SNPs associated with cerebrospinal fluid phospho-tau levels influence rate of decline in Alzheimer's disease.

Abstract

Alzheimer's Disease (AD) is a complex and multifactorial disease. While large genome-wide association studies have had some success in identifying novel genetic risk factors for AD, case-control studies are less likely to uncover genetic factors that influence progression of disease. An alternative approach to identifying genetic risk for AD is the use of quantitative traits or endophenotypes. The use of endophenotypes has proven to be an effective strategy, implicating genetic risk factors in several diseases, including anemia, osteoporosis and heart disease. In this study we identify a genetic factor associated with the rate of decline in AD patients and present a methodology for identification of other such factors. We have used an established biomarker for AD, cerebrospinal fluid (CSF) tau phosphorylated at threonine 181 (ptau(181)) levels as an endophenotype for AD, identifying a SNP, rs1868402, in the gene encoding the regulatory sub-unit of protein phosphatase B, associated with CSF ptau(181) levels in two independent CSF series (P(combined) = 1.17 x 10(-05)). We show no association of rs1868402 with risk for AD or age at onset, but detected a very significant association with rate of progression of disease that is consistent in two independent series (P(combined) = 1.17 x 10(-05)). Our analyses suggest that genetic variants associated with CSF ptau(181) levels may have a greater impact on rate of progression, while genetic variants such as APOE4, that are associated with CSF Aβ(42) levels influence risk and onset but not the rate of progression. Our results also suggest that drugs that inhibit or decrease tau phosphorylation may slow cognitive decline in individuals with very mild dementia or delay the appearance of memory problems in elderly individuals with low CSF Aβ(42) levels. Finally, we believe genome-wide association studies of CSF tau/ptau(181) levels should identify novel genetic variants which will likely influence rate of progression of AD.

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SNPs Associated with Cerebrospinal Fluid Phospho-Tau
Levels Influence Rate of Decline in Alzheimer’s Disease
Carlos Cruchaga1,2.*, John S. K. Kauwe3., Kevin Mayo1,2, Noah Spiegel1,2, Sarah Bertelsen1, Petra
Nowotny1,2, Aarti R. Shah2,4, Richard Abraham9, Paul Hollingworth5, Denise Harold5, Michael M. Owen5,
Julie Williams5, Simon Lovestone6, Elaine R. Peskind7,8, Ge Li7,8, James B. Leverenz7,8,9, Douglas
Galasko10, The Alzheimer’s Disease Neuroimaging Initiative", John C. Morris4,11,6, Anne M. Fagan2,4,13,
David M. Holtzman2,4,12,13, Alison M. Goate1,2,4,13
1Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, United States of America, 2The Hope Center Program on Protein Aggregation
and Neurodegeneration (HPAN), Washington University School of Medicine, St. Louis, Missouri, United States of America, 3Department of Biology, Brigham Young
University, Provo, Utah, United States of America, 4Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, United States of America,
5Department of Psychological Medicine, Medical Research Council (MRC) Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University,
Cardiff, United Kingdom, 6Kings College, London, United Kingdom, 7Departments of Psychiatry and Behavioral Sciences, University of Washington School of Medicine,
Seattle, Washington, United States of America, 8Veterans Affairs Northwest Network Mental Illness Research, Education, and Clinical Center, Seattle, Washington, United
States of America, 9Department of Neurology, University of Washington School of Medicine, Seattle, Washington, United States of America, 10Department of
Neurosciences, University of California San Diego, La Jolla, California, United States of America, 11Department of Pathology and Immunology, Washington University
School of Medicine, St. Louis, Missouri, United States of America, 12Department of Developmental Biology, Washington University School of Medicine, St. Louis, Missouri,
United States of America, 13Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, Missouri, United States of America
Abstract
Alzheimer’s Disease (AD) is a complex and multifactorial disease. While large genome-wide association studies have had
some success in identifying novel genetic risk factors for AD, case-control studies are less likely to uncover genetic factors
that influence progression of disease. An alternative approach to identifying genetic risk for AD is the use of quantitative
traits or endophenotypes. The use of endophenotypes has proven to be an effective strategy, implicating genetic risk
factors in several diseases, including anemia, osteoporosis and heart disease. In this study we identify a genetic factor
associated with the rate of decline in AD patients and present a methodology for identification of other such factors. We
have used an established biomarker for AD, cerebrospinal fluid (CSF) tau phosphorylated at threonine 181 (ptau181) levels as
an endophenotype for AD, identifying a SNP, rs1868402, in the gene encoding the regulatory sub-unit of protein
phosphatase B, associated with CSF ptau181levels in two independent CSF series Pcombined~1:17|10{05
association of rs1868402 with risk for AD or age at onset, but detected a very significant association with rate of progression
of disease that is consistent in two independent series Pcombined~1:71|10{05
associated with CSF ptau181levels may have a greater impact on rate of progression, while genetic variants such as APOE4,
that are associated with CSF Ab42levels influence risk and onset but not the rate of progression. Our results also suggest
that drugs that inhibit or decrease tau phosphorylation may slow cognitive decline in individuals with very mild dementia or
delay the appearance of memory problems in elderly individuals with low CSF Ab42levels. Finally, we believe genome-wide
association studies of CSF tau/ptau181levels should identify novel genetic variants which will likely influence rate of
progression of AD.
??. We show no
??. Our analyses suggest that genetic variants
Citation: Cruchaga C, Kauwe JSK, Mayo K, Spiegel N, Bertelsen S, et al. (2010) SNPs Associated with Cerebrospinal Fluid Phospho-Tau Levels Influence Rate of
Decline in Alzheimer’s Disease. PLoS Genet 6(9): e1001101. doi:10.1371/journal.pgen.1001101
Editor: Amanda J. Myers, University of Miami, Miller School of Medicine, United States of America
Received April 22, 2010; Accepted July 29, 2010; Published September 16, 2010
Copyright: ? 2010 Cruchaga et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by grants from AstraZeneca, National Institutes of Health (AG16208, P01AG03991, P50AG05681, P01AG026276, AG23185,
AG05136), the Barnes-Jewish Hospital Foundation, Ford Foundation, the Department of Veterans Affairs, and an anonymous foundation. CC has a fellowship from
Fundacion Alfonso Martin Escudero. Data collection and sharing for Alzheimer’s Disease Neuroimaging Initiative (ADNI) (Principal Investigator: Michael Weiner;
National Institutes of Health grant U01 AG024904) is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering
(NIBIB), and through generous contributions from the following: Pfizer, Inc., Wyeth Research, Bristol-Myers Squibb, Eli Lilly and Company, GlaxoSmithKline, Merck
& Co., Inc., AstraZeneca AB, Novartis Pharmaceuticals Corporation, Alzheimer’s Association, Eisai Global Clinical Development, Elan Corporation plc, Forest
Laboratories, and the Institute for the Study of Aging, with participation from the United States Food and Drug Administration. Industry partnerships are
coordinated through the Foundation for the National Institutes of Health. 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 of Neuro Imaging at the University of California, Los Angeles. The funders had no role in study design, data collection and analysis, decision to publish,
or preparation of the manuscript.
