Ge, Tian, Feng, Jianfeng, Hibar, Derrek P., Thompson, Paul M. and Nichols, Thomas E.. (2012) Increasing power for voxel-wise genome-wide association studies : the random field theory, least square kernel machines and fast permutation procedures. NeuroImage, Vol.63 (No.2). pp. 858-873. ISSN 1053-8119

Preview

Text WRAP_Feng_241112-ge_rftlskm_2012_revisedsubmitted.pdf - Accepted Version Download (1238Kb) | Preview

Abstract

Imaging traits are thought to have more direct links to genetic variation than diagnostic measures based on cognitive or clinical assessments and provide a powerful substrate to examine the influence of genetics on human brains. Although imaging genetics has attracted growing attention and interest, most brain-wide genome-wide association studies focus on voxel-wise single-locus approaches, without taking advantage of the spatial information in images or combining the effect of multiple genetic variants. In this paper we present a fast implementation of voxel- and cluster-wise inferences based on the random field theory to fully use the spatial information in images. The approach is combined with a multi-locus model based on least square kernel machines to associate the joint effect of several single nucleotide polymorphisms (SNP) with imaging traits. A fast permutation procedure is also proposed which significantly reduces the number of permutations needed relative to the standard empirical method and provides accurate small p-value estimates based on parametric tail approximation. We explored the relation between 448,294 single nucleotide polymorphisms and 18,043 genes in 31,662 voxels of the entire brain across 740 elderly subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Structural MRI scans were analyzed using tensor-based morphometry (TBM) to compute 3D maps of regional brain volume differences compared to an average template image based on healthy elderly subjects. We find method to be more sensitive compared with voxel-wise single-locus approaches. A number of genes were identified as having significant associations with volumetric changes. The most associated gene was GRIN2B, which encodes the N-methyl-d-aspartate (NMDA) glutamate receptor NR2B subunit and affects both the parietal and temporal lobes in human brains. Its role in Alzheimer's disease has been widely acknowledged and studied, suggesting the validity of the approach. The various advantages over existing approaches indicate a great potential offered by this novel framework to detect genetic influences on human brains.

