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1.  C9ORF72 hexanucleotide repeat expansions in clinical Alzheimer’s disease 
JAMA neurology  2013;70(6):736-741.
Objective
Hexanucleotide repeat expansions in C9ORF72 underlie a significant fraction of frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS). This study investigates the frequency of C9ORF72 repeat expansions in clinically diagnosed late-onset Alzheimer’s disease (AD).
Design, setting and patients
This case-control study genotyped the C9ORF72 repeat expansion in 872 unrelated familial AD cases and 888 controls recruited as part of the NIA-LOAD cohort, a multi-site collaboration studying 1000 families with two or more individuals clinically diagnosed with late-onset-AD.
Main Outcome Measure
We determined the presence or absence of the C9ORF72 repeat expansion by repeat-primed PCR, the length of the longest non-expanded allele, segregation of the genotype with disease, and clinical features of repeat expansion carriers.
Results
Three families showed large C9ORF72 hexanucleotide repeat expansions. Two additional families carried more than 30 repeats. Segregation with disease could be demonstrated in 3 families. One affected expansion carrier had neuropathology compatible with AD. In the NIA-LOAD series, the C9ORF72 repeat expansions constituted the second most common pathogenic mutation, just behind the PSEN1 A79V mutation, highlighting the heterogeneity of clinical presentations associated with repeat expansions.
Interpretation
C9ORF72 repeat expansions explain a small proportion of patients with a clinical presentation indistinguishable from AD, and highlight the necessity of screening “FTD genes” in clinical AD cases with strong family history.
doi:10.1001/2013.jamaneurol.537
PMCID: PMC3681841  PMID: 23588422
3.  Temporoparietal hypometabolism is common in FTLD and is associated with imaging diagnostic errors 
Archives of neurology  2010;68(3):329-337.
Objective
To evaluate the cause of diagnostic errors in the visual interpretation of positron emission tomography scans with 18F-fluorodeoxyglucose (FDG-PET) in patients with frontotemporal lobar degeneration (FTLD) and Alzheimer's disease (AD).
Design
Twelve trained raters unaware of clinical and autopsy information independently reviewed FDG-PET scans and provided their diagnostic impression and confidence of either FTLD or AD. Six of these raters also recorded whether metabolism appeared normal or abnormal in 5 predefined brain regions in each hemisphere – frontal cortex, anterior cingulate cortex, anterior temporal cortex, temporoparietal cortex and posterior cingulate cortex. Results were compared to neuropathological diagnoses.
Setting
Academic medical centers
Patients
45 patients with pathologically confirmed FTLD (n=14) or AD (n=31)
Results
Raters had a high degree of diagnostic accuracy in the interpretation of FDG-PET scans; however, raters consistently found some scans more difficult to interpret than others. Unanimity of diagnosis among the raters was more frequent in patients with AD (27/31, 87%) than in patients with FTLD (7/14, 50%) (p = 0.02). Disagreements in interpretation of scans in patients with FTLD largely occurred when there was temporoparietal hypometabolism, which was present in 7 of the 14 FTLD scans and 6 of the 7 lacking unanimity. Hypometabolism of anterior cingulate and anterior temporal regions had higher specificities and positive likelihood ratios for FTLD than temporoparietal hypometabolism had for AD.
Conclusions
Temporoparietal hypometabolism in FTLD is common and may cause inaccurate interpretation of FDG-PET scans. An interpretation paradigm that focuses on the absence of hypometabolism in regions typically affected in AD before considering FTLD is likely to misclassify a significant portion of FTLD scans. Anterior cingulate and/or anterior temporal hypometabolism indicates a high likelihood of FTLD, even when temporoparietal hypometabolism is present. Ultimately, the accurate interpretation of FDG-PET scans in patients with dementia cannot rest on the presence or absence of a single region of hypometabolism, but must take into account the relative hypometabolism of all brain regions.
doi:10.1001/archneurol.2010.295
PMCID: PMC3058918  PMID: 21059987
4.  Rare Variants in APP, PSEN1 and PSEN2 Increase Risk for AD in Late-Onset Alzheimer's Disease Families 
PLoS ONE  2012;7(2):e31039.
Pathogenic mutations in APP, PSEN1, PSEN2, MAPT and GRN have previously been linked to familial early onset forms of dementia. Mutation screening in these genes has been performed in either very small series or in single families with late onset AD (LOAD). Similarly, studies in single families have reported mutations in MAPT and GRN associated with clinical AD but no systematic screen of a large dataset has been performed to determine how frequently this occurs. We report sequence data for 439 probands from late-onset AD families with a history of four or more affected individuals. Sixty sequenced individuals (13.7%) carried a novel or pathogenic mutation. Eight pathogenic variants, (one each in APP and MAPT, two in PSEN1 and four in GRN) three of which are novel, were found in 14 samples. Thirteen additional variants, present in 23 families, did not segregate with disease, but the frequency of these variants is higher in AD cases than controls, indicating that these variants may also modify risk for disease. The frequency of rare variants in these genes in this series is significantly higher than in the 1,000 genome project (p = 5.09×10−5; OR = 2.21; 95%CI = 1.49–3.28) or an unselected population of 12,481 samples (p = 6.82×10−5; OR = 2.19; 95%CI = 1.347–3.26). Rare coding variants in APP, PSEN1 and PSEN2, increase risk for or cause late onset AD. The presence of variants in these genes in LOAD and early-onset AD demonstrates that factors other than the mutation can impact the age at onset and penetrance of at least some variants associated with AD. MAPT and GRN mutations can be found in clinical series of AD most likely due to misdiagnosis. This study clearly demonstrates that rare variants in these genes could explain an important proportion of genetic heritability of AD, which is not detected by GWAS.
