Search tips
Search criteria 


Logo of plosonePLoS OneView this ArticleSubmit to PLoSGet E-mail AlertsContact UsPublic Library of Science (PLoS)
PLoS One. 2013; 8(4): e58618.
Published online 2013 April 2. doi:  10.1371/journal.pone.0058618
PMCID: PMC3614978

SORL1 Is Genetically Associated with Late-Onset Alzheimer’s Disease in Japanese, Koreans and Caucasians

Mathias Toft, Editor


To discover susceptibility genes of late-onset Alzheimer’s disease (LOAD), we conducted a 3-stage genome-wide association study (GWAS) using three populations: Japanese from the Japanese Genetic Consortium for Alzheimer Disease (JGSCAD), Koreans, and Caucasians from the Alzheimer Disease Genetic Consortium (ADGC). In Stage 1, we evaluated data for 5,877,918 genotyped and imputed SNPs in Japanese cases (n = 1,008) and controls (n = 1,016). Genome-wide significance was observed with 12 SNPs in the APOE region. Seven SNPs from other distinct regions with p-values <2×10−5 were genotyped in a second Japanese sample (885 cases, 985 controls), and evidence of association was confirmed for one SORL1 SNP (rs3781834, P = 7.33×10−7 in the combined sample). Subsequent analysis combining results for several SORL1 SNPs in the Japanese, Korean (339 cases, 1,129 controls) and Caucasians (11,840 AD cases, 10,931 controls) revealed genome wide significance with rs11218343 (P = 1.77×10−9) and rs3781834 (P = 1.04×10−8). SNPs in previously established AD loci in Caucasians showed strong evidence of association in Japanese including rs3851179 near PICALM (P = 1.71×10−5) and rs744373 near BIN1 (P = 1.39×10−4). The associated allele for each of these SNPs was the same as in Caucasians. These data demonstrate for the first time genome-wide significance of LOAD with SORL1 and confirm the role of other known loci for LOAD in Japanese. Our study highlights the importance of examining associations in multiple ethnic populations.


Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive dysfunction and memory loss. Multiple rare mutations in APP, PSEN1, PSEN2 and SORL1 account for most cases of early-onset autosomal dominant AD [1], [2]. Risk of late-onset AD (LOAD), the most common type of dementia in the elderly, is associated with complex interactions between genetic and environmental factors. Until recently, APOE was the only unequivocally recognized major susceptibility gene for LOAD [1], [3]. Several genome-wide association studies (GWAS) each including more than 5,000 Caucasians identified genome-wide significant associations for LOAD with nine other loci including ABCA7, BIN1, CD2AP, CD33, CLU, CR1, EPHA1, MS4A gene cluster, and PICALM [4], [5]. To our knowledge, no large GWAS for LOAD has been performed in any Asian population. Because there is a possibility that there exist ethnic-specific LOAD susceptibility variants, we carried out a large-scale GWAS to confirm associations at known loci and identify novel loci for LOAD using a three-stage design including a discovery Japanese cohort and replication cohorts of Japanese, Korean and Caucasian subjects.



Japanese datasets

Clinically defined subjects were recruited by the Japanese Genetic Study Consortium of Alzheimer’s Disease (JGSCAD: principal investigator, Y.I.) [6], [7]. Probable AD cases were ascertained on the basis of the criteria of the National Institute of Neurological and Communicative Disorders, and Stroke-Alzheimer’s Disease and Related Disorders (NINCDS/ADRDA) [8]. The Mini-Mental State Examination [9], Clinical Dementia Rating [10], and/or Function Assessment Staging [11] were primarily used for evaluation of cognitive impairment. Elders living in an unassisted manner in the local community with no signs of dementia were used as controls. DNA was extracted from peripheral blood leukocytes using standard protocols [6]. For the purpose of this study, the Stage 1 genome-wide association study (GWAS) dataset included 2024 subjects (1008 AD cases and 1016 controls) and the Stage 2 dataset included 1870 subjects (885 AD cases and 985 controls).

Korean dataset

A total of 339 subjects with AD were recruited at the Samsung Medical Center in Seoul, Korea. All AD subjects fulfilled NINCDS-ADRDA criteria for probable AD [8]. These subjects underwent a clinical interview and neurological examination that were previously described [12]. The absence of secondary causes of cognitive deficits was assessed by laboratory tests including complete blood count, blood chemistry, vitamin B12/folate, syphilis serology, and thyroid function tests. Conventional brain MRI scans (T1-weighted, T2-weighted, and FLAIR images) confirmed the absence of territorial cerebral infarctions, brain tumors, and other structural lesions. Healthy control subjects (n = 1,129) ages 55 to 85 years were recruited from routine health examination at the same location and showed no evidence of cognitive dysfunction.

Alzheimer Disease Genetics Consortium dataset

Summarized information from tests of genetic association of AD with SNPs located in the candidate gene regions was culled from a recent large GWAS conducted by the Alzheimer Disease Genetics Consortium (ADGC) [5]. Results were computed for SNPs throughout the genome in a sample composed of 11,840 AD cases and 10,931 cognitively normal elders from 15 independent Caucasian data sets. Details of the quality control and statistical analysis procedures and genetic models has been published elsewhere [5].

This study was approved by the Boston University Institutional Review Board, Institutional Review Board of Niigata University, and the Institutional Review Boards of all participating institutions. Written informed consent was obtained from all participants. Next of kin, carer takers or guardians consented on the behalf of participants whose capacity to consent was compromised. All subjects were anonymously genotyped. The basic demographics of the cases and controls before QC in each dataset are presented in Table 1.

Table 1
Sample size and characteristics of the discovery and replication datasets.


GWAS genotyping was performed in the Stage 1 sample using Affymetrix GeneChip 6.0 microarrays containing 909,622 SNPs. Applied Biosystems’ (ABI) TaqMan Assays were used to genotype individual SNPs in the Japanese and Korean replication cohorts. APOE genotypes in the Japanese and Korean samples were determined by haplotypes derived from rs7412 and rs429358 which were genotyped using TaqMan Assays. Details of APOE genotyping in each ADGC dataset were described previously [13].

