Abstract
BackgroundAlthough many genes have been implicated as hypertension candidates, to date, few studies have integrated different types of genomic data for the purpose of biomarker selection.MethodsApplying a newly proposed sparse representation based variable selection (SRVS) method to the Genetic Analysis Workshop19 data, we analyzed a combined data set consisting of 11522 gene expressions and 354893 single-nucleotide polymorphisms (SNPs) from 397 subjects (case/control: 151/246), with the aim to identify potential biomarkers for blood pressure using both gene expression measures and SNP data.ResultsAmong the top 1000 variables (SNPs/gene expressions = 575/425) selected, the bioinformatics analysis showed that 302 were plausibly associated with blood pressure. In addition, we identified 173 variables that were associated with body weight and 84 associated with left ventricular contractility. Together, 55.9 % of the top 1000 variables showed associations with blood pressure related phenotypes(SNP/gene expression =348/211).ConclusionsOur results support the feasibility of the SRVS algorithm in integrating multiple data sets of different structure for comprehensive analysis.
Highlights
Many genes have been implicated as hypertension candidates, to date, few studies have integrated different types of genomic data for the purpose of biomarker selection
We used a sparse representation based variable selection (SRVS) method [9] to integrate a gene expression data set and a single-nucleotide polymorphisms (SNPs) data set acquired from the same subjects, for the purpose of identifying blood pressure (BP) related biomarkers, and facilitate the understanding of genetic mechanism of the BP disease
To select BP related genetic variables (SNP/gene expression), we focused on the case using Systolic Blood Pressure (SBP)-res as phenotype for Eq (3)
Summary
Many genes have been implicated as hypertension candidates, to date, few studies have integrated different types of genomic data for the purpose of biomarker selection. Many genes have been reported as hypertension candidates [8], to date, a limited number of studies have integrated different types of genomic data to select biomarkers. We used a sparse representation based variable selection (SRVS) method [9] to integrate a gene expression data set and a SNP data set acquired from the same subjects, for the purpose of identifying BP related biomarkers, and facilitate the understanding of genetic mechanism of the BP disease. The SRVS method has been shown to be feasible in identifying schizophrenia candidate biomarkers, while integrating functional magnetic resonance imaging data and SNP data [10]. It has been demonstrated that the use of multiple data types may provide higher power to identify potential biomarkers that would be missed by using independent data analysis [11]
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