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variant of Alzheimer’s disease
Jonathan M. Schott, Sebastian J. Crutch, Minerva M. Carrasquillo, James
Uphill, Tim J. Shakespeare, Natalie S. Ryan, Keir X.X. Yong, Manja
Lehmann, Nilufer Ertekin-Taner, Neil R. Graff-Radford, et al.
To cite this version:
Jonathan M. Schott, Sebastian J. Crutch, Minerva M. Carrasquillo, James Uphill, Tim J. Shakespeare,
et al.. Genetic risk factors for the posterior cortical atrophy variant of Alzheimer’s disease. Alzheimer’s
and Dementia, Elsevier, 2016, �10.1016/j.jalz.2016.01.010�. �hal-01289466�
Featured Article
Genetic risk factors for the posterior cortical atrophy variant of
Alzheimer’s disease
Q10
Jonathan M. Schott
a,
*
, Sebastian J. Crutch
a, Minerva M. Carrasquillo
b, James Uphill
c,
Tim J. Shakespeare
a, Natalie S. Ryan
a, Keir X. X. Yong
a, Manja Lehmann
a,
Nilufer Ertekin-Taner
b,d, Neil R. Graff-Radford
d, Bradley F. Boeve
e, Melissa E. Murray
b,
Qurat ul Ain Khan
b, Ronald C. Petersen
e, Dennis W. Dickson
b, David S. Knopman
e,
Gil D. Rabinovici
f, Bruce L. Miller
f, Aida Suarez Gonzalez
a,g, Eulogio Gil-N
eciga
g,
Julie S. Snowden
h, Jenny Harris
h, Stuart M. Pickering-Brown
h, Eva Louwersheimer
i,
Wiesje M. van der Flier
i, Philip Scheltens
i, Yolande A. Pijnenburg
i, Douglas Galasko
j,k,
Marie Sarazin
l, Bruno Dubois
m, Eloi Magnin
n, Daniela Galimberti
o, Elio Scarpini
o,
Stefano F. Cappa
p, John R. Hodges
q, Glenda M. Halliday
q, Lauren Bartley
q, Maria C. Carrillo
r,
Jose T. Bras
s, John Hardy
s, Martin N. Rossor
a, John Collinge
c, Nick C. Fox
a, Simon Mead
cQ2
aDepartment of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, UK
Q3
bDepartment of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
cDepartment of Neurodegenerative Disease, MRC Prion Unit, UCL Institute of Neurology, UK
dDepartment of Neurology, Mayo Clinic, Jacksonville, FL, USA
eDepartment of Neurology, Mayo Clinic, Rochester, MN, USA
fUCSF, San Francisco, CA, USA
gUniversity Hospital Virgen del Rocıo, Seville, Spain
h
Institute of Brain, Behaviour and Mental Health, University of Manchester, UK
i
Alzheimer center, Department of Neurology, VU University Medical Center, Neuroscience Campus, Amsterdam, Netherlands
j
Department of Epidemiology & biostatistics, VU University Medical Center, Amsterdam, The Netherlands
k
UC San Diego/VA San Diego Healthcare System, San Diego, CA, USA
l
INSERM U610, H^opital de la Salp^etriere, Paris, France
m
Centre des Maladies Cognitives et Comportementales, IM2A, ICM, Paris 6 University, France
n
Regional Memory Centre (CMRR), CHU Besanc¸on, Besanc¸on, France
oUniversity of Milan, Fondazione Ca Granda, IRCCS Ospedale Policlinico, Italy
p
Vita-Salute San Raffaele University, Milan, Italy
q
University of New South Wales, Sydney, Australia
rAlzheimer’s Association, Chicago, IL, USA
sDepartment of Molecular Neurosciences, UCL Institute of Neurology, UK
Abstract Introduction: The genetics underlying posterior cortical atrophy (PCA), typically a rare variant of Alzheimer’s disease (AD), remain uncertain.
Methods: We genotyped 302 PCA patients from 11 centers, calculated risk at 24 loci for AD/DLB and performed an exploratory genome-wide association study.
Results: We confirm that variation in/near APOE/TOMM40 (P5 6 ! 10214) alters PCA risk, but with smaller effect than for typical AD (PCA: odds ratio [OR]5 2.03, typical AD: OR 5 2.83, P5 .0007). We found evidence for risk in/near CR1 (P 5 7 ! 1024), ABCA7 (P5 .02) and BIN1 (P5 .04). ORs at variants near INPP5D and NME8 did not overlap between PCA and typical AD.
*Corresponding author. Tel.:11 020 3448 3011; Fax: ---.
Q4
E-mail address:j.schott@ucl.ac.uk
http://dx.doi.org/10.1016/j.jalz.2016.01.010
1552-5260/Ó 2016 The Alzheimer’s Association. Published by Elsevier Inc. All rights reserved.
