with genomic data.     

Combined Psychophysiological Assessment of ADHD: A Pilot Study of Bayesian Probability Approach Illustrated by Appraisal of ADHD in Female College Students

Manifestations of ADHD are observed at both psychological and physiological levels and assessed via various psychometric, EEG, and imaging tests. However, no test is 100% accurate in its assessment of ADHD. This study introduces a stochastic assessment combining psychometric tests with previously reported (Consistency Index) and newly developed (Alpha Blockade Index) EEG-based physiological markers of ADHD. The assessment utilizes classical Bayesian inference to refine after each step the probability of ADHD of each individual. In a pilot study involving six college females with ADHD and six matched controls, the assessment achieved correct classification for all ADHD and non-ADHD participants. In comparison, the classification of ADHD versus non-ADHD participants was < 85% for any one of the tests separately. The procedure significantly improved the score separation between ADHD versus non-ADHD groups. The final average probabilities for ADHD were 76% for the ADHD group and 8% for the control group. These probabilities correlated (r = .87) with the Brown ADD scale and (r = .84) with the ADHD-Symptom Inventory used for the screening of the participants. We conclude that, although each separate test was not completely accurate, a combination of several tests classified correctly all ADHD and all non-ADHD participants. The application of the proposed assessment is not limited to the specific tests used in this study--the assessment represents a general paradigm capable of accommodating a variety of ADHD tests into a single diagnostic assessment.     

Multiple introductions,admixture and bridgehead invasion characterize the introduction history of Ambrosia artemisiifolia in Europe and Australia

Admixture between differentiated populations is considered to be a powerful mechanism stimulating the invasive success of some introduced species. It is generally facilitated through multiple introductions; however, the importance of admixture prior to introduction has rarely been considered. We assess the likelihood that the invasive Ambrosia artemisiifolia populations of Europe and Australia developed through multiple introductions or were sourced from a historical admixture zone within native North America. To do this, we combine large genomic and sampling data sets analysed with approximate Bayesian computation and random forest scenario evaluation to compare single and multiple invasion scenarios with pre‐ and postintroduction admixture simultaneously. We show the historical admixture zone within native North America originated before global invasion of this weed and could act as a potential source of introduced populations. We provide evidence supporting the hypothesis that the invasive populations established through multiple introductions from the native range into Europe and subsequent bridgehead invasion into Australia. We discuss the evolutionary mechanisms that could promote invasiveness and evolutionary potential of alien species from bridgehead invasions and admixed source populations.     

A Novel Approach for Identifying Causal Models of Complex Diseases from Family Data

Causal models including genetic factors are important for understanding the presentation mechanisms of complex diseases. Familial aggregation and segregation analyses based on polygenic threshold models have been the primary approach to fitting genetic models to the family data of complex diseases. In the current study, an advanced approach to obtaining appropriate causal models for complex diseases based on the sufficient component cause (SCC) model involving combinations of traditional genetics principles was proposed. The probabilities for the entire population, i.e., normal–normal, normal–disease, and disease–disease, were considered for each model for the appropriate handling of common complex diseases. The causal model in the current study included the genetic effects from single genes involving epistasis, complementary gene interactions, gene–environment interactions, and environmental effects. Bayesian inference using a Markov chain Monte Carlo algorithm (MCMC) was used to assess of the proportions of each component for a given population lifetime incidence. This approach is flexible, allowing both common and rare variants within a gene and across multiple genes. An application to schizophrenia data confirmed the complexity of the causal factors. An analysis of diabetes data demonstrated that environmental factors and gene–environment interactions are the main causal factors for type II diabetes. The proposed method is effective and useful for identifying causal models, which can accelerate the development of efficient strategies for identifying causal factors of complex diseases.     

A simple procedure for joint estimation of the long‐term rates of sexuality and mutation in predominantly clonal populations,for use with dominant molecular markers

In highly clonal populations, mutation can contribute to the rate of apparent sexuality. To remove this bias, a method is presented that jointly estimates the rates of sexuality (Ns) and mutation (Nµ) for populations, based upon levels of single‐locus vs. multilocus clonal identity. This effectively haploid model assumes equilibrium, and can be used with dominant molecular data and in conjunction with organisms of various ploidies. Simulations indicate that while equilibrium can take thousands of generations to attain, and afterwards have a large evolutionary variance, the method gives approximate estimates of Ns.     

Liddle综合征触发蛋白ENaC的PY模体突变型的结合肽段的筛选

Liddle综合征是一种常染色体显性、盐敏感型的高血压综合征,其分子发病机制研究认为是上皮钠离子通道(epithelial Na+channel,ENaC)的β亚基或γ亚基的胞质侧羧基端区域低频率的点突变或缺失突变导致肾远曲小管钠离子重吸收增加.本研究提出了一种从分子水平上治疗Liddle综合征的设想,即构建一种可识别Liddle综合征患者中ENaC蛋白突变的PY模体的人工泛素连接酶E3,使其结合并降解突变的ENaC蛋白,从而使肾远曲小管上皮细胞膜上ENaC的表达数量和活性恢复.而识别PY突变体的E3,可通过用患者中的PY突变体筛选随机多肽文库获得与之结合的随机肽段,用其替换PY模体天然配体蛋白Nedd4的WW结构域,从而得到一个新的人工E3.本研究中以一种Liddle综合征突变型Y620H为诱饵蛋白,筛选新型随机多肽文库,获得了1个至少能与2种PY突变体(Y620H和P618L)特异性结合的随机肽段,为进一步构建可降解ENaC突变体的人工E3积累了重要的实验数据.     

A Bayesian MCMC Approach to Assess the Complete Distribution of Fitness Effects of New Mutations: Uncovering the Potential for Adaptive Walks in Challenging Environments

The role of adaptation in the evolutionary process has been contentious for decades. At the heart of the century-old debate between neutralists and selectionists lies the distribution of fitness effects (DFE)—that is, the selective effect of all mutations. Attempts to describe the DFE have been varied, occupying theoreticians and experimentalists alike. New high-throughput techniques stand to make important contributions to empirical efforts to characterize the DFE, but the usefulness of such approaches depends on the availability of robust statistical methods for their interpretation. We here present and discuss a Bayesian MCMC approach to estimate fitness from deep sequencing data and use it to assess the DFE for the same 560 point mutations in a coding region of Hsp90 in Saccharomyces cerevisiae across six different environmental conditions. Using these estimates, we compare the differences in the DFEs resulting from mutations covering one-, two-, and three-nucleotide steps from the wild type—showing that multiple-step mutations harbor more potential for adaptation in challenging environments, but also tend to be more deleterious in the standard environment. All observations are discussed in the light of expectations arising from Fisher’s geometric model.