A Bayesian method for evaluating and discovering disease loci associations

Background: A genome-wide association study (GWAS) typically involves examining representative SNPs in individuals from some population. A GWAS data set can concern a million SNPs and may soon concern billions. Researchers investigate the association of each SNP individually with a disease, and it is becoming increasingly commonplace to also analyze multi-SNP associations. Techniques for handling so many hypotheses include the Bonferroni correction and recently developed Bayesian methods. These methods can encounter problems. Most importantly, they are not applicable to a complex multi-locus hypothesis which has several competing hypotheses rather than only a null hypothesis. A method that computes the posterior probability of complex hypotheses is a pressing need. Methodology/Findings: We introduce the Bayesian network posterior probability (BNPP) method which addresses the difficulties. The method represents the relationship between a disease and SNPs using a directed acyclic graph (DAG) model, and computes the likelihood of such models using a Bayesian network scoring criterion. The posterior probability of a hypothesis is computed based on the likelihoods of all competing hypotheses. The BNPP can not only be used to evaluate a hypothesis that has previously been discovered or suspected, but also to discover new disease loci associations. The results of experiments using simulated and real data sets are presented. Our results concerning simulated data sets indicate that the BNPP exhibits both better evaluation and discovery performance than does a p-value based method. For the real data sets, previous findings in the literature are confirmed and additional findings are found. Conclusions/Significance: We conclude that the BNPP resolves a pressing problem by providing a way to compute the posterior probability of complex multi-locus hypotheses. A researcher can use the BNPP to determine the expected utility of investigating a hypothesis further. Furthermore, we conclude that the BNPP is a promising method for discovering disease loci associations. © 2011 Jiang et al

What a difference a term makes:the effect of educational attainment on marital outcomes in the UK

Abstract In the past, students in England and Wales born within the first 5 monthsof the academic year could leave school one term earlier than those born later inthe year. Focusing on women, those who were required to stay on an extra termmore frequently hold some academic qualification. Using having been required tostay on as an exogenous factor affecting academic attainment, we find that holding alow-level academic qualification has no effect on the probability of being currentlymarried for women aged 25 or above, but increases the probability of the husbandholding some academic qualification and being economically active.33 Halama

Stem cell differentiation increases membrane-actin adhesion regulating cell blebability, migration and mechanics

This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder in order to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/K. S. is funded by an EPSRC PhD studentship. S.T. is funded by an EU Marie Curie Intra European Fellowship (GENOMICDIFF)

A novel approach to simulate gene-environment interactions in complex diseases

Background: Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.). Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are few examples of studies on their interactions in epidemiological literature. One reason can be the incomplete knowledge of the power of statistical methods designed to search for risk factors and their interactions in these data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interactions. To this aim, a possible strategy is to challenge the different statistical methods against data sets where the underlying phenomenon is completely known and fully controllable, for example simulated ones. Results: We present a mathematical approach that models gene-environment interactions. By this method it is possible to generate simulated populations having gene-environment interactions of any form, involving any number of genetic and environmental factors and also allowing non-linear interactions as epistasis. In particular, we implemented a simple version of this model in a Gene-Environment iNteraction Simulator (GENS), a tool designed to simulate case-control data sets where a one gene-one environment interaction influences the disease risk. The main aim has been to allow the input of population characteristics by using standard epidemiological measures and to implement constraints to make the simulator behaviour biologically meaningful. Conclusions: By the multi-logistic model implemented in GENS it is possible to simulate case-control samples of complex disease where gene-environment interactions influence the disease risk. The user has full control of the main characteristics of the simulated population and a Monte Carlo process allows random variability. A knowledge-based approach reduces the complexity of the mathematical model by using reasonable biological constraints and makes the simulation more understandable in biological terms. Simulated data sets can be used for the assessment of novel statistical methods or for the evaluation of the statistical power when designing a study

Intragenic DNA methylation: implications of this epigenetic mechanism for cancer research

Epigenetics is the study of all mechanisms that regulate gene transcription and genome stability that are maintained throughout the cell division, but do not include the DNA sequence itself. The best-studied epigenetic mechanism to date is DNA methylation, where methyl groups are added to the cytosine base within cytosine–guanine dinucleotides (CpG sites). CpGs are frequently clustered in high density (CpG islands (CGIs)) at the promoter of over half of all genes. Current knowledge of transcriptional regulation by DNA methylation centres on its role at the promoter where unmethylated CGIs are present at most actively transcribed genes, whereas hypermethylation of the promoter results in gene repression. Over the last 5 years, research has gradually incorporated a broader understanding that methylation patterns across the gene (so-called intragenic or gene body methylation) may have a role in transcriptional regulation and efficiency. Numerous genome-wide DNA methylation profiling studies now support this notion, although whether DNA methylation patterns are a cause or consequence of other regulatory mechanisms is not yet clear. This review will examine the evidence for the function of intragenic methylation in gene transcription, and discuss the significance of this in carcinogenesis and for the future use of therapies targeted against DNA methylation

Neuroblastoma Cell Death is Induced by Inorganic Arsenic Trioxide (As2O3) and Inhibited by a Normal Human Bone Marrow Cell-Derived Factor

