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Neuroimage. 2010 Nov 15;53(3):1147-59. doi: 10.1016/j.neuroimage.2010.07.002. Epub 2010 Jul 17.

Discovering genetic associations with high-dimensional neuroimaging phenotypes: A sparse reduced-rank regression approach.

Collaborators (224)

Weiner M, Aisen P, Weiner M, Aisen P, Petersen R, Jack CR Jr, Jagust W, Trojanowki J, Toga AW, Beckett L, Green RC, Gamst A, Saykin AJ, Morris J, Potter WZ, Green RC, Montine T, Petersen R, Aisen P, Gamst A, Thomas RG, Donohue M, Walter S, Jack CR Jr, Dale A, Bernstein M, Felmlee J, Fox N, Thompson P, Schuff N, Alexander G, DeCarli C, Jagust W, Bandy D, Koeppe RA, Foster N, Reiman EM, Chen K, Mathis C, Morris J, Cairns NJ, Taylor-Reinwald L, Trojanowki J, Shaw L, Lee VM, Korecka M, Toga AW, Crawford K, Neu S, Beckett L, Harvey D, Gamst A, Kornak J, Saykin AJ, Foroud TM, Potkin S, Shen L, Kachaturian Z, Frank R, Snyder PJ, Molchan S, Kaye J, Dolen S, Quinn J, Schneider L, Pawluczyk S, Spann BM, Brewer J, Vanderswag H, Heidebrink JL, Lord JL, Petersen R, Johnson K, Doody RS, Villanueva-Meyer J, Chowdhury M, Stern Y, Honig LS, Bell KL, Morris JC, Mintun MA, Schneider S, Marson D, Griffith R, Clark D, Grossman H, Tang C, Marzloff G, deToledo-Morrell L, Shah RC, Duara R, Varon D, Roberts P, Albert MS, Kozauer N, Zerrate M, Rusinek H, de Leon MJ, De Santi SM, Doraiswamy PM, Petrella JR, Aiello M, Arnold S, Karlawish JH, Wolk D, Smith CD, Given CA 2nd, Hardy P, Lopez OL, Oakley M, Simpson DM, Ismail MS, Brand C, Richard J, Mulnard RA, Thai G, Mc-Adams-Ortiz C, Diaz-Arrastia DA, Martin-Cook K, DeVous D, Levey AI, Lah JJ, Cellar JS, Burns JM, Anderson HS, Laubinger MM, Apostolova L, Silverman DH, Lu PH, Graff-Radford NR, Parfitt F, Johnson H, Farlow M, Herring S, Hake AM, van Dyck CH, MacAvoy MG, Benincasa AL, Chertkow H, Bergman H, Hosein C, Black S, Stefanovic S, Caldwell C, Hsiung GY, Feldman H, Assaly M, Kertesz A, Rogers J, Trost D, Bernick C, Munic D, Wu CK, Johnson N, Mesulam M, Sadowsky C, Martinez W, Villena T, Turner RS, Johnson K, Reynolds B, Sperling RA, Rentz DM, Johnson KA, Rosen A, Tinklenberg J, Ashford W, Sabbagh M, Connor D, Jacobson S, Killiany R, Norbash A, Nair A, Obisesan TO, Jayam-Trouth A, Wang P, Lerner A, Hudson L, Ogrocki P, DeCarli C, Fletcher E, Carmichael O, Kittur S, 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, Hendin BA, Scharre DW, Kataki M, Zimmerman EA, Celmins D, Brown AD, Pearlson G, Blank K, Anderson K, Saykin AJ, Santulli RB, Englert J, Williamson JD, Sink KM, Watkins F, Ott BR, Stopa E, Tremont G, Salloway S, Malloy P, Correia S, Rosen HJ, Miller BL, Mintzer J, Longmire CF, Spicer K.

Author information

  • 1Statistics Section, Department of Mathematics, Imperial College London, UK.

Abstract

There is growing interest in performing genome-wide searches for associations between genetic variants and brain imaging phenotypes. While much work has focused on single scalar valued summaries of brain phenotype, accounting for the richness of imaging data requires a brain-wide, genome-wide search. In particular, the standard approach based on mass-univariate linear modelling (MULM) does not account for the structured patterns of correlations present in each domain. In this work, we propose sparse reduced rank regression (sRRR), a strategy for multivariate modelling of high-dimensional imaging responses (measurements taken over regions of interest or individual voxels) and genetic covariates (single nucleotide polymorphisms or copy number variations), which enforces sparsity in the regression coefficients. Such sparsity constraints ensure that the model performs simultaneous genotype and phenotype selection. Using simulation procedures that accurately reflect realistic human genetic variation and imaging correlations, we present detailed evaluations of the sRRR method in comparison with the more traditional MULM approach. In all settings considered, sRRR has better power to detect deleterious genetic variants compared to MULM. Important issues concerning model selection and connections to existing latent variable models are also discussed. This work shows that sRRR offers a promising alternative for detecting brain-wide, genome-wide associations.

Crown Copyright 2010. Published by Elsevier Inc. All rights reserved.

PMID:
20624472
[PubMed - indexed for MEDLINE]
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