Quality Control and Population Substructure

In the Stage 1 sample, SNPs with a genotype call rate (GCR) <95%, a minor allele frequency (MAF) <0.05, or significant deviation from the Hardy-Weinberg equilibrium (HWE) in controls (P<10−6) were excluded. After excluding 83,673 low quality and 298,304 low frequency SNPs, we removed 196 subjects with a GCR <95% and 41 subjects whose gender as determined by analysis of X-chromosome data using the PLINK program (ver. 1.06) [14] was inconsistent with the reported gender. The same QC procedures were applied to the Japanese and Korean replication datasets. We examined population substructure in the GWAS dataset by analyzing tagging SNPs from the genome-wide panels using the smartpca module from EIGENSTRAT software [15] in a manner described previously [5]. Subsequently, we excluded three subjects who were cryptically related to other subjects in the dataset and 49 individuals who were population outliers. The strength of association of the top 10 principal components (PCs) was tested with AD status. The first three PCs were nominally associated with AD status. A total of 574,828 SNPs and 1,735 subjects comprising 891 cases and 844 controls passed the QC and were used for imputation and in further statistical analyses.

Genotype Imputation

Genotypes for all SNPs in Japanese and Caucasians were imputed with the Markov Chain haplotyping (MaCH) software [16] using reference haplotypes in the 1000 Genomes database (version released in February 2012 for Japanese datasets and version released in December 2010 for Caucasian datasets). This procedure also filled in missing data for the genotyped SNPs. Imputation quality was determined as R2, which estimates the squared correlation between imputed and true genotypes. We applied threshold criteria for quality control assessment of imputed SNPs (R2 ≥0.8) as recommended for 1000 Genomes imputed data using the IMPUTE2 program [17]. Genotype probabilities for 5,877,918 genotyped and reliably imputed SNPs with a minor allele frequency (MAF) >0.02 were included in the Japanese GWAS.

Statistical analysis

Genotyped and imputed SNPs were tested for association with AD in the Stage 1 dataset using a logistic generalized linear model (GLM) controlling for age-at-onset (cases)/age-at-exam (controls), sex and the first three principal components from analysis of of population substructure. Stage 1 analyses were also performed based on a model adjusting for these covariates and the number of APOE ε4 alleles. SNPs in the APOE region (between map positions 45,000 kb and 45,800 kb on chromosome 19) were also tested for association in ε3/ε3 and ε3/ε4 subgroups. Genotyped SNPs were coded as 0, 1, or 2 according to the number of minor alleles under the additive genetic model. For imputed SNPs, a quantitative estimate between 0 and 2 for the dose of the minor allele were used to incorporate the uncertainty of the imputation estimates. All analyses were performed using PLINK. SNPs attaining a P value below 5×10−5 were considered for replication in Stage 2. Initially, only one SNP per region was tested in the replication sample to minimize the penalty for multiple testing. Additional SNPs from regions meeting the signifcance threshold in the replication sample were also evaluated. SNPs with a P value below 1×10−5 in the combined Stages 1 and 2 samples and nominally significant in Stage 2 (P<0.05) were advanced to Stage 3.

SNP association results obtained from individual datasets were combined by meta-analysis using the inverse variance method implemented in the software package METAL ( [18]. An additive model was assumed and the association results across datasets were combined by summing the regression coefficients weighted by the inverse variance of the coefficients. The meta-analysis P-value of the association was estimated by the summarized test statistic, after applying a genomic control within each individual study. Effect sizes were weighted by their inverse variance and a combined estimate was calculated by summing the weighted estimates and dividing by the summed weights.


The quantile-quantile plot indicated limited genomic inflation (λ = 1.04in the Stage 1 GWAS results (Fig. S1). A total of 125 SNPs from seven distinct regions showed evidence of association with P<10−4 (Table S1, Fig. S2). In addition to APOE SNP rs429358 (P = 2.46×10−49, OR [95% CI]  = 5.5 [4.4–6.9]), 12 other SNPs in the APOE region were associated with LOAD at the genome-wide significance level of P<5.0×10−8. The two most significant results in this group of SNPs were rs12610605 (PVRL2: P = 1.38×10−13, OR [95% CI]  = 1.8 [1.5–2.0]) and rs62117161 (between CEACAM16 and BCL3: P = 3.46×10−12, OR [95% CI]  = 0.47 [0.38–0.58]). Since imputation in the APOE region using the 1000 Genomes reference panel is unreliable [6], we genotyped nine SNPs from this region, spanning multiple linkage disequilibrium (LD) blocks (Fig. S3) and that were nominally significant in the APOE ε3/ε3 subgroup, in the Japanese discovery and replication samples using TaqMan assays (Table S2). Genome-wide significant results were obtained for five of these SNPs, but only the association with PPP1R37 SNP rs 17643262 remained nominally significant after adjustment for the number of APOE ε4 alleles (P = 3.96×10−4) or in analyses stratified by APOE genotype (ε3/ε3: P = 0.01; ε3/ε4: P = 0.0016).

SNPs from six other distinct chromosomal regions met Stage 2 follow-up criteria (P<5×10−5) and the top SNP from each region was genotyped in an independent Japanese sample (Table 2). Two SNPs were nominally significant in the replication sample, however the effect direction for KIAA0494 SNP rs7519866 differed from the discovery sample. Modest evidence for replication was observed only with SORL1 SNP rs4598682 (P≤0.05). Subsequently, we selected an additional four SORL1 SNPs (rs3781834, rs2282647, rs17125523, and rs3737529) for testing in the Japanese replication sample that were among the most significant in the basic or extended models in the discovery sample (Table S1) and not in LD with rs4598682 (r2<0.2, Figure S4). Two of these SNPs (rs3781834 and rs17125523) were chosen also because they were genotyped in the discovery sample and thus would minimize the effects of potential imputation artifacts in meta-analysis of the two Japanese samples. Highly significant results were obtained for SORL1 SNPs rs4598682 (P = 9.51×10−6), rs3781834 (P = 7.33×10−7), rs17125523 (P = 5.51×10−6), and rs3737529 (P = 4.14×10−6) after combining results from the discovery and replication samples (Table S3).

Table 2
Top-ranked genome-wide association results in the Japanese discovery (Stage 1) sample (P<2.5×10−5) and their replication in Japanese (Stage 2).

