Abstract Details
Activity Number:
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91
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Type:
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Contributed
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Date/Time:
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Sunday, August 4, 2013 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract - #309302 |
Title:
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Structured Brain-Wide and Genome-Wide Association Study via Multivariate Compound Lasso Using PET Images
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Author(s):
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Yanming Li*+ and Bin Nan and Ji Zhu
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Companies:
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University of Michigan and University of Michigan and University of Michigan
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Keywords:
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coordinate descent ;
genomewide association study ;
lasso ;
PET image ;
multivariate linear regression ;
volume of interest
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Abstract:
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We propose an efficient compound lasso approach to conduct a structured brain-wide-genome-wide association study modified by the Alzheimer's disease status using ADNI PET imaging data. The compound lasso method not only can handle the ultra-high dimensionalities of both 3D image and genotypes, but also takes into considerations of the anatomic brain structure and the complex gene structure in human genome. It can select at the same time the important brain subregions associated with certain genes as well as nested individual voxel-SNP associations. Our disease-status modified model also helps to select important gene-disease interactions. The proposed method is evaluated through extensive simulations. Its application to brain-wide-genome-wide ADNI data confirms several previously reported genes associated with PET image, and identifies promising genes whose associations with PET image are significantly modified by the disease status.
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Authors who are presenting talks have a * after their name.
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