A Model-Based Approach for Identifying Signatures of Ancient Balancing Selection in Genetic Data

While much effort has focused on detecting positive and negative directional selection in the human genome, relatively little work has been devoted to balancing selection. This lack of attention is likely due to the paucity of sophisticated methods for identifying sites under balancing selection. Here we develop two composite likelihood ratio tests for detecting balancing selection. Using simulations, we show that these methods outperform competing methods under a variety of assumptions and demographic models. We apply the new methods to whole-genome human data, and find a number of previously-identified loci with strong evidence of balancing selection, including several HLA genes. Additionally, we find evidence for many novel candidates, the strongest of which is FANK1, an imprinted gene that suppresses apoptosis, is expressed during meiosis in males, and displays marginal signs of segregation distortion. We hypothesize that balancing selection acts on this locus to stabilize the segregation distortion and negative fitness effects of the distorter allele. Thus, our methods are able to reproduce many previously-hypothesized signals of balancing selection, as well as discover novel interesting candidates.     

Identification of anticancer drugs for hepatocellular carcinoma through personalized genome‐scale metabolic modeling

Genome‐scale metabolic models (GEMs) have proven useful as scaffolds for the integration of omics data for understanding the genotype–phenotype relationship in a mechanistic manner. Here, we evaluated the presence/absence of proteins encoded by 15,841 genes in 27 hepatocellular carcinoma (HCC) patients using immunohistochemistry. We used this information to reconstruct personalized GEMs for six HCC patients based on the proteomics data, HMR 2.0, and a task‐driven model reconstruction algorithm (tINIT). The personalized GEMs were employed to identify anticancer drugs using the concept of antimetabolites; i.e., drugs that are structural analogs to metabolites. The toxicity of each antimetabolite was predicted by assessing the in silico functionality of 83 healthy cell type‐specific GEMs, which were also reconstructed with the tINIT algorithm. We predicted 101 antimetabolites that could be effective in preventing tumor growth in all HCC patients, and 46 antimetabolites which were specific to individual patients. Twenty‐two of the 101 predicted antimetabolites have already been used in different cancer treatment strategies, while the remaining antimetabolites represent new potential drugs. Finally, one of the identified targets was validated experimentally, and it was confirmed to attenuate growth of the HepG2 cell line.     

Structure-revealing data fusion

Background

Analysis of data from multiple sources has the potential to enhance knowledge discovery by capturing underlying structures, which are, otherwise, difficult to extract. Fusing data from multiple sources has already proved useful in many applications in social network analysis, signal processing and bioinformatics. However, data fusion is challenging since data from multiple sources are often (i) heterogeneous (i.e., in the form of higher-order tensors and matrices), (ii) incomplete, and (iii) have both shared and unshared components. In order to address these challenges, in this paper, we introduce a novel unsupervised data fusion model based on joint factorization of matrices and higher-order tensors.

Results

While the traditional formulation of coupled matrix and tensor factorizations modeling only shared factors fails to capture the underlying structures in the presence of both shared and unshared factors, the proposed data fusion model has the potential to automatically reveal shared and unshared components through modeling constraints. Using numerical experiments, we demonstrate the effectiveness of the proposed approach in terms of identifying shared and unshared components. Furthermore, we measure a set of mixtures with known chemical composition using both LC-MS (Liquid Chromatography - Mass Spectrometry) and NMR (Nuclear Magnetic Resonance) and demonstrate that the structure-revealing data fusion model can (i) successfully capture the chemicals in the mixtures and extract the relative concentrations of the chemicals accurately, (ii) provide promising results in terms of identifying shared and unshared chemicals, and (iii) reveal the relevant patterns in LC-MS by coupling with the diffusion NMR data.

Conclusions

We have proposed a structure-revealing data fusion model that can jointly analyze heterogeneous, incomplete data sets with shared and unshared components and demonstrated its promising performance as well as potential limitations on both simulated and real data.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-239) contains supplementary material, which is available to authorized users.     