These four SORL1 SNPs showing significant association in the combined samples from Stages 1 and 2 were considered for further replication in Stage 3. We added rs11218343 to this stage of the analysis because it was the most significant SORL1 SNP in the large Caucasian dataset (P = 1.0×10−7), a result which emerged after pooling the Caucasian discovery GWAS sample and unpublished data in the replication sample from our previously published GWAS [5]. These five SNPs were subsequently evaluated in Stage 3 by meta analysis including the Stage 1 and 2 Japanese, Korean and ADGC Caucasian datasets. SNPs rs11218343 (P = 2.20×10−9) and rs3781834 (P = 9.90×10−9), attained genome-wide significance in the sample of datasets from all stages (Table 3, Fig. 1). There was modest evidence of replication for rs17125523 (meta P = 3.30×10−6) and rs 3737529 (meta P = 5.10×10−6). Although the allele frequencies for the top SNPs were very different between the Asian (MAF >0.2) and Caucasian (MAF <0.05) samples (Table 3), there was no evidence of heterogeneity in the magnitude of the odds ratios or effect direction among the population groups (P>0.15, Fig. 2). There was no apparent association in the comparably smaller Korean dataset; however, the direction of the effect for each SNP was the same as in the Japanese and Caucasian datsets.

Figure 1
Regional association plot for the SORL1 region on chromosome 11 in the three-stage design.
Figure 2
Forest plots of the two most strongly associated SNPs, rs3781834 (A) and rs11218343 (B), in the SORL1 region showing the strength and pattern of significance in the Japanese discovery and each replication dataset in the model of adjusting for population ...
Table 3
Meta-analysis of top-ranked association results with SORL1 in Japanese, Korean, and Caucasian datasets.

Next, we investigated whether robust genetic associations for LOAD reported previously in Caucasians [4], [5] generalize to Japanese. After correcting for 15 tests, SNPs rs3851179 located approximately 90 kb upstream from PICALM (P = 1.71×10−5) and rs744373 located approximately 30 kb upstream from BIN1 (P = 1.39×10−4) were significantly associated with LOAD risk in the Japanese Stage 1 dataset (Table 4). Nominally significant associations were also observed for SNPs in CR1, CLU, and ABCA7. Of the eight SNPs tested in the small Korean sample, nominally signficant results (P<0.05) were obtained for one SNP in CLU and PICALM, each with the same pattern of association and comparable effect size as in Japanese.

Table 4
Association of LOAD in Asians with SNPs showing genome-wide significance in Caucasians.


Our multi-stage GWAS of LOAD identified for the first-time genome-wide significant association with SORL1. Genetic association with SORL1 was first established in a study focused on genes encoding proteins involved in vacuolar protein sorting [19]. Most, but not all, subsequent studies in Caucsians replicated this finding (summarized in Alzgene database: Confirmatory evidence of association with SORL1 SNPs has also been reported in comparatively small samples of Chinese and Japanese (reviewed in [20]). These findings are independent of previous candidate gene studies of SORL1 in Japanese (two subjects in common) and with Caucasians in the Rogaeva et al. study [19] (less than 2% overlap).

The two genome-wide significant SORL1 SNPs, rs11218343 and rs3781834 are located at chromosome positions 121,435,587 base pairs and 121,445,940 base pairs, respectively, and thus between the two previously reported strongly associated 3-marker haplotypes that extend upstream from rs641120 (121,380,965 base pairs) and downstream from rs1699102 (121,456,962 base pairs) [19]. A recent meta-analysis including more than 30,000 Caucasian and Asian subjects demonstrated that multiple SORL1 SNPs in distinct regions are associated with AD [20], a finding substantiated in an association study of SORL1 SNPs with brain MRI traits in LOAD families [21]. Further analysis of our large Caucasian sample suggests that the association peak at rs3781834 is independent of at least one of the two distinct haplotypes previously associated with AD in an independent sample of non-Hispanic Caucasians, Caribbean Hispanics and Israeli-Arabs (Fig. S5) [19], Since all of the SNPs at the association peaks reported in this study and previously are intronic, functional studies are required to determine the identity of pathogenic variants at these locations.

Remarkably, the less frequent alleles at rs11218343 and rs3781834 are protective in both Japanese and Caucasian datasets with very similar odds ratios (range 0.74 to 0.83) despite the fact that these alleles are much rarer in Caucasians (4% and 2%, respectively) than in Japanese (34% and 23%, respectively). The rarity of these SNPs in Caucasians, as well as allelic heterogeneity, may explain why SORL1 did not previously emerged as a genome-wide significant AD locus in much larger GWAS [4], [5]. Given the discovery sample size, effect size (odds ratio [OR]  = 0.74) and MAF (0.23) of the top SORL1 SNP (rs3781834) in the Japanese sample, and a significance level of 2×10−5 (i.e., threshold for including a SNP in the Stage 2 replication phase), calculation of power post hoc using the PAWE-3D program [22] confirmed that the discovery sample had sufficient power (83.7%). By comparison, the Caucasian sample of 22,771 subjects had only 52.8% power to detect association with this SNP at the observed significance level of 7.9×10−4 and OR (0.78) and a much lower MAF (0.02) than in Japanese.

The most significant result in the GWAS in Japanese was obtained for PALM2 SNP rs913360 (P = 1.8×10−7), but this SNP was not significant in the Japanese replication sample (P = 0.16) and the result for the combined Japanese datasets was less significant than in the discovery sample (P = 6.6×10−6). There was no evidence in the large Caucasian dataset supporting association for rs913360 (P = 0.38) or other PALM2 SNPs.

We obtained evidence in Japanese and Korean populations for association of AD with the same SNPs in the PICALM and BIN1 regions that were identified as genome-wide significant in multiple large GWAS in Caucasians [4], [5]. There are no previously reported association studies of these loci in Japanese. Several small association studies of PICALM in comparatively smaller Chinese samples have yielded conflicting results [23][25]. We also found nominally significant associations in the Japanese sample for previously associated SNPs in CR1, CLU, and ABCA7. Lack of asociation with EPHA1, CD2AP, MS4A6A, and CD33 may be due to insufficient power, different linkage disequilibrium structure of these regions than in Caucasisans, locus heterogeneity or intragenic heterogeneity.

