Genetic Variants in the Fat and Obesity Associated (FTO)
Gene and Risk of Alzheimer’s Disease
Christiane Reitz1,2,3, Giuseppe Tosto1, Richard Mayeux1,2,3,4,5*, Jose A. Luchsinger5, for the NIA-LOAD/
NCRAD Family Study Group and the Alzheimer’s Disease Neuroimaging Initiative"
1The Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, New York, New York, United
States of America, 2The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, New York, United States of America,
3Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, New York, United States of America, 4Department of Psychiatry, College
of Physicians and Surgeons, Columbia University, New York, New York, United States of America, 5Department of Medicine, Pathology, College of Physicians and
Surgeons, Columbia University, New York, New York, United States of America
Background: Recent studies showed that polymorphisms in the Fat and Obesity-Associated (FTO) gene have robust effects
on obesity, obesity-related traits and endophenotypes associated with Alzheimer’s disease (AD).
Methods: We used 1,877 Caucasian cases and controls from the NIA-LOAD study and 1,093 Caribbean Hispanics to further
explore the association of FTO with AD. Using logistic regression, we assessed 42 SNPs in introns 1 and 2, the region
previously reported to be associated with AD endophenotypes, which had been derived by genome-wide screenings. In
addition, we performed gene expression analyses of neuropathologically confirmed AD cases and controls of two
independent datasets (19 AD cases, 10 controls; 176 AD cases, 188 controls) using within- and between-group factors
ANOVA of log10transformed rank invariant normalized expression data.
Results: In the NIALOAD study, one SNP was significantly associated with AD and three additional markers were close to
significance (rs6499640, rs10852521, rs16945088, rs8044769, FDR p-value: 0.05,p,0.09). Two of the SNPs are in strong LD
(D9.0.9) with the previously reported SNPs. In the Caribbean Hispanic dataset, we identified three SNPs (rs17219084,
rs11075996, rs11075997, FDR p-value: 0.009,p,0.01) that were associated with AD. These results were confirmed by
haplotype analyses and in a metaanalysis in which we included the ADNI dataset. FTO had a significantly lower expresssion
in AD cases compared to controls in two independent datasets derived from human cortex and amygdala tissue,
respectively (p=2.1861025 and p,0.0001).
Conclusions: Our data support the notion that genetic variation in Introns 1 and 2 of the FTO gene may contribute to AD
Citation: Reitz C, Tosto G, Mayeux R, Luchsinger JA, for the NIA-LOAD/NCRAD Family Study Group and the Alzheimer’s Disease Neuroimaging Initiative (2012)
Genetic Variants in the Fat and Obesity Associated (FTO) Gene and Risk of Alzheimer’s Disease. PLoS ONE 7(12): e50354. doi:10.1371/journal.pone.0050354
Editor: Steven Estus, University of Kentucky, United States of America
Received January 6, 2012; Accepted October 24, 2012; Published December 12, 2012
Copyright: ? 2012 Reitz 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 the National Institutes of Health (NIH) and the National Institute on Aging: R37-AG15473, P01-AG07232, The
Blanchett Hooker Rockefeller Foundation, The Charles S. Robertson Gift from the Banbury Fund, and The Merrill Lynch Foundation. Dr. Reitz was further supported
by a Paul B. Beeson Career Development Award (K23AG034550). Data and samples from the National Institute on Aging - Late Onset Alzheimer’s Disease Family
Study, which receives government support under a cooperative agreement grant (U24AG026390), were also used in this study. Samples from the National Cell
Repository for Alzheimer’s Disease (NCRAD), which receives government support under a cooperative agreement grant (U24 AG21886) awarded by the National
Institute on Aging (NIA), were used in this study. Data collection and sharing for the ADNI project was funded by the Alzheimer’s Disease Neuroimaging Initiative
(ADNI) (National Institutes of Health Grant U01 AG024904). 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 Ltd.; AstraZeneca; Bayer HealthCare; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals Inc.; Eli Lilly and
Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; Janssen Alzheimer Immunotherapy Research &
Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; Novartis
Pharmaceuticals Corporation; Pfizer Inc.; Servier; Synarc Inc.; 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 (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. The funders had no role in study design, data collection and analysis,
decision to publish, or preparation of the manuscript.