Alzheimer’s & Dementia- (2016) 1-10
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109
Exploratory genome-wide association studies confirmed APOE and identified three novel loci:
rs76854344 near CNTNAP5 (P5 8 ! 10210OR 5 1.9 [1.5–2.3]); rs72907046 near FAM46A
(P5 1 ! 1029OR5 3.2 [2.1–4.9]); and rs2525776 near SEMA3C (P 5 1 ! 1028, OR5 3.3
[2.1–5.1]).
Discussion:We provide evidence for genetic risk factors specifically related to PCA. We identify three candidate loci that, if replicated, may provide insights into selective vulnerability and pheno-typic diversity in AD.
Ó 2016 The Alzheimer’s Association. Published by Elsevier Inc. All rights reserved.
Keywords: Posterior cortical atrophy; Alzheimer’s disease; Genetics; GWAS; Selective vulnerability; ApoE
1. Introduction Posterior
Q5 cortical atrophy (PCA) is a rare
neurodegenera-tive syndrome, typically a variant of Alzheimer’s disease (AD), although occasionally due to other pathologies including dementia with Lewy bodies, corticobasal degener-ation, and prion disease[1]. Patients with PCA present with combinations of cognitive problems attributable to posterior cortical dysfunction and in particular difficulties with higher level visual processing including simultanagnosia, optic apraxia, optic ataxia, and visual disorientation; other fea-tures may include dyslexia, dyscalculia, dysgraphia, and limb dyspraxia. In contrast with typical, amnestic AD, mem-ory is relatively spared until the disease becomes advanced. MR brain imaging in PCA typically shows parieto-occipital lobe atrophy with relative preservation of medial temporal lobe structures[2]; fluoro-deoxyglucose positron emission tomography (PET) shows prominent posterior cortical hypo-metabolism[3]; and in a single case study using the AV1451 tau PET tracer, posterior cortical tau deposition [4]. By contrast, PET imaging using amyloid-binding ligands typi-cally shows global amyloid deposition [3]. Aside from the imaging and cognitive differences, patients with PCA are typically younger than those with typical amnestic late-onset AD, usually with disease late-onset in the sixth or seventh decade[1]. PCA is almost invariably a sporadic disorder, and the risk factors for developing the syndrome are unknown. Understanding the genetic architecture of the PCA variant of AD may provide insights both into factors predisposing to young onset AD, as well as mechanisms underlying regional vulnerability in AD.
To date, only a few, single-center studies have addressed genetic risk for PCA [5–10], and due to the rarity of the syndrome all have been relatively small, the largest being a maximum of 81 cases [9]. Some, but not all, of these studies have suggested that despite their early-disease onset, patients with PCA may be less likely than expected to have an APOE E4 allele, the commonest risk factor for late-onset AD. Other studies have suggested that there may be differ-ences in some of the more recently identified genetic risks for AD in patients with PCA[9]. Recognizing the rarity of this AD variant, we formed an international consortium comprising eleven centers, using clinical diagnostic criteria
to define cases of PCA, with the principal aim of determining whether APOE E4 and genetic risks from recent genome-wide association studies (GWAS) of AD and dementia with Lewy bodies (DLB, see below) are the risk factors for the PCA variant of AD. In a second, exploratory analysis, we performed a pilot GWAS analysis to identify novel puta-tive genetic risk factors for PCA.
2. Methods
2.1. PCA patients and controls Q6
After an inaugural multidisciplinary meeting of PCA re-searchers[11], latterly formalized as the Alzheimer’s Asso-ciation’s International Society to Advance Alzheimer’s Research and Treatment (ISTAART) Professional Interest Area in Atypical AD and Associated Syndromes, an interna-tional collaborative group was established to assess genetic risk factors for PCA. Researchers identified individuals with PCA, in whom a deoxyribo nucleic acid (DNA) sample was available. Patients who the referring physician had diag-nosed with AD, had multidomain cognitive impairment ful-filling criteria for AD dementia, and had one or both of two published criteria for PCA, as proposed by Tang-Wai[5]and Mendez [12] (Table 1) were included. Additional data collected included gender, age at disease onset, age at death (where applicable), and whether there was molecular (cere-brospinal fluid or amyloid PET using locally defined ranges) evidence or pathologic confirmation of underlying AD pa-thology. Each site had appropriate local ethical approvals in place, and all participants gave informed written consent. Controls were from UK, USA, and Germany (see below).