In addition, our analyses showed numerous highly significant results for imputed SNPs in the APOE region (including CEACAM/BCL3, PVRL2, TOMM40, and LOC284352) even after adjustment for the dose of the ε4 allele. However, recognizing that the reliability of imputation is poor for SNPs in this region [13], we genotyped 10 of the significant SNPs in the Japanese discovery and replication datasets. Only one of these results, a PPP1R37 SNP, was nominally significant after adjustment for dose of ε4. Association of AD with this SNP, which is located approximately 225 kb from APOE, has not been observed previously. PVRL2 and APOE are located in a genomic region sandwiched between two recombination hotspots [26], where strong association signals for LOAD have been reproducibly detected in Caucasians [1], [5], but dissipate almost completely for all non-APOE loci after conditioning on APOE, suggesting that no other loci in this region influence LOAD susceptibility [13]. This conclusion is consistent with the observation of moderate linkage disequilibrium between the SNPs determining APOE genotype, rs7412 and rs429358 (Fig. S5), SNPs showing genomewide significant evidence for association with LOAD without adjustment for APOE genotype, and our prior LOAD association studies with SNPs in this region among Caucasians [13].

SorL1, also known as SorLA and LR11, and APP proteins are co-localized in the endosomal and Golgi compartments [27]. SorL1 through its co-dependent interaction with vps26 regulates the intracellular transport and processing of APP, resulting in reduction of amyloid beta (Aß) peptide production [20], [27], [28]. SORL1 knock-out mice carrying both pathogenic mutations in the PSEN1 (exon 9 deletion) and APP (Swedish, K595M/N596L) exhibited increased production and accumulation of Aß [29]. SORL1 variants might influence the CSF Aß42 level in AD patients [30]. Recently, Pottier et al. sequenced the exomes of 29 index cases with autosomal dominant early-onset AD who lacked mutations in APP, PSEN1 and PSEN2 [2]. Seven of these subjects had private SORL1 mutations (2 nonsense and 2 missense) that were predicted to have a pathogenic effect. By comparison, the two genome-wide significant SNPs in this study are both intronic. It is expected that future large resequencing studies of SORL1 will identify the functional variants, thus providing important clues about the mechanisms governing normal and abnormal action of SorL1 on processes leading to LOAD. The emergence of SORL1 as a genome-wide significant locus for AD confirms existing genetic and functional evidence and elevates the importance of intracellular trafficking involving retromer and the Golgi-to- endosome as a key pathway leading to AD [31], [32].

Supporting Information

Figure S1

Quantile-quantile (Q-Q) plot of observed (y-axis) vs. expected (x-axis) P-values from tests of association genome-wide (5,877,918 SNPs) adjusted for population structure, age and sex for LOAD in the Japanese discovery sample. Genomic inflation was low (λ = 1.047).


Figure S2

Manhattan plot of observed –log10P-values for genome-wide SNP association tests for LOAD (y-axis) according to chromosomal location (x-axis) in the Japanese discovery sample adjusted for population structure, age, and sex. All genome-wide significant SNPs (above the horizontal line corresponding to P = 5×10−8 on the y-axis) are located in the APOE region on chromosome 19.


Figure S3

Linkage disequilibrium (r2) among SNPs in the APOE region genotyped using TaqMan calculated in the Japanese discovery (A) and replication (B) datasets. APOE genotype is derived from haplotypes of coding SNPs rs429358 and rs7412.


Figure S4

Linkage disequilibrium (r2) among SNPs in the SORL1 region genotyped in the Japanese discovery (A) and replication (B) datasets.


Figure S5

Comparison of SORL1 association findings in the current study with association signals previously identified by Rogaeva et al. [20]. (A) Regional association plot of the SORL1 region. P-values are expressed as –log10(P) (y-axis) for every tested SNP ordered by chromosomal location (x-axis) and represented as blue rectangles for the Japanese discovery set (J1), light blue diamonds for the ADGC Caucasian set (C), pink circles for meta-analysis of Japanese discovery and Caucasian sets (J1+C), and red circles for meta-analysis of Japanese discovery, Japanese replication (J2), Korean (K), and Caucasian sets (J1+J2+K+C). The numbers below the line showing the orientation of SORL1 are the designations for associated SNPs in the Rogaeva et al. study: 8 = rs668387, 9 = rs689021, 10 = rs641120, 11 = rs4935775, 19 = rs2070045, 22 = rs1699102, 23 = rs3824968, 24 = rs2282649, and 25 = rs1010159. Recombination hotspots are indicated by the continuous blue line behind the symbols for the SNP P-values. (B) Linkage disequilibrium (r2) of the previously associated SNPs in the SORL1 region [20] in the HapMap 2 reference Japanese population (JPT). The association signal with rs3781834 (contained in Block 2) appears to be independent of one of the distinct AD-associated haplotypes reported by Rogaeva et al. [20] (including SNPs in Block 1), but not necessarily independent of the other AD-associated haplotype reported by Rogaeva et al which includes rs1699102 in Block 2 and the SNPs in Block 3.


Table S1

Top-ranked GWAS results in the Japanese GWAS dataset (P<1×104 and imputation quality0.8) with and withut adjustment for the number of APOE ε4 alleles.


Table S2

Association of individually genotyped SNPs in the APOE region in models with and without adjustment for the number of APOE ε4 alleles.


Table S3

Association results for SORL1 SNPs genotyped in the Japanese replication sample.



We deeply thank all the patients with AD and their families, and the control individuals for their participation in this study. Without their contribution, this study would have been impossible. We acknowledge the valuable contributions of Drs. Satoshi Takahashi and Yoshikatsu Fujisawa who died before publication of this paper. We also thank Drs. D. Stephen Snyder and Marilyn Miller from NIA who are ex-officio ADGC members.

