Guided exploration of genomic risk for gray matter abnormalities in schizophrenia using parallel independent component analysis with reference

Jiayu Chen; Vince D. Calhoun; Godfrey D. Pearlson; Nora Perrone-Bizzozero; Jing Sui; Jessica A. Turner; Juan R. Bustillo; Stefan Ehrlich; Scott R. Sponheim; José M. Cañive; Beng Choon Ho; Jingyu Liu

doi:10.1016/j.neuroimage.2013.05.073

Guided exploration of genomic risk for gray matter abnormalities in schizophrenia using parallel independent component analysis with reference

Jiayu Chen, Vince D. Calhoun, Godfrey D. Pearlson, Nora Perrone-Bizzozero, Jing Sui, Jessica A. Turner, Juan R. Bustillo, Stefan Ehrlich, Scott R. Sponheim, José M. Cañive, Beng Choon Ho, Jingyu Liu

Psychiatry & Behavioral Sciences

Research output: Contribution to journal › Article › peer-review

40 Scopus citations

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.

Original language	English (US)
Pages (from-to)	384-396
Number of pages	13
Journal	NeuroImage
Volume	83
DOIs	https://doi.org/10.1016/j.neuroimage.2013.05.073
State	Published - Dec 2013

Bibliographical note

Funding Information:
The authors would like to thank Jill Fries and Marilee Morgan for preprocessing the imaging and genetic data. We also want to thank the University of Iowa Hospital, Massachusetts General Hospital, the University of Minnesota, the University of New Mexico, and the Mind Research Network staff for their efforts in data collection, preprocessing, and analyses. We appreciate the valuable advice given by Rogers Silver at the Mind Research Network. This project was funded by the National Institutes of Health , grant number: 5P20RR021938 , R01EB005846 , and 1R01MH094524-01A1 .

Keywords

Multivariate
Parallel ICA with reference
SMRI
SNP
Schizophrenia
Semi-blind

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Chen, J., Calhoun, V. D., Pearlson, G. D., Perrone-Bizzozero, N., Sui, J., Turner, J. A., Bustillo, J. R., Ehrlich, S., Sponheim, S. R., Cañive, J. M., Ho, B. C., & Liu, J. (2013). Guided exploration of genomic risk for gray matter abnormalities in schizophrenia using parallel independent component analysis with reference. NeuroImage, 83, 384-396. https://doi.org/10.1016/j.neuroimage.2013.05.073

Chen, J, Calhoun, VD, Pearlson, GD, Perrone-Bizzozero, N, Sui, J, Turner, JA, Bustillo, JR, Ehrlich, S, Sponheim, SR, Cañive, JM, Ho, BC & Liu, J 2013, 'Guided exploration of genomic risk for gray matter abnormalities in schizophrenia using parallel independent component analysis with reference', NeuroImage, vol. 83, pp. 384-396. https://doi.org/10.1016/j.neuroimage.2013.05.073

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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.",

keywords = "Multivariate, Parallel ICA with reference, SMRI, SNP, Schizophrenia, Semi-blind",

author = "Jiayu Chen and Calhoun, {Vince D.} and Pearlson, {Godfrey D.} and Nora Perrone-Bizzozero and Jing Sui and Turner, {Jessica A.} and Bustillo, {Juan R.} and Stefan Ehrlich and Sponheim, {Scott R.} and Ca{\~n}ive, {Jos{\'e} M.} and Ho, {Beng Choon} and Jingyu Liu",

note = "Funding Information: The authors would like to thank Jill Fries and Marilee Morgan for preprocessing the imaging and genetic data. We also want to thank the University of Iowa Hospital, Massachusetts General Hospital, the University of Minnesota, the University of New Mexico, and the Mind Research Network staff for their efforts in data collection, preprocessing, and analyses. We appreciate the valuable advice given by Rogers Silver at the Mind Research Network. This project was funded by the National Institutes of Health , grant number: 5P20RR021938 , R01EB005846 , and 1R01MH094524-01A1 . ",

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AU - Cañive, José M.

AU - Ho, Beng Choon

AU - Liu, Jingyu

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N2 - 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.

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Guided exploration of genomic risk for gray matter abnormalities in schizophrenia using parallel independent component analysis with reference

Abstract

Bibliographical note

Keywords

Access

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