Competing Interests: ADNI is partly funded through generous contributions from the following: Abbott; Amorfix Life Sciences Ltd.; AstraZeneca; Bayer
HealthCare; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd
and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson &
Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; Novartis Pharmaceuticals Corporation;
Pfizer Inc.; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. There are no patents, products in development or marketed products to declare. This does
not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.
* E-mail: email@example.com
" Membership of the NIA-LOAD/NCRAD Family Study Group and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) is provided in the Acknowledgments.
PLOS ONE | www.plosone.org1 December 2012 | Volume 7 | Issue 12 | e50354
Alzheimer’s disease (AD) is the most common cause of
dementia, accounting for 60–80% of cases . At present, about
33.9 million people worldwide have AD, and the prevalence is
anticipated to triple over the next 40 years owing to demographic
changes and longer life expectancies . Available drugs for
dementia and AD have small effect sizes and do not clearly alter
disease progression .
As delaying symptom onset by as little as 1 year could
potentially lower AD prevalence by more than 9 million cases
over the next 40 years , there has been growing interest in
identification of preventive measures. Observational studies have
assessed a wide range of potentially modifiable risk factors, in
particular cardiovascular risk factors. While for diabetes the
association with AD seems clear [3,4], the association for most
other cardiovascular risk factors, including obesity, remains largely
inconsistent across studies. For obesity, most studies show an
increased risk , but some show an inverse risk [6,7], some show
nonlinear associations , and some show no association .
Explanations for the conflicting data include reversed causation,
residual confounding, potential survival bias, and decreased
validity of body mass index (BMI) as a measure of obesity in the
elderly . In general, measures of central obesity, particularly
waist to hip ratio (WHR), seem to be better predictors of
cardiovascular outcomes compared with BMI , and central
obesity in middle age is related to a higher risk of dementia.
Recent studies have demonstrated that polymorphisms in the
Fat and Obesity-Associated (FTO) gene have strong and robust
effects on obesity and obesity-related traits (such as body mass
index (BMI), waist circumference, waist to hip ratio, bicondilar
upper arm width and upper arm circumference) [12,13,14,15].
FTO is located on chromosome 16q12.2, has nine known splice
variants and is highly expressed in the brain. Although this gene
has nine exons, all reported polymorphisms are part of one LD
block spanning 47 kb across intron 1, exon 2 and part of intron 2
The same polymorphisms have also independent strong effects
on insulin resistance/Type 2 Diabetes, which is – as described
above- a strong risk factor for AD [12,13,14], metabolic syndrome
, obesity-related dyslipidemia , and changes in blood
pressure . In addition, several studies reported associations of
genetic variation in FTO with traits that are common endophe-
notypes of dementia. In the Alzheimer’s Disease Neuroimaging
Initiative (ADNI), the FTO polymorphism most commonly
associated with obesity and in Caucasians (rs9939609 (Intron 1))
was associated with reductions in frontal and occipital lobe
volumes . In a Swedish dataset involving 355 old men at the
age of 82 years from the Uppsala Longitudinal Study of Adult
Men (ULSAM), rs9939609 was associated with impairment in
verbal fluency . In the only study to date that assessed the
effect of genetic variation in FTO on AD risk, a longitudinal cohort
study of the Kungsholmen project that involved 1,003 Caucasians
followed for 9 years, the minor allele of rs9939609 was associated
with a 1.6-fold risk of developing AD . The advantage of
relating genetic variation with a phenotype of interest is that it
overcomes the issues of reverse causation and residual confound-
The goal of the present study was to further clarify whether
genetic variation in FTO, that is similar to or in linkage
disequilibrium (LD) with the SNPs previously reported to be
associated with obesity-related measures or AD endophenotypes is
associated with AD. We explored this question by genetic
association analyses of two independent case-control datasets that
are derived from different ethnic groups and have sufficient power
to detect modest effect sizes. In addition, we peformed a meta-
analysis that also included the publicly available ADNI dataset,
and conducted microarray gene expression analyses of two
Figure 1. Genomic organization of FTO and its neighboring genes (not drawn to scale). The FTO gene contains nine exons which are
depicted in blue rectangles. The SNPs previously reported to be associated with obesity-related measures or AD endophenotypes, as well as the SNPs
associated with AD in the present study, are located in Intron 1, Exon 2 and Intron 2.