Alzheimer’s Disease Genetics Consortium authors and affiliations

Marilyn S. Albert1, Roger L. Albin2,3, Liana G. Apostolova4, Steven E. Arnold5, Clinton T. Baldwin6, Robert Barber7, Michael M. Barmada8, Lisa L. Barnes9,10, Thomas G. Beach11, Gary W. Beecham12,13, Duane Beekly14, David A. Bennett9,15, Eileen H. Bigio16, Thomas D. Bird17, Deborah Blacker18,19, Bradley F. Boeve20, James D. Bowen21, Adam Boxer22, James R. Burke23, Joseph D. Buxbaum24-26, Nigel J. Cairns27, Laura B. Cantwell28, Chuanhai Cao29, Chris S. Carlson30, Regina M. Carney31, Minerva M. Carrasquillo32, Steven L. Carroll33, Helena C. Chui34, David G. Clark35, Jason Corneveaux36, Paul K. Crane37, David H. Cribbs38, Elizabeth A. Crocco39, Carlos Cruchaga40, Philip L. De Jager41,42, Charles DeCarli43, Steven T. DeKosky44, F. Yesim Demirci8, Malcolm Dick45, Dennis W. Dickson32, Ranjan Duara46, Nilufer Ertekin-Taner32,47, Denis Evans48, Kelley M. Faber49, Kenneth B. Fallon33, Martin R. Farlow50, Steven Ferris51, Tatiana M. Foroud49, Matthew P. Frosch52, Douglas R. Galasko53, Mary Ganguli54, Marla Gearing55,56, Daniel H. Geschwind57, Bernardino Ghetti58, John R. Gilbert12,13, Sid Gilman2, Jonathan D. Glass59, Alison M. Goate40, Neill R. Graff-Radford32,47, Robert C. Green60, John H. Growdon61, Hakon Hakonarson62, Kara L. Hamilton-Nelson12, Ronald L. Hamilton63, John Hardy64, Lindy E. Harrell35, Elizabeth Head65, Lawrence S. Honig66, Matthew J. Huentelman36, Christine M. Hulette67, Bradley T. Hyman61, Gail P. Jarvik68,69, Gregory A. Jicha70, Lee-Way Jin71, M. Ilyas Kamboh8,72, Anna Karydas22, John S.K. Kauwe73, Jeffrey A. Kaye74,75, Ronald Kim76, Edward H. Koo53, Neil W. Kowall77,78, Joel H. Kramer79, Patricia Kramer74,80, Walter A. Kukull81, Frank M. LaFerla82, James J. Lah59, Eric B. Larson37,83, James B. Leverenz84, Allan I. Levey59, Ge Li85, Chiao-Feng Lin28, Andrew P. Lieberman86, Oscar L. Lopez72, Kathryn L. Lunetta87, Constantine G. Lyketsos88, Wendy J. Mack89, Daniel C. Marson35, Eden R. Martin12,13, Frank Martiniuk90, Deborah C. Mash91, Eliezer Masliah53,92, Wayne C. McCormick37, Susan M. McCurry93, Andrew N. McDavid30, Ann C. McKee77,78, Marsel Mesulam94, Bruce L. Miller22, Carol A. Miller95, Joshua W. Miller71, Thomas J. Montine84, John C. Morris27,96, Jill R. Murrell49,58, Amanda J. Myers39, Adam C. Naj12, John M. Olichney43, Vernon S. Pankratz97, Joseph E. Parisi98,99, Elaine Peskind85, Ronald C. Petersen20, Aimee Pierce38, Wayne W. Poon45, Huntington Potter29, Joseph F. Quinn74, Ashok Raj29, Ruchita A. Rajbhandary12, Murray Raskind85, Eric M. Reiman36,100-102, Barry Reisberg51,103, Christiane Reitz66,104,105, John M. Ringman4, Erik D. Roberson35, Ekaterina Rogaeva106, Howard J. Rosen22, Roger N. Rosenberg107, Mary Sano25, Andrew J. Saykin49,108, Julie A. Schneider9,109, Lon S. Schneider34,110, William W. Seeley22, Amanda G. Smith29, Joshua A. Sonnen84, Salvatore Spina58, Robert A. Stern77, Rudolph E. Tanzi61, John Q. Trojanowski28, Juan C. Troncoso111, Debby W. Tsuang85, Otto Valladares28, Vivianna M. Van Deerlin28, Linda J. Van Eldik112, Badri N. Vardarajan6, Harry V. Vinters4,113, Jean Paul Vonsattel114, Sandra Weintraub94, Kathleen A. Welsh-Bohmer23,115, Jennifer Williamson66, Randall L. Woltjer116, Clinton B. Wright117, Steven G. Younkin32, Chang-En Yu37, Lei Yu9.

1 Department of Neurology, Johns Hopkins University, Baltimore, Maryland.

2 Department of Neurology, University of Michigan, Ann Arbor, Michigan.

3 Geriatric Research, Education and Clinical Center (GRECC)., VA Ann Arbor Healthcare System (VAAAHS), Ann Arbor, Michigan.

4 Department of Neurology, University of California Los Angeles, Los Angeles, California.

5 Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.

6 Department of Medicine (Genetics Program), Boston University, Boston, Massachusetts.

7 Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, Fort Worth, Texas.

8 Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania.

9 Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois.

10 Department of Behavioral Sciences, Rush University Medical Center, Chicago, Illinois.

11 Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Phoenix, Arizona.

12 The John P. Hussman Institute for Human Genomics, University of Miami, Miami, Florida.

13 Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami, Miami, Florida.

14 National Alzheimer's Coordinating Center, University of Washington, Seattle, Washington.

15 Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois.

16 Department of Pathology, Northwestern University, Chicago, Illinois.

17 Department of Neurology, University of Washington, Seattle, Washington.

18 Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts.

19 Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts.

20 Department of Neurology, Mayo Clinic, Rochester, Minnesota.

21 Swedish Medical Center, Seattle, Washington.

22 Department of Neurology, University of California San Francisco, San Fransisco, California.

23 Department of Medicine, Duke University, Durham, North Carolina.

24 Department of Neuroscience, Mount Sinai School of Medicine, New York.

25 Department of Psychiatry, Mount Sinai School of Medicine, New York.

26 Departments of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York.

27 Department of Pathology and Immunology, Washington University, St. Louis, Missouri.

28 Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.

