Dataset Information

Clique-finding for heterogeneity and multidimensionality in biomarker epidemiology research: the CHAMBER algorithm.

ABSTRACT: Commonly-occurring disease etiology may involve complex combinations of genes and exposures resulting in etiologic heterogeneity. We present a computational algorithm that employs clique-finding for heterogeneity and multidimensionality in biomedical and epidemiological research (the "CHAMBER" algorithm).This algorithm uses graph-building to (1) identify genetic variants that influence disease risk and (2) predict individuals at risk for disease based on inherited genotype. We use a set-covering algorithm to identify optimal cliques and a Boolean function that identifies etiologically heterogeneous groups of individuals. We evaluated this approach using simulated case-control genotype-disease associations involving two- and four-gene patterns. The CHAMBER algorithm correctly identified these simulated etiologies. We also used two population-based case-control studies of breast and endometrial cancer in African American and Caucasian women considering data on genotypes involved in steroid hormone metabolism. We identified novel patterns in both cancer sites that involved genes that sulfate or glucuronidate estrogens or catecholestrogens. These associations were consistent with the hypothesized biological functions of these genes. We also identified cliques representing the joint effect of multiple candidate genes in all groups, suggesting the existence of biologically plausible combinations of hormone metabolism genes in both breast and endometrial cancer in both races.The CHAMBER algorithm may have utility in exploring the multifactorial etiology and etiologic heterogeneity in complex disease.

SUBMITTER: Mushlin RA

PROVIDER: S-EPMC2653643 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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Clique-finding for heterogeneity and multidimensionality in biomarker epidemiology research: the CHAMBER algorithm.

Mushlin Richard A RA Gallagher Stephen S Kershenbaum Aaron A Rebbeck Timothy R TR

PloS one 20090316 3

<h4>Background</h4>Commonly-occurring disease etiology may involve complex combinations of genes and exposures resulting in etiologic heterogeneity. We present a computational algorithm that employs clique-finding for heterogeneity and multidimensionality in biomedical and epidemiological research (the "CHAMBER" algorithm).<h4>Methodology/principal findings</h4>This algorithm uses graph-building to (1) identify genetic variants that influence disease risk and (2) predict individuals at risk for ...[more]

PMID: 19287484

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Project description:BackgroundThere is a growing awareness of the need to adequately integrate sex and gender into health-related research. Although it is widely known that the entangled dimensions sex/gender are not comprehensively considered in most studies to date, current publications of conceptual considerations and guidelines often only give recommendations for certain stages of the research process and - to the best of our knowledge - there is a lack of a detailed guidance that accompanies each step of the entire research process. The interdisciplinary project "Integrating gender into environmental health research" (INGER) aimed to fill this gap by developing a comprehensive checklist that encourages sex/gender transformative research at all stages of the research process of quantitative health research. In the long term this contributes to a more sex/gender-equitable research.MethodsThe checklist builds on current guidelines on sex/gender in health-related research. Starting from important key documents, publications from disciplines involved in INGER were collected. Furthermore, we used a snowball method to include further relevant titles. The identification of relevant publications was continued until saturation was reached. 55 relevant publications published between 2000 and 2021 were identified, assessed, summarised and included in the developed checklist. After noticing that most publications did not cover every step of the research process and often considered sex/gender in a binary way, the recommendations were modified and enriched based on the authors' expertise to cover every research step and to add further categories to the binary sex/gender categories.ResultsThe checklist comprises 67 items in 15 sections for integrating sex/gender in quantitative health-related research and addresses aspects of the whole research process of planning, implementing and analysing quantitative health studies as well as aspects of appropriate language, communication of results to the scientific community and the public, and research team composition.ConclusionThe developed comprehensive checklist goes beyond a binary consideration of sex/gender and thus enables sex/gender-transformative research. Although the project INGER focused on environmental health research, no aspects that were specific to this research area were identified in the checklist. The resulting comprehensive checklist can therefore be used in different quantitative health-related research fields.

Dataset Information

Clique-finding for heterogeneity and multidimensionality in biomarker epidemiology research: the CHAMBER algorithm.

Publications

Clique-finding for heterogeneity and multidimensionality in biomarker epidemiology research: the CHAMBER algorithm.

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