Comparing Machine Learning and Logistic Regression Methods for Predicting Hypertension Using a Combination of Gene Expression and Next-Generation Sequencing Data

Machine learning methods continue to show promise in the analysis of data from genetic association studies because of the high number of variables relative to the number of observations. However, few best practices exist for the application of these methods. We extend a recently proposed supervised machine learning approach for predicting disease risk by genotypes to be able to incorporate gene expression data and rare variants. We then apply 2 different versions of the approach (radial and linear support vector machines) to simulated data from Genetic Analysis Workshop 19 and compare performance to logistic regression. Method performance was not radically different across the 3 methods, although the linear support vector machine tended to show small gains in predictive ability relative to a radial support vector machine and logistic regression. Importantly, as the number of genes in the models was increased, even when those genes contained causal rare variants, model predictive ability showed a statistically significant decrease in performance for both the radial support vector machine and logistic regression. The linear support vector machine showed more robust performance to the inclusion of additional genes. Further work is needed to evaluate machine learning approaches on larger samples and to evaluate the relative improvement in model prediction from the incorporation of gene expression data

Written submission from the School of Law, Politics and Sociology, University of Sussex (OEU0007)to the Women and equalities Committee inquiry: ensuring strong equalities legislation after EU exit

Undoubtedly, the UK’s equalities legislation has become stronger in recent years providing important protection for people who experience discrimination. Nevertheless, this has happened in the context of widening economic inequality, cuts in public services and restrictions on access to justice – all of which make it harder for victims of discrimination to realise the rights that exist on paper. If the UK leaves the EU, the next few years will be a period of great political, economic and social instability when it will be vital to ensure that protection against discrimination is strengthened not weakened and that a culture of support for equality and human rights is promoted throughout the UK. The rights that must be protected benefit everyone in the UK, not only supporting marginalised people and victims of discrimination but also making workplaces fairer for all and underpinning the legitimacy of our democratic institutions

Toward an Integrated Model of Capsule Regulation in Cryptococcus neoformans

Cryptococcus neoformans is an opportunistic fungal pathogen that causes serious human disease in immunocompromised populations. Its polysaccharide capsule is a key virulence factor which is regulated in response to growth conditions, becoming enlarged in the context of infection. We used microarray analysis of cells stimulated to form capsule over a range of growth conditions to identify a transcriptional signature associated with capsule enlargement. The signature contains 880 genes, is enriched for genes encoding known capsule regulators, and includes many uncharacterized sequences. One uncharacterized sequence encodes a novel regulator of capsule and of fungal virulence. This factor is a homolog of the yeast protein Ada2, a member of the Spt-Ada-Gcn5 Acetyltransferase (SAGA) complex that regulates transcription of stress response genes via histone acetylation. Consistent with this homology, the C. neoformans null mutant exhibits reduced histone H3 lysine 9 acetylation. It is also defective in response to a variety of stress conditions, demonstrating phenotypes that overlap with, but are not identical to, those of other fungi with altered SAGA complexes. The mutant also exhibits significant defects in sexual development and virulence. To establish the role of Ada2 in the broader network of capsule regulation we performed RNA-Seq on strains lacking either Ada2 or one of two other capsule regulators: Cir1 and Nrg1. Analysis of the results suggested that Ada2 functions downstream of both Cir1 and Nrg1 via components of the high osmolarity glycerol (HOG) pathway. To identify direct targets of Ada2, we performed ChIP-Seq analysis of histone acetylation in the Ada2 null mutant. These studies supported the role of Ada2 in the direct regulation of capsule and mating responses and suggested that it may also play a direct role in regulating capsule-independent antiphagocytic virulence factors. These results validate our experimental approach to dissecting capsule regulation and provide multiple targets for future investigation

High glucose disrupts oligosaccharide recognition function via competitive inhibition : a potential mechanism for immune dysregulation in diabetes mellitus