Competing Interests: The work presented in this report is also the subject of a pending patent filed by Washington University in St. Louis, Missouri, United
States of America, in which Drs. C. Cruchaga, A. M. Goate, D. M. Holtzman and A. Fagan are named as inventors. The patent is currently under option to
AstraZeneca Pharmaceuticals, LP. Drs. Goate and Holtzman have acted as consultants for AstraZeneca Pharmaceuticals, LP, in 2008/2009.
* E-mail: cruchagc@psychiatry.wustl.edu
. These authors contributed equally to this work.
" A complete listing of the investigators for the Alzheimer’s Disease Neuroimaging Initiative is provided in the Acknowledgments.
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Introduction
Genetic studies have helped to further our understanding of the
pathogenic mechanism of several diseases, including AD. To date
only the e4 allele of apolipoprotein E (APOE4), present in 50% of
late onset AD (LOAD) cases, has been convincingly demonstrated
to influence risk for LOAD. The traditional method for searching
for genetic risk factors involves the comparison of genes in AD
cases and non-demented elderly controls. AD is a complex and
multifactorial disease, and as a result very large datasets have been
necessary to identify these genetic risk factors [1–2]. An alternative
to the standard case-control study design is to use quantitative
traits or endophenotypes. Quantitative traits have been used
to successfully identify new genetic factors implicated in anemia
[3–6], osteoporosis [7] and heart disease [8–11]. The advantages
of quantitative traits are that they provide higher power than
regular case-control analyses, a biological model of disease and the
possible effects of the associated genetic variation and may
decrease the clinical heterogeneity of the samples. This is likely to
be true for Alzheimer’s Disease (AD) because up to 30% of
individuals in screened elderly non-demented control samples
show evidence of AD pathology at autopsy [12], and a similar
number have biomarker profiles consistent with preclinical AD
[13–15], thus reducing the power of a case-control design.
Both Ab and tau protein play an important role in AD, are
detectable in cerebrospinal fluid (CSF) in all individuals, and have
been used as biomarkers for diagnosis [16–18]. Patients with AD
show lower CSF Ab42levels [19] that inversely correlate with the
presence of fibrillar Ab in the brain (as measured by Pittsburgh
Compound B (PET-PIB) retention) in demented individuals [14]
and plaque counts in brain samples [20]. Several studies suggest
that PET-PIB retention and CSF Ab42levels could help to identify
individuals with AD pathology before the onset of clinically
detectable disease (preclinical AD) [14,21]. The CSF levels of total
tau and tau phosphorylated at threonine 181 (ptau181) are
increased in AD [12,14]. Elevated CSF tau levels are associated
with neuronal damage and are also observed in stroke [22] and
traumatic brain injury immediately after injury [23], however
increases in CSF ptau181 levels appear to be specific to AD
[24–26]. In several previous studies we have successfully applied
this endophenotype-based approach, leveraging the information
from both CSF Ab and tau to identify genetic polymorphisms
implicated in AD risk [27–29].
Tau activity depends on its state of phosphorylation [30], which
is regulated by several kinases, phosphatases and other tau-related
proteins [31]. Hyperphosphorylation of tau destabilizes the
microtubule network, leading to impaired axonal transport and
ultimately to neurofibrillary tangle formation and neuronal death
(For review see [32]). In the present study we have evaluated 355
single nucleotide polymorphisms (SNPs) in 34 genes involved in
tau modification or metabolism for association with CSF levels of
ptau181, then determined the effects of those variants on AD risk,
onset and rate of progression.
Results
Association with CSF ptau181levels: Initial screening
Based on bibliographic data we selected 384 SNPs localized in
34 genes related to tau metabolism (tau kinases, phosphatases, tau
O-glcNAcylation or tau degradation (Table S1). 355 SNPs passed
quality control (Hardy-Weinberg equilibrium and call rate
.95%). Association of SNPs with CSF ptau181 levels was
evaluated by ANCOVA in 353 CSF samples from the Washington
University Alzheimer Disease Research Center (WU-ADRC-CSF)
(Tables 1 and 2). Clinical Dementia Rating (CDR), age and APOE
e4 genotype were included as covariates in the analyses. Eighteen
SNPs, located in 7 different genes showed significant association
with CSF ptau181 levels in the WU-ADRC-CSF series after
multiple test correction (Table 3). The SNP with the most
significant p-value, rs1868402, is located in intron 5 of the
regulatory subunit of the protein phosphatase B gene, also known
as calcineurin B (PPP3R1; MIM#: 601302). The association of
rs1868402 with CSF ptau181 levels showed the best fit in the
dominant model, with minor alleles carriers showing significantly
higher CSF ptau181levels (P=5.90610204, Figure 1A and Figure
S1). All subsequent analyses for rs1868402 used the dominant
model. Six other SNPs in PPP3R1, which are in high linkage
disequilibrium (LD) with rs1868402 (Figure S2, and Table S2),
also showed association with CSF ptau181. Based on the linkage
disequilibrium (LD) in PPP3R1, we selected rs1868402 and
rs6546366 for replication. The remaining eleven SNPs that were
significant after multiple test correction were also selected for
replication (Table 3).
