ABSTRACT In addition to apolipoprotein E (_APOE_), recent large genome-wide association studies (GWASs) have identified nine other genes/loci (_CR1, BIN1, CLU_, _PICALM, MS4A4/MS4A6E, CD2AP,

CD33, EPHA1_ and _ABCA7_) for late-onset Alzheimer's disease (LOAD). However, the genetic effect attributable to known loci is about 50%, indicating that additional risk genes for LOAD

remain to be identified. In this study, we have used a new GWAS data set from the University of Pittsburgh (1291 cases and 938 controls) to examine in detail the recently implicated nine

new regions with Alzheimer's disease (AD) risk, and also performed a meta-analysis utilizing the top 1% GWAS single-nucleotide polymorphisms (SNPs) with _P_<0.01 along with four

(_P_<0.05) either within or adjacent to five genes (_PICALM, BIN1, ABCA7, MS4A4/MS4A6E_ and _EPHA1_), significant signals were observed 69–180 kb outside of the remaining four genes

(_CD33_, _CLU_, _CD2AP_ and _CR1_). Meta-analysis on the top 1% SNPs revealed a suggestive novel association in the _PPP1R3B_ gene (top SNP rs3848140 with _P_=3.05E–07). The association of

this SNP with AD risk was consistent in all five samples with a meta-analysis odds ratio of 2.43. This is a potential candidate gene for AD as this is expressed in the brain and is involved

in lipid metabolism. These findings need to be confirmed in additional samples. SIMILAR CONTENT BEING VIEWED BY OTHERS NOVEL ALZHEIMER’S DISEASE RISK VARIANTS IDENTIFIED BASED ON

WHOLE-GENOME SEQUENCING OF _APOE_ Ε4 CARRIERS Article Open access 19 May 2021 EXOME SEQUENCING IDENTIFIES RARE DAMAGING VARIANTS IN _ATP8B4_ AND _ABCA1_ AS RISK FACTORS FOR ALZHEIMER’S

DISEASE Article Open access 21 November 2022 EXOME-WIDE AGE-OF-ONSET ANALYSIS REVEALS EXONIC VARIANTS IN _ERN1_ AND _SPPL2C_ ASSOCIATED WITH ALZHEIMER’S DISEASE Article Open access 26

February 2021 INTRODUCTION Alzheimer's disease (AD), especially the late-onset form (LOAD), is a complex multifactorial neurodegenerative disease and the leading cause of dementia among

the elderly people.1 Currently, there are ∼5 million AD cases in the United States and about 35 million cases worldwide.2 Due to its long clinical course, AD is a major public health

problem. Genetic susceptibility at multiple genes and interactions among them and/or environmental factors likely influence the risk of AD. AD has a strong genetic basis with heritability

estimates up to 80%.3 However, genetic variants in the four well-established genes for AD; amyloid precursor protein and presenilin 1 and 2 (_PSEN1_ and _PSEN2_) for the rare early-onset,

and apolipoprotein E (_APOE_) for the common LOAD explain less than half of this heritability. To identify the remaining genes for the common LOAD, efforts have been focused on conducting

genome-wide association studies (GWASs)4, 5, 6, 7, 8, 9, 10, 11 because this approach is hypothesis free and conceptually would identify all known and unknown genes. However, with the

exception of the _APOE_ region, no other significant associations were replicated across these initial GWAS. This highlights the difficulties in identifying the remaining LOAD genes that are

thought to make a relatively small contribution to the overall risk of disease and thus would require much large sample size than used in earlier GWASs.4, 5, 6, 7, 8, 9, 10, 11 Recent large

GWASs have identified nine additional genes/loci for LOAD, including _CR1, BIN1, CLU_ (a.k.a. _APOJ_), _PICALM, MS4A4/MS4A6E, CD2AP, CD33, EPHA1_ and _ABCA7._12, 13, 14, 15, 16 Although up

to 80% of the AD risk is attributable to genetic factors,3 all of the known LOAD genes (including _APOE_ and new ones) account for about 50% of the total genetic variance. This indicates

that additional risk genes for LOAD remain to be identified. In this study, we used 2229 samples from the University of Pittsburgh Alzheimer's Disease Research Center (ADRC) that were

genotyped using the Illumina Omni1-Quad chip to examine the extent of associations of the recently implicated nine non-_APOE_ risk loci for LOAD in this sample. In addition, we performed a