Diabetic complications include infection and cardiovascular disease. Within the immune system, host-pathogen and regulatory host-host interactions operate through binding of oligosaccharides by C-type lectin. A number of C-type lectins recognise oligosaccharides rich in mannose and fucose – sugars with similar structures to glucose. This raises the possibility that high glucose conditions in diabetes affect protein-oligosaccharide interactions via competitive inhibition. Mannose binding lectin, soluble DC-SIGN & DC-SIGNR, and surfactant protein D, were tested for carbohydrate binding in the presence of glucose concentrations typical of diabetes, via surface plasmon resonance and affinity chromatography. Complement activation assays were performed in high glucose. DC-SIGN and DC-SIGNR expression in adipose tissues was examined via immunohistochemistry. High glucose inhibited C-type lectin binding to high-mannose glycoprotein and binding of DC-SIGN to fucosylated ligand (blood group B) was abrogated in high glucose. Complement activation via the lectin pathway was inhibited in high glucose and also in high trehalose - a nonreducing sugar with glucoside stereochemistry. DC-SIGN staining was seen on cells with DC morphology within omental and subcutaneous adipose tissues. We conclude that high glucose disrupts C-type lectin function, potentially illuminating new perspectives on susceptibility to infectious and inflammatory disease in diabetes. Mechanisms involve competitive inhibition of carbohydrate-binding within sets of defined proteins, in contrast to broadly indiscriminate, irreversible glycation of proteins

Machine learning and data mining in complex genomic data a review on the lessons learned in Genetic Analysis Workshop Nineteen

In the analysis of current genomic data, application of machine learning and data mining techniques has become more attractive given the rising complexity of the projects. As part of the Genetic Analysis Workshop 19, approaches from this domain were explored, mostly motivated from two starting points. First, assuming an underlying structure in the genomic data, data mining might identify this and thus improve downstream association analyses. Second, computational methods for machine learning need to be developed further to efficiently deal with the current wealth of data. In the course of discussing results and experiences from the machine learning and data mining approaches, six common messages were extracted. These depict the current state of these approaches in the application to complex genomic data. Although some challenges remain for future studies, important forward steps were taken in the integration of different data types and the evaluation of the evidence. Mining the data for underlying genetic or phenotypic structure and using this information in subsequent analyses proved to be extremely helpful and is likely to become of even greater use with more complex data sets

Challenges in quantifying changes in the global water cycle

Human influences have likely already impacted the large-scale water cycle but natural variability and observational uncertainty are substantial. It is essential to maintain and improve observational capabilities to better characterize changes. Understanding observed changes to the global water cycle is key to predicting future climate changes and their impacts. While many datasets document crucial variables such as precipitation, ocean salinity, runoff, and humidity, most are uncertain for determining long-term changes. In situ networks provide long time-series over land but are sparse in many regions, particularly the tropics. Satellite and reanalysis datasets provide global coverage, but their long-term stability is lacking. However, comparisons of changes among related variables can give insights into the robustness of observed changes. For example, ocean salinity, interpreted with an understanding of ocean processes, can help cross-validate precipitation. Observational evidence for human influences on the water cycle is emerging, but uncertainties resulting from internal variability and observational errors are too large to determine whether the observed and simulated changes are consistent. Improvements to the in situ and satellite observing networks that monitor the changing water cycle are required, yet continued data coverage is threatened by funding reductions. Uncertainty both in the role of anthropogenic aerosols, and due to large climate variability presently limits confidence in attribution of observed changes

Assessing learning and memory in pigs

In recent years, there has been a surge of interest in (mini) pigs (Sus scrofa) as species for cognitive research. A major reason for this is their physiological and anatomical similarity with humans. For example, pigs possess a well-developed, large brain. Assessment of the learning and memory functions of pigs is not only relevant to human research but also to animal welfare, given the nature of current farming practices and the demands they make on animal health and behavior. In this article, we review studies of pig cognition, focusing on the underlying processes and mechanisms, with a view to identifying. Our goal is to aid the selection of appropriate cognitive tasks for research into pig cognition. To this end, we formulated several basic criteria for pig cognition tests and then applied these criteria and knowledge about pig-specific sensorimotor abilities and behavior to evaluate the merits, drawbacks, and limitations of the different types of tests used to date. While behavioral studies using (mini) pigs have shown that this species can perform learning and memory tasks, and much has been learned about pig cognition, results have not been replicated or proven replicable because of the lack of validated, translational behavioral paradigms that are specially suited to tap specific aspects of pig cognition. We identified several promising types of tasks for use in studies of pig cognition, such as versatile spatial free-choice type tasks that allow the simultaneous measurement of several behavioral domains. The use of appropriate tasks will facilitate the collection of reliable and valid data on pig cognition