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Testing for association with multiple traits in generalized estimation equations, with application to neuroimaging data.

There is an increasing need to develop and apply powerful statistical tests to detect multiple traits–single locus associations, as arising from neuroimaging genetics and other studies. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI), in addition to genome-wide single nucleoti... Full description

Journal Title: NeuroImage August 1, 2014, Vol.96, pp.309-325
Main Author: Zhang, Yiwei
Other Authors: Xu, Zhiyuan , Shen, Xiaotong , Pan, Wei , Zhang, Yiwei
Format: Electronic Article Electronic Article
Language: English
Subjects:
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ID: E-ISSN: 1095-9572 ; DOI: 10.1016/j.neuroimage.2014.03.061
Link: http://search.proquest.com/docview/1532479443/?pq-origsite=primo
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title: Testing for association with multiple traits in generalized estimation equations, with application to neuroimaging data.
format: Article
creator:
  • Zhang, Yiwei
  • Xu, Zhiyuan
  • Shen, Xiaotong
  • Pan, Wei
  • Zhang, Yiwei
subjects:
  • Algorithms–Diagnosis
  • Alzheimer Disease–Genetics
  • Brain–Physiology
  • Brain Mapping–Methods
  • Genetic Association Studies–Methods
  • Genetic Predisposition to Disease–Genetics
  • Humans–Physiology
  • Nerve Net–Methods
  • Neuroimaging–Methods
  • Reproducibility of Results–Methods
  • Sensitivity and Specificity–Methods
  • Gee
  • Gwas
  • Neuroimaging Genetics
  • Score Test
  • Statistical Power
  • Sum of Powered Score (Spu) Test
  • Aspu Test
ispartof: NeuroImage, August 1, 2014, Vol.96, pp.309-325
description: There is an increasing need to develop and apply powerful statistical tests to detect multiple traits–single locus associations, as arising from neuroimaging genetics and other studies. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI), in addition to genome-wide single nucleotide polymorphisms (SNPs), thousands of neuroimaging and neuropsychological phenotypes as intermediate phenotypes for Alzheimer's disease, have been collected. Although some classic methods like MANOVA and newly proposed methods may be applied, they have their own limitations. For example, MANOVA cannot be applied to binary and other discrete traits. In addition, the relationships among these methods are not well understood. Importantly, since these tests are not data adaptive, depending on the unknown association patterns among multiple traits and between multiple traits and a locus, these tests may or may not be powerful. In this paper we propose a class of data-adaptive weights and the corresponding weighted tests in the general framework of generalized estimation equations (GEE). A highly adaptive test is proposed to select the most powerful one from this class of the weighted tests so that it can maintain high power across a wide range of situations. Our proposed tests are applicable to various types of traits with or without covariates. Importantly, we also analytically show relationships among some existing and our proposed tests, indicating that many existing tests are special cases of our proposed tests. Extensive simulation studies were conducted to compare and contrast the power properties of various existing and our new methods. Finally, we applied the methods to an ADNI dataset to illustrate the performance of the methods. We conclude with the recommendation for the use of the GEE-based Score test and our proposed adaptive test for their high and complementary performance. •Meeting the pressing need for more powerful analysis of multivariate neuroimaging traits•Introducing to the neuroimaging community some recently proposed association tests•Developing new, more powerful and versatile association tests•Establishing connections among the existing and new association tests•Demonstrating the use and performance of the methods with simulated and the ADNI data
language: eng
source:
identifier: E-ISSN: 1095-9572 ; DOI: 10.1016/j.neuroimage.2014.03.061
fulltext: fulltext
issn:
  • 10959572
  • 1095-9572
url: Link