2.2. Genetic and statistical analyses
DNA samples were analyzed at the MRC Prion Unit, Department of Neurodegenerative Disease, Institute of Neurology, UCL. PCA samples were genotyped on Illumina 660 arrays (n5 54, UCL cohort only) and OmniExpress ar-rays (n5 239, all cohorts); in total, 293 passed sample qual-ity control, implemented using PLINK. Controls were genotyped on Illumina 550 (n5 809, KORA F4 German, PMID: 16032514), OmniExpress (n5 1185, Geisinger US
110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231
http://www.geisinger.org), Illumina 2.5 M (n 5 1882, KORA F3 German), Illumina 5M (n5 1651, Framingham US, see acknowledgments), and Illumina 1.2 M (n 5 5020, WTCCC2 UK, see acknowledgments) arrays. Physical locations refer to the Feb 2009 (GRCh37/hg19) as-sembly. We excluded SNPs with a minor allele frequency (MAF), 1% (n 5 70,042 from OmniExpress case arrays); genotyping rate,99% (n 5 85,086 from OmniExpress case arrays); or Hardy-Weinberg Equilibrium (HWE) exact test P , 1023in controls. Cases with call rate,98% (n 5 6) as well as ethnic outliers (n5 2) were excluded after visual-ization of multidimensional scaling plots. Related and dupli-cate cases were removed by IBS/IBD calculation (n5 1) and re-examination of patient data, as they became available post genotyping (n 5 2). A Pi-Hat (proportion identity by descent) threshold of .0.1875 was used, which should exclude first and second degree relatives. Two duplicates removed due to later availability of patient data were also included within the six cases removed for low call rate thus bringing the total number of cases removed to nine. Owing to the multitude of different genotyping platforms, control comparisons were carried out sequentially and ex-clusions removed at each stage. A total of 840 KORAF4 German controls were originally genotyped; 17 with call rate,98%, six with Pi-Hat . 0.1875 and eight MDS out-liers were removed. A total of 1950 KORAF3 German con-trols were originally downloaded; three with call rate,98%,
57 with Pi-Hat . 0.1875, and eight MDS outliers were removed. A total of 1264 Geisinger US controls were origi-nally downloaded; two with call rate,98%, 69 with Pi-Hat .0.1875, and eight MDS outliers were removed. 2467 FHS US controls were originally downloaded; 16 with call rate ,98%, 793 with Pi-Hat .0.1875 and seven MDS outliers were removed. 5050 WTCCC2 UK controls were generated from available raw IDAT files; 26 with call rate,98%, four with Pi-Hat. 0.1875, and zero MDS outliers were removed (see Supplementary Table 1). All remaining cases and controls were finally visualized on an MDS plot (see
Supplementary Fig. 1), outlier detection was performed us-ing PLINK v1.07, and no further outliers were detected. IBS/ IBD estimation of the final cases and controls also lead to no further exclusions based on relatedness. Shapeitv2 was used, in conjunction with the 1000 Genomes Phase 1 Integrated variant set (b37 March 2012 release), to align all data rela-tive to the posirela-tive strand [13]. To avoid potential down-stream cross platform confusion, however, we removed any A/T or G/C transversions to phase each chromosome from each platform separately before imputation using Impute2 (v2.3.0). GTOOL (v0.6.6) was used to extract and collate samples into their respective cohorts for association testing [14]. Association testing was performed using SNPtest_v2.5-beta4 employing the frequentist (additive model) score method which involves weighting by the likeli-hood of each imputed genotype [15]. Four population
Table 1
Clinical criteria for PCA
Tang-Wai et al, 2004[5] Mendez et al, 2002[11]
Core features
Insidious onset and gradual progression
Presentation of visual complaints in the absence of significant primary ocular disease
Relative preservation of anterograde memory and insight early in the disorder
Disabling visual impairment throughout the disorder Absence of stroke or tumor
Absence of early parkinsonism and hallucinations Any of the following findings
Simultanagnosia with or without optic ataxia or ocular apraxia Constructional dyspraxia
Visual field defect
Environmental disorientation
Any of the elements of Gerstmann syndrome Supportive features
Alexia Presenile onset
Ideomotor or dressing apraxia Prosopagnosia
Investigations
Neuropsychological deficits referable to parietal and/or occipital regions
Focal or asymmetric atrophy in parietal and/or occipital regions on structural imaging
Focal or asymmetric hypoperfusion/hypometabolism in parietal and/ or occipital regions on functional
imaging.
Core diagnostic features (all must be present) Insidious onset and gradual progression
Presentation with visual complaints with intact primary visual functions
Evidence of predominant complex visual disorder on examination Elements of Balint’s syndrome
Visual agnosia Dressing apraxia
Environmental disorientation
Proportionally less impaired deficits in memory and verbal fluency Relatively preserved insight with or without depression
Supportive diagnostic features Presenile onset
Alexia
Elements of Gerstmann’s syndrome Ideomotor apraxia
Physical examination within normal limits Investigations
Neuropsychology: predominantly impaired perceptual deficits Brain imaging: predominantly occipitoparietal
abnormality (especially on functional neuroimaging) with relative sparing of frontal and mesiotemporal
regions.
J.M. Schott et al. / Alzheimer’s & Dementia- (2016) 1-10 3
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covariates derived from IBS/IBD analysis in PLINK were used in the association analysis[16,17]. The case-control as-sociation test statistic inflation factor was 1.06 (Supplementary Fig. 2). Any association statistics mentioned in the results section are shown with standard genomic control (PGC) corrected and uncorrected P values.
Post association-testing QC excluded markers with a MAF ,1% and departure from HWE, in both combined and any single control cohort P, 1024. We also excluded makers with SNPtestv2 derived “info metric” and “add info metric” below 0.9. The final autosomal analysis thus included 5.9 M markers.
