Nonparametric coalescent inference of mutation spectrum history and demography

As populations boom and bust, the accumulation of genetic diversity is modulated, encoding histories of living populations in present-day variation. Many methods exist to decode these histories, and all must make strong model assumptions. It is typical to assume that mutations accumulate uniformly across the genome at a constant rate that does not vary between closely related populations. However, recent work shows that mutational processes in human and great ape populations vary across genomic regions and evolve over time. This perturbs the mutation spectrum (relative mutation rates in different local nucleotide contexts). Here, we develop theoretical tools in the framework of Kingman’s coalescent to accommodate mutation spectrum dynamics. We present mutation spectrum history inference (mushi), a method to perform nonparametric inference of demographic and mutation spectrum histories from allele frequency data. We use mushi to reconstruct trajectories of effective population size and mutation spectrum divergence between human populations, identify mutation signatures and their dynamics in different human populations, and calibrate the timing of a previously reported mutational pulse in the ancestors of Europeans. We show that mutation spectrum histories can be placed in a well-studied theoretical setting and rigorously inferred from genomic variation data, like other features of evolutionary history.

Over the past decade, population geneticists have developed many sophisticated methods for inferring population demography and have consistently found that simple isolated populations of constant size are far from the norm (reviewed in refs. 1–3). Population expansions and founder events, as well as migration between species and geographic regions, have been inferred from virtually all high-resolution genetic datasets. We now recognize that inferring these nonequilibrium demographies is often essential for understanding the histories of adaptation and global migration. Population genetics has uncovered many features of human history that were once virtually unknowable by other means, revealing a complex series of migrations, population replacements, and admixture networks among human groups and extinct hominoids.Although demographic inference methods can model complex population histories, the germline mutation process that creates variation has long received a comparatively simple treatment. A single parameter, μ, is used to represent the mutation rate per generation at all loci, in all individuals, and at all times. In humans, μ is estimated from parent–child trio sequencing studies, and modest variation in μ can have major effects on the interpretation of inferred parameters, such as times of admixture and population divergence. In other organisms, for which trio sequence data are usually unavailable, μ is estimated from sequence divergence between species with a fossil-calibrated divergence time, and these estimates come with still higher uncertainty.A growing body of evidence indicates that simple, constant mutation rate models may not adequately describe how variation accumulates on either inter- or intraspecific timescales (4–7). Germline mutation rates appear to have evolved during the speciation of great apes and the divergence of modern human populations (reviewed in ref. 8). Much of this evolution might be caused by nearly neutral drift (9), but a contributing factor could be selection on traits, like life history and chromatin structure, that indirectly affect mutation accumulation. Because mutation is intimately tied to the basic housekeeping process of cell division, gamete production, and embryonic development, the accumulation of mutations is likely to be complexly coupled to other biological processes (10–12).It is difficult to disentangle past changes in mutation rate from past changes in effective population size, which modulate levels of polymorphism even when the mutation rate stays constant. However, evolution of the mutation process can be indirectly detected by measuring its effects on the mutation spectrum: the relative mutation rates among different local nucleotide contexts. Hwang and Green (13) modeled the triplet context dependence of the substitution process in a mammalian phylogeny, finding varying contributions from replication errors, cytosine deamination, and biased gene conversion and showing that the relative rates of these processes varied between different mammalian lineages. Many cancers also exhibit somatic hypermutability of certain triplet motifs due to different DNA damage agents and failure points in the DNA repair process (14, 15). Harris (6) and Harris and Pritchard (7) examined the variation of triplet spectra between closely related populations, counting single-nucleotide variants in each triplet mutation type as a proxy for mutational input. They found that human triplet spectra distinctly cluster by continental ancestry group and that historical pulses in mutation activity influence the distribution of allele frequencies in certain mutation types. The divergence of mutation spectra among human continental groups has been replicated in independently generated datasets (7, 16), and similar patterns have been observed in other species, including great apes (17), mice (18), and yeast (19). Some of the mutation spectrum divergence between mice and yeast lineages has been mapped to mutator alleles (19, 20).Emerging from the literature is a picture of a mutation process evolving within and between populations, anchored to genomic features and accented by spectra of local nucleotide context. If probabilistic models of population genetic processes are to keep pace with these empirical findings, mutation deserves a richer treatment in state-of-the-art inference tools. In this paper, we build on classical theoretical tools to introduce fast nonparametric inference of population-level mutation spectrum history (MuSH)—the relative mutation rate in different local nucleotide contexts across time—alongside inference of demographic history. Whereas previous work has uncovered mutation spectrum evolution using summary statistics of standing