Literature DB >> 28736311

Imaging-wide association study: Integrating imaging endophenotypes in GWAS.

Zhiyuan Xu1, Chong Wu1, Wei Pan2.   

Abstract

A new and powerful approach, called imaging-wide association study (IWAS), is proposed to integrate imaging endophenotypes with GWAS to boost statistical power and enhance biological interpretation for GWAS discoveries. IWAS extends the promising transcriptome-wide association study (TWAS) from using gene expression endophenotypes to using imaging and other endophenotypes with a much wider range of possible applications. As illustration, we use gray-matter volumes of several brain regions of interest (ROIs) drawn from the ADNI-1 structural MRI data as imaging endophenotypes, which are then applied to the individual-level GWAS data of ADNI-GO/2 and a large meta-analyzed GWAS summary statistics dataset (based on about 74,000 individuals), uncovering some novel genes significantly associated with Alzheimer's disease (AD). We also compare the performance of IWAS with TWAS, showing much larger numbers of significant AD-associated genes discovered by IWAS, presumably due to the stronger link between brain atrophy and AD than that between gene expression of normal individuals and the risk for AD. The proposed IWAS is general and can be applied to other imaging endophenotypes, and GWAS individual-level or summary association data.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Alzheimer's disease; IWAS; MRI; SPU test; SSU test; Sum test; TWAS; aSPU test

Mesh:

Year:  2017        PMID: 28736311      PMCID: PMC5671364          DOI: 10.1016/j.neuroimage.2017.07.036

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  47 in total

Review 1.  Genomic similarity and kernel methods II: methods for genomic information.

Authors:  Daniel J Schaid
Journal:  Hum Hered       Date:  2010-07-03       Impact factor: 0.444

Review 2.  Genomic similarity and kernel methods I: advancements by building on mathematical and statistical foundations.

Authors:  Daniel J Schaid
Journal:  Hum Hered       Date:  2010-07-03       Impact factor: 0.444

3.  Voxelwise gene-wide association study (vGeneWAS): multivariate gene-based association testing in 731 elderly subjects.

Authors:  Derrek P Hibar; Jason L Stein; Omid Kohannim; Neda Jahanshad; Andrew J Saykin; Li Shen; Sungeun Kim; Nathan Pankratz; Tatiana Foroud; Matthew J Huentelman; Steven G Potkin; Clifford R Jack; Michael W Weiner; Arthur W Toga; Paul M Thompson
Journal:  Neuroimage       Date:  2011-04-08       Impact factor: 6.556

4.  High dimensional endophenotype ranking in the search for major depression risk genes.

Authors:  David C Glahn; Joanne E Curran; Anderson M Winkler; Melanie A Carless; Jack W Kent; Jac C Charlesworth; Matthew P Johnson; Harald H H Göring; Shelley A Cole; Thomas D Dyer; Eric K Moses; Rene L Olvera; Peter Kochunov; Ravi Duggirala; Peter T Fox; Laura Almasy; John Blangero
Journal:  Biol Psychiatry       Date:  2011-10-07       Impact factor: 13.382

5.  A powerful and adaptive association test for rare variants.

Authors:  Wei Pan; Junghi Kim; Yiwei Zhang; Xiaotong Shen; Peng Wei
Journal:  Genetics       Date:  2014-05-15       Impact factor: 4.562

6.  Bayesian Generalized Low Rank Regression Models for Neuroimaging Phenotypes and Genetic Markers.

Authors:  Hongtu Zhu; Zakaria Khondker; Zhaohua Lu; Joseph G Ibrahim
Journal:  J Am Stat Assoc       Date:  2014       Impact factor: 5.033

7.  A Powerful Framework for Integrating eQTL and GWAS Summary Data.

Authors:  Zhiyuan Xu; Chong Wu; Peng Wei; Wei Pan
Journal:  Genetics       Date:  2017-09-11       Impact factor: 4.562

8.  Integrative approaches for large-scale transcriptome-wide association studies.

Authors:  Alexander Gusev; Arthur Ko; Huwenbo Shi; Gaurav Bhatia; Wonil Chung; Brenda W J H Penninx; Rick Jansen; Eco J C de Geus; Dorret I Boomsma; Fred A Wright; Patrick F Sullivan; Elina Nikkola; Marcus Alvarez; Mete Civelek; Aldons J Lusis; Terho Lehtimäki; Emma Raitoharju; Mika Kähönen; Ilkka Seppälä; Olli T Raitakari; Johanna Kuusisto; Markku Laakso; Alkes L Price; Päivi Pajukanta; Bogdan Pasaniuc
Journal:  Nat Genet       Date:  2016-02-08       Impact factor: 38.330

Review 9.  Genetic studies of quantitative MCI and AD phenotypes in ADNI: Progress, opportunities, and plans.

