Construction of gene clusters resembling genetic causal mechanisms for common complex disease with an application to young-onset hypertension

BMC Genomics

Methodology Article

Wen-Harn Pan¹  Ke-Shiuan Lynn²  Han-Ying Yang²  Wen-Lian Hsu²  Chen-Hua Lu² 

[1] Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan;National Health Research Institutes, Mialoli, Taiwan;Institute of Information Science, Academia Sinica, Taipei, Taiwan;

关键词: Genetic causal mechanism;    Sufficient cause;    Data-mining;    Young-onset hypertension;    Complex disease;   

DOI  :  10.1186/1471-2164-14-497

 received in 2013-03-06, accepted in 2013-07-19,  发布年份 2013

来源: Springer

【 摘 要 】

BackgroundLack of power and reproducibility are caveats of genetic association studies of common complex diseases. Indeed, the heterogeneity of disease etiology demands that causal models consider the simultaneous involvement of multiple genes. Rothman’s sufficient-cause model, which is well known in epidemiology, provides a framework for such a concept. In the present work, we developed a three-stage algorithm to construct gene clusters resembling Rothman’s causal model for a complex disease, starting from finding influential gene pairs followed by grouping homogeneous pairs.ResultsThe algorithm was trained and tested on 2,772 hypertensives and 6,515 normotensives extracted from four large Caucasian and Taiwanese databases. The constructed clusters, each featured by a major gene interacting with many other genes and identified a distinct group of patients, reproduced in both ethnic populations and across three genotyping platforms. We present the 14 largest gene clusters which were capable of identifying 19.3% of hypertensives in all the datasets and 41.8% if one dataset was excluded for lack of phenotype information. Although a few normotensives were also identified by the gene clusters, they usually carried less risky combinatory genotypes (insufficient causes) than the hypertensive counterparts. After establishing a cut-off percentage for risky combinatory genotypes in each gene cluster, the 14 gene clusters achieved a classification accuracy of 82.8% for all datasets and 98.9% if the information-short dataset was excluded. Furthermore, not only 10 of the 14 major genes but also many other contributing genes in the clusters are associated with either hypertension or hypertension-related diseases or functions.ConclusionsWe have shown with the constructed gene clusters that a multi-causal pie-multi-component approach can indeed improve the reproducibility of genetic markers for complex disease. In addition, our novel findings including a major gene in each cluster and sufficient risky genotypes in a cluster for disease onset (which coincides with Rothman’s sufficient cause theory) may not only provide a new research direction for complex diseases but also help to reveal the disease etiology.

【 授权许可】

Unknown   
© Lynn et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.