Three phenotypically distinct cell types are present in human neuroblastomas (NB) and NB cell lines: I-type stem cells, N-type neuroblastic precursors, and S-type Schwannian/melanoblastic precursors. The stimulation of human N-type neuroblastoma cell proliferation by normal human bone marrow monocytic cell conditioned medium (BMCM) has been demonstrated in vitro, a finding consistent with the high frequency of bone marrow (BM) metastases in patients with advanced NB. Inorganic arsenic trioxide (As2O3), already clinically approved for the treatment of several hematological malignancies, is currently under investigation for NB. Recent studies show that As2O3 induces apoptosis in NB cells. We examined the impact of BMCM on growth and survival of As2O3-treated NB cell lines, to evaluate the response of cultured NB cell variants to regulatory agents. We studied the effect of BMCM on survival and clonogenic growth of eleven As2O3-treated NB cell lines grown in sparsely seeded, non-adherent, semi-solid cultures. As2O3 had a strong inhibitory effect on survival of all tested NB cell lines. BMCM augmented cell growth and survival and reversed the inhibitory action of As2O3 in all tested cell lines, but most strongly in N-type cells. While As2O3 effectively reduced survival of all tested NB cell lines, BMCM effectively impacted its inhibitory action. Better understanding of micro-environmental regulators affecting human NB tumor cell growth and survival may be seminal to the development of therapeutic strategies and clinically effective agents for this condition

Proposal for a method to estimate nutrient shock effects in bacteria

Plating methods are still the golden standard in microbiology; however, some studies have shown that these techniques can underestimate the microbial concentrations and diversity. A nutrient shock is one of the mechanisms proposed to explain this phenomenon. In this study, a tentative method to assess nutrient shock effects was tested. Findings To estimate the extent of nutrient shock effects, two strains isolated from tap water (Sphingomonas capsulata and Methylobacterium sp.) and two culture collection strains (E. coli CECT 434 and Pseudomonas fluorescens ATCC 13525) were exposed both to low and high nutrient conditions for different times and then placed in low nutrient medium (R2A) and rich nutrient medium (TSA). The average improvement (A.I.) of recovery between R2A and TSA for the different times was calculated to more simply assess the difference obtained in culturability between each medium. As expected, A.I. was higher when cells were plated after the exposition to water than when they were recovered from high-nutrient medium showing the existence of a nutrient shock for the diverse bacteria used. S. capsulata was the species most affected by this phenomenon. This work provides a method to consistently determine the extent of nutrient shock effects on different microorganisms and hence quantify the ability of each species to deal with sudden increases in substrate concentration. <br/

Spatial effects should be allowed for in primary care and other community-based cluster RCTS

<p>Abstract</p> <p>Background</p> <p>Typical advice on the design and analysis of cluster randomized trials (C-RCTs) focuses on allowance for the clustering at the level of the unit of allocation. However often C-RCTs are also organised spatially as may occur in the fields of Public Health and Primary Care where populations may even overlap.</p> <p>Methods</p> <p>We allowed for spatial effects on the error variance by a multiple membership model. These are a form of hierarchical model in which each lower level unit is a member of more than one higher level unit. Membership may be determined through adjacency or through Euclidean distance of centroids or in other ways such as the proportion of overlapping population. Such models may be estimated for Normal, binary and Poisson responses in Stata (v10 or above) as well as in WinBUGS or MLWin. We used this to analyse a dummy trial and two real, previously published cluster-allocated studies (one allocating general practices within one City and the other allocating general practices within one County) to investigate the extent to which ignoring spatial effects affected the estimate of treatment effect, using different methods for defining membership with Akaike's Information Criterion to determine the "best" model.</p> <p>Results</p> <p>The best fitting model included both a fixed North-South gradient and a random cluster effect for the dummy RCT. For one of the real RCTs the best fitting model included both a random practice effect plus a multiple membership spatial term, while for the other RCT the best fitting model ignored the clustering but included a fixed North-South gradient. Alternative models which fitted only slightly less well all included spatial effects in one form or another, with some variation in parameter estimates (greater when less well fitting models were included).</p> <p>Conclusions</p> <p>These particular results are only illustrative. However, we believe when designing C-RCTs in a primary care setting the possibility of spatial effects should be considered in relation to the intervention and response, as well as any explanatory effect of fixed covariates, together with any implications for sample size and methods for planned analyses.</p

Does the road to happiness depend on the retirement decision? Evidence from Italy

This study estimates the causal effect of retirement decision on well-being in Italy. To do so, the authors exploit the exogenous variation provided by the changes in the eligibility criteria for pensions that were enacted in Italy in 1995 (Dini’s law) and in 1997 (Prodi’s law, from the names of the prime ministers at the time of their introduction). A sizeable and positive impact of retirement decision is found on satisfaction with leisure time and on frequency of meeting friends. Furthermore, the results are generalized, allowing for the estimation of different moments from different data sources

Living biointerfaces based on non-pathogenic bacteria to direct cell differentiation

Genetically modified Lactococcus lactis, non-pathogenic bacteria expressing the FNIII7-10 fibronectin fragment as a protein membrane have been used to create a living biointerface between synthetic materials and mammalian cells. This FNIII7-10 fragment comprises the RGD and PHSRN sequences of fibronectin to bind α5β1 integrins and triggers signalling for cell adhesion, spreading and differentiation. We used L. lactis strain to colonize material surfaces and produce stable biofilms presenting the FNIII7-10 fragment readily available to cells. Biofilm density is easily tunable and remains stable for several days. Murine C2C12 myoblasts seeded over mature biofilms undergo bipolar alignment and form differentiated myotubes, a process triggered by the FNIII7-10 fragment. This biointerface based on living bacteria can be further modified to express any desired biochemical signal, establishing a new paradigm in biomaterial surface functionalisation for biomedical applications