Association with CSF ptau181levels: Replication in an
independent CSF series and combined analyses
In a replication series of 493 independent CSF samples from
ADNI (ADNI-CSF) and University of Washington (UW) (Tables 1
and 2) only the SNP located in calcineurin B, rs1868402,
replicated and passed the FDR filter (P=0.005 ptau181, Table 3,
Figure S1). In this series rs1868402 also showed the best fit in the
dominant model and minor allele carriers have higher CSF
ptau181levels. In the replication CSF series, rs6546366 (PPP3R1),
showed no association with CSF ptau181levels (Table 3). The lack
of association of rs6546366 with CSF ptau181levels is probably
due to a lower level of LD with rs1868402 in the replication series
(r2=0.65) compared with the LD between these two SNPs in the
WU-ADRC-CSF series (r2=0.80). This result indicates that
rs1868402, or another unknown variant in LD with rs1868402,
is the variant that drives the association with CSF ptau181levels.
We also performed a combined analysis by combining the
residuals for CSF ptau181after correcting for the covariates (see
Materials and Methods). In this analysis we did not included site or
platform as a covariate because there were no significant
differences between datasets and/or platform for the ptau181
residuals. Inclusion of site/platform as a covariate did not
significantly change thep-value.
rs1868402 showed the most significant association with CSF
ptau181 levels (combined analysis P~1:71|10{05), with a p-
value that was significant after Bonferroni correction for the entire
study (a; 0:05=384~1:30|10{04). Minor allele carriers have a
2.4 fold increased risk of being in the highest quartile of the CSF
ptau181 distribution compared to the lowest quartile (odds
ratio=2.37, 95% confidence interval 1.59–3.54). None of the
other SNPs were significant after Bonferroni correction (Table 3).
Inthe combineddata,
Association with CSF ptau181levels: Context-dependent
effects
It has been demonstrated in our longitudinal data and that of
othersthattheincreaseinCSFtauandptau181levelsseeninmildAD
is preceded by decreases in CSF Ab42levels [14,21]. This likely
reflects deposition of Ab in the brain [14]. Individualswith CSF Ab42
levels less than 500 pg/ml in the WU-ADRC-CSF, and less than
192 pg/ml in the ADNI-CSF series, have evidence of Ab deposition
in the brain, as detected by PET-PIB [14,21]. We used these CSF
Ab42thresholds to stratify the WU-ADRC-CSF and ADNI-CSF
samples into individuals with low or high CSF Ab42levels (with and
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without likely Ab deposition in the brain). The difference in CSF
Ab42threshold levels between the WU-ADRC-CSF and ADNI-CSF
series is due to different antibodies and procedures used to measure
the CSF levels (see Materials and Methods). The data necessary to
examine the correlation between the CSF Ab42levels and PET-PIB
signal is not availablein the UW CSF series and therefore thisdataset
could not be included in this analysis. In these analyses we calculated
the p-value and the Odds ratios for rs1868402 with CSF ptau181
levelsbycomparingthefrequencyof this SNP in the lowestversusthe
highest quartile of the CSF ptau181levels after correcting for the
covariates. When we stratified the WU-ADRC-CSF and ADNI-CSF
series by CSF Ab42levels, we observed very significant associa-
tion for rs1868402 with CSF ptau181 in the low Ab42 stratum
(combined analysis P~1:13|10{04, Odds Ratio 3.48; 95% con-
fidenceinterval 1.8–6.7) and a nominallysignificant association in the
high Ab42stratum (combined analysis P=0.023; Odds Ratio 2.54;
95% confidence interval 1.0–6.75, Table 4)..Both the high and low
Ab42 level strata have sufficient power to detect the association
between rs1868402 with CSF ptau181levels (high Ab420.985; low
Ab42=0.975;a=0.05).Thenominallysignificantp-valueinthe high
Ab stratum may indicate a moderate effect on CSF ptau181levels in
healthy individuals, but it is clear that when AD pathology is present
the effect of this SNP is more marked.
Rs1868402 explains 4.62% of the variability in CSF ptau181
levels in individuals with low CSF Ab42levels in the WU-ADRC-
CSF+ADNI-CSF samples, which is similar to the variability
explained by other SNPs and endophenotypes [3–11]. It is
important to note that in the low CSF Ab42 group there are
individuals diagnosed with DAT (CDR.0, n=183, 58%) and
non-demented individuals (CDR=0, n=134, 42%) with possible
Ab deposition in the brain and brain atrophy (presymptomatic
AD) [12]. In the high Ab42stratum 80% of the samples (n=242)
had a CDR=0.
Implication of SNPs associated with CSF ptau181levels in
AD: Association with rate of progression of AD but not
risk for AD or age at onset
The premise of this endophenotype-based approach is that a
SNP, such as rs1868402 that shows strong, replicable association
with an important AD biomarker should also modulate risk, onset
and/or progression of AD. We tested whether rs1868402
influences risk for AD, age at onset and disease progression. We
found no association between rs1868402 and risk for AD (P=0.10,
Table 5) or age at onset (P=0.19 Figure 2) in 1106 cases and 1216
controls of European descent.
To examine disease progression we used two longitudinal
datasets: 109 subjects from WU-ADRC-CSF (399 observations)
and 150 subjects from ADNI-CSF (620 observations). Association
with rate of progression was evaluated by comparing the change in
sum of boxes of the CDR (SB-CDR) per year (slope) by genotype
including age at the first visit, gender, APOE genotype and initial
CDR as covariates. CSF ptau181and Ab42levels were also included
in the model to correct for the potential association between these
Author Summary
Alzheimer’s disease (AD) is the most common neurode-
generative disease affecting more than 4.5 million people
in the US. Genetic studies of AD have previously identified
pathogenic mutations in three genes (APP, PSEN1 and
PSEN2) and polymorphisms in APOE as risk factors. These
findings have led to a better understanding of the
underlying disease mechanisms. However, half of all AD
cases have no known genetic risk factors for disease. Most
studies are designed to identify variants associated with
risk or age at onset, but rarely cover other important facets
of AD, such as disease progression or duration. In this
study we have used an established AD biomarker
(cerebrospinal fluid tau phosphorylated at threonine 181,
ptau181) to find genetic variants that influence levels of
ptau181in the cerebrospinal fluid. This novel and powerful
approach has allowed us to identify a genetic factor
located in the regulatory subunit of the calcineurin that is
also strongly associated with rate of progression of AD.
This study is important because it defines a strategy to find
novel genetic factors influencing different facets of AD
pathobiology including risk, onset and progression.
Table 1. Summary of sample characteristics.
