Integrating fMRI and SNP data for biomarker identification for schizophrenia with a sparse representation based variable selection method

Hongbao Cao,Junbo Duan,Vince Calhoun,Yu-Ping Wang,Dongdong Lin

doi:10.1186/1755-8794-6-s3-s2

Hongbao Cao, Junbo Duan + Show 3 more

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https://doi.org/10.1186/1755-8794-6-s3-s2

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Abstract

BackgroundIn recent years, both single-nucleotide polymorphism (SNP) array and functional magnetic resonance imaging (fMRI) have been widely used for the study of schizophrenia (SCZ). In addition, a few studies have been reported integrating both SNPs data and fMRI data for comprehensive analysis.MethodsIn this study, a novel sparse representation based variable selection (SRVS) method has been proposed and tested on a simulation data set to demonstrate its multi-resolution properties. Then the SRVS method was applied to an integrative analysis of two different SCZ data sets, a Single-nucleotide polymorphism (SNP) data set and a functional resonance imaging (fMRI) data set, including 92 cases and 116 controls. Biomarkers for the disease were identified and validated with a multivariate classification approach followed by a leave one out (LOO) cross-validation. Then we compared the results with that of a previously reported sparse representation based feature selection method.ResultsResults showed that biomarkers from our proposed SRVS method gave significantly higher classification accuracy in discriminating SCZ patients from healthy controls than that of the previous reported sparse representation method. Furthermore, using biomarkers from both data sets led to better classification accuracy than using single type of biomarkers, which suggests the advantage of integrative analysis of different types of data.ConclusionsThe proposed SRVS algorithm is effective in identifying significant biomarkers for complicated disease as SCZ. Integrating different types of data (e.g. SNP and fMRI data) may identify complementary biomarkers benefitting the diagnosis accuracy of the disease.

Highlights

In recent years, both single-nucleotide polymorphism (SNP) array and functional magnetic resonance imaging have been widely used for the study of schizophrenia (SCZ)
Chen et al proposed parallel independent component analysis to identify genomic risk components associated with brain function abnormalities and detected significant biomarkers from both functional magnetic resonance imaging (fMRI) data and SNP data that are strongly correlated [7]
When comparing the fMRI voxels selected, we showed that the sparse representation based variable selection (SRVS) method were capable of selecting fMRI voxels that were clustered in specific regions, as shown in Figure 5 (a)

Summary

Introduction

Both single-nucleotide polymorphism (SNP) array and functional magnetic resonance imaging (fMRI) have been widely used for the study of schizophrenia (SCZ). A few studies have been reported integrating both SNPs data and fMRI data for comprehensive analysis. Chen et al proposed parallel independent component analysis (paraICA) to identify genomic risk components associated with brain function abnormalities and detected significant biomarkers from both fMRI data and SNP data that are strongly correlated [7]. Parallel ICA is an effective method for the joint analysis of multiple modalities including interconnections between them [8]. Utilizing this method, Meda et al detected three fMRI components significantly correlated with two distinct gene components in SCZ study [11]. A novel sparse representation based variable selection (SRVS) method was proposed and applied to an integrative analysis of two types of data: fMRI and SNP, aiming to obtain comprehensive analysis

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Discussion

Conclusion