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Fast and Powerful Genome Wide Association Analysis of Dense Genetic Data with High Dimensional Imaging Phenotypes

Habib Ganjgahi, Anderson M. Winkler, David C. Glahn, John Blangero, Brian Donohue, Peter Kochunov, Thomas E. Nichols
doi: https://doi.org/10.1101/179150
Habib Ganjgahi
1Department of Statistics, University of Oxford, Oxford, UK
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Anderson M. Winkler
2Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK
7Big Data Analytics Group, Hospital Israelita Albert Einstein, São Paulo, SP, Brazil
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David C. Glahn
4Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
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John Blangero
5South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
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Brian Donohue
3Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
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Peter Kochunov
3Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
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Thomas E. Nichols
2Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK
8Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, UK
9Department of Statistics, University of Warwick, Coventry, UK
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  • For correspondence: thomas.nichols@bdi.ox.ac.uk
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ABSTRACT

Genome wide association (GWA) analysis of brain imaging phenotypes can advance our understanding of the genetic basis of normal and disorder-related variation in the brain. GWA approaches typically use linear mixed effect models to account for non-independence amongst subjects due to factors such as family relatedness and population structure. The use of these models with high-dimensional imaging phenotypes presents enormous challenges in terms of computational intensity and the need to account multiple testing in both the imaging and genetic domain. Here we present method that makes mixed models practical with high-dimensional traits by a combination of a transformation applied to the data and model, and the use of a non-iterative variance component estimator. With such speed enhancements permutation tests are feasible, which allows inference on powerful spatial tests like the cluster size statistic.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted August 21, 2017.
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Fast and Powerful Genome Wide Association Analysis of Dense Genetic Data with High Dimensional Imaging Phenotypes
Habib Ganjgahi, Anderson M. Winkler, David C. Glahn, John Blangero, Brian Donohue, Peter Kochunov, Thomas E. Nichols
bioRxiv 179150; doi: https://doi.org/10.1101/179150
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Fast and Powerful Genome Wide Association Analysis of Dense Genetic Data with High Dimensional Imaging Phenotypes
Habib Ganjgahi, Anderson M. Winkler, David C. Glahn, John Blangero, Brian Donohue, Peter Kochunov, Thomas E. Nichols
bioRxiv 179150; doi: https://doi.org/10.1101/179150

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