GWAS analysis in the Pittsburgh sample and conducted a meta-analysis on the top 1% single-nucleotide polymorphisms (SNPs) by incorporating additional 2727 cases and 3336 controls from prior

studies to identify new loci for AD. SUBJECTS AND METHODS STAGE 1 SAMPLE Genomic DNA from 1440 AD cases and 1000 controls were genotyped using the Illumina Human Omni1-Quad as part of the

stage 1 discovery sample. All subjects were European Americans. AD cases (mean age-at-onset (AAO) 72.6±6.4 years; 65.6% women; 23.5% autopsy-confirmed) were derived from the University of

Pittsburgh ADRC, all of whom met the National Institute of Neurological and Communication Disorders and Stroke (NINCDS)/Alzheimer's Disease and Related Disorders Association (ADRDA)

criteria for probable or definite AD. The University of Pittsburgh ADRC follows a standard evaluation protocol, including medical history, general medical and neurological examinations,

psychiatric interview, neuropsychological testing and magnetic resonance imaging scan. Controls were cognitively normal subjects that were recruited from the same geographical area as the

cases. They were 60 years or older (mean age 74.07±6.20 years; 59.8% women; 0.2% autopsy-confirmed), had no psychiatric or neurological disorders, and did not meet criteria for mild

cognitive impairment or dementia. All subjects were recruited with informed consent, and the study was approved by the University of Pittsburgh Institutional Review Board. STAGE 2 SAMPLES A

total of 2727 cases and 3336 controls were derived from four prior studies and they are briefly described below. MAYO SAMPLE The Mayo sample comprised 844 AD cases (mean AAO: 74±4.8 years;

57.2% women) and 1255 controls (mean age: 73.2±4.4 years; 51.7% women) that were previously genotyped using the Illumina HumanHap300 BeadChip.11 AD diagnosis was established using the

NINCDS/ADRDA criteria. ALZHEIMER'S DISEASE NEUROIMAGING INITIATIVE (ADNI) SAMPLE This data set consisted of 188 AD cases with a clinical diagnosis of AD at baseline visit and 193

controls (mean age: 78.6±5.3 years; 46.3% women) that were previously genotyped using the Illumina 610-Quad BeadChip.17 AD cases were between the ages of 55 and 90 (mean AAO: 71.9±8.1 years;

44.6% women) and met NINCDS/ADRDA criteria for AD. Details of the clinical evaluation and sample characterization are described elsewhere.18, 19 The ADNI data used in this report were

obtained from the ADNI database (adni.loni.ucla.edu). The initial goal of ADNI was to recruit 800 adults, aged 55–90, to participate in research on the sensitivity and specificity of

neuroimaging and other biomarkers for detecting and monitoring AD pathology _in vivo_. In ADNI, ∼200 cognitively normal older individuals and 400 people with amnestic mild cognitive

impairment were followed for 3 years and 200 people with mild early-stage AD who were followed for 2 years. For up-to-date information, see http://www.adni-info.org. UNIVERSITY OF

MIAMI/VANDERBILT UNIVERSITY/MT. SINAI SCHOOL OF MEDICINE SAMPLE The University of Miami/Vanderbilt University/Mt. Sinai School of Medicine data set contains 1186 cases (mean AAO: 74.1±7.8

years; 64% women) and 1135 controls (mean age: 74.0±8.3; 61% women) ascertained at the University of Miami, Vanderbilt University and Mt. Sinai School of Medicine. Cases met NINCDS/ADRDA

criteria for probable or definite AD with AAO >60 years. Cognitively healthy controls were unrelated individuals from the same catchment areas.16 MIRAGE STUDY SAMPLE MIRAGE is a

family-based genetic epidemiological study of AD that enrolled AD cases and unaffected sibling controls at 17 clinical centers in the United States, Canada, Germany and Greece. Briefly,

families were ascertained through a proband meeting the NINCDS/ADRDA criteria for definite or probable AD. Unaffected sibling controls were verified as cognitively healthy based on a

