Literature DB >> 32822005

Neuroimaging PheWAS (Phenome-Wide Association Study): A Free Cloud-Computing Platform for Big-Data, Brain-Wide Imaging Association Studies.

Lu Zhao1, Ishaan Batta1, William Matloff1, Caroline O'Driscoll1, Samuel Hobel1, Arthur W Toga2.   

Abstract

Large-scale, case-control genome-wide association studies (GWASs) have revealed genetic variations associated with diverse neurological and psychiatric disorders. Recent advances in neuroimaging and genomic databases of large healthy and diseased cohorts have empowered studies to characterize effects of the discovered genetic factors on brain structure and function, implicating neural pathways and genetic mechanisms in the underlying biology. However, the unprecedented scale and complexity of the imaging and genomic data requires new advanced biomedical data science tools to manage, process and analyze the data. In this work, we introduce Neuroimaging PheWAS (phenome-wide association study): a web-based system for searching over a wide variety of brain-wide imaging phenotypes to discover true system-level gene-brain relationships using a unified genotype-to-phenotype strategy. This design features a user-friendly graphical user interface (GUI) for anonymous data uploading, study definition and management, and interactive result visualizations as well as a cloud-based computational infrastructure and multiple state-of-art methods for statistical association analysis and multiple comparison correction. We demonstrated the potential of Neuroimaging PheWAS with a case study analyzing the influences of the apolipoprotein E (APOE) gene on various brain morphological properties across the brain in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Benchmark tests were performed to evaluate the system's performance using data from UK Biobank. The Neuroimaging PheWAS system is freely available. It simplifies the execution of PheWAS on neuroimaging data and provides an opportunity for imaging genetics studies to elucidate routes at play for specific genetic variants on diseases in the context of detailed imaging phenotypic data.

Entities:  

Keywords:  Discovery science; Genetics; High-performance computing; Magnetic resonance imaging; Web-based system

Mesh:

Year:  2021        PMID: 32822005      PMCID: PMC7897334          DOI: 10.1007/s12021-020-09486-4

Source DB:  PubMed          Journal:  Neuroinformatics        ISSN: 1539-2791


  77 in total

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Authors:  R W Cox
Journal:  Comput Biomed Res       Date:  1996-06

2.  FreeSurfer 5.3 versus 6.0: are volumes comparable? A Chronic Effects of Neurotrauma Consortium study.

Authors:  Erin D Bigler; Marc Skiles; Benjamin S C Wade; Tracy J Abildskov; Nick J Tustison; Randall S Scheibel; Mary R Newsome; Andrew R Mayer; James R Stone; Brian A Taylor; David F Tate; William C Walker; Harvey S Levin; Elisabeth A Wilde
Journal:  Brain Imaging Behav       Date:  2020-10       Impact factor: 3.978

3.  Matapax: an online high-throughput genome-wide association study pipeline.

Authors:  Liam H Childs; Jan Lisec; Dirk Walther
Journal:  Plant Physiol       Date:  2012-02-21       Impact factor: 8.340

4.  Distinct genetic influences on cortical surface area and cortical thickness.

Authors:  Matthew S Panizzon; Christine Fennema-Notestine; Lisa T Eyler; Terry L Jernigan; Elizabeth Prom-Wormley; Michael Neale; Kristen Jacobson; Michael J Lyons; Michael D Grant; Carol E Franz; Hong Xian; Ming Tsuang; Bruce Fischl; Larry Seidman; Anders Dale; William S Kremen
Journal:  Cereb Cortex       Date:  2009-03-18       Impact factor: 5.357

5.  Association between low density lipoprotein and rheumatoid arthritis genetic factors with low density lipoprotein levels in rheumatoid arthritis and non-rheumatoid arthritis controls.

Authors:  Katherine P Liao; Dorothée Diogo; Jing Cui; Tianxi Cai; Yukinori Okada; Vivian S Gainer; Shawn N Murphy; Namrata Gupta; Daniel Mirel; Ashwin N Ananthakrishnan; Peter Szolovits; Stanley Y Shaw; Soumya Raychaudhuri; Susanne Churchill; Isaac Kohane; Elizabeth W Karlson; Robert M Plenge
Journal:  Ann Rheum Dis       Date:  2013-05-28       Impact factor: 19.103

6.  Patterns of cortical thickness according to APOE genotype in Alzheimer's disease.

Authors:  Leticia Gutiérrez-Galve; Manja Lehmann; Nicola Z Hobbs; Matthew J Clarkson; Gerard R Ridgway; Sebastian Crutch; Sebastien Ourselin; Jonathan M Schott; Nick C Fox; Josephine Barnes
Journal:  Dement Geriatr Cogn Disord       Date:  2009-11-26       Impact factor: 2.959

7.  Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort.