Samplen
Age (yrs)
Mean ± SD (range) Male (%)
APOE
e 4+ + (%)CDR
WU-ADRC-CSFCSF/Progression 35368611 (45–94) 39400=72%: .0.5=18%
ADNI-CSF CSF/Progression236 7566 (56–91) 56 470=40%: .0.5=60%
UW CSF2576968 (55–88) 5050 0=52%: .0.5=48%
Brain Samplescases 82 8667 (72–102) 4541
.0.5=100%
controls 398569 (64–107)41230=100%
WU-ADRC-CCcases 3408367 (69–101)35 56
.0.5=100%
control281 7868 (60–102)39 210=100%
ADNI-CCcases 1007168 (52–91) 55 65
.0.5=100%
control1237765 (61–92) 5326 0=100%
MRCcases6667667 (60–97)27 61
.0.5=100%
control812 7666 (61–97) 37 230=100%
Sample size (n), age, percentage of males, percentage of APOE4 allele carriers, and clinical dementia rating (CDR) for each sample. For the cases age at onset is shown
and for controls the age at last assessment.
Washington University Alzheimer’s Disease Research Center (ADRC), Alzheimer’s Disease Neuroimaging Initiative (ADNI) and for the University of Washington, Seattle
(UW). Cerebrospinal Fluid (CSF). Case-control (CC).
doi:10.1371/journal.pgen.1001101.t001
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phenotypes with progression [33]. Because the association of
rs1868402 with CSF ptau181was mainly in individuals with low
CSF Ab42 levels, association with progression was analyzed in
individuals with low CSF Ab42levels (less than 500 pg/ml in the
WU-ADRC-CSF, and less than 192 pg/ml in the ADNI-CSF
series), and an initial CDR of 0 or 0.5. Individuals with CDR 0 and
0.5 were selected to maximize the amount of information on
progression per individual and to avoid possible ceiling effects from
individuals who began the study with advanced levels of dementia.
Carriers of the rs1868402 allele associated with higher CSF
ptau181levels showed an increase of 0.58 SB-CDR per year, which
is six-fold faster than the rate seen in individuals homozygous for
the allele associated with low CSF ptau181 levels (P=0.0026;
Table 6, Figure 3A and Figure S3), and almost two times faster
than the average change for the entire series (SB-CDR per year for
the entire series 0.31). The association of rs1868402 with
progression replicated in the ADNI-CSF series (P=0.014) with a
Pcombined=1.96610205. In addition, we also used the ADNI
samples with no CSF data (ADNI-CC, Table 6) to replicate the
association with rate of progression in an independent sample. In
this dataset rs1868402 also showed a significant association with
rate of progression (P=0.018, Table 6) and in the same direction
as in the previous analyses.
We tested whether rs1868402 interacts with rs3785883, a SNP
located in MAPT, which is also associated with CSF ptau181levels
[27]. Rs3785883 also showed a p-value for association with CSF
ptau181levels of 0.008 in the combined series of this study.We
found significant epistasis between these SNPs. Individuals
carrying allelesassociated with
rs1868402 and rs3785883 showed an increase of 1.02 SB-CDR
per year on average, whereas those carrying the alleles associated
with lower CSF ptau181at each SNP showed essentially no change
in the SB-CDR per year (combined analyses P~1:43|10{05;
Figure 3B; Table 6). The interaction between rs1868402 and
rs3785883 also replicated in the ADNI-CSF and ADNI-CC series
(Table 6).
higherCSFptau181
for
Gene expression
Since rs1868402 is located in a region that encodes a tau
phosphatase, we tested next whether the SNP is associated with
PPP3R1 mRNA levels and tau pathology in brain. We extracted
total RNA from the parietal lobe of 82 AD cases and 39 non-
demented elderly individuals. The allele of rs1868402, associated
with higher CSF ptau181levels, showed significantly lower PPP3R1
mRNA levels (P=0.010; Figure 1B), and higher tangle pathology
as measured by Braak stage (P=0.005; Figure 1C) in brain
samples with Ab pathology, but not in neuropathologically normal
samples. We also found that there was a correlation between
PPP3R1 mRNA levels and Braak stage in these samples (P=0.018;
Figure 1D). Rs12713636, in LD with rs1868402 (D9=1,
R2=0.75) also shows association with PPP3R1 mRNA levels and
in the same direction in the publicly available GEO GSE8919 [34]
dataset (P=0.015).
Discussion
In the present study we have used a novel and powerful
endophenotype-based approach to identify a novel genetic factor
implicated in AD. Most genetic studies in AD have focused on
identifying genetic factors that modulate risk or age at onset of
Table 2. Summary of biomarker characteristics.
ADRCADNI UW
Aß42
5646244 (175–1295)170656 (53–300) 131640 (61–231)
Tau3766241 (88–1358) 98656 (28–495)72651 (9–281)
ptau181 63632 (24–241)1868 (8–115) 63634 (14–281)
CSF Aß42, tau and ptau181levels for the Washington University Alzheimer’s
Disease Reseach Center (ADRC), Alzheimer’s Disease Neuroimaging Initiative
(ADNI) and for the University of Washington, Seattle (UW). For each phenotype
the mean in pg/ml with the standard deviation and range is shown.
doi:10.1371/journal.pgen.1001101.t002
Table 3. SNPs associated with CSF ptau181levels in the initial series, the replication series and the combined dataset.
gene rs MAFWU-ADRC-CSFReplication series Combined Series
uncorrected
FDR
p-valueuncorrected
FDR
p-valueuncorrected effectc
PPP3R1rs1868402A
0.375.90610204
0.0250.0050.0341.17610205*
2 20.30 (0.06)
PPP3R1rs6546366A
0.350.0040.030 0.271 0.476 0.003
20.24(0.08)
F2rs2070852A
0.320.0080.0410.6900.5230.114
20.14 (0.08)
FYNrs927010B
0.26 5.92610204
0.025 0.4250.4760.081
20.26 (0.17)
FYN rs77680460.390.0040.0300.4650.476 0.107
20.08 (0.06)
GSK3b
rs3755557B
0.120.0040.0300.4420.4760.794 0.06 (0.30)
GSK3b
rs7431209A
0.240.0050.0300.9460.5870.590 0.03 (0.08)
MAPTrs7210728A
0.360.0050.0410.5290.4760.110 0.11 (0.08)
MGEA5rs2305192B
0.300.0080.041 0.7950.5420.046
20.31 (0.14)
PRKCArs72184250.210.0050.0300.3580.4760.218 0.07 (0.07)
NPs that passed FDR correction in the WU-ADRC-CSF series were followed up in a replication series composed of CSF samples from ADNI-CSF and UW. Both series (WU-
ADRC-CSF and Replication) were combined to increase the statistical power. In the WU-ADRC-CSF and replication series; p-values in bold denote values that are
significant after FDR correction. In the Combined Series we applied Bonferroni correction. Threshold for Bonferroni correction is 1.3610204.