FTO and Alzheimer’s Disease
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The two datasets used for the discovery single marker analyses
included (1) 1,877 cases and controls from the NIA-LOAD study
, and (2) 1,093 cases and controls from a Caribbean Hispanic
For the NIALOAD study, recruitment took place throughout
the United States at 18 participating AD centers (ADCs), each of
which had received approval by their institutional review board. A
collaborative effort by each ADC, the NIA, the Alzheimer’s
Disease Education and Referral Center, and the Alzheimer’s
Association led to national media coverage, which facilitated
recruitment. A toll-free number at the National Cell Repository
for Alzheimer’s Disease (http://ncrad.iu.edu) was made available.
When qualifying families contacted the National Cell Repository,
research staff referred the family to the geographically closest
participating ADC for evaluation. The recruitment criteria
included a family with multiple members affected with LOAD
that could provide clinical information and a biological sample for
DNA extraction. The proband had to have a diagnosis of definite
or probable LOAD  with onset after 60 years of age and a full
sibling with definite, probable, or possible LOAD with onset after
60 years of age. A third biologically related family member was
required, who could have been a first-, second-, or third-degree
relative of the affected sibling pairs and 60 years or older if
unaffected or 50 years or older if diagnosed as having LOAD or
mild cognitive impairment . Unaffected persons were required
to have had documented cognitive testing and clinical examination
results to verify the clinical designation. A minimal data set
included demographic variables, diagnosis, age at onset, method of
diagnosis, Clinical Dementia Rating Scale score , and the
presence of other relevant health problems. Each ADC was
required to use standard research criteria for the diagnosis of
LOAD . Participants with advanced disease or those living in a
remote location who could not complete a detailed in-person
evaluation contributed blood samples, and the site investigator
conducted a detailed review of medical records to document the
presence or absence of LOAD.
The 1,093 Caribbean Hispanic subjects were selected from the
Washington Heights–Inwood Columbia Aging Project (WHICAP)
study and the Estudio Familiar de Influencia Genetica de
Alzheimer (EFIGA) study. The WHICAP study  is a
population-based epidemiologic study of randomly selected elderly
individuals residing in northern Manhattan, New York, compris-
ing three ethnic groups: non-Hispanic white, Caribbean Hispanic,
and African American. For the current study, only individuals who
were self-reported Hispanic of Caribbean origin were included. In
addition, we selected one affected individual from each family
participating in the EFIGA study of Caribbean Hispanic families
with LOAD . Both studies followed the same clinical
diagnostic methods. The participants originated from the Domin-
ican Republic and Puerto Rico. Approximately 60.3% of the
affected individuals were participants in the WHICAP epidemi-
ologic study, and the remaining 39.7% of the participants were
from the EFIGA study. All unaffected individuals were partici-
pants in the WHICAP epidemiologic study. For the familial cases,
we selected one proband from each family to create a cohort of
unrelated individuals. We selected persons with definite or
probable LOAD over those with possible LOAD to limit the
effects of comorbidity. Data were available from medical,
neurological, and neuropsychological evaluations  collected
from 1999 through 2007. The standardized neuropsychological
test battery covered multiple domains and included the Mini-
Mental State Examination , the Boston Naming Test , the
Controlled Word Association Test from the Boston Diagnostic
Aphasia Evaluation , the Wechsler Adult Intelligence Scale–
Revised similarities subtest , the Mattis Dementia Rating Scale
, the Rosen Drawing Test , the Benton Visual Retention
Test , the multiple-choice version of the Benton Visual
Retention Test , and the Selective Reminding Test . The
diagnosis of dementia was established on the basis of all available
information gathered from the initial and follow-up assessments
and medical records. The diagnosis of LOAD was based on the
National Institute of Neurological Disorders and Stroke–Alzhei-
mer’s Disease and Related Disorders Association criteria .