Strategies for imputation to whole genome sequence using a single or multi-breed reference population in cattle

Background

The advent of low cost next generation sequencing has made it possible to sequence a large number of dairy and beef bulls which can be used as a reference for imputation of whole genome sequence data. The aim of this study was to investigate the accuracy and speed of imputation from a high density SNP marker panel to whole genome sequence level. Data contained 132 Holstein, 42 Jersey, 52 Nordic Red and 16 Brown Swiss bulls with whole genome sequence data; 16 Holstein, 27 Jersey and 29 Nordic Reds had previously been typed with the bovine high density SNP panel and were used for validation. We investigated the effect of enlarging the reference population by combining data across breeds on the accuracy of imputation, and the accuracy and speed of both IMPUTE2 and BEAGLE using either genotype probability reference data or pre-phased reference data. All analyses were done on Bovine autosome 29 using 387,436 bi-allelic variants and 13,612 SNP markers from the bovine HD panel.

Results

A combined breed reference population led to higher imputation accuracies than did a single breed reference. The highest accuracy of imputation for all three test breeds was achieved when using BEAGLE with un-phased reference data (mean genotype correlations of 0.90, 0.89 and 0.87 for Holstein, Jersey and Nordic Red respectively) but IMPUTE2 with un-phased reference data gave similar accuracies for Holsteins and Nordic Red. Pre-phasing the reference data only lead to a minor decrease in the imputation accuracy, but gave a large improvement in computation time. Pre-phasing with BEAGLE was substantially faster than pre-phasing with SHAPEIT2 (2.5 hours vs. 52 hours for 242 individuals), and imputation with pre-phased data was faster in IMPUTE2 than in BEAGLE (5 minutes vs. 50 minutes per individual).

Conclusion

Combining reference populations across breeds is a good option to increase the size of the reference data and in turn the accuracy of imputation when only few animals are available. Pre-phasing the reference data only slightly decreases the accuracy but gives substantial improvements in speed. Using BEAGLE for pre-phasing and IMPUTE2 for imputation is a fast and accurate strategy.     

Role of IL-6 in Exercise Training- and Cold-Induced UCP1 Expression in Subcutaneous White Adipose Tissue

Expression of brown adipose tissue (BAT) associated proteins like uncoupling protein 1 (UCP1) in inguinal WAT (iWAT) has been suggested to alter iWAT metabolism. The aim of this study was to investigate the role of interleukin-6 (IL-6) in exercise training and cold exposure-induced iWAT UCP1 expression. The effect of daily intraperitoneal injections of IL-6 (3 ng/g) in C57BL/6 mice for 7 days on iWAT UCP1 expression was examined. In addition, the expression of UCP1 in iWAT was determined in response to 3 days of cold exposure (4°C) and 5 weeks of exercise training in wild type (WT) and whole body IL-6 knockout (KO) mice. Repeated injections of IL-6 in C57BL/6 mice increased UCP1 mRNA but not UCP1 protein content in iWAT. Cold exposure increased iWAT UCP1 mRNA content similarly in IL-6 KO and WT mice, while exercise training increased iWAT UCP1 mRNA in WT mice but not in IL-6 KO mice. Additionally, a cold exposure-induced increase in iWAT UCP1 protein content was blunted in IL-6 KO mice, while UCP1 protein content in iWAT was lower in both untrained and exercise trained IL-6 KO mice than in WT mice. In conclusion, repeated daily increases in plasma IL-6 can increase iWAT UCP1 mRNA content and IL-6 is required for an exercise training-induced increase in iWAT UCP1 mRNA content. In addition IL-6 is required for a full induction of UCP1 protein expression in response to cold exposure and influences the UCP1 protein content iWAT of both untrained and exercise trained animals.     

A Statistical Model for Estimation of Fish Density Including Correlation in Size,Space, Time and between Species from Research Survey Data

Trawl survey data with high spatial and seasonal coverage were analysed using a variant of the Log Gaussian Cox Process (LGCP) statistical model to estimate unbiased relative fish densities. The model estimates correlations between observations according to time, space, and fish size and includes zero observations and over-dispersion. The model utilises the fact the correlation between numbers of fish caught increases when the distance in space and time between the fish decreases, and the correlation between size groups in a haul increases when the difference in size decreases. Here the model is extended in two ways. Instead of assuming a natural scale size correlation, the model is further developed to allow for a transformed length scale. Furthermore, in the present application, the spatial- and size-dependent correlation between species was included. For cod (Gadus morhua) and whiting (Merlangius merlangus), a common structured size correlation was fitted, and a separable structure between the time and space-size correlation was found for each species, whereas more complex structures were required to describe the correlation between species (and space-size). The within-species time correlation is strong, whereas the correlations between the species are weaker over time but strong within the year.     