Modified Telephone Interview of Cognitive Status score of ⩾86. For this study, we used 1262 subjects (509 cases and 753 controls)16 from the MIRAGE Caucasian data set. GENOTYPING AND QUALITY

CONTROL OF GENOTYPE DATA IN THE PITTSBURGH SAMPLE The stage 1 Pittsburgh sample was genotyped using the Illumina Omni1-Quad chip (containing probes for 1 016 423 SNPs and/or copy-number

variations) at the Feinstein Institute of Medical Research (Manhasset, NY). Genotypes for two _APOE_ SNPs rs429358 (_E*4_) and rs7412 (_E*2_) were determined either as previously described20

or using TaqMan SNP genotyping assays. Exclusion criteria for individual samples included consent and diagnostic criteria (five controls were excluded because of updated diagnostic

criteria, or refusal to participate further in this study), high genotype failure rate (141 individuals were removed because of a genotype failure rate >2%) and cryptic relatedness (65

individuals were removed because they displayed an average degree of sharing (identity by state or IBS) >0.4 with other members of the data set). Exclusion criteria for markers included

minor allele frequency (189 727 SNPs were removed because of minor allele frequency <1%), deviation from Hardy–Weinberg expectations in controls (2239 SNPs gave a Hardy–Weinberg

expectations test _P_-value ⩽1E–06), and high genotype failure rates (22 385 SNPs were removed because of genotype failure rates >2%). The final sample after all exclusions was applied

consisted of 1291 cases and 938 controls. IMPUTATION Genotype posterior probabilities were imputed with MACH v.1.0 (http://www.sph.umich.edu/csg/abecasis/MACH/), on all data sets using

haplotypes from the HapMap CEU v3 data release as a reference sample. The imputation generated data for >3 million SNPs that were subsequently filtered to exclude SNPs with r2<0.3 and

2 543 888 were included in the analysis. Additional details of the imputation procedure were published previously.16 POPULATION STRATIFICATION Population stratification was examined using a

multi-dimensional scaling method as implemented in PLINK.21 Four components were conservatively determined to be relevant to the determination of population origin based on the visual

examination of principle component plots. ASSOCIATION ANALYSES Initial association analysis was performed in the University of Pittsburgh ADRC's GWAS data set. After standard quality

control filters for both genotypes and samples and imputing for unobserved genotypes, a total of ∼2.5 million SNPs were examined in 2229 subjects (1291 cases and 938 controls) for

association analysis. Association of SNPs with AD risk was tested using logistic regression under an additive model that included age, sex and the first four principal components as

covariates. All SNPs with _P_-values of <0.01 were carried forward to the meta-analysis. Significance values from logistic regression analyses were used for ranking purposes only, and so

were not adjusted for multiple testing. All analyses were done in R and/or PLINK. Meta-analysis was done using a fixed-effect methodology, as implemented in PLINK. Heterogeneity testing was

accomplished using Cochran's Q statistic, summarized as the _I_2 statistic (the percentage of total variation across studies that is due to heterogeneity rather than chance).22, 23

Values closer to 0 indicate no heterogeneity, whereas larger numbers indicate increasing degrees of heterogeneity between studies. RESULTS ASSOCIATION ANALYSIS IN NINE NON-APOE AD LOCI We

first examined the association signals in the Pittsburgh GWAS data in recently reported nine non-_APOE_ AD gene regions (_CR1, BIN1, CLU, PICALM, MS4A4/MS4A6E, CD2AP, CD33, EPHA1_ and

_ABCA7_)12, 13, 14, 15, 16 in 2229 cases and controls that passed quality control for both genotypes and samples and included imputed data for unobserved genotypes. Although this sample was

included as one of several replication samples in one GWAS that reported the latter five gene region,16 the extent of replications in these five and the other four regions in this sample has

previously not been examined closely. We observed significant associations (_P_<0.05) either within or adjacent to five genes (_PICALM, BIN1, ABCA7, MS4A4/MS4A6E_ and _EPHA1_). On the

other hand, significant signals were observed 69–180 kb outside of the remaining four genes (_CD33_, _CLU_, _CD2AP_ and _CR1_). The regional association plots containing SNPs within ∼500 kb

on either side of these nine genes are presented in Supplementary Figures 1–9. In the _PICALM_ gene and its surrounding sequence, 69 SNPs showed significant associations (Supplementary