We assessed whether the genetic risks for typical AD and PCA were different by comparing odds ratios (OR) between our study and those published in typical AD
[18]. We employed a Wald-type test, first calculating the standard error of the difference in log (OR) from the re-ported CIs. We then divided the empirical difference in log (OR) by its standard error, and thus derived a z-score and P value, relying on the approximate normality of log (OR) estimates from large samples. In the main analysis of candidate SNPs, we used a Bonferroni corrected asso-ciation threshold of P, .002 based on the testing of a lead SNP from 24 independent loci derived from studies of AD
[18] and DLB[19].
Although the typical samples sizes that are required to discover novel genome-wide significant risk factors in complex disorders are in the thousands, there are some precedents of strong genetic effects detected with small but phenotypically homogenous samples, including vari-ants at APOE in AD [20], PRNP in prion disease [21], and complement factor H in age related macular degener-ation [22]. We therefore performed an exploratory genome-wide association study using established method-ologies that account for population structure and with imputation of SNPs not present on the genotyping arrays. As PCA is a clinical syndrome that may be due to pathol-ogies other than AD, we also assessed the odds ratios at SNPs of interest in a subset of patients with biomarker/ pathological evidence for AD.
3. Results
3.1. Patients and demographics
A total of 302 samples fulfilling entry criteria to the study were available from eleven centers: University College London (n 5 94); Mayo Clinic, USA (n 5 77); University of California San Francisco, USA (n5 25); University of Sev-ille, Spain (n5 25); University of Manchester, UK (n 5 20); VU University, Netherlands (n5 17); University of California San Diego, USA (n5 16); INSERM, France (n 5 10); Univer-sity of Milan, Italy (n5 6); CHU Besanc¸on, France (n 5 6); and University of New South Wales, Australia (n5 6). Demo-graphics are shown inTable 2. All samples fulfilled Tang-Wai clinical criteria for PCA[5], and in the 225 samples for which data were available, 97.3% also fulfilled Mendez criteria[12];
41% of the cohort was male. Mean (6SD) age at symptom onset was 58.96 6.9 years, and 82.5% had young onset de-mentia, as defined by age at onset of,65 years. Thirty-four patients had died, with a mean age at death of 68.0 (67.7) years. Molecular or pathologic evidence for underlying Alz-heimer pathology was available for 82 (27%), of whom 52 had a CSF profile compatible with AD; 32 had a positive am-yloid PET scan; and 15 had autopsy proven AD. None had evidence for pathology or biomarkers for non-AD pathology. DNA from nine individuals failed array quality control, and statistical analyses were done on the remaining 293/ 302 samples (seeTable 2). We also considered the associa-tions in the sub-sample of 82 with biomarker/autopsy evidence for underlying AD pathology.
3.2. Comparisons at known genetic loci for AD and DLB Results of the genetic analysis of candidate risk factors for the whole PCA cohort are shown inTable 2. First, we consid-ered 24 SNPs known to be genetic risk factors in AD and/or DLB. The best proxy genotyped for the APOE E4 AD-risk allele, rs2075650, located on chromosome 19 in the TOMM40 gene and 13kb upstream of APOE, was identified as a strong risk factor for PCA (OR 2.03, [95% CI5 1.68– 2.46], P 5 6 ! 10214, PGC 5 3 ! 10213; Fig. 1 and
Fig. 2A). rs3818361 located on chromosome 1 in CR1 was also significantly associated (ORs 5 1.38 [1.14–1.67], P5 7 ! 1024, PGC5 1 ! 1023; Fig. 2B). rs3764650 in
ABCA7 (OR5 1.39 [1.07–1.8], P 5 .02, PGC5 .02) and Table 2
Clinical features and demographics
Total number of DNA samples received 302
Number (%) male 124 (41%)
Mean6 SD age at onset (y) 58.96 6.9
Number (%) with young onset dementia (onset,65 y) 249 (83%)
Number with biomarker/path evidence for AD* 82 (27%)
Number (%) with known age of death 34
Mean6 SD age at death (y) 67.96 7.7
9 samples failed genetic QC
Total number of DNA samples passing QC and entering analysis
293
Number (%) male 120 (41%)
Mean6 SD age at onset (y) 58.86 6.9
Number (%) with young onset dementia (onset,65 y) 243 (83%)
Number with biomarker/path evidence for AD* 77 (26%)
Number (%) with known age of death 33
Mean6 SD age at death (y) 67.86 7.8
*No individual with biomarker/path evidence for a non-AD diagnosis were included. 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475
rs744373 upstream of BIN1 (OR5 1.2 [1.01–1.43], P 5 .04, PGC5 .05) reached nominal significance but did not surpass
our Bonferroni corrected threshold of P, .002. Other candi-date SNPs showed no evidence of association.