How Can Inequalities in Mortality Be Reduced? A Quantitative Analysis of 6 Risk Factors in 21 European Populations

Background

Socioeconomic inequalities in mortality are one of the greatest challenges for health policy in all European countries, but the potential for reducing these inequalities is unclear. We therefore quantified the impact of equalizing the distribution of six risk factors for mortality: smoking, overweight, lack of physical exercise, lack of social participation, low income, and economic inactivity.

Methods

We collected and harmonized data on mortality and risk factors by educational level for 21 European populations in the early 2000s. The impact of the risk factors on mortality in each educational group was determined using Population Attributable Fractions. We estimated the impact on inequalities in mortality of two scenarios: a theoretical upward levelling scenario in which inequalities in the risk factor were completely eliminated, and a more realistic best practice scenario, in which inequalities in the risk factor were reduced to those seen in the country with the smallest inequalities for that risk factor.

Findings

In general, upward levelling of inequalities in smoking, low income and economic inactivity hold the greatest potential for reducing inequalities in mortality. While the importance of low income is similar across Europe, smoking is more important in the North and East, and overweight in the South. On the basis of best practice scenarios the potential for reducing inequalities in mortality is often smaller, but still substantial in many countries for smoking and physical inactivity.

Interpretation

Theoretically, there is a great potential for reducing inequalities in mortality in most European countries, for example by equity-oriented tobacco control policies, income redistribution and employment policies. Although it is necessary to achieve substantial degrees of upward levelling to make a notable difference for inequalities in mortality, the existence of best practice countries with more favourable distributions for some of these risk factors suggests that this is feasible.     

Care and choice in dealing with the invasive cane toad in Western Australia

This article examines the meeting between the community group Kimberley Toad Busters (KTB) and established science and government in terms of an intersection of two logics of engagement. A logic of choice and a logic of care meet in the first example where I look at the conflicts and tensions arising in a public forum on control of cane toads in the Kimberley region of Western Australia. The second part of the article presents two examples of KTB's own practices to demonstrate that the tension between the two logics also figures within these practices. Nature is enacted both similarly and differently in these events through the tension between the logic of care and the logic of choice. In the first instance the logics figure as mutually exclusive and generative of an expanding gulf between lay and scientific knowledge, while in the second the two emerge in an unfinished productive tension.     

Drought footprint on European ecosystems between 1999 and 2010 assessed by remotely sensed vegetation phenology and productivity

Drought affects more people than any other natural disaster but there is little understanding of how ecosystems react to droughts. This study jointly analyzed spatio‐temporal changes of drought patterns with vegetation phenology and productivity changes between 1999 and 2010 in major European bioclimatic zones. The Standardized Precipitation and Evapotranspiration Index (SPEI) was used as drought indicator whereas changes in growing season length and vegetation productivity were assessed using remote sensing time‐series of Normalized Difference Vegetation Index (NDVI). Drought spatio‐temporal variability was analyzed using a Principal Component Analysis, leading to the identification of four major drought events between 1999 and 2010 in Europe. Correspondence Analysis showed that at the continental scale the productivity and phenology reacted differently to the identified drought events depending on ecosystem and land cover. Northern and Mediterranean ecosystems proved to be more resilient to droughts in terms of vegetation phenology and productivity developments. Western Atlantic regions and Eastern Europe showed strong agglomerations of decreased productivity and shorter vegetation growing season length, indicating that these ecosystems did not buffer the effects of drought well. In a climate change perspective, increase in drought frequency or intensity may result in larger impacts over these ecosystems, thus management and adaptation strategies should be strengthened in these areas of concerns.