Item Type:	Journal Article
Subjects:	Q Science > QH Natural history > QH426 Genetics
Divisions:	Faculty of Science > Computer Science Faculty of Science > Statistics Faculty of Science > WMG (Formerly the Warwick Manufacturing Group)
Library of Congress Subject Headings (LCSH):	Imaging systems in genetics, Brain -- Genetics, Alzheimer's disease -- Genetic aspects
Journal or Publication Title:	NeuroImage
Publisher:	Elsevier
ISSN:	1053-8119
Date:	1 November 2012
Volume:	Vol.63
Number:	No.2
Page Range:	pp. 858-873
Identification Number:	10.1016/j.neuroimage.2012.07.012
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
References:	R.J. Adler. The geometry of random fields. Wiley, New York, 1981. R.J. Adler and J.E. Taylor. Random fields and geometry. Springer Verlag, 2007. H. Akil, S. Brenner, E. Kandel, K.S. Kendler, M.C. King, E. Scolnick, J.D. Watson, and H.Y. Zoghbi. The future of psychiatric research: Genomes and neural circuits. Science, 327(5973):1580, 2010. D. Aldous. Probability approximations via the poisson clumping heuristic. Applied Mathematical Sciences, 77, 1989. M.J. Anderson and J. Robinson. Permutation tests for linear models. Australian & New Zealand Journal of Statistics, 43(1):75–88, 2001. H. Bi and C.I. Sze. N-methyl–aspartate receptor subunit NR2A and NR2B messenger RNA levels are altered in the hippocampus and entorhinal cortex in Alzheimer’s disease. Journal of the Neurological Sciences, 200(1-2):11–18, 2002. M.N. Braskie, N. Jahanshad, J.L. Stein, M. Barysheva, K.L. McMahon, G.I. de Zubicaray, N.G. Martin, M.J. Wright, J.M. Ringman, A.W. Toga, et al. Common Alzheimer’s disease risk variant within the CLU gene affects white matter microstructure in young adults. The Journal of Neuroscience, 31(18):6764, 2011. J. Cao. The size of the connected components of excursion sets of �2, t and F fields. Advances in Applied Probability, 31:579–595, 1999. J. Cao and K.J. Worsley. Applications of random fields in human brain mapping. Spatial Statistics: Methodological Aspects and Applications, 159:170–182, 2001. L.R. Cardon, N. Craddock, P. Deloukas, A. Duncanson, D.P. Kwiatkowski, M.I. McCarthy, W.H. Ouwehand, N.J. Samani, J.A. Todd, P. Donnelly, et al. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447 (7145):661–678, 2007. H.A. David and H.N. Nagaraja. Order statistics. Wiley Online Library, 1970. J. Fan and J. Lv. Sure independence screening for ultrahigh dimensional feature space. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 70(5):849– 911, 2008. N.B. Farber, J.W. Newcomer, and J.W. Olney. The glutamate synapse in neuropsychiatric disorders: focus on schizophrenia and Alzheimer’s disease. Progress in Brain Research, 116:421–437, 1998. N. Filippini, A. Rao, S. Wetten, R.A. Gibson, M. Borrie, D. Guzman, A. Kertesz, I. Loy- English, J. Williams, T. Nichols, et al. Anatomically-distinct genetic associations of APOE 4 allele load with regional cortical atrophy in Alzheimer’s disease. NeuroImage, 44(3):724–728, 2009. G.M. Fox, P.L. Holst, H.T. Chute, R.A. Lindberg, A.M. Janssen, R. Basu, and A.A. Welcher. cdna cloning and tissue distribution of five human eph-like receptor proteintyrosine kinases. Oncogene, 10(5):897, 1995. T.M. Frayling, N.J. Timpson, M.N. Weedon, E. Zeggini, R.M. Freathy, C.M. Lindgren, J.R.B. Perry, K.S. Elliott, H. Lango, N.W. Rayner, et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science, 316(5826):889, 2007. K.A. Frazer, D.G. Ballinger, D.R. Cox, D.A. Hinds, L.L. Stuve, R.A. Gibbs, J.W. Belmont, A. Boudreau, P. Hardenbol, S.M. Leal, et al. A second generation human haplotype map of over 3.1 million SNPs. Nature, 449(7164):851–861, 2007. K.J. Friston, K.J. Worsley, R.S.J. Frackowiak, J.C. Mazziotta, and A.C. Evans. Assessing the significance of focal activations using their spatial extent. Human Brain Mapping, 1: 210–220, 1994. K.J. Friston, A. Holmes, J.B. Poline, C.J. Price, and C.D. Frith. Detecting activations in pet and fmri: levels of inference and power. NeuroImage, 4(3):223–235, 1996. K.J. Friston, J.T. Ashburner, S.J. Kiebel, T.E. Nichols, and W.D. Penny. Statistical parametric mapping: the analysis of funtional brain images. Elsevier/Academic Press, 2007. I.I. Gottesman and T.D. Gould. The endophenotype concept in psychiatry: etymology and strategic intentions. American Journal of Psychiatry, 160(4):636, 2003. E.J. Gumbel. Statistics of extremes. Dover Pubns, 2004. S. Hayasaka and T.E. Nichols. Validating cluster size inference: random field and permutation methods. NeuroImage, 20(4):2343–2356, 2003. S. Hayasaka, K.L. Phan, I. Liberzon, K.J.Worsley, and T.E. Nichols. Nonstationary clustersize inference with random field and permutation methods. NeuroImage, 22(2):676–687, 2004. D.P. Hibar, O. Kohannim, J.L. Stein, M.C. Chiang, and P.M. Thompson. Multilocus