Authors:  Li Shen; Sungeun Kim; Shannon L Risacher; Kwangsik Nho; Shanker Swaminathan; John D West; Tatiana Foroud; Nathan Pankratz; Jason H Moore; Chantel D Sloan; Matthew J Huentelman; David W Craig; Bryan M Dechairo; Steven G Potkin; Clifford R Jack; Michael W Weiner; Andrew J Saykin
Journal:  Neuroimage       Date:  2010-01-25       Impact factor: 6.556

8.  Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data.

Authors:  Joshua C Denny; Lisa Bastarache; Marylyn D Ritchie; Robert J Carroll; Raquel Zink; Jonathan D Mosley; Julie R Field; Jill M Pulley; Andrea H Ramirez; Erica Bowton; Melissa A Basford; David S Carrell; Peggy L Peissig; Abel N Kho; Jennifer A Pacheco; Luke V Rasmussen; David R Crosslin; Paul K Crane; Jyotishman Pathak; Suzette J Bielinski; Sarah A Pendergrass; Hua Xu; Lucia A Hindorff; Rongling Li; Teri A Manolio; Christopher G Chute; Rex L Chisholm; Eric B Larson; Gail P Jarvik; Murray H Brilliant; Catherine A McCarty; Iftikhar J Kullo; Jonathan L Haines; Dana C Crawford; Daniel R Masys; Dan M Roden
Journal:  Nat Biotechnol       Date:  2013-12       Impact factor: 54.908

9.  Phenome-wide association study (PheWAS) for detection of pleiotropy within the Population Architecture using Genomics and Epidemiology (PAGE) Network.

Authors:  Sarah A Pendergrass; Kristin Brown-Gentry; Scott Dudek; Alex Frase; Eric S Torstenson; Robert Goodloe; Jose Luis Ambite; Christy L Avery; Steve Buyske; Petra Bůžková; Ewa Deelman; Megan D Fesinmeyer; Christopher A Haiman; Gerardo Heiss; Lucia A Hindorff; Chu-Nan Hsu; Rebecca D Jackson; Charles Kooperberg; Loic Le Marchand; Yi Lin; Tara C Matise; Kristine R Monroe; Larry Moreland; Sungshim L Park; Alex Reiner; Robert Wallace; Lynn R Wilkens; Dana C Crawford; Marylyn D Ritchie
Journal:  PLoS Genet       Date:  2013-01-31       Impact factor: 5.917

10.  The UK Biobank resource with deep phenotyping and genomic data.

Authors:  Clare Bycroft; Colin Freeman; Desislava Petkova; Gavin Band; Lloyd T Elliott; Kevin Sharp; Allan Motyer; Damjan Vukcevic; Olivier Delaneau; Jared O'Connell; Adrian Cortes; Samantha Welsh; Alan Young; Mark Effingham; Gil McVean; Stephen Leslie; Naomi Allen; Peter Donnelly; Jonathan Marchini
Journal:  Nature       Date:  2018-10-10       Impact factor: 49.962

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  3 in total

Review 1.  Maturation and application of phenome-wide association studies.

Authors:  Shiying Liu; Dana C Crawford
Journal:  Trends Genet       Date:  2022-01-03       Impact factor: 11.639

Review 2.  Using Phecodes for Research with the Electronic Health Record: From PheWAS to PheRS.

Authors:  Lisa Bastarache
Journal:  Annu Rev Biomed Data Sci       Date:  2021-07-20

3.  Linking Brain Structure, Activity, and Cognitive Function through Computation.

Authors:  Katrin Amunts; Javier DeFelipe; Cyriel Pennartz; Alain Destexhe; Michele Migliore; Philippe Ryvlin; Steve Furber; Alois Knoll; Lise Bitsch; Jan G Bjaalie; Yannis Ioannidis; Thomas Lippert; Maria V Sanchez-Vives; Rainer Goebel; Viktor Jirsa
Journal:  eNeuro       Date:  2022-03-11
  3 in total

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