In the subset of individuals with either biomarker (CSF or amyloid PET) or pathologic evidence for underlying AD (n 5 82), rs2075650 (at the APOE/TOMM40 locus, subse-quently referred to as APOE) was again identified as a risk
factor with a similar OR to the whole group (OR5 2.00 [1.39–2.89] P5 9 ! 1025, PGC5 1 ! 1024). rs3818361
(CR1) and rs3764650 (ABCA7) both showed nominally sig-nificant differences compared with controls (CR1 OR5 1.7 [1.20–2.41], P 5 .003, PGC 5 .004; ABCA7 OR 5 1.83
[1.17–2.86], P5 .009, PGC5 .01). There was no evidence
for an effect of BIN1 (OR5 1.08 [0.76–1.52], P 5 .65) in the biomarker cohort.
web 4C =FPO web 4C =FPO
Fig. 1. Manhattan plot of autosomes with threshold for genome-wide significance (P, 5 ! 1028) indicated by the red line. Four loci achieved statistical
sig-nificance at APOE (chromosome 19), SEMA3C (chromosome 7), FAM46A (chromosome 6), and CNTNAP5 (chromosome 2).
web 4C =FPO web 4C =FPO
Fig. 2. SNPannotation and proxy (SNAP) plots for five regions of interest. These plots illustrate the statistical evidence of association at a locus together withQ1
information about nearby genes and linkage disequilibrium between the most strongly associated SNP and its neighbors on the chromosome.
J.M. Schott et al. / Alzheimer’s & Dementia- (2016) 1-10 5
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3.3. Comparing risk for PCA to typical AD
Comparing odds ratios and the nominal risk conferred in the whole PCA cohort against the most recently published meta-meta studies of typical AD [16], the effect size seen at the APOE locus was significantly less strong in PCA than for typical AD (PCA: OR 2.03, typical AD: OR 2.83, P5 .0007, PGC5 .001, see methods). Although there was
no evidence for risk in PCA vs controls, the risk effects at rs35349669 in INPP5D (P 5 .02, PGC 5 .02) and
rs2718058 upstream of NME8 (P 5 .03, PGC5 .04) both
were nominally different in PCA than typical AD. 3.4. Exploratory GWAS
As several different array platforms were used in the study, only 210,670 SNPs were genotyped across all samples. In this set, there was little evidence of inflation in the association test statistic (l 5 1.05). Only the proxy for the APOE E4 AD-risk allele, rs2075650, achieved genome-wide significance. We went on to analyze 5.9-M SNPs in the imputed data set. Aside from chromosome 19 (APOE locus), three loci on chromosomes 7, 2, and 6 respec-tively were of interest (Fig. 1). rs2525776 on chromosome 7, upstream of SEMA3C (OR 3.3 [2.1–5.1], P5 1.4 ! 1028, PGC5 4 ! 1028; Fig. 2C); rs76854344 on chromosome
2, upstream of CNTNAP5, (OR 1.9 [1.5–2.3], P 5 8.0 !
10210, PGC5 2 ! 1029;Fig. 2D), and rs72907046 on
chro-mosome 6, downstream of FAM46A (OR 3.2 [2.1–4.9], P5 1.1 ! 1029, PGC5 3 ! 1029;Fig. 2E) were all
asso-ciated with PCA. Restricting the analysis to the 82 individ-uals with biomarker/pathology evidence for underlying AD pathology, the corresponding odds ratios were similar: 3.8 [1.8–8.2], P 5 2.8 ! 1024 for rs2525776; 1.8 [1.2– 2.7], P5 2.1 ! 1023for rs76854344; and 2.5 [1.0–6.1], P5 2.7 ! 1022)for rs72907046. None of these three lociQ7
showed any evidence of association with typical AD on the IGAP AD-risk GWAS meta-analysis. A full list of sug-gestive associations P , 1024 based on the SNPs repre-sented on case and control arrays (the intersection SNPs) is available in a Supplementary Table 2, and the entire data set is available at the NHGRI-EBI GWAS Catalog.
