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

Abstract

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.

A literature mining-based approach for identification of cellular pathways associated with chemoresistance in cancer

“…It is worth noting that this network consisted of 72 proteins, showing that the input genes are highly connected to each other. As expected, genes with similar functions are often clustered in the same pathway or biological process [64]. Similar results were found for other cancers (data not shown).…”

Incorporation of protein binding effects into likelihood ratio test for exome sequencing data

“…The location of ZBTB4 (7,362,685 to 7,387,582 bp) is inside of BP15_H (the blood pressure QTL#15[human]) on chr17. DNAH9 gene cluster is reported to be related to young-onset hypertension [30]. Through common microRNAs, LUC7L2 was found to be associated with hydrochlorothiazide (HCTZ)-induced uric acid elevations in an antihypertensive responses study of African Americans [31].…”

Exploring Different Stages of Alzheimer's Disease through Topological Analysis of Differentially Expressed Genetic Networks

“…In Tables I, and II [23], GO:0032350(regulation of hormone metabolic process) [24], GO:0006569(tryptophan catabolic process) [25], GO:0046218(indolalkylamine catabolic process) [26], GO:0004718(protein tyrosine kinase activity) [27], GO:0015459(potassium channel regulator activity) [28], GO:0016247(channel regulator activity) [29], GO:0015457(Transport) [30], GO:0008076(voltage-gated potassium channel complex) [31].…”