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Neuroimage. 2014 Aug 1;96:309-25. doi: 10.1016/j.neuroimage.2014.03.061. Epub 2014 Apr 1.

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

Collaborators (418)

Weiner MW, Aisen P, Petersen R, Jack CR Jr, Jagust W, Trojanowki JQ, Toga AW, Beckett L, Green RC, Saykin AJ, Morris J, Shaw LM, Khachaturian Z, Sorensen G, Carrillo M, Kuller L, Raichle M, Paul S, Davies P, Fillit H, Hefti F, Holtzman D, Mesulam M, Potter W, Snyder P, Schwartz A, Green RC, Montine T, Petersen R, Thomas RG, Donohue M, Walter S, Gessert D, Sather T, Jiminez G, Beckett L, Harvey D, Donohue M, Bernstein M, Fox N, Thompson P, Schuff N, DeCarli C, Borowski B, Gunter J, Senjem M, Vemuri P, Jones D, Kantarci K, Ward C, Jagust W, Koeppe RA, Foster N, Reiman EM, Chen K, Mathis C, Landau S, Morris JC, Cairns NJ, Householder E, Taylor-Reinwald L, Shaw LM, Trojanowki JQ, Lee V, Korecka M, Figurski M, Crawford K, Neu S, Saykin AJ, Foroud TM, Potkin S, Shen L, Faber K, Kim S, Nho K, Thal L, Khachaturian Z, Buckholtz N, Snyder PJ, Potter W, Paul S, Albert M, Frank R, Khachaturian Z, Hsiao J, Kaye J, Quinn J, Lind B, Carter R, Dolen S, Schneider LS, Pawluczyk S, Beccera M, Teodoro L, Spann BM, Brewer J, Vanderswag H, Fleisher A, Heidebrink JL, Lord JL, Petersen R, Mason SS, Albers CS, Knopman D, Johnson K, Doody RS, Villanueva-Meyer J, Chowdhury M, Rountree S, Dang M, Stern Y, Honig LS, Bell KL, Ances B, Morris JC, Carroll M, Leon S, Householder E, Mintun MA, Schneider S, Oliver A, Marson D, Griffith R, Clark D, Geldmacher D, Brockington J, Roberson E, Grossman H, Mitsis E, deToledo-Morrell L, Shah RC, Duara R, Varon D, Greig MT, Roberts P, Albert M, Onyike C, D' Agostino D 2nd, Kielb S, Galvin JE, Pogorelec DM, Cerbone B, Michel CA, Rusinek H, de Leon MJ, Glodzik L, De Santi S, Doraiswamy P, Petrella JR, Wong TZ, Arnold SE, Karlawish JH, Wolk D, Smith CD, Jicha G, Hardy P, Sinha P, Oates E, Conrad G, Lopez OL, Oakley M, Simpson DM, Porsteinsson AP, Goldstein BS, Martin K, Makino KM, Ismail M, Brand C, Mulnard RA, Thai G, Mc-Adams-Ortiz C, Womack K, Mathews D, Quiceno M, Diaz-Arrastia R, King R, Weiner M, Martin-Cook K, DeVous M, Levey AI, Lah JJ, Cellar JS, Burns JM, Anderson HS, Swerdlow RH, Apostolova L, Tingus K, Woo E, Silverman DH, Lu PH, Bartzokis G, Graff-Radford NR, Parfitt F, Kendall T, Johnson H, Farlow MR, Hake AM, Matthews BR, Herring S, Hunt C, van Dyck CH, Carson RE, MacAvoy MG, Chertkow H, Bergman H, Hosein C, Black S, Stefanovic B, Caldwell C, Hsiung GY, Feldman H, Mudge B, Assaly M, Kertesz A, Rogers J, Trost D, Bernick C, Munic D, Kerwin D, Mesulam MM, Lipowski K, Wu CK, Johnson N, Sadowsky C, Martinez W, Villena T, Turner RS, Johnson K, Reynolds B, Sperling RA, Johnson KA, Marshall G, Frey M, Yesavage J, Taylor JL, Lane B, Rosen A, Tinklenberg J, Sabbagh MN, Belden CM, Jacobson SA, Sirrel SA, Kowall N, Killiany R, Budson AE, Norbash A, Johnson PL, Obisesan TO, Wolday S, Allard J, Lerner A, Ogrocki P, Hudson L, Fletcher E, Carmichael O, Olichney J, DeCarli C, Kittur N, Borrie M, Lee TY, Bartha R, Johnson S, Asthana S, Carlsson CM, Potkin SG, Preda A, Nguyen D, Tariot P, Fleisher A, Reeder S, Bates V, Capote H, Rainka M, Scharre DW, Kataki M, Adeli A, Zimmerman EA, Celmins D, Brown AD, Pearlson GD, Blank K, Anderson K, Santulli RB, Kitzmiller TJ, Schwartz ES, Sink KM, Williamson JD, Garg P, Watkins F, Ott BR, Querfurth H, Tremont G, Salloway S, Malloy P, Correia S, Rosen HJ, Miller BL, Mintzer J, Spicer K, Bachman D, Finger E, Pasternak S, Rachinsky I, Rogers J, Kertesz A, Drost D, Pomara N, Hernando R, Sarrael A, Schultz SK, Ponto LL, Shim H, Smith KE, Relkin N, Chaing G, Raudin L, Smith A, Fargher K, Raj BA, Petersen R, Green RC, Harvey D, Jagust W, Morris JC, Saykin AJ, Shaw LM, Trojanowki JQ, Neylan T, Grafman J, Green RC, Montine T, Petersen R, Thomas RG, Donohue M, Gessert D, Sather T, Davis M, Morrison R, Jiminez G, Hayes J, Finley S, Harvey D, Bernstein M, Borowski B, Gunter J, Senjem M, Koeppe RA, Foster N, Reiman EM, Chen K, Landau S, Morris JC, Cairns NJ, Trojanowki JQ, Lee V, Korecka M, Figurski M, Crawford K, Neu S, Saykin AJ, Foroud TM, Potkin S, Shen L, Faber K, Kim S, Nho K, Friedl K, Pawluczyk S, Beccera M, Brewer J, Vanderswag H, Stern Y, Honig LS, Bell KL, Fleischman D, Arfanakis K, Shah RC, Duara R, Varon D, Doraiswamy P, Petrella JR, James O, Porsteinsson AP, Goldstein B, Martin KS, Mulnard RA, Thai G, McAdams-Ortiz C, Mintzer J, Massoglia D, Brawman-Mintzer O, Sadowsky C, Martinez W, Villena T, Jagust W, Landau S, Turner RS, Behan K, Reynolds B, Sperling RA, Johnson KA, Marshall G, Sabbagh MN, Jacobson SA, Sirrel SA, Obisesan TO, Wolday S, Allard J, Fruehling J, Harding S, Peskind ER, Petrie EC, Li G, Yesavage JA, Furst AJ, Relkin N, Chaing G, Ravdin L.

Author information

1
Division of Biostatistics, School of Public Health, Minneapolis, MN 55455, USA.
2
School of Statistics, University of Minnesota, Minneapolis, MN 55455, USA.
3
Division of Biostatistics, School of Public Health, Minneapolis, MN 55455, USA. Electronic address: weip@biostat.umn.edu.

Abstract

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.

KEYWORDS:

GEE; GWAS; Neuroimaging genetics; Score test; Statistical power; Sum of powered score (SPU) test; aSPU test

PMID:
24704269
PMCID:
PMC4043944
DOI:
10.1016/j.neuroimage.2014.03.061
[Indexed for MEDLINE]
Free PMC Article

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