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. 2016 Oct 18;10(Suppl 7):283-288.
doi: 10.1186/s12919-016-0044-7. eCollection 2016.

Integrating multiple genomic data: sparse representation based biomarker selection for blood pressure

Hongbao Cao  1 , Wei Guo  1 , Haide Qin  1 , Mengyuan Xu  1 , Benjamin Lehrman  1 , Yu Tao  1 , Yin-Yao Shugart  1
Affiliations

Integrating multiple genomic data: sparse representation based biomarker selection for blood pressure

Hongbao Cao et al. BMC Proc. .

Abstract

Background: Although 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.

Methods: Applying 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.

Results: Among 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).

Conclusions: Our results support the feasibility of the SRVS algorithm in integrating multiple data sets of different structure for comprehensive analysis.

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Figures

Fig. 1
Fig. 1
Blood pressure phenotypes of 397 subjects. SBP-res and DBP-res are the residual y of regression problem given by Eq. (1) for SBP and DBP, respectively; x-axis represents the subjects; y-axis represents the blood pressure phenotypes at each subject
Fig. 2
Fig. 2
Number of SNPs and gene expressions selected in the top 100 to 1000 variables selected
Fig. 3
Fig. 3
LOD analysis results for the top 1000 variables selected. a Pie plot of the variable distribution for the top 1000 variables. b Bar plot for the number of variables linked to left ventricular, body weight and blood pressure in the top 100 to 1000 variables selected
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