For each SNP the rs number and P values for association with ptau181before and after FDR correction are shown.
ADominant model.
BRecessive model.
Ceffects and the standard error of the mean are given in units of standard deviation.
doi:10.1371/journal.pgen.1001101.t003
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Figure 1. Rs1868402 is associated with CSF ptau181levels, PPP3R1 mRNA expression levels and tangle counts. A. Association of
rs1868402 with CSF ptau181levels (WU-ADRC-CSF n=353) was tested by an Analyses of Covariance (ANCOVA) including CDR, age and APOE
genotype as covariates. B: Minor allele carriers of rs1868402 have significantly lower PPP3R1 mRNA levels in individuals with AD pathology (n=82). C:
Minor allele carriers of rs1868402 have significantly higher numbers of tangles (n=82). D: PPP3R1 mRNA expression correlates with tangle counts in
individuals with AD pathological changes (n=82). The p-value is for the correlation between mRNA levels and genotypes.
doi:10.1371/journal.pgen.1001101.g001
Table 4. Rs 1868402 is associated with CSF ptau181levels in individuals with Ab deposition.
GeneRs ##stratumnptau181
EffectB
OR (95%)
PPP3R1rs1868402A
Total sample589 2.62610205
2 20.34 (0.08) 2.78 (1.73–4.54)
Low Ab levels300 1.13610204
2 20.47 (0.12)3.48 (1.80–6.75)
High Ab levels2890.023
2 20.18 (0.10)2.54 (1.00–5.75)
The p-value and the OR for rs1868402 were calculated by comparing the rs1868402 allele frequency in the lowest quartile vs the highest quartile of CSF ptau181levels,
after correction for covariates in the WU-ADRC-CSF (n=353)+ADNI-CSF (n=266) samples. CSF Samples from the UW could not be included in this analysis because there
is no study analyzing the correlation between the CSF Ab42levels and PET-PIB signal in this dataset, and therefore the threshold of CSF Ab42levels for PIB positivity is
unknown. OR (95%)=Odds ratio with the 95% confidence interval: Odds ratios were calculated comparing the highest vs lowest quartile of CSF ptau181levels. Samples
were stratified based on CSF Ab42levels as an approximation of Ab deposition. For the ADRC samples individuals with Ab42levels less than 500 pg/ml were considered
positive for Ab deposition and for ADNI samples the Ab42cutoff value of 192 pg/ml was used.
Values in boldface indicate significant p-values.
ADominant model.
Beffects and the standard error of the mean are given in units of standard deviation.
doi:10.1371/journal.pgen.1001101.t004
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disease [1–2]. The genetic factors influence other important facets
of AD, such as rate of progression or disease duration remain
poorly understood. Here we report that the minor allele of
rs1868402 shows significant, replicable association with higher
CSF ptau181levels and faster rate of progression of AD. We failed
to detect evidence for association with risk for disease or age at
onset. This is consistent with the known pathobiology of AD, in
which Ab aggregation and deposition is an early preclinical event,
followed by increased CSF tau and ptau181 levels and tau
pathology during the clinical phase of the disease. Under this
model, factors that affect tau phosphorylation and aggregation
may be expected to modify disease progression but not risk for
disease, while genes that influence Ab aggregation, such as APOE4
[35], would be expected to influence CSF Ab42levels and risk/age
at onset of disease but not CSF tau/ptau181 levels or rate of
progression of disease. Table 7 illustrates that this is the case in our
data. Genetic analyses of CSF Ab42and tau variation therefore
allow identification of genetic factors that influence different
components of the disease process.
Our results suggest that rs1868402, or another variant in LD
with it, may reduce calcineurin expression/activity leading to an
increase in tau phosphorylation increasing tau pathology and
neurodegeneration in individuals with Ab deposition. The
resulting increase in tau-related pathology would then increase
the rate of progression of AD. Several studies provide support for a
role of calcineurin in AD pathogenesis. Inhibition of calcineurin in
mouse brains by cyclosporin A or FK506 or rat brain by antisense
oligonucleotides led to enhanced tau phosphorylation [36–38]. In
mice the increase in tau phosphorylation was accompanied by
impaired spatial memory, a characteristic feature of AD [36].
Finally, in AD patients, calcineurin activity is decreased and
correlates with neuropathologic changes [39].
Our analyses suggest that genetic variants associated with CSF
Ab42levels also influence risk and age at onset (e.g. APOE) but
variants associated with CSF ptau181levels have a greater impact
on rate of progression (Table 7). Genome-wide association studies
of CSF tau/ptau181levels should identify novel genetic variants
which will likely influence rate of progression of AD. Variants that
influence disease progression may have significant clinical benefit.
For example, these variants have the potential to predict more
accurately the time from diagnosis to functional impairment that
may require nursing home placement. Stratification of samples by
such SNPs will enable cheaper and more efficient clinical trials by
selecting individuals expected to have faster rates of progression.
By targeting different facets of AD biology this approach can
identify a broader range of potential therapeutic targets than a
conventional case-control design. Drugs that inhibit or decrease
tau phosphorylation would be expected to decrease cognitive
decline in individuals with very mild dementia or delay the
appearance of memory problems in elderly individuals with low
CSF Ab42 levels. Finally, we believe that this approach is
applicable to other common neurological and psychiatric
disorders, where biomarkers of disease have been identified, and
underlines the value and importance of finding such markers in
other diseases.