29 USF Health Byrd Alzheimer's Institute, University of South Florida, Tampa, Florida.

30 Fred Hutchinson Cancer Research Center, Seattle, Washington.

31 Department of Psychiatry, Vanderbilt University, Nashville, Tennessee.

32 Department of Neuroscience, Mayo Clinic, Jacksonville, Florida.

33 Department of Pathology, University of Alabama at Birmingham, Birmingham, Alabama.

34 Department of Neurology, University of Southern California, Los Angeles, California.

35 Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama.

36 Neurogenomics Division, Translational Genomics Research Institute, Phoenix, Arizona.

37 Department of Medicine, University of Washington, Seattle, Washington.

38 Department of Neurology, University of California Irvine, Irvine, California.

39 Department of Psychiatry and Behavioral Sciences, Miller School of Medicine, University of Miami, Miami, Florida.

40 Department of Psychiatry and Hope Center Program on Protein Aggregation and Neurodegeneration, Washington University School of Medicine, St. Louis, Missouri.

41 Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Department of Neurology & Psychiatry, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.

42 Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts.

43 Department of Neurology, University of California Davis, Sacramento, California.

44 University of Virginia School of Medicine, Charlottesville, Virginia.

45 Institute for Memory Impairments and Neurological Disorders, University of California Irvine, Irvine, California.

46 Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, Florida.

47 Department of Neurology, Mayo Clinic, Jacksonville, Florida.

48 Rush Institute for Healthy Aging, Department of Internal Medicine, Rush University Medical Center, Chicago, Illinois.

49 Department of Medical and Molecular Genetics, Indiana University, Indianapolis, Indiana.

50 Department of Neurology, Indiana University, Indianapolis, Indiana.

51 Department of Psychiatry, New York University, New York.

52 C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital, Charlestown, Massachusetts.

53 Department of Neurosciences, University of California San Diego, La Jolla, California.

54 Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania.

55 Department of Pathology and Laboratory Medicine, Emory University, Atlanta, Georgia.

56 Emory Alzheimer's Disease Center, Emory University, Atlanta, Georgia.

57 Neurogenetics Program, University of California Los Angeles, Los Angeles, California.

58 Department of Pathology and Laboratory Medicine, Indiana University, Indianapolis, Indiana.

59 Department of Neurology, Emory University, Atlanta, Georgia.

60 Division of Genetics, Department of Medicine and Partners Center for Personalized Genetic Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.

61 Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts.

62 Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.

63 Department of Pathology (Neuropathology), University of Pittsburgh, Pittsburgh, Pennsylvania.

64 Institute of Neurology, University College London, Queen Square, London.

65 Sanders-Brown Center on Aging, Department of Molecular and Biomedical Pharmacology, University of Kentucky, Lexington, Kentucky.

66 Taub Institute on Alzheimer's Disease and the Aging Brain, Department of Neurology, Columbia University, New York.

67 Department of Pathology, Duke University, Durham, North Carolina.

68 Department of Genome Sciences, University of Washington, Seattle, Washington.

69 Department of Medicine (Medical Genetics), University of Washington, Seattle, Washington.

70 Sanders-Brown Center on Aging, Department Neurology, University of Kentucky, Lexington, Kentucky.

71 Department of Pathology and Laboratory Medicine, University of California Davis, Sacramento, California.

72 University of Pittsburgh Alheimer's Disease Research Center, Pittsburgh, Pennsylvania.

73 Department of Biology, Brigham Young University, Provo, Utah.

74 Department of Neurology, Oregon Health & Science University, Portland, Oregon.

75 Department of Neurology, Portland Veterans Affairs Medical Center, Portland, Oregon.

76 Department of Pathology and Laboratory Medicine, University of California Irvine, Irvine, California.

77 Department of Neurology, Boston University, Boston, Massachusetts.

78 Department of Pathology, Boston University, Boston, Massachusetts.

79 Department of Neuropsychology, University of California San Francisco, San Fransisco, California.

80 Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, Oregon.

81 Department of Epidemiology, University of Washington, Seattle, Washington.

82 Department of Neurobiology and Behavior, University of California Irvine, Irvine, California.

83 Group Health Research Institute, Group Health, Seattle, Washington.

84 Department of Pathology, University of Washington, Seattle, Washington.

85 Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington.

86 Department of Pathology, University of Michigan, Ann Arbor, Michigan.

87 Department of Biostatistics, Boston University, Boston, Massachusetts.

88 Department of Psychiatry, Johns Hopkins University, Baltimore, Maryland.

89 Department of Preventive Medicine, University of Southern California, Los Angeles, California.

90 Department of Medicine - Pulmonary, New York University, New York.

91 Department of Neurology, University of Miami, Miami, Florida.

92 Department of Pathology, University of California San Diego, La Jolla, California.

93 School of Nursing Northwest Research Group on Aging, University of Washington, Seattle, Washington.

94 Cognitive Neurology and Alzheimer's Disease Center, Northwestern University, Chicago, Illinois.

95 Department of Pathology, University of Southern California, Los Angeles, California.

96 Department of Neurology, Washington University, St. Louis, Missouri.

97 Department of Biostatistics, Mayo Clinic, Rochester, Minnesota.

98 Department of Anatomic Pathology, Mayo Clinic, Rochester, Minnesota.

99 Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota.

100 Arizona Alzheimer’s Consortium, Phoenix, Arizona.

101 Banner Alzheimer's Institute, Phoenix, Arizona.

102 Department of Psychiatry, University of Arizona, Phoenix, Arizona.

103 Alzheimer's Disease Center, New York University, New York.

104 Gertrude H. Sergievsky Center, Columbia University, New York.

105 Department of Neurology, Columbia University, New York.

106 Tanz Centre for Research in Neurodegenerative Disease, University of Toronto, Toronto, Ontario.

107 Department of Neurology, University of Texas Southwestern, Dallas, Texas.

108 Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, Indiana.

