Journal article
Guided exploration of genomic risk for gray matter abnormalities in schizophrenia using parallel independent component analysis with reference
NeuroImage (Orlando, Fla.), Vol.83, pp.384-396
12/2013
DOI: 10.1016/j.neuroimage.2013.05.073
PMCID: PMC3797233
PMID: 23727316
Abstract
One application of imaging genomics is to explore genetic variants associated with brain structure and function, presenting a new means of mapping genetic influences on mental disorders. While there is growing interest in performing genome-wide searches for determinants, it remains challenging to identify genetic factors of small effect size, especially in limited sample sizes. In an attempt to address this issue, we propose to take advantage of a priori knowledge, specifically to extend parallel independent component analysis (pICA) to incorporate a reference (pICA-R), aiming to better reveal relationships between hidden factors of a particular attribute. The new approach was first evaluated on simulated data for its performance under different configurations of effect size and dimensionality. Then pICA-R was applied to a 300-participant (140 schizophrenia (SZ) patients versus 160 healthy controls) dataset consisting of structural magnetic resonance imaging (sMRI) and single nucleotide polymorphism (SNP) data. Guided by a reference SNP set derived from ANK3, a gene implicated by the Psychiatric Genomic Consortium SZ study, pICA-R identified one pair of SNP and sMRI components with a significant loading correlation of 0.27 (p=1.64×10−6). The sMRI component showed a significant group difference in loading parameters between patients and controls (p=1.33×10−15), indicating SZ-related reduction in gray matter concentration in prefrontal and temporal regions. The linked SNP component also showed a group difference (p=0.04) and was predominantly contributed to by 1030 SNPs. The effect of these top contributing SNPs was verified using association test results of the Psychiatric Genomic Consortium SZ study, where the 1030 SNPs exhibited significant SZ enrichment compared to the whole genome. In addition, pathway analyses indicated the genetic component majorly relating to neurotransmitter and nervous system signaling pathways. Given the simulation and experiment results, pICA-R may prove a promising multivariate approach for use in imaging genomics to discover reliable genetic risk factors under a scenario of relatively high dimensionality and small effect size.
•A novel reference guided multivariate approach to reveal relationships of features.•Designed for imaging genomics to extract specific genetic factors from the genome•Simulation and real data application demonstrate its feasibility.•Schizophrenia-related gray mater reduction related to multiple genetic variants
Details
- Title: Subtitle
- Guided exploration of genomic risk for gray matter abnormalities in schizophrenia using parallel independent component analysis with reference
- Creators
- Jiayu Chen - University of New MexicoVince D Calhoun - University of New MexicoGodfrey D Pearlson - Yale UniversityNora Perrone-Bizzozero - University of New MexicoJing Sui - Mind Research NetworkJessica A Turner - Mind Research NetworkJuan R Bustillo - University of New MexicoStefan Ehrlich - Technische Universität DresdenScott R Sponheim - University of MinnesotaJosé M Cañive - University of New MexicoBeng-Choon Ho - University of IowaJingyu Liu - University of New Mexico
- Resource Type
- Journal article
- Publication Details
- NeuroImage (Orlando, Fla.), Vol.83, pp.384-396
- DOI
- 10.1016/j.neuroimage.2013.05.073
- PMID
- 23727316
- PMCID
- PMC3797233
- NLM abbreviation
- Neuroimage
- ISSN
- 1053-8119
- eISSN
- 1095-9572
- Publisher
- Elsevier Inc
- Grant note
- DOI: 10.13039/100000002, name: National Institutes of Health, award: 5P20RR021938, R01EB005846, 1R01MH094524-01A1
- Language
- English
- Date published
- 12/2013
- Academic Unit
- Psychiatry
- Record Identifier
- 9984280830602771
Metrics
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