@article{b63fa01a3ba74b158f69fef939800008,
title = "Susceptibility of brain atrophy to TRIB3 in Alzheimer's disease, evidence from functional prioritization in imaging genetics",
abstract = "The joint modeling of brain imaging information and genetic data is a promising research avenue to highlight the functional role of genes in determining the pathophysiological mechanisms of Alzheimer's disease (AD). However, since genome-wide association (GWA) studies are essentially limited to the exploration of statistical correlations between genetic variants and phenotype, the validation and interpretation of the findings are usually nontrivial and prone to false positives. To address this issue, in this work, we investigate the functional genetic mechanisms underlying brain atrophy in AD by studying the involvement of candidate variants in known genetic regulatory functions. This approach, here termed functional prioritization, aims at testing the sets of gene variants identified by high-dimensional multivariate statistical modeling with respect to known biological processes to introduce a biology-driven validation scheme. When applied to the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, the functional prioritization allowed for identifying a link between tribbles pseudokinase 3 (TRIB3) and the stereotypical pattern of gray matter loss in AD, which was confirmed in an independent validation sample, and that provides evidence about the relation between this gene and known mechanisms of neurodegeneration.",
keywords = "Alzheimer's disease, Brain atrophy, Imaging-genetics, Neuroimaging, TRIB3",
author = "Marco Lorenzi and Andre Altmann and Boris Gutman and Selina Wray and Charles Arber and Hibar, {Derrek P.} and Neda Jahanshad and Schott, {Jonathan M.} and Alexander, {Daniel C.} and Thompson, {Paul M.} and Sebastien Ourselin",
note = "Funding Information: ACKNOWLEDGMENTS. M.L., J.M.S., D.C.A., and S.O. received support from European Union{\textquoteright}s Horizon 2020 Research and Innovation Programme Grant 666992 (EuroPOND) for this work. A.A. holds an MRC eMedLab Medical Bioinformatics Career Development Fellowship. This work was supported by Medical Research Council Grant MR/L016311/1. The contribution to this work by B.G. and P.M.T. was funded by NIH “Big Data to Knowledge” Grant U54 EB020403 (principal investigator: P.M.T.). S.W. and C.A. are supported by the National Institute for Health Research (NIHR) Queen Square Biomedical Research Unit in Dementia and Alzheimer{\textquoteright}s Research UK and the NIHR University College London (UCL) Hospitals Biomedical Research Centre. J.M.S. acknowledges the support of the NIHR UCL Hospitals Biomedical Research Centre; the Wolfson Foundation; Engineering and Physical Sciences Research Council (EPSRC) Grant EP/J020990/1; Medical Research Council (MRC) Grant MR/L023784/1; Alzheimer Research UK (ARUK) Grants ARUK-Network 2012-6-ICE, ARUK-PG2017-1946, and ARUK-PG2017-1946; Brain Research Trust Grant UCC14191; and European Union{\textquoteright}s Horizon 2020 Research and Innovation Programme Grant 666992. EPSRC Grants EP/J020990/01 and EP/M020533/1 support the work of D.C.A. and S.O. on this topic. S.O. receives funding from EPSRC Grants EP/H046410/1 and EP/K005278, MRC Grant MR/J01107X/1, EU-FP7 Project VPH-DARE@IT Grant FP7-ICT-2011-9-601055, the NIHR Biomedical Research Unit (Dementia) at the UCL, and NIHR University College London Hospitals Biomedical Research Centre (NIHR BRC UCLH/UCL High Impact Initiative-BW. Publisher Copyright: {\textcopyright} 2018 National Academy of Sciences. All rights reserved.",
year = "2018",
month = mar,
day = "20",
doi = "10.1073/pnas.1706100115",
language = "English",
volume = "115",
pages = "3162--3167",
journal = "Proceedings of the National Academy of Sciences of the United States of America",
issn = "0027-8424",
publisher = "National Academy of Sciences",
number = "12",
}