Authors:  Andrew J Saykin; Li Shen; Xiaohui Yao; Sungeun Kim; Kwangsik Nho; Shannon L Risacher; Vijay K Ramanan; Tatiana M Foroud; Kelley M Faber; Nadeem Sarwar; Leanne M Munsie; Xiaolan Hu; Holly D Soares; Steven G Potkin; Paul M Thompson; John S K Kauwe; Rima Kaddurah-Daouk; Robert C Green; Arthur W Toga; Michael W Weiner
Journal:  Alzheimers Dement       Date:  2015-07       Impact factor: 21.566

10.  Adaptive gene- and pathway-trait association testing with GWAS summary statistics.

Authors:  Il-Youp Kwak; Wei Pan
Journal:  Bioinformatics       Date:  2015-12-10       Impact factor: 6.937

View more
  20 in total

1.  A Powerful Framework for Integrating eQTL and GWAS Summary Data.

Authors:  Zhiyuan Xu; Chong Wu; Peng Wei; Wei Pan
Journal:  Genetics       Date:  2017-09-11       Impact factor: 4.562

2.  Incorporating spatial-anatomical similarity into the VGWAS framework for AD biomarker detection.

Authors:  Meiyan Huang; Yuwei Yu; Wei Yang; Qianjin Feng
Journal:  Bioinformatics       Date:  2019-12-15       Impact factor: 6.937

3.  Integrating eQTL data with GWAS summary statistics in pathway-based analysis with application to schizophrenia.

Authors:  Chong Wu; Wei Pan
Journal:  Genet Epidemiol       Date:  2018-02-07       Impact factor: 2.135

4.  Some statistical consideration in transcriptome-wide association studies.

Authors:  Haoran Xue; Wei Pan
Journal:  Genet Epidemiol       Date:  2019-12-10       Impact factor: 2.135

5.  Integrating germline and somatic genetics to identify genes associated with lung cancer.

Authors:  Jack Pattee; Xiaowei Zhan; Guanghua Xiao; Wei Pan
Journal:  Genet Epidemiol       Date:  2019-12-10       Impact factor: 2.135

6.  Brain Imaging Genomics: Integrated Analysis and Machine Learning.

Authors:  Li Shen; Paul M Thompson
Journal:  Proc IEEE Inst Electr Electron Eng       Date:  2019-10-29       Impact factor: 10.961

7.  A gene-level methylome-wide association analysis identifies novel Alzheimer's disease genes.

Authors:  Chong Wu; Jonathan Bradley; Yanming Li; Lang Wu; Hong-Wen Deng
Journal:  Bioinformatics       Date:  2021-02-01       Impact factor: 6.937

8.  Progress and Research Priorities in Imaging Genomics for Heart and Lung Disease: Summary of an NHLBI Workshop.

Authors:  Donna K Arnett; Ramachandran S Vasan; Matthew Nayor; Li Shen; Gary M Hunninghake; Peter Kochunov; R Graham Barr; David A Bluemke; Ulrich Broeckel; Peter Caravan; Susan Cheng; Paul S de Vries; Udo Hoffmann; Márton Kolossváry; Huiqing Li; James Luo; Elizabeth M McNally; George Thanassoulis
Journal:  Circ Cardiovasc Imaging       Date:  2021-08-13       Impact factor: 8.589

9.  MethReg: estimating the regulatory potential of DNA methylation in gene transcription.

Authors:  Tiago C Silva; Juan I Young; Eden R Martin; X Steven Chen; Lily Wang
Journal:  Nucleic Acids Res       Date:  2022-05-20       Impact factor: 19.160

10.  InTACT: An adaptive and powerful framework for joint-tissue transcriptome-wide association studies.

Authors:  Ye Eun Bae; Lang Wu; Chong Wu
Journal:  Genet Epidemiol       Date:  2021-07-13       Impact factor: 2.135

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.