variation, we shift perspective to focus on inference of the MuSH, which we model on the same footing as demography.Demographic inference requires us to invert the map that takes population history to the patterns of genetic diversity observable today. This task is often simplified by first compressing these genetic diversity data into a summary statistic such as the sample frequency spectrum (SFS), the distribution of derived allele frequencies among sampled haplotypes. The SFS is a well-studied population genetic summary statistic that is sensitive to demographic history. Inverting the map from demographic history to SFS is a notoriously ill-posed problem, in that many different population histories can have identical expected SFS (21–25). One way to deal with the ill posedness of demographic inference is to specify a parametric model of population size change, usually piecewise linear or piecewise exponential. An alternative, which generalizes to other inverse problems, is to allow a more general space of solutions but to regularize by penalizing histories that contain biologically unrealistic features (e.g., high-frequency population size oscillations). Both approaches shrink the set of feasible solutions to the inverse problem so that it becomes well posed and can be thought of as leveraging prior knowledge. In particular, regularization constrains the population size from changing on arbitrarily small timescales since significant population size change usually takes at least a few generations.In this paper, we extend a coalescent framework for demographic inference to accommodate inference of the MuSH from an SFS that is resolved into different local k-mer nucleotide contexts. This is a richer summary statistic that we call the k-SFS where, for example, k=3 means triplet context. We show using coalescent theory that the k-SFS is related to the MuSH by a linear transformation while depending nonlinearly on the demographic history. We infer both demographic history and MuSH by optimizing a cost that balances a data-fitting term using the forward map from coalescent theory, along with regularization terms that favor solutions with low complexity. Our open-source software mushi (mutation spectrum history inference) is available in ref. 26 as a Python package with extensive documentation. Using default settings and modest hardware, mushi takes only a few seconds to infer histories from population-scale sample frequency data.The recovered MuSH is a rich object that illuminates dimensions of population history and addresses biological questions about the evolution of the mutation process. After validating with data simulated under known histories, we use mushi to independently infer histories for each of the 26 populations (from 5 superpopulations defined by continental ancestry) from the 1000 Genomes Project (1KG) Consortium (27) using recent high-coverage sequencing data (28). We demonstrate that mushi is a powerful tool for demographic inference that has several advantages over existing demographic inference methods and then go on to describe the illuminated features of human MuSH.We recover demographic features that are robust to regularization parameter choices, including the out-of-Africa event and the more recent bottleneck in the ancestors of modern Finns, and we find that effective population sizes converge ancestrally within each superpopulation, despite being inferred independently. Decomposing human MuSH into mutation signatures varying through time in each population, we see global divergence in the mutation process that impacts many mutation types and reflects population and superpopulation relatedness. Finally, we revisit the timing of a previously reported ancient pulse of elevated TCC → TTC mutation rate, active primarily in the ancestors of Europeans and absent in East Asians (6, 7, 29, 30). We find that the extent of the pulse into the ancient past is sensitive to the choice of demographic history model but that all demographic models that fit the k-SFS yield a pulse timing that is significantly older than previously thought, seemingly arising near the divergence time of East Asians and Europeans.With mushi, we can quickly reconstruct demographic history and MuSH without strong model specification requirements. This adds an approach to the toolbox for researchers interested only in demographic inference. For researchers studying the mutation spectrum, demographic history is necessary for time calibration of events in mutation history, so we expect that jointly modeling demography and MuSH will be important for studying mutational spectrum evolution in population genetics.     

Reasons for drinking less and their relationship to sustained remission from problem drinking

AIMS: To compare representative general population and treated samples on their reasons for drinking less and whether particular reasons were related to sustained remission from problem drinking for either group. PARTICIPANTS AND DESIGN: A total of 659 problem drinking adults in a Northern California county identified through a probability survey in the general population (n = 239) and a survey of consecutive admissions to public and private alcohol and drug programs (n = 420), who reported drinking less at the one-year follow-up and provided reasons for reducing their drinking, were assessed 1-, 3-, and 5-years post-baseline regarding their problem drinking status. MEASUREMENTS: Logistic regression models were used to predict sustained remission from problem drinking. RESULTS: While the treated sample endorsed a majority of reasons in significantly higher proportions than the general population sample, the same three reasons were significant for both groups in predicting sustained remission from problem drinking: hitting rock bottom, experiencing a traumatic event and undergoing a spiritual awakening. Interventions by medical personnel and family members were either non-significant predictors or significantly negatively related to sustained improvement for both general population and treated problem drinkers. CONCLUSIONS: General population and treatment samples have similar reasons for cutting down as they relate to sustained remission from problem drinking.     