The clinical characteristics of these two datasets are summarized
in Table 1. As described above, for both datasets, the diagnoses of
‘probable’ or ‘possible’ AD were defined based on the National
Institute of Neurological and Communication Disorders and
Stroke–Alzheimer’s Disease and Related Disorders Association
(NINCDS-ADRDA) diagnosis criteria at clinics specializing in
memory disorders or in clinical investigations. Although both
datasets were subsets of larger family samples, all samples used in
the present study were unrelated. From each family, one affected
individual with definite or probable LOAD was selected, and
unrelated, unaffected individuals served as controls. Persons were
classified as ‘‘controls’’ when they were without cognitive
impairment or dementia at last visit [23,24]. Informed consent
was obtained in written form from all participants using
procedures approved by institutional review boards at each of
the clinical research centers collecting human subjects. Whether
the participants had the capacity to consent was assessed by in-
person interview of the participant and/or next of kin, carers or
guardians. Next of kin, carers or guardians consented on the
behalf of participants whose capacity to consent was reduced.
Recruitment for the Caribbean Hispanic Study was approved by
the Institutional Review Board of the Columbia University
Medical Center. Recruitment for the NIALOAD Study was
approved by the relevant institutional review boards of the
participating centers (ie. the IRBs of Boston University, Columbia
University, Duke University, Indiana University, Massachusetts
General Hospital, Mayo Clinic, Mount Sinai School of Medicine,
Oregon Health & Science University, Rush University Medical
Center, University of Alabama at Birmingham, University of
California Los Angeles; University of Kentucky; University of
Pennsylvania; University of Pittsburgh; University of Southern
California; University of Texas Southwestern; University of
Washington; Washington University Medical Center; University
of Miami; Northwestern University; Emory University).The study
was conducted according to the principles expressed in the
Declaration of Helsinki.
The publicly available ADNI data used in the preparation of
this article were obtained from the Alzheimer’s Disease Neuroim-
aging Initiative (ADNI) database (adni.loni.ucla.edu). The ADNI
was launched in 2003 by the National Institute on Aging (NIA),
the National Institute of Biomedical Imaging and Bioengineering
(NIBIB), the Food and Drug Administration (FDA), private
pharmaceutical companies and non-profit organizations, as a
$60 million, 5-year public-private partnership. The primary goal
of ADNI has been to test whether serial magnetic resonance
imaging (MRI), positron emission tomography (PET), other
biological markers, and clinical and neuropsychological assessment
can be combined to measure the progression of mild cognitive
impairment (MCI) and early Alzheimer’s disease (AD). Determi-
nation of sensitive and specific markers of very early AD
progression is intended to aid researchers and clinicians to develop
new treatments and monitor their effectiveness, as well as lessen
FTO and Alzheimer’s Disease
PLOS ONE | www.plosone.org3 December 2012 | Volume 7 | Issue 12 | e50354
the time and cost of clinical trials. The Principal Investigator of
this initiative is Michael W. Weiner, MD, VA Medical Center and
University of California – San Francisco. ADNI is the result of
efforts of many coinvestigators 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. Also this study complied with the Declaration
For both studies, we used the results from direct genotyping of
single nucleotide polymorphisms (SNPs) in FTO that was
conducted as part of genome-wide studies described previously
[23,24]. For the analyses described in this study, we focused on the
SNPs in Intron1, Exon 2 and Intron 2, ie. all SNPs in the regions
previously reported to be associated with obesity measures,
diabetes, brain volume and verbal fluency. Information on
platforms used for APOE genotyping is given in Table S1.