109 Department of Pathology (Neuropathology), Rush University Medical Center, Chicago, Illinois.

110 Department of Psychiatry, University of Southern California, Los Angeles, California.

111 Department of Pathology, Johns Hopkins University, Baltimore, Maryland.

112 Sanders-Brown Center on Aging, Department of Anatomy and Neurobiology, University of Kentucky, Lexington, Kentucky.

113 Department of Pathology & Laboratory Medicine, University of California Los Angeles, Los Angeles, California.

114 Taub Institute on Alzheimer's Disease and the Aging Brain, Department of Pathology, Columbia University, New York.

115 Department of Psychiatry & Behavioral Sciences, Duke University, Durham, North Carolina.

116 Department of Pathology, Oregon Health & Science University, Portland, Oregon.

117 Evelyn F. McKnight Brain Institute, Department of Neurology, Miller School of Medicine, University of Miami, Miami, Florida.

Funding Statement

This study was supported by a Grants-in-Aid for Scientific Research on Priority Areas, ‘Advanced Brain Science Project’ (Y.I.), ‘Comprehensive Genomics’ (R.K., K.T. and K.Y.), and ‘Applied Genomics’ (S.T.) and ‘Integrated Database Project’ (A.K.) from the Ministry of Education, Culture, Sports, Science and Technology of Japan, ‘Japanese Alzheimer’s Disease Neuroimaging Initiative’ (R.K.) by New Energy and Industrial Technology Development Organization (NEDO) in Japan, and by a grant from the Korea Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea (A120030). The United States National Institutes of Health (NIH), National Institute on Aging (NIH-NIA) supported this work through the following grants: ADGC, U01 AG032984, RC2 AG036528; NACC, U01 AG016976; NCRAD, U24 AG021886; NIA LOAD, U24 AG026395, U24 AG026390; BU ADC P30; MIRAGE R01 AG025259; Banner Sun Health Research Institute P30 AG019610; Boston University, P30 AG013846, U01 AG10483, R01 CA129769, R01 MH080295, R01 AG017173, R01AG33193; Columbia University, P50 AG008702, R37 AG015473; Duke University, P30 AG028377, AG05128; Emory University, AG025688; Group Health Research Institute, UO1 AG06781, UO1 HG004610; Indiana University, P30 AG10133; Johns Hopkins University, P50 AG005146, R01 AG020688; Massachusetts General Hospital, P50 AG005134; Mayo Clinic, P50 AG016574; Mount Sinai School of Medicine, P50 AG005138, P01 AG002219; New York University, P30 AG08051, MO1RR00096, and UL1 RR029893; Northwestern University, P30 AG013854; Oregon Health & Science University, P30 AG008017, R01 AG026916; Rush University, P30 AG010161, R01 AG019085, R01 AG15819, R01 AG17917, R01 AG30146; TGen, R01 NS059873; University of Alabama at Birmingham, P50 AG016582, UL1RR02777; University of Arizona, R01 AG031581; University of California, Davis, P30 AG010129; University of California, Irvine, P50 AG016573, P50, P50 AG016575, P50 AG016576, P50 AG016577; University of California, Los Angeles, P50 AG016570; University of California, San Diego, P50 AG005131; University of California, San Francisco, P50 AG023501, P01 AG019724; University of Kentucky, P30 AG028383; University of Michigan, P50 AG008671; University of Pennsylvania, P30 AG010124; University of Pittsburgh, P50 AG005133, AG030653; University of Southern California, P50 AG005142; University of Texas Southwestern, P30 AG012300; University of Miami, R01 AG027944, AG010491, AG027944, AG021547, AG019757; University of Washington, P50 AG005136; Vanderbilt University, R01 AG019085; and Washington University, P50 AG005681, P01 AG03991. The Kathleen Price Bryan Brain Bank at Duke University Medical Center is funded by NINDS grant #NS39764, NIMH MH60451 and by Glaxo Smith Kline. Genotyping of the TGEN2 cohort was supported by Kronos Science. The TGen series was also funded by NIA grant AG034504 to AJM, The Banner Alzheimer’s Foundation, The Johnnie B. Byrd Sr. Alzheimer’s Institute, the Medical Research Council, and the state of Arizona and also includes samples from the following sites: Newcastle Brain Tissue Resource (funding via the Medical Research Council, local NHS trusts and Newcastle University), MRC London Brain Bank for Neurodegenerative Diseases (funding via the Medical Research Council), South West Dementia Brain Bank (funding via numerous sources including the Higher Education Funding Council for England (HEFCE), Alzheimer’s Research Trust (ART), BRACE as well as North Bristol NHS Trust Research and Innovation Department and DeNDRoN), The Netherlands Brain Bank (funding via numerous sources including Stichting MS Research, Brain Net Europe, Hersenstichting Nederland Breinbrekend Werk, International Parkinson Fonds, Internationale Stiching Alzheimer Onderzoek), Institut de Neuropatologia, Servei Anatomia Patologica, Universitat de Barcelona. Marcelle Morrison-Bogorad, PhD, Tony Phelps, PhD, and Walter Kukull, PhD, are thanked for helping to co-ordinate this collection. ADNI Funding for ADNI is through the Northern California Institute for Research and Education by grants from Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., Alzheimer's Association, Alzheimer's Drug Discovery Foundation, the Dana Foundation, and by the National Institute of Biomedical Imaging and Bioengineering and NIA grants U01 AG024904, RC2 AG036535, K01 AG030514. The authors thank Drs. D. Stephen Snyder and Marilyn Miller from NIA who are ex-officio ADGC members. Support was also from the Alzheimer’s Association (LAF, IIRG-08-89720; MP-V, IIRG-05-14147) and the US Department of Veterans Affairs Administration, Office of Research and Development, Biomedical Laboratory Research Program. P.S.G.-H. is supported by the Wellcome Trust, Howard Hughes Medical Institute, and Canadian Institute of Health Research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


