Inference of population splits and mixtures from genome-wide allele frequency data

Many aspects of the historical relationships between populations in a species are reflected in genetic data. Inferring these relationships from genetic data, however, remains a challenging task. In this paper, we present a statistical model for inferring the patterns of population splits and mixtures in multiple populations. In this model, the sampled populations in a species are related to their common ancestor through a graph of ancestral populations. Using genome-wide allele frequency data and a Gaussian approximation to genetic drift, we infer the structure of this graph. We applied this method to a set of 55 human populations and a set of 82 dog breeds and wild canids. In both species, we show that a simple bifurcating tree does not fully describe the data; in contrast, we infer many migration events. While some of the migration events that we find have been detected previously, many have not. For example, in the human data we infer that Cambodians trace approximately 16% of their ancestry to a population ancestral to other extant East Asian populations. In the dog data, we infer that both the boxer and basenji trace a considerable fraction of their ancestry (9% and 25%, respectively) to wolves subsequent to domestication, and that East Asian toy breeds (the Shih Tzu and the Pekingese) result from admixture between modern toy breeds and "ancient" Asian breeds. Software implementing the model described here, called TreeMix, is available at http://treemix.googlecode.comComment: 28 pages, 6 figures in main text. Attached supplement is 22 pages, 15 figures. This is an updated version of the preprint available at http://precedings.nature.com/documents/6956/version/

Direct exfoliation and dispersion of two-dimensional materials in pure water via temperature control

The high-volume synthesis of two-dimensional (2D) materials in the form of platelets is desirable for various applications. While water is considered an ideal dispersion medium, due to its abundance and low cost, the hydrophobicity of platelet surfaces has prohibited its widespread use. Here we exfoliate 2D materials directly in pure water without using any chemicals or surfactants. In order to exfoliate and disperse the materials in water, we elevate the temperature of the sonication bath, and introduce energy via the dissipation of sonic waves. Storage stability greater than one month is achieved through the maintenance of high temperatures, and through atomic and molecular level simulations, we further discover that good solubility in water is maintained due to the presence of platelet surface charges as a result of edge functionalization or intrinsic polarity. Finally, we demonstrate inkjet printing on hard and flexible substrates as a potential application of water-dispersed 2D materials.close1

A Genealogical Interpretation of Principal Components Analysis

Principal components analysis, PCA, is a statistical method commonly used in population genetics to identify structure in the distribution of genetic variation across geographical location and ethnic background. However, while the method is often used to inform about historical demographic processes, little is known about the relationship between fundamental demographic parameters and the projection of samples onto the primary axes. Here I show that for SNP data the projection of samples onto the principal components can be obtained directly from considering the average coalescent times between pairs of haploid genomes. The result provides a framework for interpreting PCA projections in terms of underlying processes, including migration, geographical isolation, and admixture. I also demonstrate a link between PCA and Wright's fst and show that SNP ascertainment has a largely simple and predictable effect on the projection of samples. Using examples from human genetics, I discuss the application of these results to empirical data and the implications for inference

Seasonal variations in carbon, nitrogen and phosphorus concentrations and C:N:P stoichiometry in different organs of a Larix principis-rupprechtii Mayr. plantation in the Qinling Mountains, China

Understanding how concentrations of elements and their stoichiometry change with plant growth and age is critical for predicting plant community responses to environmental change. Weusedlong-term field experiments to explore how the leaf, stem and root carbon (C), nitrogen (N) and phosphorous (P) concentrations and their stoichiometry changed with growth and stand age in a L.principis-rupprechtii Mayr. plantation from 2012–2015 in the Qinling Mountains, China. Our results showed that the C, N and P concentrations and stoichiometric ratios in different tissues of larch stands were affected by stand age, organ type andsampling month and displayed multiple correlations with increased stand age in different growing seasons. Generally, leaf C and N concentrations were greatest in the fast-growing season, but leaf P concentrations were greatest in the early growing season. However, no clear seasonal tendencies in the stem and root C, N and P concentrations were observed with growth. In contrast to N and P, few differences were found in organ-specific C concentrations. Leaf N:P was greatest in the fast-growing season, while C:N and C:P were greatest in the late-growing season. No clear variations were observed in stem and root C:N, C:P andN:Pthroughout the entire growing season, but leaf N:P was less than 14, suggesting that the growth of larch stands was limited by N in our study region. Compared to global plant element concentrations and stoichiometry, the leaves of larch stands had higher C, P, C:NandC:PbutlowerNandN:P,andtherootshadgreater PandC:NbutlowerN,C:Pand N:P. Our study provides baseline information for describing the changes in nutritional elements with plant growth, which will facilitates plantation forest management and restoration, and makes avaluable contribution to the global data pool on leaf nutrition and stoichiometry

Identification of lung cancer with high sensitivity and specificity by blood testing

