An ICA with reference approach in identification of genetic variation and associated brain networks
- 1The Mind Research Network, Albuquerque, NM, USA
- 2Department of Radiology, Harvard Medical School, Boston, MA, USA
- 3Department of Neurosciences, University of New Mexico School of Medicine, Albuquerque, NM, USA
- 4Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
- 5Veterans Affairs Medical Center, San Francisco, CA, USA
- 6Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
To address the statistical challenges associated with genome-wide association studies, we present an independent component analysis (ICA) with reference approach to target a specific genetic variation and associated brain networks. First, a small set of single nucleotide polymorphisms (SNPs) are empirically chosen to reflect a feature of interest and these SNPs are used as a reference when applying ICA to a full genomic SNP array. After extracting the genetic component maximally representing the characteristics of the reference, we test its association with brain networks in functional magnetic resonance imaging (fMRI) data. The method was evaluated on both real and simulated datasets. Simulation demonstrates that ICA with reference can extract a specific genetic factor, even when the variance accounted for by such a factor is so small that a regular ICA fails. Our real data application from 48 schizophrenia patients (SZs) and 40 healthy controls (HCs) include 300K SNPs and fMRI images in an auditory oddball task. Using SNPs with allelic frequency difference in two groups as a reference, we extracted a genetic component that maximally differentiates patients from controls (p < 4 × 10−17), and discovered a brain functional network that was significantly associated with this genetic component (p < 1 × 10−4). The regions in the functional network mainly locate in the thalamus, anterior and posterior cingulate gyri. The contributing SNPs in the genetic factor mainly fall into two clusters centered at chromosome 7q21 and chromosome 5q35. The findings from the schizophrenia application are in concordance with previous knowledge about brain regions and gene function. All together, the results suggest that the ICA with reference can be particularly useful to explore the whole genome to find a specific factor of interest and further study its effect on brain.
Keywords: independent component analysis with reference, genome-wide association study, brain network, schizophrenia, single nucleotide polymorphisms, functional magnetic resonance imaging
Citation: Liu J, Ghassemi MM, Michael AM, Boutte D, Wells W, Perrone-Bizzozero N, Macciardi F, Mathalon DH, Ford JM, Potkin SG, Turner JA and Calhoun VD (2012) An ICA with reference approach in identification of genetic variation and associated brain networks. Front. Hum. Neurosci. 6:21. doi: 10.3389/fnhum.2012.00021
Received: 05 November 2011; Accepted: 04 February 2012;
Published online: 22 February 2012.
Copyright: © 2012 Liu, Ghassemi, Michael, Boutte, Wells, Perrone-Bizzozero, Macciardi, Mathalon, Ford, Potkin, Turner and Calhoun. This is an open-access article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits non-commercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.
*Correspondence: Jingyu Liu, The Mind Research Network, 1101 Yale Boulevard, Albuquerque, NM 87131, USA. e-mail: firstname.lastname@example.org