genetic analysis of brain images. Frontiers in Genetics, 2(73), 2011a. D.P. Hibar, J.L. Stein, O. Kohannim, N. Jahanshad, A.J. Saykin, L. Shen, S. Kim, N. Pankratz, T. Foroud, M.J. Huentelman, et al. Voxelwise gene-wide association study (vGeneWAS): multivariate gene-based association testing in 731 elderly subjects. NeuroImage, 56:1875–1891, 2011b. R.W. Hoerl. Ridge analysis 25 years later. American Statistician, 39:186–192, 1985. A.P. Holmes. Statistical issues in functional brain mapping. PhD thesis, 1994. A.P. Holmes, RC Blair, G. Watson, and I. Ford. Nonparametric analysis of statistic images from functional mapping experiments. Journal of Cerebral Blood Flow & Metabolism, 16(1):7–22, 1996. M.R. Hynd, H.L. Scott, and P.R. Dodd. Differential expression of N-methyl-d-aspartate receptor NR2 isoforms in Alzheimer’s disease. Journal of Neurochemistry, 90(4):913– 919, 2004. E. Inoue, M. Deguchi-Tawarada, A. Togawa, C. Matsui, K. Arita, S. Katahira-Tayama, T. Sato, E. Yamauchi, Y. Oda, and Y. Takai. Synaptic activity prompts -secretase– mediated cleavage of epha4 and dendritic spine formation. The Journal of cell biology, 185(3):551, 2009. International Human Genome Sequencing Consortium. Finishing the euchromatic sequence of the human genome. Nature, 431:931–945, 2004. C.R. Jack Jr, M.A. Bernstein, N.C. Fox, P. Thompson, G. Alexander, D. Harvey, B. Borowski, P.J. Britson, et al. The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. Journal of Magnetic Resonance Imaging, 27(4):685–691, 2008. H. Jiang and J. Jia. Association between NR2B subunit gene (GRIN2B) promoter polymorphisms and sporadic Alzheimer’s disease in the North Chinese population. Neuroscience letters, 450(3):356–360, 2009. A.H. Joyner et al. A common MECP2 haplotype associates with reduced cortical surface area in humans in two independent populations. Proceedings of the National Academy of Sciences, 106(36):15483, 2009. P.E. Kennedy and B.S. Cade. Randomization tests for multiple regression. Communications in Statistics-Simulation and Computation, 25(4):923–936, 1996. S.J. Kiebel, J.B. Poline, K.J. Friston, A.P. Holmes, and K.J. Worsley. Robust smoothness estimation in statistical parametric maps using standardized residuals from the general linear model. NeuroImage, 10(6):756–766, 1999. T.A. Knijnenburg, L.F.A. Wessels, M.J.T. Reinders, and I. Shmulevich. Fewer permutations, more accurate p-values. Bioinformatics, 25(12):i161, 2009. O. Kohannim, D.P. Hibar, J.L. Stein, N. Jahanshad, C.R. Jack, M.W. Weiner, A.W. Toga, and P.M. Thompson. Boosting power to detect genetic associations in imaging using multi-locus, genome-wide scans and ridge regression. In Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on, pages 1855–1859. IEEE, 2011. O. Kohannim, D.P. Hibar, N. Jahanshad, J.L. Stein, X. Hua, A.W. Toga, C.R. Jack Jr., M.W. Weiner, P.M. Thompson, et al. Predicting temporal lobe volume on MRI from genotypes using L1-L2 regularized regression. ISBI, in press, 2012. L.C. Kwee, D. Liu, X. Lin, D. Ghosh, and M.P. Epstein. A powerful and flexible multilocus association test for quantitative traits. The American Journal of Human Genetics, 82(2): 386–397, 2008. R. Lamprecht and J. LeDoux. Structural plasticity and memory. Nature Reviews Neuroscience, 5(1):45–54, 2004. A. Leow, S.C. Huang, A. Geng, J. Becker, S. Davis, A. Toga, and P. Thompson. Inverse consistent mapping in 3D deformable image registration: its construction and statistical properties. In Information Processing in Medical Imaging, pages 493–503. Springer, 2005. A.D. Leow, I. Yanovsky, M.C. Chiang, A.D. Lee, A.D. Klunder, A. Lu, J.T. Becker, S.W. Davis, A.W. Toga, and P.M. Thompson. Statistical properties of jacobian maps and the realization of unbiased large-deformation nonlinear image registration. Medical Imaging, IEEE Transactions on, 26(6):822–832, 2007. D. Liu, X. Lin, and D. Ghosh. Semiparametric regression of multidimensional genetic pathway data: Least-squares kernel machines and linear mixed models. Biometrics, 63 (4):1079–1088, 2007. J. Mazziotta, A. Toga, A. Evans, P. Fox, J. Lancaster, K. Zilles, R.Woods, T. Paus, G. Simpson, B. Pike, et al. A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 356(1412):1293, 2001. A. Meyer-Lindenberg, K.K. Nicodemus, M.F. Egan, J.H. Callicott, V. Mattay, and D.R. Weinberger. False positives in imaging genetics. NeuroImage, 40(2):655–661, 2008. T. Nichols and S. Hayasaka. Controlling the familywise error rate in functional neuroimaging: a comparative review. Statistical methods in medical research, 12(5):419–446, 2003. T.E. Nichols and A.P. Holmes. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Human Brain Mapping, 15(1):1–25, 2002. C.G. Parsons, A. St¨offler, and W. Danysz. Memantine: a NMDA receptor antagonist that improves memory by restoration of homeostasis in the glutamatergic system-too little activation is bad, too much is even worse. Neuropharmacology, 53(6):699–723, 2007. J. Pickands III. Statistical inference using extreme order statistics. the Annals of Statistics, pages 119–131, 1975. J.B. Poline and B.M. Mazoyer. Analysis of individual positron emission tomography activation maps by detection of high signal-to-noise-ratio pixel clusters. Journal of Cerebral Blood Flow & Metabolism, 13(3):425–437, 1993. J.B. Poline, K.J.Worsley, A.C. Evans, and K.J. Friston. Combining spatial extent and peak intensity to test for activations in functional imaging. NeuroImage, 5(2):83–96, 1997. S.G. Potkin, G. Guffanti, A. Lakatos, J.A. Turner, F. Kruggel, J.H. Fallon, A.J. Saykin, A. Orro, S. Lupoli, E. Salvi, et al. Hippocampal atrophy as a quantitative trait in a genome-wide association study identifying novel susceptibility genes for alzheimer’s disease. PLoS One, 4(8):e6501, 2009. P.E. Roland, B. Levin, R. Kawashima, and S. A° kerman. Three-dimensional analysis of clustered voxels in 15O-butanol brain activation images. Human Brain Mapping, 1(1): 3–19, 1993. D. Schaid. Genomic similarity and kernel methods ii: methods for genomic information. Hum Hered, 70:132–140, 2010a. D.J. Schaid. Genomic similarity and kernel methods i: advancements by building on mathematical and statistical foundations. Hum Hered, 70:109–131, 2010b. L. Shen, S. Kim, S.L. Risacher, K. Nho, S. Swaminathan, J.D. West, T. Foroud, N. Pankratz, J.H. Moore, C.D. Sloan, et al. Whole genome association study of brainwide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort. NeuroImage, 2010. M. Silver, G. Montana, and T.E. Nichols. False positives in neuroimaging genetics using voxel-based morphometry data. NeuroImage, 54(2):992–1000, 2011. A.M. Sim´on, R.L. de Maturana, A. Ricobaraza, L. Escribano, L. Schiapparelli, M. Cuadrado-Tejedor, A. P´erez-Mediavilla, J. Avila, J. Del R´ıo, and D. Frechilla. Early changes in hippocampal eph receptors precede the onset of memory decline in mouse models of alzheimer’s disease. Journal of Alzheimer’s Disease, 17(4):773–786, 2009. R. Smith. Threshold methods for sample extremes. In J.T. De Oliveira, editor, Statistical extremes and applications, pages 6211–6638. D. Reidel Dordrecht, The Netherlands, 1984. J.L. Stein, X. Hua, S. Lee, A.J. Ho, A.D. Leow, A.W. Toga, A.J. Saykin, L. Shen, T. Foroud, N. Pankratz, et al. Voxelwise genome-wide association study (vGWAS). NeuroImage, 53(3):1160–1174, 2010a. J.L. Stein, X. Hua, J.H. Morra, S. Lee, D.P. Hibar, A.J. Ho, A.D. Leow, A.W. Toga, J.H. Sul, H.M. Kang, et al. Genome-wide analysis reveals novel genes influencing temporal lobe structure with relevance to neurodegeneration in alzheimer’s disease. NeuroImage, 51(2):542–554, 2010b. R. Sultana, D. Boyd-Kimball, J. Cai, W.M. Pierce, J.B. Klein, M. Merchant, and D.A. Butterfield. Proteomics analysis of the alzheimer’s disease hippocampal proteome. Journal of Alzheimer’s Disease, 11(2):153–164, 2007. Y.P. Tang, E. Shimizu, G.R. Dube, C. Rampon, G.A. Kerchner, M. Zhuo, G. Liu, and J.Z. Tsien. Genetic enhancement of learning and memory in mice. Nature, 401(6748):63–69, 1999. J.E. Taylor and R.J. Adler. Euler characteristics for gaussian fields on manifolds. Annals of Probability, 31:533–563, 2003. R. Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58:267–288, 1996. M. Vounou, T.E. Nichols, and G. Montana. Discovering genetic associations with highdimensional neuroimaging phenotypes: A sparse reduced-rank regression approach. NeuroImage, 53(3):1147–1159, 2010. K.J. Worsley. Local maxima and the expected Euler characteristic of excursion sets of �2 , F and T fields. Advances in Applied Probability, pages 13–42, 1994. K.J. Worsley. The geometry of random images. CHANCE, 9:27–40, 1996. K.J. Worsley. Non-stationary FWHM and its effect on statistical inference of fMRI data. NeuroImage, 15:S346, 2002. K.J. Worsley, AC Evans, S. Marrett, and P. Neelin. A three-dimensional statistical analysis for CBF activation studies in human brain. Journal of Cerebral Blood Flow and Metabolism, 12:900–900, 1992. K.J. Worsley, S. Marrett, P. Neelin, A.C. Vandal, K.J. Friston, A.C. Evans, et al. A unified statistical approach for determining significant signals in images of cerebral activation. Human Brain Mapping, 4(1):58–73, 1996. K.J. Worsley, M. Andermann, T. Koulis, D. MacDonald, and A.C. Evans. Detecting changes in nonisotropic images. Human Brain Mapping, 8(2-3):98–101, 1999. K. Yashiro and B.D. Philpot. Regulation of NMDA receptor subunit expression and its implications for LTD, LTP, and metaplasticity. Neuropharmacology, 55(7):1081–1094, 2008.
URI:	http://wrap.warwick.ac.uk/id/eprint/50672

Request changes to a record

Actions (login required)

View Item