Figure 1). The top 11 SNPs with _P_<E–03 were located in four different introns of the _PICALM_ gene. The most significant SNP, rs17817992 (_P_=1.37E–04), was located in intron 2 followed

by rs12790526 (_P_=1.43E–04) in intron 2, rs12795381 (_P_=1.68E–04) in intron 12 and rs12802399 (_P_=1.72E–04) in intron 2. The originally reported genome-wide significant SNP/rs3851179,13

which is present about 89 kb 5′ to _PICALM_, was observed at _P_=4.6E–02 in our sample. In the _BIN1_ gene, 17 SNPs showed significant associations (Supplementary Figure 2), mostly in the

5′region, including the 5 top significant SNPs/rs11680911 (_P_=2.9E–04), rs7561528 (_P_=3.0E–04), rs6743470 (_P_=1.18E–03), rs17014923 (_P_=1.64E–03) and rs9394826 (_P_=2.15E–03). Among our

top five significant SNPs, rs7561528 was genome-wide significant in the discovery sample of the Alzheimer Disease Genetics Consortium.16 The originally reported genome-wide significant

SNP/rs744373 in the initial GWAS after stage 3 meta-analysis14 was observed at _P_=1.02E–02 in our sample. In the _ABCA7_ gene, seven SNPs showed significant association (Supplementary

Figure 3), including the two most significant SNPs, rs4147916 in intron 34 (_P_=3.18E–03) and rs4147918 in exon 37 (_P_=3.19E–03) that is associated with an amino-acid change (Gln → Arg).

The reported genome-wide significant SNP (rs3764650)15 gave a non-significant but similar trend of association in our sample (_P_=1.68E–01). There are multiple genes in the _MS4A_ gene

clusters on chromosome 11. While one GWAS reported genome-wide significant SNPs in the _MS4A4A_ gene,16 another GWAS found genome-wide significant SNPs in the _MS4A6A_ and _MS4A4E_ genes.15

In our data set, we found a total of 39 SNPs with _P_<0.05 in the _MS4A_ gene cluster (Supplementary Figure 4) and the 5 most significant SNPs were located in the _MS4A4A_ gene and its

surrounding sequence: rs11827324 (_P_=2.58E–03), rs4939338 (_P_=3.39E–03), rs10792263 (_P_=4.85E–03), rs11824773 (_P_=8.06E–03) and rs11824734 (_P_=6.93E–03). The genome-wide significant SNP

in the Alzheimer Disease Genetics Consortium (rs4938933) gave the same trend of association in our sample (_P_=8.80E–02). There were eight significant SNPs in the _EPHA1_ gene

(Supplementary Figure 5), including the top significant SNPs: rs10241042 (_P_=2.27E–03), rs1525119 (_P_=1.02E–02) and rs10233030 (_P_=1.29E–02). The reported genome-wide significant SNP

(rs11767557)15, 16 was observed with a similar trend of association in our sample (_P_=1.11E–01). For the other four genes, significant SNPs (_P_<0.05) were observed 69, 125, 143 and 180 

kb outside of the _CD33_, _CLU_, _CR1_ and _CD2AP_ genes, respectively. There were four SNPs with _P_<0.05 and another seven with _P_-values between 0.05 and 0.10 located in an intergenic

region about 69 kb distal to the _CD33_ gene. Likewise, there were four significant SNPs (_P_=0.012–0.027) about 125 kb proximal to the _CD33_ gene (Supplementary Figure 6). The reported

genome-wide significant SNP (rs3865444)15, 16 gave a non-significant but similar trend of association in our sample (_P_=2.50E–01). Overall, recombination was found to be low in this region.