Materials and Methods
Subjects and endophenotypes
The cerebrospinal fluid discovery series includes 353 individuals
enrolled in longitudinal studies at the WU-ADRC. CSF collection
and Ab42, tau and ptau181 measurements were performed as
described previously [14]. Table 1 shows the demographic data for
the CSF series and Table 2 shows a description of the CSF
biomarker in each dataset. The CSF replication series consists of
236 individuals from the ADNI dataset and 257 individuals from
the University of Washington (UW, Seattle). All CSF samples were
from individuals of European descent. Written consent was
obtained from all participants. While there are differences in the
absolute levels of the biomarker measurements between the two
studies that likely reflect differences in the methods used for
quantification (regular ELISA vs Luminex), ascertainment, and/or
Table 5. Rs1868402 is not associated with risk for AD.
MAF
SeriesCasesControlsMinor AlleleCases Controls p-value
rs1868402MRC 666812C0.5120.471 0.06
WU ADRC-CC 340281C 0.497 0.4700.25
ADNI-CC 100 123C 0.5050.4460.11
Total11061216C 0.504 0.4670.10
rs1868402 was genotyped in the MRC, WU-ADRC-CC and ADNI-CC series. Number of cases and controls, minor allele and minor allele frequency (MAF) for each series
and for the combined series are showed. P-value for the dominant (rs1868402) model were calculated by logistic regression including APOE, age, gender and series as
covariates.
doi:10.1371/journal.pgen.1001101.t005
Figure 2. Survival curves comparing age at onset of LOAD
between the different genotypes of rs1868402. Survival fractions
were calculated using the Kaplan-Meier method and significant
differences were calculated by Log-rank test. Association with age at
onset was calculated in a combined series with samples from WU-
ADRC-CC, ADNI-CC and MRC.
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in handling of the CSF after collection, CSF ptau181levels in the
WU-ADRC-CSF, ADNI-CSF, and UW samples show similar
characteristics. CSF ptau181levels show a 10–17 fold difference
between individuals in each dataset, are normally distributed after
log-log transformation, and have similar covariates in each dataset
(see statistical analyses).
Risk for disease and age at onset analyses were analyzed in a
total of 1106 late-onset AD (LOAD) cases and 1216 age-gender-
ethnicity matched non-demented controls (Table 5). These
samples were ascertained at the WU-ADRC, MRC genetic
resource for late-onset AD (UK, MRC Sample [40]), and ADNI.
Cases received a diagnosis of dementia of the Alzheimer’s type
(DAT), using criteria equivalent to the National Institute of
Neurological and Communication Disorders and Stroke-Alzhei-
mer’s Disease and Related Disorders Association for probable AD
[41–42]. All individuals were of European descent and written
consent was obtained from all participants.
Association of rate of progression of dementia with genetic
variants was tested in two longitudinal series from the WU-ADRC
and ADNI. The WU-ADRC-CSF series includes 109 individuals
with clinical data from at least two time points starting with a
CDR of 0 or 0.5 in the first interview, and a diagnosis of DAT
(dementia Alzheimer Type) at the last visit. There are an average
of 3.8 observations per individual, which varies from 2 to 14 with
an average follow up time of 3.2 years. The second series with
longitudinal data is the ADNI series: 459 individuals (236 with
CSF data and another 223 with no CSF data) have had a clinical
examination at least two time points with an average of 4.1
observations per individual, which varies between two and six
observations, however the average follow up time is only 1.9 years.
To study the association with progression rate in the CSF samples
we analyze only the 150 samples with low (,192pg/ml) CSF
Ab42, as explained in the Statistical Analyses section.
SNP selection and genotyping
Based on bibliographic data, we selected 384 SNPs in the most
relevant tau kinases, phosphatases, and in other genes implicated
in other posttranslational modifications of tau, or tau degradation
[45–47] (Table S1). Tagging SNPs (r2.0.8), based on CEU-
HapMap data, were selected for each of these genes. We used
Pupasuite software [48] to select potentially functional variants in
the selected genes and flanking regions. SNPs were genotyped
Table 6. Association with rate of disease progression.
WU-ADRC-CSFADNI-CSF Combined CSF series
ADNI samples
No CSF
rs18684020.0026 0.014 1.71610205
0.0187
rs1868402*rs3785883 4.00610204
0.010 1.43610205
0.0123
Rs1868402 is associated with progression rate and shows epistasis with rs3785883, which is also associated with CSF tau levels [27].
Rate of progression were analyzed by the change of the Clinical Dementia Rating sum of boxes (CDR-SB) score per year in individuals with low CSF Ab42levels
(ADRC,500pg/ml; ADNI,192pg/ml) and in samples with no CSF data. Association of SNPs with progression was calculated with mixed linear models (proc mixed) after
controlling for age, sex, APOE, CDR and/or CSF ptau181and Ab42.
doi:10.1371/journal.pgen.1001101.t006
Figure 3. Genetic variants associated with CSF ptau181levels are also associated with rate of progression of AD. Rate of progression is
defined as the change in the Clinical Dementia Rating sum of boxes (SB-CDR) score per year. Association of SNPs with progression was calculated
using a mixed linear model (PROC MIXED) after controlling for age, sex, APOE, initial CDR, CSF ptau181and Ab42levels. A. Minor allele carriers of
rs1868402, are associated with higher CSF ptau181levels, and show a 6 fold faster progression than homozygotes for the major allele (CDR-SB/year:
0.58 vs 0.09; p=0.0026) in individuals from the WU-ADRC-CSF with low CSF Ab42levels (,500pg/ml). For this SNP the dominant model was used
because it showed the best fit in all the analyses. B. rs3785883 genotypes do not have significantly different progression rates P=0.057. The
genotype frequency distribution for rs3785883 with disease progression is most likely not significant due to the low statistical power. AA carriers
show a CDR-SB of 1.01, AG of 0.47 and GG 0.26 (p=0.057). The additive model was used because it showed the best fit. C. Rs1868402 and rs3785883
show an epistatic interaction. Carriers of the alleles associated with higher CSF ptau181levels (CT+CC for rs1868402 and AA for 3785883) showed a
CDR-SB/year of 1.02 vs 20.006 for carriers of alleles associated with lowest CSF ptau181levels. LP indicates lumbar puncture.
doi:10.1371/journal.pgen.1001101.g003
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using the Illumina Golden Gate, Sequenom and/or Taqman
genotyping technologies. Only SNPs with a genotyping call rate
higher than 95% and in Hardy-Weinberg equilibrium were used
in the analyses.