4. Discussion
We report findings from a consortium to study genetic risk factors in PCA, a rare predominantly early-onset cogni-tive disorder characterized by progressive and dispropor-tionately posterior cortical dysfunction and atrophy, and usually associated with AD-type pathology. Our primary aim was to explore the relationship between PCA and a pre-determined list of candidate SNPs derived from studies of
Table 3
Results of the main analysis for candidate SNPs which were discovered in typical AD (ref[16], except *Seshadri et al. JAMA 2010:303; 1832–40,yHollingworth
et al. Nat Genet 2011:43:429–35) or DLB[17]
Candidate
SNP Chr Nearest gene
Typical AD/DLB OR
PCA vs control
P value PCA or PCA or CI
P Value for comparison of OR (PCA vs typical AD)
Cases MAF Control MAF rs3818361 1 CR1 1.17 6.71E204 1.38 (1.14–1.67) .09 0.24 0.19 rs744373 2 BIN1 1.17 0.04 1.20 (1.01–1.43) .77 0.32 0.28 rs35349669 2 INPP5D 1.08 0.21 0.89 (0.75–1.04) .02 0.46 0.49 rs6825004 4 SCARB2 0.78 0.42 0.92 (0.77–1.1) .38 0.29 0.31 rs7687945 4 SNCA 0.75 0.92 1.00 (0.85–1.18) .08 0.49 0.49 rs190982 5 MEF2C 0.93 0.66 0.99 (0.83–1.18) .50 0.40 0.40 rs10948363 6 CD2AP 1.10 0.67 0.97 (0.8–1.17) .19 0.27 0.27 rs11767557 7 EPHA1 0.90 0.39 0.92 (0.74–1.13) .73 0.19 0.20 rs2718058 7 NME8 0.93 0.17 1.12 (0.95–1.33) .03 0.39 0.36 rs1476679 7 ZCWPW1 0.91 0.73 1.05 (0.88–1.25) .12 0.31 0.30 rs11136000 8 CLU 0.87 0.27 0.91 (0.77–1.09) .58 0.37 0.40 rs28834970 8 PTK2B 1.10 0.27 1.10 (0.93–1.3) .98 0.37 0.35 rs10838725 11 CELF1 1.08 0.98 1.01 (0.84–1.2) .45 0.32 0.31 rs670139y 11 MS4A4E 1.08 0.77 0.97 (0.82–1.14) .19 0.40 0.41 rs983392 11 MS4A6A 0.90 0.71 1.04 (0.88–1.22) .09 0.42 0.41 rs3851179 11 PICALM 0.87 0.39 0.94 (0.79–1.11) .40 0.35 0.37 rs11218343 11 SORL1 0.77 0.57 0.81 (0.4–1.65) .88 0.01 0.02 rs17125944 14 FERMT2 1.14 0.73 0.94 (0.7–1.27) .21 0.08 0.09 rs10498633 14 SLC24A4 RIN3 0.91 0.14 0.86 (0.7–1.06) .62 0.20 0.23 rs3764650 19 ABCA7 1.20 0.02 1.39 (1.07–1.8) .28 0.12 0.09 rs2075650 19 APOE 2.83 6.24E214 2.03 (1.68–2.46) .0007 0.25 0.14 rs3865444y 19 CD33 0.91 0.69 0.95 (0.8–1.14) .61 0.30 0.31 rs597668* 19 EXOC3L2 BLOC1S3 MARK4 1.18 0.59 1.04 (0.84–1.3) .30 0.17 0.16 rs7274581 20 CASS4 0.88 0.52 1.12 (0.85–1.48) .09 0.10 0.09
OR, odds ratio; MAF, minor allele frequency; CI, confidence interval.
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typical AD and DLB. Our main findings are the identifica-tion of genetic risk for PCA at some of the known AD-risk loci, but not at the two DLB-risk loci that were tested (Table 3). We demonstrate PCA-risk association with vari-ants in or near APOE, CR1, ABCA7 and BIN1 in the whole cohort, but only those at APOE, CR1, and ABCA7 remain nominally significant in the small molecularly defined sub-group. We also show evidence for nonoverlapping CIs of ge-netic risk at APOE, INPP5D, and NME8. Although this is by far the largest study of PCA to date, our relatively small sam-ple size remains underpowered to detect genome-wide significant associations at risk loci with small effect sizes, such as those shown to associate in AD-risk GWAS. Howev-er, our exploratory GWAS nominates the three novel loci, SEMA3C, CNTNAP5, and FAM46A, which achieved genome-wide significance, as potential genes of interest in PCA.
Although still small for genetic studies, this collection is the result of a global collaborative effort and is considerably larger than any previous studies of PCA reporting varying evidence that APOE is a risk factor [5–10]. Although the vast majority of samples in previous studies overlap with the present study, individual studies comprise,w25% of the current total. We found robust genetic association at APOE but with an odds ratio significantly smaller than those seen in typical AD. APOE is the best established and strongest risk factor for sporadic AD and has been associated not only with increased risk per se, but also with earlier disease onset[23], the rate of hippocampal atro-phy[24]and more memory led disease. However, the situa-tion is more complex in early-onset AD which is associated both with a greater proportion of nonamnestic presentations, and perhaps a relatively reduced proportion of APOE E4 car-riers[25]. Studies investigating APOE risk for PCA (or as it was sometimes previously defined, biparietal AD[6]), have shown mixed results, perhaps due to differences in case defi-nition, and the limited sample size of each study. In this study of PCA, by far the largest yet published, we confirm that APOE is a risk factor for PCA, but that it is a weaker risk factor than for typical AD.
Addressing risk factors other than APOE, the largest sin-gle previous study—which included samples also used in this analysis—found nominally significant association with SNPs in or near CLU, BIN1, and ABCA7[9]. We found ev-idence of association in the same direction for the SNP at ABCA7 both in the whole cohort and those with biomarker evidence for AD; for BIN1 in the whole cohort alone; but could not confirm that SNPs near to CLU are risk factors. Aside from APOE, only variants in CR1 surpassed our statis-tical threshold for multiple testing in the whole cohort; these variants also showed nominal significance in the biomarker positive subgroup. Estimates of effect size were greater in PCA than those in typical AD at each associated locus aside from APOE. Although these differences were only nomi-nally statistically significant, the four risk loci we report are among the strongest known common genetic risk factors
for typical AD (Table 3). CR1 has multiple functions including the regulation of complement and phagocytosis of immune complexes and pathogens, which are increas-ingly though to be relevant to AD pathogenesis [26]. ABCA7 may play a role in AD through regulation of phago-cytosis or lipid metabolism. BIN1 mechanisms in AD are un-clear but may be involved in endocytosis and the recycling of endocytic vesicles[27].
