Molecular epidemiology

Summary

Molecular epidemiology is a branch of epidemiology and medical science that focuses on the contribution of potential genetic and environmental risk factors, identified at the molecular level, to the etiology, distribution and prevention of disease within families and across populations. This field has emerged from the integration of molecular biology into traditional epidemiological research. Molecular epidemiology improves our understanding of the pathogenesis of disease by identifying specific pathways, molecules and genes that influence the risk of developing disease. More broadly, it seeks to establish understanding of how the interactions between genetic traits and environmental exposures result in disease. History The term "molecular epidemiology" was first coined by Edwin D. Kilbourne in a 1973 article entitled "The molecular epidemiology of influenza". The term became more formalized with the formulation of the first book on molecular epidemiology titled Molecular E

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https://en.wikipedia.org/wiki/Molecular_epidemiology

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Epidemiology is a cornerstone of public health. Understanding the distribution and dynamics of diseases is critically important to manage or prevent them. Modern digital approaches are used increasingly in epidemiology. This course teaches both the basics of epidemiology as well as those approaches.

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Large-scale variational inference for Bayesian joint regression modelling of high-dimensional genetic data

Hélène Ruffieux

Genetic association studies have become increasingly important in understanding the molecular bases of complex human traits. The specific analysis of intermediate molecular traits, via quantitative trait locus (QTL) studies, has recently received much attention, prompted by the advance of high-throughput technologies for quantifying gene, protein and metabolite levels. Of great interest is the detection of weak trans-regulatory effects between a genetic variant and a distal gene product. In particular, hotspot genetic variants, which remotely control the levels of many molecular outcomes, may initiate decisive functional mechanisms underlying disease endpoints. This thesis proposes a Bayesian hierarchical approach for joint analysis of QTL data on a genome-wide scale. We consider a series of parallel sparse regressions combined in a hierarchical manner to flexibly accommodate high-dimensional responses (molecular levels) and predictors (genetic variants), and we present new methods for large-scale inference. Existing approaches have limitations. Conventional marginal screening does not account for local dependencies and association patterns common to multiple outcomes and genetic variants, whereas joint modelling approaches are restricted to relatively small datasets by computational constraints. Our novel framework allows information-sharing across outcomes and variants, thereby enhancing the detection of weak trans and hotspot effects, and implements tailored variational inference procedures that allow simultaneous analysis of data for an entire QTL study, comprising hundreds of thousands of predictors, and thousands of responses and samples. The present work also describes extensions to leverage spatial and functional information on the genetic variants, for example, using predictor-level covariates such as epigenomic marks. Moreover, we augment variational inference with simulated annealing and parallel expectation-maximisation schemes in order to enhance exploration of highly multimodal spaces and allow efficient empirical Bayes estimation. Our methods, publicly available as packages implemented in R and C++, are extensively assessed in realistic simulations. Their advantages are illustrated in several QTL applications, including a large-scale proteomic QTL study on two clinical cohorts that highlights novel candidate biomarkers for metabolic disorders.

EPFL2019

Efficient inference for genetic association studies with multiple outcomes

Anthony Christopher Davison, Jörg Hager, Hélène Ruffieux

Combined inference for heterogeneous high-dimensional data is critical in modern biology, where clinical and various kinds of molecular data may be available from a single study. Classical genetic association studies regress a single clinical outcome on many genetic variants one by one, but there is an increasing demand for joint analysis of many molecular outcomes and genetic variants in order to unravel functional interactions. Unfortunately, most existing approaches to joint modeling are either too simplistic to be powerful or are impracticable for computational reasons. Inspired by Richardson and others (2010, Bayesian Statistics 9), we consider a sparse multivariate regression model that allows simultaneous selection of predictors and associated responses. As Markov chain Monte Carlo (MCMC) inference on such models can be prohibitively slow when the number of genetic variants exceeds a few thousand, we propose a variational inference approach which produces posterior information very close to that of MCMC inference, at a much reduced computational cost. Extensive numerical experiments show that our approach outperforms popular variable selection methods and tailored Bayesian procedures, dealing within hours with problems involving hundreds of thousands of genetic variants and tens to hundreds of clinical or molecular outcomes.

Oxford University Press2017

Background: Clostridium botulinum and related clostridia express extremely potent toxins known as botulinum neurotoxins (BoNTs) that cause severe, potentially lethal intoxications in humans. These BoNT-producing bacteria are categorized in seven major toxinotypes (A through G) and several subtypes. The high diversity in nucleotide sequence and genetic organization of the gene cluster encoding the BoNT components poses a great challenge for the screening and characterization of BoNT-producing strains. Methodology/Principal Findings: In the present study, we designed and evaluated the performances of a resequencing microarray (RMA), the PathogenId v2.0, combined with an automated data approach for the simultaneous detection and characterization of BoNT-producing clostridia. The unique design of the PathogenID v2.0 array allows the simultaneous detection and characterization of 48 sequences targeting the BoNT gene cluster components. This approach allowed successful identification and typing of representative strains of the different toxinotypes and subtypes, as well as the neurotoxin-producing C. botulinum strain in a naturally contaminated food sample. Moreover, the method allowed fine characterization of the different neurotoxin gene cluster components of all studied strains, including genomic regions exhibiting up to 24.65% divergence with the sequences tiled on the arrays. Conclusions/Significance: The severity of the disease demands rapid and accurate means for performing risk assessments of BoNT-producing clostridia and for tracing potentials sources of contamination in outbreak situations. The RMA approach constitutes an essential higher echelon component in a diagnostics and surveillance pipeline. In addition, it is an important asset to characterise potential outbreak related strains, but also environment isolates, in order to obtain a better picture of the molecular epidemiology of BoNT-producing clostridia.

Public Library of Science2013

Large-scale variational inference for Bayesian joint regression modelling of high-dimensional genetic data

Hélène Ruffieux

EPFL2019