In the _CLU_ region, 41 significant SNPs were present in the _PTK2B_ and _CHRNA2_ genes, both of which are within 100–125 kb of the _CLU_ gene (Supplementary Figure 7). Although no SNP

showed an association at _P_=0.05 in the _CLU_ gene, several SNPs demonstrated the reported trend of association. The 14 most significant SNPs were present in the _PTK2B_ gene with

_P_<4.0E–03. The top three SNPs in the _PTK2B_ gene were rs373625 in intron 25 (_P_=2.72E–03) and rs9657295 and rs11135993 in intron 27 (_P_=3.06E–03). The reported associations in the

_CD2AP_ and _CR1_ genes were not replicated in our sample, as significant SNPs were located ∼150 kb from these two genes (Supplementary Figures 8 and 9). GWAS ANALYSIS We next examined the

entire GWAS data set (∼2.5 million SNPs) in the Pittsburg sample. Figure 1a shows the comparison of observed _P_-values with the _P_-values for a null distribution. The observed _P_-value

conformed to the null distribution until the tail of the distribution where it deviated, suggesting little evidence of stratification but compelling evidence of disease associations. As

expected, several SNPs in the _APOE_ region on chromosome 19 demonstrated genome-wide significant (_P_<5E–08) associations (Figure 2). The most significant SNP was rs429358 (_E*4_) in the

_APOE_ gene (_P_=2.52E–53) followed by rs4420638 (_P_=1.97E–42), rs6857 (_P_=6.75E–38), rs2075650 (_P_=5.67E–29), rs157582 (_P_=5.56E–28) and rs157580 (_P_=2.77E–10) in the

_APOE_/_APOC1_/_TOMM40_ gene region. The regional association plot including all SNPs in the _APOE_ region is given in Figure 3. After removing SNPs in the _APOE, CR1, BIN1, CLU, PICALM,

MS4A4/MS4A6E, CD2AP, CD33, EPHA1_ and _ABCA7_ regions, a deviation of _P_-values from the null distribution remained in the quantile-quantile plot, although within the 95% confidence

interval of the expectation (Figure 1b). Since no other SNPs outside the _APOE_ region were genome-wide significant in the Pittsburgh discovery sample, we performed a meta-analysis by

combining the results of the top ∼1% SNPs (∼25 000) with _P_<0.01 of the discovery sample with the four existing data sets (2727 AD cases and 3336 controls) for these SNPs. Of the known

loci, _APOE_, _BIN1_ and _PICALM_ revealed genome-wide significant associations along with five suggestive new loci with _P_<1E–05 in the meta-analysis (Table 1). All SNPs with

_P_<1E–04 in the meta-analysis are presented in Supplementary Table. Four of the top five SNPs outside the _APOE_ and nine other known loci were either directly genotyped or had proxy

genotyped SNPs with _P_<1E–04 on Illumina arrays (see Table 1 and Supplementary Table), thus eliminating potential spurious associations due to imputation artifact. The most significant

SNP, rs3848140 (_P_=3.05E–07), located in the _PPP1R3B_ (encoding protein phosphatase 1, regulatory subunit 3B) region on chromosome 8 at position 9.04 Mb was actually genotyped and there

were three additional significant SNPs in this gene at _P_<1E–06. The association of this SNP with AD risk was consistent in all five samples with meta odds ratio of 2.43. Figure 4 shows

the regional plot for SNPs within 500 kb on either side of the _PPP1R3B_ index SNP and the meta _P-_values for markers which had _P_<IE–04 in the meta-analysis. The regional plots for the

remaining four top AD loci are shown in Supplementary Figures 10–13. To remove any variation in AD due to the established effect of _APOE_, we also analyzed the data after adjusting for the

effect of _APOE*4_, but found no appreciable difference in _P_-values for the non-_APOE_ loci (Table 1), indicating that their effects are independent of _APOE_. DISCUSSION In this study,

we have used a new GWAS data set to (i) examine in detail the recently implicated nine new regions with AD risk, and to (ii) perform a meta-analysis utilizing the top 1% GWAS SNPs (∼25 000)

with _P_<0.01 along with four independent data sets for these SNPs in an effort to identify new AD loci. Five of the nine loci are replicated in the Pittsburgh sample as we found multiple

nominal significant SNPs in or around five genes (_PICALM, BIN1, ABCA7, MS4A4/MS4A6E_ and _EPHA1_), suggesting that these are the relevant genes for AD in these regions. As associations in

these genes have been validated in multiple studies,13, 16 it is acceptable to consider _P_<0.05 statistically significant in follow-up studies24 like ours. However, the identity of the

potential causal genes in the remaining four regions _(CD33_, _CLU_, _CD2AP_ and _CR1_) is not clear in the Pittsburgh sample, as significant SNPs were observed 69–180 kb outside of these

genes. The power in our Pittsburgh sample to detect these nine loci for the effect sizes reported in a previous GWAS16 is 0.38, 0.43, 0.39, 0.31, 0.38, 0.30, 0.28, 0.28 and 0.47 for _PICALM,