Although they did not confer significant alteration of PCA risk, we found that odds ratio confidence intervals for SNPs at or near to INPP5D and NME8 in PCA did not over-lap those of typical AD and showed directionally opposite effects, INPP5D was identified as a risk factor for AD in a recent large meta-analysis and plays an important role in a number of inflammatory processes. There is little evidence for the function of NME8 in the central nervous system, although a role in modification of oxidative stress has been proposed[28]. Although these findings were only nominally significant and need independent replication, they do raise the possibility that syndromic variants of AD may be differ-entially associated with alterations in certain risk genes, perhaps through altered responses to inflammation or stress. The results of our exploratory genome-wide study impli-cate three potential strong risk loci, near to CNTNAP5, FAM46A, and upstream of SEMA3C. The regions of strong LD with these associations did not include directly geno-typed SNPs across all platforms, and therefore false-positive associations related to differential accuracy of imputation between case and control arm of the study remain possible. With the caveat that these findings must therefore be considered preliminary and require follow-up replication in an independent sample and by direct genotyping, it is notable that all three genes have roles in processes poten-tially relevant to PCA. Contactin-associated protein-like 5 gene (CNTNAP5) belongs to a subgroup of the neurexin family of multidomain transmembrane proteins involved in cell adhesion and intercellular communication in the central nervous system and has been implicated as a risk factor for bipolar disorder and autism spectrum disorders[29]. Family with sequence similarity 46, member A1 (FAM46A), origi-nally C6orf37, is preferentially expressed within the neural retina[30] and has been implicated in cell signaling path-ways related to retinal neurodegeneration [31]. Class III semaphorins including Semaphorin 3C (SEMA3C) have been examined as potential modifying factors in neurode-generation through interactions with plexins and neuropi-lins. SEMA3C has been identified as a chemotrophic molecule influencing attractive guidance for cortical axon development[32]; the expression of SEMA3C and its recep-tors have been shown to influence the maturation of the vi-sual system [33]; and SEMA3C is also expressed in the hippocampus, where it has a role in influencing the afferent connections of the developing hippocampus and in partic-ular the ingrowth of septo-hippocampal connections [34], the major cholinergic connections implicated in learning and memory [35]. Finally, SEMA3C expression has been
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shown to correlate with functional network connectivity within the brain[36]. Although at this stage speculative, it is possible therefore that perhaps subtle differences in cortical development might influence where pathology starts and/or how it spreads through the brain if a neurodegenera-tion process is initiated later in life. The fact that all three of these novel genetic risks showed nominal associations in the relatively small subset of individuals with biomarker evi-dence for AD and the absence of similar evievi-dence of associ-ation in any of these genes with IGAP studies of typical AD suggest that if confirmed, these loci may be specific risks for PCA due to Alzheimer’s disease.
The main limitation of our study is the necessarily modest sample size of this very rare disorder, noting that the case numbers presented here were only achievable through the establishment of an international consortium. We plan to continue to collect further samples to allow for replication in due course. Based on standard power cal-culations in case-control studies, even in the favorable sit-uation of completely accurate imputation of the functional SNP we are only adequately powered to detect effect sizes of OR .1.5, for common candidate SNPs, and the only common genetic risk factor of this strength in typical AD is APOE. We made comparisons with studies of patients diagnosed with typical AD; however, these patients and/or studies are different in multiple ways including the genotyping platforms used, later age at clinical onset, potential pathologic heterogeneity, and most probably dif-ferences in geographical location, all of which could confound the comparison. Although PCA is underpinned by AD pathology in most cases, we only had evidence for underlying AD in a proportion (w1/4), and we cannot confirm that the genetic risks we have determined are spe-cific for the AD variant of PCA rather than the syndrome of PCA or for young onset AD. However, allowing for the fact that the confidence intervals are inevitably large, it is notable that the estimates for the odds ratios for APOE, CR1, and ABCA7, and the putative genes identified in our exploratory GWAS were similar or larger in the proportion with molecular evidence for AD, suggesting that the risk we identify are likely to be for the AD variant of PCA, rather than for the syndrome per se.
One of the major outstanding issues in neurodegenerative disease research is an explanation for the often very striking phenotypic heterogeneity underpinned by the same broad core pathology. Possibilities for phenotype modification include demographic and environmental factors, including age at onset, or perhaps more likely complex gene and/or environment interactions and/or factors related to the mis-folded proteins and their propagation, tissue or network selectivity and toxicity. The results of this study suggest that subtle differences in established risk factors may be associated with some of this heterogeneity and provide test-able suggestions for novel genes that may influence the development of the hippocampal and visual system, which may influence the development of the PCA phenotype
rela-tive to other syndromes. If confirmed in future studies, next generation sequencing may be useful in determining whether these findings might be underpinned by rare variants with large effect sizes. More broadly, genetic investigation of well-phenotyped AD variants may provide important in-sights into disease biology in typical AD.
