Polygenic risk score associated prefrontal-subcortical and salience networks in depression
Poster No:
679
Submission Type:
Abstract Submission
Authors:
Chuang Liang1, Jiayu Chen2, Juan Bustillo3, Peter Kochunov4, Nora Perrone-Bizzozero5, Yonggui Yuan6, Wenhao Jiang6, Jing Sui7, Rongtao Jiang8, Chunzhi Zhao1, Xiao Yang9, Zening Fu2, Yuhui Du10, Daoqiang Zhang1, Christopher Abbott3, Vince Calhoun11, Shile Qi1
Institutions:
1Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, 2GSU, Atlanta, GA, 3University of New Mexico, Albuquerque, NM, 4University of Maryland School of Medicine, Baltimore, MD, 5University of New Mexico School of Medicine, Albuquerque, NM, 6Zhongda Hospital, Medical School, Southeast University, Nanjing, Jiangsu, 7Beijing Normal University, Beijing, China, 8Yale School of Medicine, New Haven, CT, 9West China Hospital of Sichuan University, Chengdu, Sichuan, 10Shanxi University, Taiyuan, Shanxi, 11GSU/GATech/Emory, Atlanta, GA
First Author:
Co-Author(s):
Introduction:
Major depressive disorder (MDD) is a highly prevalent and heritable (~40%) psychiatric disorder with widespread functional and structural brain abnormalities1-3. Polygenic risk score (PRS)4 reflects cumulative risk by aggregating the effects of all MDD relevant single nucleotide polymorphisms (SNPs), that provides a promising approach to examine the underlying polygenic architecture of MDD and the impact of genetic factors on its neurobiological mechanisms5. However, previous studies focused on simple correlation analysis between PRS and brain phenotypes based on single modality, without identifying MDD-PRS associated multimodal brain patterns and further assessing its biomarker properties including diagnosis, prediction, and treatment prognosis.
Methods:
Healthy subjects with neuroimaging data from UKB (n=34874) were used as discovery cohort. Multimodal brain imaging data of MDD and healthy control (HC) from multiple studies were collected as validation datasets: cohort I with 571 MDDs and 336 HCs; cohorts II6 with 263 MDDs and 286 HCs; cohort III7 with 124 pre- and post-electroconvulsive therapy (ECT) MDDs and 56 HCs. MDD PRS (calculated by PRSice8 with a threshold of PSNP≤5.0e−08) were used as reference, with age and education as covariates, to guide a two-way MRI (fractional amplitude of low frequency fluctuations, fALFF + gray matter volume, GMV) fusion within UKB to identify the multimodal brain patterns associated with PRS and unrelated with covariates (Fig. 1a). We validated the repeatability of PRS patterns under different PSNP thresholds (1.0e-04 and 0.05) and calculating methods (PRScs9, Fig. 1b). The masks of the identified brain patterns were then used to extract fALFF and GMV from 3 independent MDD cohorts. Group difference analysis between MDD and HC, longitudinal difference between pre-ECT and post-ECT MDDs (Fig. 1c), symptom prediction (Fig. 1d), and treatment response prediction (Fig. 1e) were performed based on these MDD-PRS associated features.
·Figure1. Flowchart of the study design.
Results:
(1) The prefrontal-subcortical (including caudate and parahippocampal gyrus) network, salience network (SAN, including anterior cingulate cortex and insula), and middle temporal cortex (MTC) were the major multimodal brain patterns associated with MDD-PRS and unrelated to age and education (Fig. 2a). (2) The identified PRS-associated patterns were highly replicable within UKB under PSNP≤5.0e−08/1.0e−04/0.05 and PRScs, where the spatial correlation of fALFF and GMV were 0.34-0.88 and 0.28-0.98, respectively (Fig. 2b). (3) The PRS related fALFF (cohort I)/GMV (cohort I/II/III) showed significant group differences between MDD and HC (Fig. 2c), and can predict the Hamilton Depression Rating Scale (HDRS) scores in cohorts II (r=0.39) and III (pre-MDD: r=0.48; post-MDD: r=0.39, Fig. 2d). (4) PRS-associated patterns showed longitudinal differences between pre-ECT and post-ECT MDD rather than HC. (5) PRS-associated features in pre-ECT MDD can longitudinally predict HDRS of post-ECT MDD (r=0.43) and further predict the ECT treatment response with an accuracy of 70.2%.
·Figure2. The identified PRS-associated multimodal brain patterns (a-b) and its biomarker properties including diagnosis (c), prediction (d), and treatment prognosis (e-f).
Conclusions:
This is the first attempt to identify PRS related multimodal brain patterns for MDD and further test its diagnostic, predictive, and therapeutic prognostic properties as biomarkers. MDD-PRS associated prefrontal-subcortical network, SAN and MTC were consistently decreased in MDD, that can predict symptoms at both cross sectional and longitudinal data, as well as predict the ECT treatment response. In summary, the identified prefrontal-subcortical network, SAN and MTC may be the neural basis of genetic risk in MDD, which can serve as potential diagnostic, predictive, and treatment prognostic biomarkers to assist clinical scientists in developing personalize treatment strategies for MDD.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Genetics:
Genetic Association Studies 1
Modeling and Analysis Methods:
Multivariate Approaches
Keywords:
MRI
Multivariate
Psychiatric Disorders
Other - polygenic risk score; prediction; treatment prognosis
1|2Indicates the priority used for review
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
2. Euesden, J. (2015). PRSice: polygenic risk score software. Bioinformatics, 31(9), 1466-1468.
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9. Wigmore, E. M. (2017). Do regional brain volumes and major depressive disorder share genetic architecture? A study of Generation Scotland (n= 19 762), UK Biobank (n= 24 048) and the English Longitudinal Study of Ageing (n= 5766). Translational Psychiatry, 7(8), e1205-e1205.