BIN1, ABCA7, MS4A4, EPHA1, CD33_, _CLU_, _CD2AP_ and _CR1,_ respectively. Although the overall power is low and this would reduce our confidence in our negative results, it enhances the

interest in our positive results even further. There were only 11 SNPs present in the _CD33_ gene on the 1M chip. This may explain why we did not observe any significant SNP in this gene,

although the reported genome-wide significant SNP (rs3865444) gave a _P_-value of 0.25 with the same effect size in our sample. Also, recombination is low in the _CD33_ region; thus, it is

likely that functional SNPs may be present in a broader region around _CD33_. Although none of the small number of 15 SNPs present in the _CLU_ gene on the 1M chip showed significant

association at _P_<0.05, a number of them showed the trend of association reported in earlier studies. However, several SNPs in a nearby _PTK2B_ gene that is expressed in adult brain,25

revealed significant associations. Perhaps, genetic variations in the _CLU_ and _PTK2B_ genes, both of which are expressed in the brain, are relevant to AD risk and thus resequencing a

broader region in and around these genes may reveal functional variants. However, a recent sequencing study found no functional SNPs in the coding region of the _CLU_ gene and, likewise, no

significant association was found between the previously associated _CLU_ SNPs with AD risk with expression quantitative trait loci,26 suggesting that the biological role of _CLU_ is AD is

not yet clear. While findings in our sample suggest the involvement of broader regions around the _CD33_ and _CLU_ genes, the evidence of association of the _CR1_ and _CD2AP_ with AD is weak

or absent. The GWAS analysis in the Pittsburgh data set found only the _APOE/TOMM40/APOC1_ region to be genome-wide significant, which is due to the established association with the

_APOE*4_ SNP. Meta-analysis of the top 25 000 SNPs from the Pittsburgh GWAS data, in conjunction with four replication data sets revealed five suggestive non-_APOE_ loci. The top SNP was

located in the _PPP1R3B_ gene, which is a potential candidate gene for AD as it is expressed in human brain.27 Genetic variation in _PPP1R3B_ has been shown to be associated with plasma high

density lipoprotein and cholesterol levels and the risk of coronary artery disease.28, 29 Overexpression of the mouse ortholog _Ppp1r3b_ in mouse liver results in significantly lower high

density lipoprotein levels.28 As high density lipoproteins levels and particle size affect the clearance of β-amyloid in mouse brain,30 it is conceivable that the _PPP1R3B_-associated effect

on AD risk is mediated through its impact on high density lipoproteins levels. Although the association of the top _PPP1R3B_ SNP did not meet the strict criteria for genome-wide

significance, it was consistent in direction in all samples as reflected in Odds ratio values (see Table 1). This suggests that this gene region should be followed up in additional samples.

If these findings are confirmed, then this would be another cholesterol-related gene involved in AD along with _APOE_, _CLU_ and _ABCA7_. In conclusion, 6 of the 10 established AD loci,

including the _APOE_ locus, have been replicated in our new GWAS data set, while broader regions in the remaining four loci are suspected. Although we did not identify additional non-_APOE_

SNPs meeting a conservative threshold of genome-wide significance, we have identified five suggestive loci that warrant follow-up in additional samples. REFERENCES * Evans DA, Funkenstein

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National Institute on Aging grants AG030653 (MIK), AG005133 (ADRC), AG027224 (RAS), AG07562 and AG023651 (MG) and AG18023 (SGY). We thank Drs Peter Gregersen and Annette Lee of the Feinstein

Institute of Medical Research where the genotyping of the discovery sample was performed. We acknowledge Drs Neill R Graff-Radford, Dennis W Dickson and Ronald C Petersen for their key

contribution in collecting the Mayo replication sample. One of the replication data sets was funded by the Alzheimer′s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health