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titleTesting for association with multiple traits in generalized estimation equations, with application to neuroimaging data.
creatorZhang, Yiwei ; Xu, Zhiyuan ; Shen, Xiaotong ; Pan, Wei ; Zhang, Yiwei
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ispartofNeuroImage, August 1, 2014, Vol.96, pp.309-325
identifierE-ISSN: 1095-9572 ; DOI: 10.1016/j.neuroimage.2014.03.061
subjectAlgorithms–Diagnosis ; Alzheimer Disease–Genetics ; Brain–Physiology ; Brain Mapping–Methods ; Genetic Association Studies–Methods ; Genetic Predisposition to Disease–Genetics ; Humans–Physiology ; Nerve Net–Methods ; Neuroimaging–Methods ; Reproducibility of Results–Methods ; Sensitivity and Specificity–Methods ; Gee ; Gwas ; Neuroimaging Genetics ; Score Test ; Statistical Power ; Sum of Powered Score (Spu) Test ; Aspu Test
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descriptionThere is an increasing need to develop and apply powerful statistical tests to detect multiple traits–single locus associations, as arising from neuroimaging genetics and other studies. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI), in addition to genome-wide single nucleotide polymorphisms (SNPs), thousands of neuroimaging and neuropsychological phenotypes as intermediate phenotypes for Alzheimer's disease, have been collected. Although some classic methods like MANOVA and newly proposed methods may be applied, they have their own limitations. For example, MANOVA cannot be applied to binary and other discrete traits. In addition, the relationships among these methods are not well understood. Importantly, since these tests are not data adaptive, depending on the unknown association patterns among multiple traits and between multiple traits and a locus, these tests may or may not be powerful. In this paper we propose a class of data-adaptive weights and the corresponding weighted tests in the general framework of generalized estimation equations (GEE). A highly adaptive test is proposed to select the most powerful one from this class of the weighted tests so that it can maintain high power across a wide range of situations. Our proposed tests are applicable to various types of traits with or without covariates. Importantly, we also analytically show relationships among some existing and our proposed tests, indicating that many existing tests are special cases of our proposed tests. Extensive simulation studies were conducted to compare and contrast the power properties of various existing and our new methods. Finally, we applied the methods to an ADNI dataset to illustrate the performance of the methods. We conclude with the recommendation for the use of the GEE-based Score test and our proposed adaptive test for their high and complementary performance. •Meeting the pressing need for more powerful analysis of multivariate neuroimaging traits•Introducing to the neuroimaging community some recently proposed association tests•Developing new, more powerful and versatile association tests•Establishing connections among the existing and new association tests•Demonstrating the use and performance of the methods with simulated and the ADNI data
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titleTesting for association with multiple traits in generalized estimation equations, with application to neuroimaging data.
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0Algorithms–Diagnosis
1Alzheimer Disease–Genetics
2Brain–Physiology
3Brain Mapping–Methods
4Genetic Association Studies–Methods
5Genetic Predisposition to Disease–Genetics
6Humans–Physiology
7Nerve Net–Methods
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9Saykin, Andrew J
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11Shaw, Leslie M
12Khachaturian, Zaven
13Sorensen, Greg
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15Kuller, Lew
16Raichle, Marc
17Paul, Steven
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19Fillit, Howard
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22Mesulam, M Marcel
23Potter, William
24Snyder, Peter
25Schwartz, Adam
26Montine, Tom
27Thomas, Ronald G
28Donohue, Michael
29Walter, Sarah
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31Sather, Tamie
32Jiminez, Gus
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37Schuff, Norbert
38Decarli, Charles
39Borowski, Bret
40Gunter, Jeff
41Senjem, Matt
42Vemuri, Prashanthi
43Jones, David
44Kantarci, Kejal
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titleTesting for association with multiple traits in generalized estimation equations, with application to neuroimaging data.
authorZhang, Yiwei ; Xu, Zhiyuan ; Shen, Xiaotong ; Pan, Wei ; Zhang, Yiwei
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4Genetic Association Studies–Methods
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14Morris, John
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19Kuller, Lew
20Raichle, Marc
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34Gessert, Devon
35Sather, Tamie
36Jiminez, Gus
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38Bernstein, Matthew
39Fox, Nick
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41Schuff, Norbert
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45Senjem, Matt
46Vemuri, Prashanthi
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57Cairns, Nigel J
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