Association between brain structure and risk SNPs for depression in adolescence
Poster No:
863
Submission Type:
Abstract Submission
Authors:
Jingyu Liu1, Chan Aek Panichvatana2, Vince Calhoun3
Institutions:
1GSU, Atlanta, GA, 2Georgia State University, Atlanta, GA, 3GSU/GATech/Emory, Decatur, GA
First Author:
Co-Author(s):
Introduction:
The global rising prevalence of depressive and anxiety symptoms in children and adolescents posts a pressing issue for public health and warrants special attention of the research community. Multiple factors, ranging from genetic vulnerabilities to environmental stressors, influence the risk for these mental disorders. This study aims to investigate how risk genetic mutations of major depression associate with brain structural variations during the key developmental phase. To leverage big data collected across cohorts in different countries, we have implemented a decentralized data-analyses algorithm, decentralized parallel independent component analysis (dpICA). dpICA enables analyzing MRI images and single nucleotide polymorphism (SNP) data located at different centers, without sharing the raw data, and computing statistical models and identifying the associations between genetic and brain structure.
Methods:
We have investigated data from three cohorts: Adolescent Brain and Cognitive Development Study (USA, age of 9-10), Consortium on Vulnerability to Externalizing Disorders and Addictions (India, age of 6-17) and IMAGEN (Europe, age of 14). Risk SNPs were selected based on a recent genome-wide association study of major depression. We chose SNPs associated with major depression with p < 1e-3, and passing quality control and pruning, leading to 1664 risk SNPs for further analyses. T1 weighted MRI images from all three cohorts went through a SPM12 standard pipeline to derive gray matter images normalized into MNI space. Finally gray matter and SNPs from 6209 independent participants of ABCD cohort, 1526 participants of IMAGEN, and 571 participants of cVEDA were analyzed through dpICA. Briefly, data from each cohort were first reduced through principal component analysis (PCA), and only limited number of PCs with large variances were passed to one global site, At the global site, PCs from three sites were concatenated and further merged through another PCA. Then, three global PCs of SNP data associated significantly with race were removed. The rest of global PCs of SNP data and global PCs of MRI data were analyzed with parallel ICA, which extracts independent gray matter components and SNP components, and iteratively maximizes the correlation between participants' loadings of SNP and gray matter components.
Results:
Among 25 gray matter components and 27 SNP components, one SNP component was identified to be significantly associated with one gray matter component with the overall correlation r= 0.06 (p<1e-6), and correlations in each cohort were 0.07, 0.11 and 0.17 for ABCD, IMAGEN and cVEDA respectively. There are 19 main contribution SNPs, with the highest contributing SNPs being rs2782446 and rs8002150 in chromosome 13 noncoding regions. The associated brain regions are from superior parietal, precuneus, posterior cingulate, lingual gyrus and cerebellum.
·Gray matter component associated with SNPs
Conclusions:
Our pilot analyses indicate that SNPs with risk for major depression, mainly adult depression, have showed associations with gray matter variation during children and adolescent development phase, even though the effect size is small. Further stability and generalizability of our finding needs to be performed.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Genetics:
Genetic Association Studies 1
Keywords:
Data analysis
Other - depression
1|2Indicates the priority used for review
Provide references using author date format
Zhang, Y. (2020), 'The Consortium on Vulnerability to Externalizing Disorders and Addictions (c-VEDA): an accelerated longitudinal cohort of children and adolescents in India.' Molecular Psychiatry 25, 1618–1630
Maricic, L.(2020), 'The IMAGEN study: a decade of imaging genetics in adolescents.' Molecular Psychiatry 25, 2648–2671
Volkow ND, (2018), 'The conception of the ABCD study: From substance use to a broad NIH collaboration. Developmental Cognitive Neuroscience, 32, 4-7,
Panichvatana, C.A.,(2023),'Decentralized Parallel Independent Component Analysis for Multimodal, Multisite Data,' IEEE Annu Int Conf IEEE Eng Med Biol Soc.