Effects of Multiple Genetic Loci on Age at Onset in Late-Onset Alzheimer Disease

Naj, Adam C.; Jun, Gyungah; Reitz, Christiane; Kunkle, Brian W.; Perry, William; Park, Yo Son; Beecham, Gary W.; Rajbhandary, Ruchita; Hamilton‐Nelson, Kara L.; Wang, Li-San; Kauwe, John; Huentelman, Matthew J.; Myers, Amanda; Bird, Thomas D.; Boeve, Bradley F.; Baldwin, Clinton T.; Jarvik, Gail P.; Crane, Paul K.; Rogaeva, Ekaterina; Barmada, M. Michael; Demirci, F. Yesim; Cruchaga, Carlos; Kramer, Patricia L.; Ertekin-Taner, Nilüfer; Hardy, John; Graff‐Radford, Neill R.; Green, Robert C.; Larson, Eric B.; George-Hyslop, Peter St; Buxbaum, Joseph D.; Evans, Denis A.; Schneider, Julie A.; Lunetta, Kathryn L.; Kamboh, M. Ilyas; Saykin, Andrew J.; Reiman, Eric M.; Jager, Philip L. De; Bennett, David A.; Morris, John C.; Montine, Thomas J.; Goate, Alison M.; Blacker, Deborah; Tsuang, Debby W.; Hákonarson, Hákon; Kukull, Walter A.; Foroud, Tatiana; Martin, Eden R.; Haines, Jonathan L.; Mayeux, Richard; Farrer, Lindsay A.; Schellenberg, Gerard D.; Pericak‐Vance, Margaret A.; Albert, Marilyn; Albin, Roger L.; Apostolova, Liana G.; Arnold, Steven E.; Barber, Robert W.; Barnes, Lisa L.; Beach, Thomas G.; Becker, James T.; Beekly, Duane; Bigio, Eileen H.; Bowen, James D.; Boxer, Adam L.; Burke, James R.; Cairns, Nigel J.; Cantwell, Laura B.; Cao, Chuanhai; Carlson, Chris; Carney, Regina M.; Carrasquillo, Minerva M.; Carroll, Steven L.; Chui, Helena C.; Clark, David G.; Corneveaux, Jason J.; Cribbs, David H.; Crocco, Elizabeth; DeCarli, Charles; DeKosky, Steven T.; Dick, Malcolm; Dickson, Dennis W.; Duara, Ranjan; Faber, Kelley; Fallon, Kenneth B.; Farlow, Martin R.; Ferris, Steven; Frosch, Matthew P.; Galasko, Douglas; Ganguli, Mary; Gearing, Marla; Geschwind, Daniel H.; Ghetti, Bernardino; Gilbert, John R.; Glass, Jonathan D.; Growdon, John H.; Hamilton, Ronald L.; Harrell, Lindy E.; Head, Elizabeth; Honig, Lawrence S.; Hulette, Christine M.; Hyman, Bradley T.; Jicha, Gregory A.; Jin, Lee‐Way; Karydas, Anna; Kaye, Jeffrey; Kim, Ronald; Koo, Edward H.; Kowall, Neil W.; Kramer, Joel H.; LaFerla, Frank M.; Lah, James J.; Leverenz, James B.; Levey, Aĺlan I.; Li, Ge; Lieberman, Andrew P.; Lin, Chiao‐Feng; López, Oscar L.; Lyketsos, Constantine G.; Mack, Wendy J.; Martiniuk, Frank; Mash, Deborah C.; Masliah, Eliezer; McCormick, Wayne C.; McCurry, Susan M.; McDavid, Andrew; McKee, Ann C.; Mesulam, Marsel; Miller, Bruce L.; Miller, Carol A.; Miller, Joshua W.; Murrell, Jill R.; Olichney, John; Pankratz, V. Shane; Parisi, Joseph E.; Paulson, Henry L.; Peskind, Elaine R.; Petersen, Ronald C.; Pierce, Aimee; Poon, Wayne W.; Potter, Huntington; Quinn, Joseph F.; Raj, Ashok; Raskind, Murray A.; Reisberg, Barry; Ringman, John M.; Roberson, Erik D.; Rosen, Howard J.; Rosenberg, Roger N.; Sano, Mary; Schneider, Lon S.; Seeley, William W.; Smith, Amanda; Sonnen, Joshua A.; Spina, Salvatore; Stern, Robert A.; Tanzi, Rudolph E.; Thornton‐Wells, Tricia A.; Trojanowski, John Q.; Troncoso, Juan C.; Valladares, Otto; Deerlin, Vivianna M. Van; Eldik, Linda J. Van; Vardarajan, Badri N.; Vinters, Harry V.; Vonsattel, Jean Paul; Weıntraub, Sandra; Welsh‐Bohmer, Kathleen A.; Williamson, Jennifer; Wishnek, Sarah; Woltjer, Randall L.; Wright, Clinton B.; Younkin, Steven G.; Yu, Chang‐En; Yu, Lei

doi:10.1001/jamaneurol.2014.1491

Cited by 176 publications

(157 citation statements)

References 32 publications

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“…Moreover, AAO has also been used as the phenotype in large-scale GWASs, which revealed the association of CR1, BIN1, and PICALM with AAO among patients with LOAD. However, the effect of these genes in AD did not exceed that of APOE [57].…”

Section: Clinical Features As the Phenotypementioning

confidence: 86%

An Overview of Genome-Wide Association Studies in Alzheimer’s Disease

Shen

Jia

2016

Neurosci. Bull.