Acknowledgments
We acknowledge the support of the Alzheimer’s Association’s International Society to Advance Alzheimer’s Research and Treatment (ISTAART), Alzheimer’s Research UK, the UK Alzheimer’s Society and the Raena Frankel-Pollen Fund. This work was supported by the NIHR UCL/UCLH Biomed-ical Research Centre, the NIHR Queen Square Dementia Biomedical Research Unit, the Wolfson Foundation, and the UK Medical Research Council. The Dementia Research Centre is an Alzheimer’s Research UK Coordinating Center. N.C.F. and M.N.R. are NIHR senior investigators. J.T.B. re-ceives funding from the UK Alzheimer’s Society. This work was also supported by the Medical Research Council Demen-tias Platform UK (MR/L023784/1 and MR/009076/1); Alz-heimer’s Research UK (Senior Research Fellowship to S.C., Research Fellowship to T.S.); the Dunhill Medical Trust (grant number R337/0214 to S.C.); ESRC/NIHR (ES/K006711/1 to S.C.); EPSRC (EP/M006093/1 to S.C.); Alzheimer’s Associa-tion (MNIRGD 2013 award to M.M.C.); Mayo Alzheimer’s Disease Research Center (P50 AG0016574 to D.W.D., N.E.-T., N.R.G.-R., B.F.B., D.S.K., R.C.P.); National Institute on Aging (R01 AG032990 and U01 AG046139 to N.E.-T.; R01-AG045611 to G.D.R.; and P50-AG023501 to B.L.M. and G.D.R.); National Institute of Neurological Disorders and Stroke (R01 NS080820 to N.E.-T.); John Douglas French Alzheimer’s Foundation (to G.D.R., B.L.M.); andState of Cal-Q8
ifornia Department of Health Services Alzheimer’s Disease Research Centre of California (04-33516, B.L.M.). D.G. and E.S. are supported by Italian Ministry of Health (Ricerca Cor-rente). W.M.v.d.F., P.S. receive funding from PERADES-JPND. E.L. receives funding from Stichting Dioraphte. Case ascertainment in Australia was funded by grants from the Na-tional Health and Medical Research Council of Australia (NHMRC program grant #1037746) and the Australian Research Council Centre of Excellence in Cognition and its Disorders Memory Node (#CE110001021); G.H. is a NHMRC senior principal research fellow (#1079679). We would like to thank the participants in the Framingham, Geisinger, WTCCC, and KORA studies used as controls in this article. The Fra-mingham Heart Study is conducted and supported by the Na-tional Heart, Lung, and Blood Institute (NHLBI) in collaboration with Boston University (Contract No. N01-HC-25195). This article was not prepared in collaboration with investigators of the Framingham Heart Study and does not necessarily reflect the opinions or views of the Framing-ham Heart Study, Boston University, or NHLBI. SHARe Illu-mina genotyping was provided under an agreement between Illumina and Boston University. Samples and data in this study
842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963
were also provided by the Geisinger MyCode Project. Funding for the MyCode project was provided by a grant from Commonwealth of Pennsylvania and the Clinic Research Fund of Geisinger Clinic. Funding support for the genotyping of the MyCode cohort was provided by a Geisinger Clinic operating funds and an award from the Clinic Research Fund. The data sets used for the analyses described in this article were obtained from dbGaP at http://www.ncbi. nlm.nih.gov/gap through dbGaP accession number phs000381.v1.p1. This study also makes use of the data gener-ated by the Wellcome Trust Case Control Consortium. A full list of the investigators who contributed to the generation of the data is available from www.wtccc.org.uk. Funding for the project was provided by the Wellcome Trust under award 076113.
Q9
Supplementary data
Supplementary data related to this article can be found online athttp://dx.doi.org/10.1016/j.jalz.2016.01.010.
RESEARCH IN CONTEXT
1. Systematic review: Reviewing the literature for pub-lications investigating the genetics of posterior cortical atrophy (PCA), there is conflicting evidence for the role of APOE in the PCA variant of Alz-heimer’s disease AD), and limited evidence for the more recently identified genetic risks for AD. 2. Interpretation: Through the establishment of an
inter-national consortium to create the largest study exploring the genetics of PCA to date, we demon-strate that (1) APOE is a risk factor for PCA but con-fers a smaller risk than for typical AD; (2) some of the genetic risks for typical AD are also associated with PCA risk; and (3) nominate three novel risk loci for PCA.
3. Future directions: These data provide clear directions and testable hypotheses for future studies, including (1) the establishment of a replication cohort and (2) investigation of the identified genes as factors influ-encing selective vulnerability in AD.
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