TY - JOUR
T1 - Association of blood-based transcriptional risk scores with biomarkers for Alzheimer disease
AU - AddNeuroMed consortium and the Alzheimer's Disease Neuroimaging Initiative
AU - Park, Young Ho
AU - Hodges, Angela
AU - Simmons, Andrew
AU - Lovestone, Simon
AU - Weiner, Michael W.
AU - Kim, Sang Yun
AU - Saykin, Andrew J.
AU - Nho, Kwangsik
AU - Aisen, Paul
AU - Petersen, Ronald
AU - Jack, Clifford R.
AU - Jagust, William
AU - Trojanowki, John Q.
AU - Toga, Arthur W.
AU - Beckett, Laurel
AU - Green, Robert C.
AU - Saykin, Andrew J.
AU - Morris, John
AU - Shaw, Leslie M.
AU - Khachaturian, Zaven
AU - Sorensen, Greg
AU - Carrillo, Maria
AU - Kuller, Lew
AU - Raichle, Marc
AU - Paul, Steven
AU - Davies, Peter
AU - Fillit, Howard
AU - Hefti, Franz
AU - Holtzman, David
AU - Marcel Mesulam, M.
AU - Potter, William
AU - Snyder, Peter
AU - Hendrix, James
AU - Vasanthakumar, Aparna
AU - Montine, Tom
AU - Rafii, Michael
AU - Chow, Tiffany
AU - Raman, Rema
AU - Jimenez, Gustavo
AU - Donohue, Michael
AU - Gessert, Devon
AU - Harless, Kelly
AU - Salazar, Jennifer
AU - Cabrera, Yuliana
AU - Walter, Sarah
AU - Hergesheimer, Lindsey
AU - Beckett, Laurel
AU - Harvey, Danielle
AU - Donohue, Michael
AU - Bernstein, Matthew
N1 - Publisher Copyright:
Copyright © 2020 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.
PY - 2020/12
Y1 - 2020/12
N2 - Objective To determine whether transcriptional risk scores (TRSs), a summation of polarized expression levels of functional genes, reflect the risk of Alzheimer disease (AD). Methods Blood transcriptome data were from Caucasian participants, which included AD, mild cognitive impairment, and cognitively normal controls (CN) in the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 661) and AddNeuroMed (n = 674) cohorts. To calculate TRSs, we selected functional genes that were expressed under the control of the AD risk loci and were identified as being responsible for AD by using Bayesian colocalization and mendelian randomization methods. Regression was used to investigate the association of the TRS with diagnosis (AD vs CN) and MRI biomarkers (entorhinal thickness and hippocampal volume). Regression was also used to evaluate whether expression of each functional gene was associated with AD diagnosis. Results The TRS was significantly associated with AD diagnosis, hippocampal volume, and entorhinal cortical thickness in the ADNI. The association of the TRS with AD diagnosis and entorhinal cortical thickness was also replicated in AddNeuroMed. Among functional genes identified to calculate the TRS, CD33 and PILRA were significantly upregulated, and TRAPPC6A was significantly downregulated in patients with AD compared with CN, all of which were identified in the ADNI and replicated in AddNeuroMed. Conclusions The blood-based TRS is significantly associated with AD diagnosis and neuroimaging biomarkers. In blood, CD33 and PILRA were known to be associated with uptake of β-amyloid and herpes simplex virus 1 infection, respectively, both of which may play a role in the pathogenesis of AD. Classification of evidence The study is rated Class III because of the case control design and the risk of spectrum bias.
AB - Objective To determine whether transcriptional risk scores (TRSs), a summation of polarized expression levels of functional genes, reflect the risk of Alzheimer disease (AD). Methods Blood transcriptome data were from Caucasian participants, which included AD, mild cognitive impairment, and cognitively normal controls (CN) in the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 661) and AddNeuroMed (n = 674) cohorts. To calculate TRSs, we selected functional genes that were expressed under the control of the AD risk loci and were identified as being responsible for AD by using Bayesian colocalization and mendelian randomization methods. Regression was used to investigate the association of the TRS with diagnosis (AD vs CN) and MRI biomarkers (entorhinal thickness and hippocampal volume). Regression was also used to evaluate whether expression of each functional gene was associated with AD diagnosis. Results The TRS was significantly associated with AD diagnosis, hippocampal volume, and entorhinal cortical thickness in the ADNI. The association of the TRS with AD diagnosis and entorhinal cortical thickness was also replicated in AddNeuroMed. Among functional genes identified to calculate the TRS, CD33 and PILRA were significantly upregulated, and TRAPPC6A was significantly downregulated in patients with AD compared with CN, all of which were identified in the ADNI and replicated in AddNeuroMed. Conclusions The blood-based TRS is significantly associated with AD diagnosis and neuroimaging biomarkers. In blood, CD33 and PILRA were known to be associated with uptake of β-amyloid and herpes simplex virus 1 infection, respectively, both of which may play a role in the pathogenesis of AD. Classification of evidence The study is rated Class III because of the case control design and the risk of spectrum bias.
UR - http://www.scopus.com/inward/record.url?scp=85096974482&partnerID=8YFLogxK
U2 - 10.1212/NXG.0000000000000517
DO - 10.1212/NXG.0000000000000517
M3 - Article
C2 - 33134515
AN - SCOPUS:85096974482
SN - 2376-7839
VL - 6
JO - Neurology: Genetics
JF - Neurology: Genetics
IS - 6
M1 - e517
ER -