Gene expression
Expression studies were carried out using cDNA obtained from
the parietal lobe of 82 AD cases and 39 non-demented individuals
(CDR=0) obtained through the WU-ADRC Neuropathology
Core (Brain samples; Table 1). AD changes were measured using
Braak and Braak staging [43]. All AD cases had a Braak and
Braak score of 5 or 6. Among the non-demented individuals 24
brains had a Braak and Braak score ranging from 1–4 indicating
the presence of some tangle pathology.
Total RNA was extracted from the parietal lobe of 82 AD cases
and 39 non-demented individuals, using the RNeasy mini kit
(Qiagen) following the manufacturer’s protocol. cDNAs were
prepared from the total RNA, using the High-Capacity cDNA
Archive kit (ABI). Gene expression was analyzed by real-time PCR,
using an ABI-7500 real-time PCR system. Real-time PCR assays
were used to quantify PPP3R1 cDNA levels. Taqman assays for
GAPDH (sequences available on request) PPP3R1 (ABI: C_
12044272_10) and cyclophilin A (ABI: 4326316E) were used to
quantify the gene expression levels. Each real-time PCR run included
within-plate duplicates and each experiment was performed, at least
twice for each sample. Real-time data were analyzed using the
comparative Ct method. The Ct values of each sample were
normalized with the Ct value for the housekeeping genes, GADPH
and cyclophilin, and were corrected for the PCR efficiency of each
assay [44], although the efficiency of all reactions was close to 100%.
Only samples with a standard error of ,0.15% were analyzed.
Statistical analyses
CSF ptau181values were log-log transformed to approximate a
normaldistribution.Analysisofthecovariance(ANCOVA)wasused
to test for association between genotypes and CSF ptau181levels. In
order to identify the covariates that affect CSF ptau181levels, we
performed a stepwise discriminant analysis including CDR, age,
gender and APOE genotype. CDR, age, and APOE genotype were
identifiedassignificantcovariatesintheWU-ADRC-CSFseriesand,
CDR and APOE genotype in the replication series (this series has a
narrower age range than the WU-ADRC-CSF series). These
covariates were included in the respective ANCOVA. Each SNP
was tested using an additive model with minor allele homozygotes
coded as 0, heterozygotes coded as 1, and major allele homozygotes
coded as 2. When the additive model was significant after multiple
test correction, dominant and recessive models were tested to
determine whether the y were a better fit. Because the CSF ptau181
levels in the WU-ADRC-CSF, ADNI-CSF and UW samples were
measured using different platforms (Innotest plate ELISA vs AlzBia3
bead-based ELISA, respectively) we were not able to combine the
raw data, rather we combined the residual values of the CSF ptau181
obtained aftercorrectingforthecovariates.Nosignificantdifferences
in the residuals from the different series were found, indicating that
the differences in the CSF levels due to the different platforms were
corrected by using the residuals.
Multiple test correction: Initial tests for association of SNPs with
CSF ptau181levels were evaluated using a False Discovery Rate
(FDR) filter of 0.1 to correct for multiple testing [49]. In the initial
screening only p-values more significant than 8:1|10{3passed
FDR filter of 0.1. In the replication series p-values more significant
than 5:0|10{3passed FDR filter. To reduce the probability of
false positives we also used the more stringent Bonferroni
correction to adjust the alpha level in the analysis of association
with CSF ptau181levels in the combined samples. In this case, the
threshold for Bonferroni correction for the combined sample is
1:3|10{04(a; 0:05=384~1:3|10{04). No multiple test correc-
tion was applied for association with risk for disease, age at onset
or progression because only one or two SNPs with specific
hypotheses were tested for association. To calculate the impact of
rs1868402 on the CSF ptau181levels, we calculated the Odds
Ratio (OR) of this SNP by comparing its frequency in the highest
vs lowest quartile of the residuals for CSF ptau181levels.
Allelic association with risk for AD was tested using logistic
regression including APOE, gender, age and series as covariates.
Association with AAO was carried out using the Kaplan-Meier
method and tested for significant differences, using a log-rank test.
Association with rate of disease progression was evaluated as
described previously [33]. Briefly, progression of disease was
measured by the change in sum of boxes on the CDR (clinical
dementia rate; SB-CDR) per year. CDR is a global measurement of
the severity of symptoms of dementia. CDR evaluates cognitive and
functional performance in six areas (memory, orientation, judgment
and problem solving, community affairs, home and hobbies and,
personal care), each of these areas has a possible score of 0, 0.5, 1, 2
or 3. The sum of boxes can vary between 0 and 18. Higher scores
indicate more significant memory problems and correlate with
neurodegeneration [50]. The change in SB-CDR per year fitted a
linear model in both series and therefore we used a mixed linear
model (PROC MIXED; SAS Institute Inc) to determine whether
there is a relationship between the slope of the SB-CDR score and
time as a function of genotype after controlling for initial age,
gender, APOE, initial CDR, CSF ptau181and Ab42levels. Because
the association between rs1868402 and CSF ptau181was driven by
individuals with low CSF Ab42levels, association with progression
in the CSF datasets was analyzed only in individuals with low CSF
Ab42levels (less than 500 pg/ml in the WU-ADRC-CSF series [14]
or 192 pg/ml in the ADNI-CSF series [21]). We analyzed whether
Table 7. Variants that modify CSF Ab42levels affect risk for AD, whereas variants associated with CSF ptau181levels affect rate of
progression.
Association withCSF ptau181*CSF Ab42*Risk** age at onset**Progression***
rs18684021.17610205*0.530.310.471.31610204
APOE40.018 3.961029*
3.9610240
4.061027
0.136
P-values for the association of rs1868402 (PPP3R1) and APOE4 with CSF Aß42and ptau181levels, risk for AD, age at onset and disease progression are shown.
*Sample size=846; WU-ADRC-CSF+ADNI-CSF+UW.
**Sample size=2322 MRC+WU ADRC-CC+ADNI-CC.
***Sample size=259; WU-ADRC-CSF+ADNI-CSF with low CSF Ab42levels.
Significant p-values are in bold.
doi:10.1371/journal.pgen.1001101.t007
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the combination of genotypes of rs1868402 and rs3785883 predicts
the rate of progression better than either of these SNPs alone. To do
that we included an interaction term rs1868402*rs3785883 in the
‘‘proc mixed’’ SAS program, that combine the genotypes for the
two SNPs. We also combined the data from the WU-ADRC-CSF
and ADNI-CSF to increase the statistical power.