doi:10.1371/journal.pone.0031039
PMCID: PMC3270040  PMID: 22312439
5.  Common variants in MS4A4/MS4A6E, CD2uAP, CD33, and EPHA1 are associated with late-onset Alzheimer’s disease 
Naj, Adam C | Jun, Gyungah | Beecham, Gary W | Wang, Li-San | Vardarajan, Badri Narayan | Buros, Jacqueline | Gallins, Paul J | Buxbaum, Joseph D | Jarvik, Gail P | Crane, Paul K | Larson, Eric B | Bird, Thomas D | Boeve, Bradley F | Graff-Radford, Neill R | De Jager, Philip L | Evans, Denis | Schneider, Julie A | Carrasquillo, Minerva M | Ertekin-Taner, Nilufer | Younkin, Steven G | Cruchaga, Carlos | Kauwe, John SK | Nowotny, Petra | Kramer, Patricia | Hardy, John | Huentelman, Matthew J | Myers, Amanda J | Barmada, Michael M | Demirci, F. Yesim | Baldwin, Clinton T | Green, Robert C | Rogaeva, Ekaterina | St George-Hyslop, Peter | Arnold, Steven E | Barber, Robert | Beach, Thomas | Bigio, Eileen H | Bowen, James D | Boxer, Adam | Burke, James R | Cairns, Nigel J | Carlson, Chris S | Carney, Regina M | Carroll, Steven L | Chui, Helena C | Clark, David G | Corneveaux, Jason | Cotman, Carl W | Cummings, Jeffrey L | DeCarli, Charles | DeKosky, Steven T | Diaz-Arrastia, Ramon | Dick, Malcolm | Dickson, Dennis W | Ellis, William G | Faber, Kelley M | Fallon, Kenneth B | Farlow, Martin R | Ferris, Steven | Frosch, Matthew P | Galasko, Douglas R | Ganguli, Mary | Gearing, Marla | Geschwind, Daniel H | Ghetti, Bernardino | Gilbert, John R | Gilman, Sid | Giordani, Bruno | 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 | Johnson, Nancy | Karlawish, Jason | Karydas, Anna | Kaye, Jeffrey A | Kim, Ronald | Koo, Edward H | Kowall, Neil W | Lah, James J | Levey, Allan I | Lieberman, Andrew P | Lopez, Oscar L | Mack, Wendy J | Marson, Daniel C | Martiniuk, Frank | Mash, Deborah C | Masliah, Eliezer | McCormick, Wayne C | McCurry, Susan M | McDavid, Andrew N | McKee, Ann C | Mesulam, Marsel | Miller, Bruce L | Miller, Carol A | Miller, Joshua W | Parisi, Joseph E | Perl, Daniel P | Peskind, Elaine | Petersen, Ronald C | Poon, Wayne W | Quinn, Joseph F | Rajbhandary, Ruchita A | Raskind, Murray | Reisberg, Barry | Ringman, John M | Roberson, Erik D | Rosenberg, Roger N | Sano, Mary | Schneider, Lon S | Seeley, William | Shelanski, Michael L | Slifer, Michael A | Smith, Charles D | Sonnen, Joshua A | Spina, Salvatore | Stern, Robert A | Tanzi, Rudolph E | Trojanowski, John Q | Troncoso, Juan C | Deerlin, Vivianna M Van | Vinters, Harry V | Vonsattel, Jean Paul | Weintraub, Sandra | Welsh-Bohmer, Kathleen A | Williamson, Jennifer | Woltjer, Randall L | Cantwell, Laura B | Dombroski, Beth A | Beekly, Duane | Lunetta, Kathryn L | Martin, Eden R | Kamboh, M. Ilyas | Saykin, Andrew J | Reiman, Eric M | Bennett, David A | Morris, John C | Montine, Thomas J | Goate, Alison M | Blacker, Deborah | Tsuang, Debby W | Hakonarson, Hakon | Kukull, Walter A | Foroud, Tatiana M | Haines, Jonathan L | Mayeux, Richard | Pericak-Vance, Margaret A | Farrer, Lindsay A | Schellenberg, Gerard D
Nature genetics  2011;43(5):436-441.
The Alzheimer Disease Genetics Consortium (ADGC) performed a genome-wide association study (GWAS) of late-onset Alzheimer disease (LOAD) using a 3 stage design consisting of a discovery stage (Stage 1) and two replication stages (Stages 2 and 3). Both joint and meta-analysis analysis approaches were used. We obtained genome-wide significant results at MS4A4A [rs4938933; Stages 1+2, meta-analysis (PM) = 1.7 × 10−9, joint analysis (PJ) = 1.7 × 10−9; Stages 1–3, PM = 8.2 × 10−12], CD2AP (rs9349407; Stages 1–3, PM = 8.6 × 10−9), EPHA1 (rs11767557; Stages 1–3 PM = 6.0 × 10−10), and CD33 (rs3865444; Stages 1–3, PM = 1.6 × 10−9). We confirmed that CR1 (rs6701713; PM = 4.6×10−10, PJ = 5.2×10−11), CLU (rs1532278; PM = 8.3 × 10−8, PJ = 1.9×10−8), BIN1 (rs7561528; PM = 4.0×10−14; PJ = 5.2×10−14), and PICALM (rs561655; PM = 7.0 × 10−11, PJ = 1.0×10−10) but not EXOC3L2 are LOAD risk loci1–3.
doi:10.1038/ng.801
PMCID: PMC3090745  PMID: 21460841

Results 1-5 (5)