1. Ertekin-Taner N (2010) Genetics of Alzheimer disease in the pre- and post-GWAS era. Alzheimer’s Res Ther 2: 3. [PMC free article] [PubMed]
2. Pottier C, Hannequin D, Coutant S, Rovelet-Lecrux A, Wallon D, et al. (2012) High frequency of potentially pathogenic SORL1 mutations in autosomal dominant early-onset Alzheimer disease. Mol Psychiatry 17: 875–879 [PubMed]
3. Farrer LA, Cupples LA, Haines JL, Hyman BT, Kukull WA, et al. (1997) Effects of age, gender and ethnicity on the association of apolipoprotein E genotype and Alzheimer disease. JAMA 278: 1349–1356 [PubMed]
4. Hollingworth P, Harold D, Sims R, Gerrish A, Lambert JC, et al. (2012) Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer's disease. Nat Genet 43: 429–435 [PMC free article] [PubMed]
5. Naj AC, Jun G, Beecham GW, Wang L-S, Vardarajan BN, et al. (2011) Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer's disease. Nat Genet 43: 436–441 [PMC free article] [PubMed]
6. Kuwano R, Kuwano R, Miyashita A, Arai H, Asada T, et al. (2006) Dynamin-binding protein gene on chromosome 10q is associated with late-onset Alzheimer's disease. Hum Mol Genet 15: 2170–2182 [PubMed]
7. Miyashita A, Arai H, Asada T, Imagawa M, Matsubara E, et al. (2007) Genetic association of CTNNA3 with late-onset Alzheimer's disease in females. Hum Mol Genet 16: 2854–2869 [PubMed]
8. McKhann G, Drachman D, Folstein M, Katzman R, Price D, et al. (1984) Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology 34: 939–944 [PubMed]
9. Folstein MF, Folstein SE, McHugh PR (1975) “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12: 189–198 [PubMed]
10. Morris JC (1993) The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 43: 2412–2414 [PubMed]
11. Reisberg B (1988) Functional assessment staging (FAST). Psychopharmacol Bull 24: 653–659 [PubMed]
12. Seo SW, Im K, Lee JM, Kim YH, Kim ST, et al. (2007) Cortical thickness in single- versus multiple-domain amnestic mild cognitive impairment. Neuroimage 36: 289–297 [PubMed]
13. Jun G, Vardarajan BN, Buros J, Yu C-E, Hawk MV, et al. (2012) A comprehensive search for Alzheimer disease susceptibility loci in the APOE region. Arch Neurol 69: 1270–1279 [PMC free article] [PubMed]
14. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81: 559–575 [PubMed]
15. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, et al. (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38: 904–909 [PubMed]
16. Li M, Boehnke M, Abecasis GR (2006) Efficient study designs for test of genetic association using sibship data and unrelated cases and controls. Am J Hum Genet 78: 778–792 [PubMed]
17. Donnelly P, Marchini J (2009) A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genetics 5: e100529 [PMC free article] [PubMed]
18. Willer CJ, Li Y, Abecasis GR (2010) METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26: 2190–2191 [PMC free article] [PubMed]
19. Rogaeva E, Meng Y, Lee JH, Gu Y-J, Zou F, et al. (2007) The sortilin-related receptor SORL1 is functionally and genetically associated with Alzheimer's disease. Nat Genet 39: 168–177 [PMC free article] [PubMed]
20. Reitz C, Rogaeva E, Lee JH, Tokuhiro S, Bettens K, et al. (2011) Association of SORL1 gene variants with Alzheimer's disease: A meta-analysis. Arch Neurol 68: 99–106 [PMC free article] [PubMed]
21. Cuenco KT, Lunetta K, Baldwin CT, McKee AC, Guo J, et al. (2008) Distinct variants in SORL1 are associated with cerebrovascular and neurodegenerative changes related to Alzheimer disease. Arch Neurol 65: 1640–1648 [PMC free article] [PubMed]
22. Gordon D, Haynes C, Blumenfeld J, Finch SJ (2005) PAWE-3D: visualizing power for association with error in case-control genetic studies of complex traits. Bioinformatics 21: 3935–3937 [PubMed]
23. Li HL, Shi SS, Guo QH, Ni W, Dong Y, et al. (2011) PICALM and CR1 variants are not associated with sporadic Alzheimer's disease in Chinese patients. J Alzheimers Dis 25: 111–117 [PubMed]
24. Yu JT, Song JH, Ma T, Zhang W, Yu NN, et al. (2011) Genetic association of PICALM polymorphisms with Alzheimer's disease in Han Chinese. J Neurol Sci 300: 78–80 [PubMed]
25. Chen LH, Kao PY, Fan YH, Ho DT, Chan CS, et al. (2012) Polymorphisms of CR1, CLU and PICALM confer susceptibility of Alzheimer's disease in a southern Chinese population. Neurobiol Aging 33: 210.e1–7 [PubMed]
26. Takei N, Miyashita A, Tsukie T, Arai H, Asada T, et al. (2009) Genetic association study on in and around the APOE in late-onset Alzheimer disease in Japanese. Genomics 93: 441–448 [PubMed]
27. Andersen OM, Reiche J, Schmidt V, Gotthardt M, Spoelgen R, et al. (2005) Neuronal sorting protein-related receptor sorLA/LR11 regulates processing of the amyloid precursor protein. Proc Natl Acad Sci USA 102: 13461–13466 [PubMed]
28. Fjorback AW, Seaman M, Gustafsen C, Mehmedbasic A, Gokool S, et al. (2012) Retromer binds the FANSHY sorting motif in SorLA to regulate amyloid precursor protein sorting and processing. J Neurosci 32: 1467–1480 [PubMed]
29. Dodson SE, Andersen OM, Karmali V, Fritz JJ, Cheng D, et al. (2008) Loss of LR11/SORLA enhances early pathology in a mouse model of amyloidosis: evidence for a proximal role in Alzheimer's disease. J Neurosci 28: 12877–12886 [PMC free article] [PubMed]
30. Kölsch H, Jessen F, Wiltfang J, Lewczuk P, Dichgans M, et al. (2008) Influence of SORL1 gene variants: association with CSF amyloid-beta products in probable Alzheimer's disease. Neurosci Lett 440: 68–71 [PubMed]
31. Vardarajan BN, Breusegem SY, Harbour ME, Rivka Inzelberg, Friedland R, et al. (2012) Identification of Alzheimer disease associated variants in genes that regulate retromer function. Neurobiol Aging 33: 2231.e15–2231.e30 [PMC free article] [PubMed]
32. Choy RW-Y, Cheng Z, Schekman R (2012) Amyloid precursor protein (APP) traffics from the cell surface via endosomes for amyloid β (Aβ) production in the trans-Golgi network. Proc Natl Acad Sci USA 109: 2077–2082 [PubMed]

Articles from PLoS ONE are provided here courtesy of Public Library of Science