Association between cDNA levels, tau pathology (Braak tangle
stage) and genotypes were carried out using ANCOVA. Stepwise
discriminant analysis was used to determine the significant
covariates (age, gender, postmortem interval, APOE genotype,
and CDR). One-tailed P-values were calculated, because a priori
predictions were made based on the associations with CSF ptau181
levels. We also used the GEO dataset GSE8919 [51] to analyze
the association between rs12713636, in LD with rs1868402
(D9=1, R2=0.75) and PPP3R1 gene expression. In this dataset
there are genotype and expression data from 486 Late onset
Alzheimer Diseases cases and 279 neuropathologically confirmed
controls. We only extracted the normalized PPP3R1 mRNA levels
and the genotype data for rs12713636. Genotypes for rs12713636
were used because rs1868402 was not included in this dataset.
ADNI material and methods
Data used in the preparation of this article were obtained from
the ADNI database (www.loni.ucla.edu/ADNI). The ADNI was
launched in 2003 by the National Institute on Aging, the National
Institute of Biomedical Imaging and Bioengineering, the Food and
Drug Administration, private pharmaceutical companies and non-
profit organizations, as a $60 million, 5-year public-private
partnership. The Principal Investigator of this initiative is Michael
W. Weiner, M.D. ADNI is the result of efforts of many co-
investigators from a broad range of academic institutions and
private corporations, and subjects have been recruited from over
50 sites across the U.S. and Canada. The initial goal of ADNI was
to recruit 800 adults, ages 55 to 90, to participate in the research -
approximately 200 cognitively normal older individuals to be
followed for 3 years, 400 people with MCI to be followed for 3
years, and 200 people with early AD to be followed for 2 years.’’
For up-to-date information see www.adni-info.org.
Supporting Information
Figure S1
higher CSF ptau181levels. The mean and the standard error of the
mean (SEM) for the raw and residuals CSF ptau181levels for the
WU-ADRC-CSF, ADNI-CSF and UW series is shown. A. Raw
CSF ptau181levels for the WU-ADRC-CSF series by rs1868402
genotype. CC+CT: 64.4562.52. TT: 57.2062.49 pg/ml. B. Raw
CSF ptau181 levels for the ADNI-CSF series by rs1868402
genotype. CC+CT: 35.9561.85. TT: 30.2261.44 pg/ml. C. Raw
CSF ptau181 levels for the UW series by rs1868402 genotype.
CC+CT: 67.4663.53. TT: 61.4262.71 pg/ml. D. Residuals CSF
ptau181 levels for the WU-ADRC-CSF series by rs1868402
genotype. CC+CT: 0.1760.07. TT: 20.2260.08. E. Residuals
CSF ptau181 levels for the ADNI-CSF series by rs1868402
genotype. CC+CT: 0.1360.09. TT: 20.1660.08. F. Residuals
CSF ptau181 levels for the UW series by rs1868402 genotype.
CC+CT: 0.1060.08. TT: 20.1260.09.
Found at: doi:10.1371/journal.pgen.1001101.s001 (0.06 MB
DOC)
Minor allele carriers of rs1868402 present significantly
Figure S2
icantly associated with CSF tau levels in the ADRC series. Color
represents D9=1 and numbers correspond to r2.
Found at: doi:10.1371/journal.pgen.1001101.s002 (0.16 MB
DOC)
Linkage disequilibrium among PPP3R1 SNPs signif-
Figure S3
are also associated with rate of progression. Average progression
rate by genotype with the 95% confidence interval. Solid lines
represent the average progression rate. Dotted lines represent the
95% confidence interval. The lines for the different genotypes are
color code. A. Minor allele carriers of rs1868402, are associated
with higher CSF ptau181 levels, and show a 6-fold faster
progression than homozygotes for the major allele (CDR-SB/
year: 0.58 vs. 0.09; p=0.0026) in individuals from the WU-
ADRC-CSF with low CSF Ab42levels (,500pg/ml). B. rs3785883
genotypes do not have significantly different progression rates
P=0.057. The genotype frequency distribution for rs3785883
with disease progression is most likely not significant due to the low
statistical power. AA carriers show a CDR-SB of 1.01, AG of 0.47
and GG 0.26 (p=0.057).
Found at: doi:10.1371/journal.pgen.1001101.s003 (0.23 MB
DOC)
Genetic variants associated with CSF ptau181levels
Table S1
official and the most common alias of the gene, the activity related
to tau, chromosomal position, and gene size in Kb are shown. A.
Tag SNP. SNPs that capture 80% of the common sequence
diversity within the gene. B. Only validated SNP with a minor
allele frequency .0.1. C. Number of SNPs that passed quality
controls.
Found at: doi:10.1371/journal.pgen.1001101.s004 (0.09 MB
DOC)
Genes and SNPs/gene genotyped in this study. The
Table S2
the discovery series (WU-ADRC-CSF). SNPs associated with CSF
ptau181 levels after FDR correction are shown. MAF=Minor
Allele Frequency. A: Dominant model. B: Recessive model.
Found at: doi:10.1371/journal.pgen.1001101.s005 (0.03 MB
DOC)
SNPs in PPP3R1 associated with CSF ptau181levels in
Acknowledgments
Some of the data used in the preparation of this article were obtained from
the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (www.
loni.ucla.edu/ADNI). 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. ADNI
investigators: (complete listing available at www.loni.ucla.edu/
ADNI/Collaboration/ADNI_Authorship_list.pdf).
The authors thank the Clinical Core of the ADRC for clinical and
cognitive assessments of the participants, the Genetics Core of the ADRC
for APOE genotypes and the Biomarker Core of the Adult Children Study
for the CSF collection and assays.
Author Contributions
Conceived and designed the experiments: CC JSKK AMG. Performed the
experiments: CC KM NS ARS. Analyzed the data: CC JSKK SB PN.
Contributed reagents/materials/analysis tools: RA PH DH MMO JW SL
ERP GL JBL DG The Alzheimer’s Disease Neuroimaging Initiative JCM
AMF DMH. Wrote the paper: CC AMG.
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Rs1868402 Is Associated with CSF ptau Levels
PLoS Genetics | www.plosgenetics.org 10September 2010 | Volume 6 | Issue 9 | e1001101