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Genome-wide association studies (GWASs) have revealed a plethora of putative susceptibility genes for Alzheimer's disease (AD). With the sole exception of the APOE gene, these AD susceptibility genes have not been unequivocally validated in independent studies. No single novel functional risk genetic variant has been identified. In this review, we evaluate recent GWASs of AD, and discuss their significance, limitations, and challenges in the investigation of the genetic spectrum of AD.

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Section: Clinical Features As the Phenotypementioning

confidence: 86%

An Overview of Genome-Wide Association Studies in Alzheimer’s Disease

Shen

Jia

2016

Neurosci. Bull.

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“…1,9 Variants of CR1, BIN1, and PICALM seem to have a combined effect over age of AD onset that does not supersede the APOE effect, 10 but might also impact the course of the dementia syndrome, and should be assessed in future studies.…”

Section: Discussionmentioning

confidence: 99%

“…Late-onset AD was considered when the dementia syndrome began after patients turned 60 years old. 1,9,10 All patients were assessed for sex, schooling, cerebrovascular risk factors (arterial hypertension, diabetes mellitus, dyslipidemias, obesity, alcohol use, smoking), use of lipid-lowering drugs, APOE haplotypes and time since dementia onset to reach Clinical Dementia Rating (CDR) 11 scores >1.0, and Mini-Mental State Examination (MMSE) 12 scores of 20 and 15. Framingham projections of 10-year absolute coronary heart disease risk 13 were estimated for all patients, based on sex, age, total cholesterol, high-density lipoprotein cholesterol, any cigarette smoking in the previous month, and treated or untreated systolic blood pressure; to predict the impact of this composite score over cognitive and functional decline all along the course of AD, only the earliest calculated coronary heart disease risk values were taken into account for statistics in our study.…”

Section: Participants and Diagnostic Assessmentmentioning

confidence: 99%

Predictors of Cognitive and Functional Decline in Patients With Alzheimer Disease Dementia From Brazil

Oliveira

Chen

Smith

et al. 2016

Alzheimer Disease &Amp; Associated Disorders

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Little is known on how risk factors for Alzheimer disease (AD) dementia affect disease progression, much less for populations with low mean schooling, whereas the transcription of APOE may be regulated by nongenetic factors. In this 44-month cohort study, 214 consecutive outpatients with late-onset AD were assessed for rates of cognitive and functional decline by way of Clinical Dementia Rating and Mini-Mental State Examination (MMSE) scores, keeping blinded assessment of APOE haplotypes. Subjects were evaluated for sex, schooling, age of dementia onset, and cerebrovascular risk factors (including Framingham risk scores). Of the 214 patients, there were 146 (68.2%) women and 113 (52.8%) APOE4+ carriers. The mean age of AD onset was 73.4±6.5 years-old, negatively correlated with time to Clinical Dementia Rating >1.0 (β=-0.132; ρ<0.001), MMSE=20 (β=-0.105; ρ<0.001), and MMSE=15 (β=-0.124; ρ=0.003), more significantly for women and APOE4+ carriers. Mean schooling was 4.18±3.7 years, correlated with time to MMSE=20 and MMSE=15 for women and APOE4+ carriers. Body mass index was correlated with time to MMSE=20 only for men (ρ=0.006). The 10-year coronary heart disease risk was correlated with time to MMSE=20 only for APOE4+ carriers (ρ=0.015). These outcomes suggest interactions among genomic effects of cognitive reserve, cerebral perfusion, and hormonal changes over mechanisms of neurodegeneration.

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“…The additive model transforms a SNP from base pair letters into a count based on, usually, the number of minor alleles: a major homozygote is "0," a heterozygote is "1," and a minor homozygote is "2." This {0,1,2} coding scheme has been used in a wide array of studies such as the onset of AD [30], and cerebrospinal fluid (CSF) tau levels [31], even though we generally acknowledge that the standard approaches cannot detect non-additive or complex effects [32,33]. Furthermore, with open resources such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), researchers have the opportunity to .…”

Section: Issues With the Additive Modelmentioning

confidence: 99%

Multivariate genotypic analyses that identify specific genotypes to characterize disease and control groups in ADNI

Beaton

Rieck

Alhazmi³

et al. 2017

Preprint

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INTRODUCTIONGenetic contributions to Alzheimer’s Disease (AD) are likely polygenic and not necessarily explained by uniformly applied linear and additive effects. In order to better understand the genetics of AD, we require statistical techniques to address both polygenic and possible non-additive effects.METHODSWe used partial least squares-correspondence analysis (PLS-CA)—a method designed to detect multivariate genotypic effects. We used ADNI-1 (N = 756) as a discovery sample with two forms of PLS-CA: diagnosis-based and ApoE-based. We used ADNI-2 (N= 791) as a validation sample with a diagnosis-based PLS-CA.RESULTSWith PLS-CA we identified some expected genotypic effects (e.g., APOE/TOMM40, and APP) and a number of new effects that include, for examples, risk-associated genotypes in RBFOX1 and GPC6 and control-associated genotypes in PTPN14 and CPNE5.DISCUSSIONThrough the use of PLS-CA, we were able to detect complex (multivariate, genotypic) genetic contributions to AD, which included many non-additive and non-linear risk and possibly protective effects.

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Effects of Multiple Genetic Loci on Age at Onset in Late-Onset Alzheimer Disease

Cited by 176 publications

References 32 publications

An Overview of Genome-Wide Association Studies in Alzheimer’s Disease

An Overview of Genome-Wide Association Studies in Alzheimer’s Disease

Predictors of Cognitive and Functional Decline in Patients With Alzheimer Disease Dementia From Brazil

Multivariate genotypic analyses that identify specific genotypes to characterize disease and control groups in ADNI

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