ArticleBMC genetics2008
Analysis of heterogeneity and epistasis in physiological mixed populations by combined structural equation modelling and latent class analysis.
Article in BMC genetics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 14 citations in OpenAlex.
- Genetic heterogeneity: Challenges, impacts, and methods through an associative lens.Genetic epidemiology · 2022Review
- Heterogeneity Analysis and Diagnosis of Complex Diseases Based on Deep Learning Method.Scientific reports · 2018Article
- Network-based analysis of the sphingolipid metabolism in hypertension.Frontiers in genetics · 2015Article
- Practical aspects of genome-wide association interaction analysis.Human genetics · 2014Review
- Next generation genetics.Frontiers in genetics · 2014Article
- Genetics of the ceramide/sphingosine-1-phosphate rheostat in blood pressure regulation and hypertension.BMC genetics · 2011Article
- Investigating the effects of climatic variables and reservoir on the incidence of hemorrhagic fever with renal syndrome in Huludao City, China: a 17-year data analysis based on structure equation model.BMC infectious diseases · 2009Article
- Role of genetic heterogeneity and epistasis in bladder cancer susceptibility and outcome: a learning classifier system approach.Journal of the American Medical Informatics Association : JAMIAArticle
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBiological systems are interacting, molecular networks in which genetic variation contributes to phenotypic heterogeneity. This heterogeneity is traditionally modelled as a dichotomous trait (e.g. affected vs. non-affected). This is far too simplistic considering the complexity and genetic variations of such networks.
methodsIn this study on type 2 diabetes mellitus, heterogeneity was resolved in a latent class framework combined with structural equation modelling using phenotypic indicators of distinct physiological processes. We modelled the clinical condition "the metabolic syndrome", which is known to be a heterogeneous and polygenic condition with a clinical endpoint (type 2 diabetes mellitus). In the model presented here, genetic factors were not included and no genetic model is assumed except that genes operate in networks. The impact of stratification of the study population on genetic interaction was demonstrated by analysis of several genes previously associated with the metabolic syndrome and type 2 diabetes mellitus.
resultsThe analysis revealed the existence of 19 distinct subpopulations with a different propensity to develop diabetes mellitus within a large healthy study population. The allocation of subjects into subpopulations was highly accurate with an entropy measure of nearly 0.9. Although very few gene variants were directly associated with metabolic syndrome in the total study sample, almost one third of all possible epistatic interactions were highly significant. In particular, the number of interactions increased after stratifying the study population, suggesting that interactions are masked in heterogenous populations. In addition, the genetic variance increased by an average of 35-fold when analysed in the subpopulations.
conclusionThe major conclusions from this study are that the likelihood of detecting true association between genetic variants and complex traits increases tremendously when studied in physiological homogenous subpopulations and on inclusion of epistasis in the analysis, whereas epistasis (i.e. genetic networks) is ubiquitous and should be the basis in modelling any biological process.
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