Cognitive dysfunction in children with brain tumors at diagnosis

Background

Survivors of brain tumors have a high risk for a wide range of cognitive problems. These dysfunctions are caused by the lesion itself and its surgical removal, as well as subsequent treatments (chemo‐ and/or radiation therapy). Multiple recent studies have indicated that children with brain tumors (BT) might already exhibit cognitive problems at diagnosis, i.e., before the start of any medical treatment. The aim of the present study was to investigate the baseline neuropsychological profile in children with BT compared to children with an oncological diagnosis not involving the central nervous system (CNS).

Methods

Twenty children with BT and 27 children with an oncological disease without involvement of the CNS (age range: 6.1–16.9 years) were evaluated with an extensive battery of neuropsychological tests tailored to the patient's age. Furthermore, the child and his/her parent(s) completed self‐report questionnaires about emotional functioning and quality of life. In both groups, tests were administered before any therapeutic intervention such as surgery, chemotherapy, or irradiation. Groups were comparable with regard to age, gender, and socioeconomic status.

Results

Compared to the control group, patients with BTs performed significantly worse in tests of working memory, verbal memory, and attention (effect sizes between 0.28 and 0.47). In contrast, the areas of perceptual reasoning, processing speed, and verbal comprehension were preserved at the time of measurement.

Conclusion

Our results highlight the need for cognitive interventions early in the treatment process in order to minimize or prevent academic difficulties as patients return to school. Pediatr Blood Cancer 2015;62:1805–1812. © 2015 The Authors. Pediatric Blood & Cancer, published by Wiley Periodicals, Inc.     

产科疾病编码常见问题分析

通过列举具体疾病,分析产科疾病编码中常见的三类问题,包括出现在孕产期不同时期的同一疾病名称,其疾病诊断命名和疾病编码不同;引发同一种疾病状态的致病因素很多,在产科疾病编码过程中必须要具体区分引发疾病产生的具体原因;某些疾病临床表现近似,诊断编码归类时容易混淆.初步探讨了产科疾病编码的一些特点,分析产生编码错误的原因并且提出了减少编码错误的措施,希望能够减少在产科疾病编码过程中出现类似错误,提高产科疾病编码的正确率.     

Clinical management of voice and breathing problems in two patients with vagus nerve stimulation therapy

We report two cases highlighting the diversity of vagal nerve stimulation (VNS)‐related effects on voice and breathing in patients with refractory epilepsy. The patients had both implantation and stimulation‐related side effects, which lasted for several months, impacting on their quality of life. The adverse effects appear to be due to recurrent laryngeal nerve paralysis‐related vocal cord hypofunction and stimulation‐related vocal fold spasms, however, their inter‐relationship is complex. In one of the patients, we were able to utilize the novel programming capabilities of the VNS device to reduce the laryngeal side effects without compromising therapeutic efficacy. [Published with video sequences].     

Interactive virtual reality assessment of aggressive social information processing in boys with behaviour problems: A pilot study

Children's aggressive behaviour is partly determined by how they process social information (e.g., making hostile interpretations or aiming to seek revenge). Such aggressive social information processing (SIP) may be most evident if children are emotionally engaged in actual social interactions. Current methods to assess aggressive SIP, however, often ask children to reflect on hypothetical vignettes. This pilot study therefore examined a new method that actually involves children in emotionally engaging social interactions: interactive virtual reality (VR). We developed a virtual classroom where children could play games with virtual peers. A sample of boys (N = 32; ages 8–13) from regular and special education reported on their SIP in distinct VR contexts (i.e., neutral, instrumental gain and provocation). They also completed a standard vignette-based assessment of SIP. Results demonstrated good convergent validity of interactive VR assessment of SIP, as indicated by significant moderate to large correlations of VR-assessed SIP with vignette-assessed SIP for all SIP variables except anger. Interactive VR showed improved measurement sensitivity (i.e., larger variances in SIP compared to vignettes) for aggressive responding, but not for other SIP variables. Discriminant validity (i.e., distinct SIP patterns across contexts) of interactive VR was supported for provocation contexts, but not for instrumental gain contexts. Last, children were more enthusiastic about the VR assessment compared to the vignette-based assessment. These findings suggest that interactive VR may be a promising tool, allowing for the assessment of children's aggressive SIP in standardized yet emotionally engaging social interactions.