Xianglian Meng1, Jin Li2, Qiushi Zhang3, Feng Chen2, Chenyuan Bian2, Xiaohui Yao4, Jingwen Yan5,6, Zhe Xu1, Shannon L Risacher5, Andrew J Saykin5, Hong Liang7, Li Shen8. 1. School of Computer Information & Engineering, Changzhou Institute of Technology, Changzhou, 213032, China. 2. College of Automation, Harbin Engineering University, Harbin, 150001, China. 3. School of Computer Science, Northeast Electric Power University, Jilin, 132012, China. 4. Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA. 5. Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA. 6. Department of BioHealth Informatics, Indiana University School of Informatics and Computing, Indianapolis, IN, 46202, USA. 7. College of Automation, Harbin Engineering University, Harbin, 150001, China. lh@hrbeu.edu.cn. 8. Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA. li.shen@pennmedicine.upenn.edu.
Abstract
BACKGROUND: Genome-wide association studies (GWAS) have identified many individual genes associated with brain imaging quantitative traits (QTs) in Alzheimer's disease (AD). However single marker level association discovery may not be able to address the underlying biological interactions with disease mechanism. RESULTS: In this paper, we used the MGAS (Multivariate Gene-based Association test by extended Simes procedure) tool to perform multivariate GWAS on eight AD-relevant subcortical imaging measures. We conducted multiple iPINBPA (integrative Protein-Interaction-Network-Based Pathway Analysis) network analyses on MGAS findings using protein-protein interaction (PPI) data, and identified five Consensus Modules (CMs) from the PPI network. Functional annotation and network analysis were performed on the identified CMs. The MGAS yielded significant hits within APOE, TOMM40 and APOC1 genes, which were known AD risk factors, as well as a few new genes such as LAMA1, XYLB, HSD17B7P2, and NPEPL1. The identified five CMs were enriched by biological processes related to disorders such as Alzheimer's disease, Legionellosis, Pertussis, and Serotonergic synapse. CONCLUSIONS: The statistical power of coupling MGAS with iPINBPA was higher than traditional GWAS method, and yielded new findings that were missed by GWAS. This study provides novel insights into the molecular mechanism of Alzheimer's Disease and will be of value to novel gene discovery and functional genomic studies.
BACKGROUND: Genome-wide association studies (GWAS) have identified many individual genes associated with brain imaging quantitative traits (QTs) in Alzheimer's disease (AD). However single marker level association discovery may not be able to address the underlying biological interactions with disease mechanism. RESULTS: In this paper, we used the MGAS (Multivariate Gene-based Association test by extended Simes procedure) tool to perform multivariate GWAS on eight AD-relevant subcortical imaging measures. We conducted multiple iPINBPA (integrative Protein-Interaction-Network-Based Pathway Analysis) network analyses on MGAS findings using protein-protein interaction (PPI) data, and identified five Consensus Modules (CMs) from the PPI network. Functional annotation and network analysis were performed on the identified CMs. The MGAS yielded significant hits within APOE, TOMM40 and APOC1 genes, which were known AD risk factors, as well as a few new genes such as LAMA1, XYLB, HSD17B7P2, and NPEPL1. The identified five CMs were enriched by biological processes related to disorders such as Alzheimer's disease, Legionellosis, Pertussis, and Serotonergic synapse. CONCLUSIONS: The statistical power of coupling MGAS with iPINBPA was higher than traditional GWAS method, and yielded new findings that were missed by GWAS. This study provides novel insights into the molecular mechanism of Alzheimer's Disease and will be of value to novel gene discovery and functional genomic studies.
Authors: Marko Gosak; Rene Markovič; Jurij Dolenšek; Marjan Slak Rupnik; Marko Marhl; Andraž Stožer; Matjaž Perc Journal: Phys Life Rev Date: 2017-11-03 Impact factor: 11.025
Authors: Sangkyu Lee; Sarah Kerns; Harry Ostrer; Barry Rosenstein; Joseph O Deasy; Jung Hun Oh Journal: Int J Radiat Oncol Biol Phys Date: 2018-01-31 Impact factor: 7.038
Authors: Laura N D'Aoust; Anna C Cummings; Renee Laux; Denise Fuzzell; Laura Caywood; Lori Reinhart-Mercer; William K Scott; Margaret A Pericak-Vance; Jonathan L Haines Journal: PLoS One Date: 2015-02-10 Impact factor: 3.240