<p>Abstract</p> <p>Background</p> <p>Lung cancer is a very frequent and lethal tumor with an identifiable risk population. Cytological analysis and chest X-ray failed to reduce mortality, and CT screenings are still controversially discussed. Recent studies provided first evidence for the potential usefulness of autoantigens as markers for lung cancer.</p> <p>Methods</p> <p>We used extended panels of arrayed antigens and determined autoantibody signatures of sera from patients with different kinds of lung cancer, different common non-tumor lung pathologies, and controls without any lung disease by a newly developed computer aided image analysis procedure. The resulting signatures were classified using linear kernel Support Vector Machines and 10-fold cross-validation.</p> <p>Results</p> <p>The novel approach allowed for discriminating lung cancer patients from controls without any lung disease with a specificity of 97.0%, a sensitivity of 97.9%, and an accuracy of 97.6%. The classification of stage IA/IB tumors and controls yielded a specificity of 97.6%, a sensitivity of 75.9%, and an accuracy of 92.9%. The discrimination of lung cancer patients from patients with non-tumor lung pathologies reached an accuracy of 88.5%.</p> <p>Conclusion</p> <p>We were able to separate lung cancer patients from subjects without any lung disease with high accuracy. Furthermore, lung cancer patients could be seprated from patients with other non-tumor lung diseases. These results provide clear evidence that blood-based tests open new avenues for the early diagnosis of lung cancer.</p

Outcomes associated with matching patients' treatment preferences to physicians' recommendations: study methodology

<p>Abstract</p> <p>Background</p> <p>Patients often express strong preferences for the forms of treatment available for their disease. Incorporating these preferences into the process of treatment decision-making might improve patients' adherence to treatment, contributing to better outcomes. We describe the methodology used in a study aiming to assess treatment outcomes when patients' preferences for treatment are closely matched to recommended treatments.</p> <p>Method</p> <p>Participants included patients with moderate and severe psoriasis attending outpatient dermatology clinics at the University Medical Centre Mannheim, University of Heidelberg, Germany. A self-administered online survey used conjoint analysis to measure participants' preferences for psoriasis treatment options at the initial study visit. Physicians' treatment recommendations were abstracted from each participant's medical records. The Preference Matching Index (PMI), a measure of concordance between the participant's preferences for treatment and the physician's recommended treatment, was determined for each participant at t₁(initial study visit). A clinical outcome measure, the Psoriasis Area and Severity Index, and two participant-derived outcomes assessing treatment satisfaction and health related quality of life were employed at t₁, t₂(twelve weeks post-t₁) and t₃(twelve weeks post-t₂). Change in outcomes was assessed using repeated measures analysis of variance. The association between participants' PMI scores at t₁and outcomes at t₂and t₃was evaluated using multivariate regressions analysis.</p> <p>Discussion</p> <p>We describe methods for capturing concordance between patients' treatment preferences and recommended treatment and for assessing its association with specific treatment outcomes. The methods are intended to promote the incorporation of patients' preferences in treatment decision-making, enhance treatment satisfaction, and improve treatment effectiveness through greater adherence.</p

Robust and Task-Independent Spatial Profile of the Visual Word Form Activation in Fusiform Cortex

Written language represents a special category of visual information. There is strong evidence for the existence of a cortical region in ventral occipitotemporal cortex for processing the visual form of written words. However, due to inconsistent findings obtained with different tasks, the level of specialization and selectivity of this so called visual word form area (VWFA) remains debated. In this study, we examined category selectivity for Chinese characters, a non-alphabetic script, in native Chinese readers. In contrast to traditional approaches of examining response levels in a restricted predefined region of interest (ROI), a detailed distribution of the BOLD signal across the mid-fusiform cortical surface and the spatial patterns of responses to Chinese characters were obtained. Results show that a region tuned for Chinese characters could be consistently found in the lateral part of the left fusiform gyrus in Chinese readers, and this spatial pattern of selectivity for written words was not influenced by top-down tasks such as phonological or semantic modulations. These results provide strong support for the robust spatial coding of category selective response in the mid-fusiform cortex, and demonstrate the utility of the spatial distribution analysis as a more meaningful approach to examine functional magnetic resonance imaging (fMRI) data

Sensitive Detection of Chromosomal Segments of Distinct Ancestry in Admixed Populations

Identifying the ancestry of chromosomal segments of distinct ancestry has a wide range of applications from disease mapping to learning about history. Most methods require the use of unlinked markers; but, using all markers from genome-wide scanning arrays, it should in principle be possible to infer the ancestry of even very small segments with exquisite accuracy. We describe a method, HAPMIX, which employs an explicit population genetic model to perform such local ancestry inference based on fine-scale variation data. We show that HAPMIX outperforms other methods, and we explore its utility for inferring ancestry, learning about ancestral populations, and inferring dates of admixture. We validate the method empirically by applying it to populations that have experienced recent and ancient admixture: 935 African Americans from the United States and 29 Mozabites from North Africa. HAPMIX will be of particular utility for mapping disease genes in recently admixed populations, as its accurate estimates of local ancestry permit admixture and case-control association signals to be combined, enabling more powerful tests of association than with either signal alone