Literature DB >> 21208483

Pathway-based analysis of GWAS datasets: effective but caution required.

Peilin Jia1, Lily Wang, Herbert Y Meltzer, Zhongming Zhao.   

Abstract

Pathway-based analysis is rapidly emerging as an alternative but powerful approach for searching for disease causal genes from genomic datasets and has been applied to many complex diseases recently, but it is only now beginning to be applied in psychiatry. Here, we discuss critical issues in the pathway-based approach by specifically comparing the first pathway analysis of genome-wide association studies (GWAS) datasets in neuropsychiatric disorders by O'Dushlaine and colleagues (Molecular Psychiatry 2010, doi:10.1038/mp.2010.7) with our analysis. We also computed the power of gene set enrichment analysis, hypergeometric test, and SNP ratio test in order to assist future applications of these methods in pathway-based analysis of GWAS datasets. Overall, we suggest that the pathway-based approach is effective but caution is needed in interpreting the results of such analysis.

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Mesh:

Year:  2010        PMID: 21208483     DOI: 10.1017/S1461145710001446

Source DB:  PubMed          Journal:  Int J Neuropsychopharmacol        ISSN: 1461-1457            Impact factor:   5.176


  35 in total

1.  Pathway analysis of genome-wide association study for bone mineral density.

Authors:  Young Ho Lee; Sung Jae Choi; Jong Dae Ji; Gwan Gyu Song
Journal:  Mol Biol Rep       Date:  2012-04-25       Impact factor: 2.316

Review 2.  Network.assisted analysis to prioritize GWAS results: principles, methods and perspectives.

Authors:  Peilin Jia; Zhongming Zhao
Journal:  Hum Genet       Date:  2014-02       Impact factor: 4.132

3.  A powerful subset-based method identifies gene set associations and improves interpretation in UK Biobank.

Authors:  Diptavo Dutta; Peter VandeHaar; Lars G Fritsche; Sebastian Zöllner; Michael Boehnke; Laura J Scott; Seunggeun Lee
Journal:  Am J Hum Genet       Date:  2021-03-16       Impact factor: 11.025

Review 4.  Gene set analysis of genome-wide association studies: methodological issues and perspectives.

Authors:  Lily Wang; Peilin Jia; Russell D Wolfinger; Xi Chen; Zhongming Zhao
Journal:  Genomics       Date:  2011-04-30       Impact factor: 5.736

5.  Genome-wide pathway analysis of a genome-wide association study on psoriasis and Behcet's disease.

Authors:  Young Ho Lee; Sung Jae Choi; Jong Dae Ji; Gwan Gyu Song
Journal:  Mol Biol Rep       Date:  2011-12-27       Impact factor: 2.316

6.  Integrative analysis of GWASs, human protein interaction, and gene expression identified gene modules associated with BMDs.

Authors:  Hao He; Lei Zhang; Jian Li; Yu-Ping Wang; Ji-Gang Zhang; Jie Shen; Yan-Fang Guo; Hong-Wen Deng
Journal:  J Clin Endocrinol Metab       Date:  2014-08-13       Impact factor: 5.958

7.  Network-assisted Causal Gene Detection in Genome-wide Association Studies: An Improved Module Search Algorithm.

Authors:  Peilin Jia; Zhongming Zhao
Journal:  IEEE Int Workshop Genomic Signal Process Stat       Date:  2011

8.  Integrating Genome-Wide Association Study and Brain Expression Data Highlights Cell Adhesion Molecules and Purine Metabolism in Alzheimer's Disease.

Authors:  Zimin Xiang; Meiling Xu; Mingzhi Liao; Yongshuai Jiang; Qinghua Jiang; Rennan Feng; Liangcai Zhang; Guoda Ma; Guangyu Wang; Zugen Chen; Bin Zhao; Tiansheng Sun; Keshen Li; Guiyou Liu
Journal:  Mol Neurobiol       Date:  2014-09-10       Impact factor: 5.590

9.  Genetic variants in Hippo signalling pathway-related genes affect the risk of colorectal cancer.

Authors:  Hengyang Shen; Yixuan Meng; Tao Hu; Shuwei Li; Mulong Du; Junyi Xin; Dongying Gu; Meilin Wang; Zan Fu
Journal:  Arch Toxicol       Date:  2020-10-03       Impact factor: 5.153

10.  A developmental stage-specific network approach for studying dynamic co-regulation of transcription factors and microRNAs during craniofacial development.

Authors:  Fangfang Yan; Peilin Jia; Hiroki Yoshioka; Akiko Suzuki; Junichi Iwata; Zhongming Zhao
Journal:  Development       Date:  2020-12-24       Impact factor: 6.868

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