Grant U01 AG024904; RC2 AG036535). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from

the following: Abbott; Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Amorfix Life Sciences; AstraZeneca; Bayer HealthCare; BioClinica; Biogen Idec; Bristol-Myers

Squibb Company; Eisai; Elan Pharmaceuticals; Eli Lilly and Company; F Hoffmann-La Roche and its affiliated company Genentech; GE Healthcare; Innogenetics, N.V.; Janssen Alzheimer

Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace; Merck & Co.; Meso Scale Diagnostics, LLC.; Novartis

Pharmaceuticals Corporation; Pfizer; Servier; Synarc; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in

Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (http://www.fnih.org). The grantee organization is the Northern California

Institute for Research and Education, and the study is coordinated by the Alzheimer′s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the

Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129, K01 AG030514 and the Dana Foundation. Some sample

collection was supported by the NIA National Cell Repository for Alzheimer's Disease (NCRAD; U24 AG021886) and additional support for data analysis was provided by R01 AG19771 and P30

AG10133.. We thank the following people for their contributions to the ADNI genotyping project: Tatiana Foroud, Kelley Faber, Li Shen, Steven G Potkin, Matthew J Huentelman, David W Craig,

Sungeun Kim, Kwangsik Nho and Bryan M DeChairo. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh,

Pittsburgh, PA, USA M I Kamboh, F Y Demirci, X Wang, R L Minster, E Feingold & M M Barmada * University of Pittsburgh Alzheimer's Disease Research Center, Pittsburgh, PA, USA M I

Kamboh & O L Lopez * Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA M I Kamboh, R A Sweet & M Ganguli * Department of Neuroscience, Mayo

Clinic College of Medicine, Jacksonville, FL, USA M M Carrasquillo, V S Pankratz & S G Younkin * Departments of Radiology and Imaging Sciences and Medical and Molecular Genetics, Indiana

University School of Medicine, Indianapolis, IN, USA A J Saykin * Department of Medicine, Genetics Program, Boston University School of Medicine and Public Health, Boston, MA, USA G Jun, C

Baldwin, M W Logue, J Buros & L Farrer * Department of Biostatistics, Boston University School of Medicine and Public Health, Boston, MA, USA G Jun, M W Logue & L Farrer * Department

of Opthalmology, Boston University School of Medicine and Public Health, Boston, MA, USA G Jun & L Farrer * Center for Human Genetics, Boston University School of Medicine and Public

Health, Boston, MA, USA C Baldwin * Department of Neurology, Boston University School of Medicine and Public Health, Boston, MA, USA L Farrer * Department of Epidemiology, Boston University

School of Medicine and Public Health, Boston, MA, USA L Farrer * The John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, USA M A Pericak-Vance * Department of

Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA J L Haines * Department of Neurology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA R A

Sweet & O L Lopez * Mental Illness Research, Education, and Clinical Center, VA Pittsburgh Healthcare System, Pittsburgh, PA, USA R A Sweet * Office of the Dean and Department of

Neurology, University of Virginia School of Medicine, Charlottesville, VA, USA S T DeKosky Authors * M I Kamboh View author publications You can also search for this author inPubMed Google

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View author publications You can also search for this author inPubMed Google Scholar CONSORTIA FOR THE ALZHEIMER'S DISEASE NEUROIMAGING INITIATIVE CORRESPONDING AUTHOR Correspondence

to M I Kamboh. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no conflict of interest. ADDITIONAL INFORMATION Data used in preparation of this article were obtained from the

Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.ucla.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or

provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at:

http://adni.loni.ucla.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. Supplementary Information accompanies the paper on the Translational Psychiatry website SUPPLEMENTARY

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Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Kamboh, M., Demirci, F., Wang, X. _et al._ Genome-wide association study of Alzheimer's disease. _Transl Psychiatry_ 2,

e117 (2012). https://doi.org/10.1038/tp.2012.45 Download citation * Received: 05 April 2012 * Accepted: 10 April 2012 * Published: 15 May 2012 * Issue Date: May 2012 * DOI:

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available for this article. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative KEYWORDS * Alzheimer's disease * genome-wide association study *

meta-analysis * _PPP1R3B_ * _PTK2B_ * single-nucleotide polymorphisms