Microarray gene expression
For the first microarray gene expression dataset, we used brain
tissue from 19 pathologically confirmed AD cases and 10
pathologically confirmed controls from the New York Brain Bank
(www.nybb.hs.columbia.edu). For each of these brains, expression
profiling was performed separately for RNA isolated from the
cerebellum, the parietal-occipital neocortex and the amygdala.
Frozen brain tissue was ground over liquid nitrogen and stored at
280uC until use. Total RNA was extracted and purified using
TRIzol Plus RNA purification kit (Invitrogen). Quantification and
qualification of all RNA preparations was performed using an
Agilent Bioanalyzer (RNA 6000 nano-kit) and only samples with
RNA integrity number (RIN).8 were used in the subsequent
RNA amplification and hybridization steps. The Genechip
expression two-cycle target labeling kit (Affymetrix) was used for
all samples according to Affymetrix protocols. Finally, the
Affymetrix GeneChipH Human Exon 1.0 ST Arrays was used
for the expression profiling. The three-region approach allowed us
to enhance the signal-to-noise ratio , and to determine those
changes in expression patterns of candidate genes that are specific
for late-onset AD and consistent with distribution of AD
pathology. The second gene expression dataset was a publicly
available dataset consisting of expression data derived from
various regions of the human cortex of 188 neuropathologically
confirmed controls and 176 neuropathologically confirmed AD
cases that was obtained using the Illumina HumanRefseq-8
Expression BeadChip platforms (http://labs.med.miami.edu/
myers/LFuN/LFuN.html). While for the New York Brain Bank
dataset exon level data were available, for the publicly available
dataset only gene-level data were accessible.
We restricted the analyses to the SNPs in Intron 1, Exon 2 and
Intron 2 in the FTO gene. First, SNP marker data were assessed
for deviations from Hardy-Weinberg equilibrium (HWE) at
p,0.0001 in controls. Independently for each of the case-control
datasets, multivariate logistic regression analyses in PLINK
(http://pngu.mgh.harvard.edu/,purcell/plink/), were used to
assess genotypic and allelic associations with AD risk, first
adjusting for age and sex, and then in addition adjusting for
APOE-e4. In order to account for population stratification, in the
Caribbean Hispanic dataset all analyses were in addition adjusted
for the first three principal components derived by EIGEN-
htm). The False Discovery Rate (FDR) , which controls the
expected proportion of incorrectly rejected null hypotheses (type I
errors) and provides a sensible balance between the number of true
and false positives [41,42], was used to account for the error in
multiple comparisons. As secondary analyses, we performed 3-
SNP sliding-window haplotype analyses using the same covariates
for adjustment. Finally, we obtained the publicly available data on
the FTO gene by the ADNI study  and performed a meta-
analysis using PLINK (http://pngu.mgh.harvard.edu/,purcell/
plink/metaanal.shtml). To determine the strength of associations
between the individual FTO SNPs and AD, we calculated a pooled
OR for each marker using fixed and random effects models using
PLINK. In these analyses, the individual studies were weighted in
to the final statistics based on the standard errors (SE) of the
individual ORs. The p values for each SNP were corrected for
multiple testing using the FDR. Between-dataset heterogeneity was
tested with the chi-square distributed Q statistic.
Statistical Analysis for the gene expression data
To determine whether FTO expression levels differ between AD
and control brains, we performed both within- and between-group
Table 1. Characteristics of the study samples.
Characteristics NIA-LOAD (n=1,877)
Affected with AD 993 549
Onset: affecteds71.666.9 79.968.0
Age at last exam: unaffecteds 76.168.478.866.4
Proportion of females (%)62.3% 69.7
APOE allele frequency (%)
e4 31.2 18.2
FTO and Alzheimer’s Disease
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factors ANOVA using PARTEK GENOMICS SUITE 6.4
(http://www.partek.com/partekgs) of log10 transformed Rank
invariant normalized expression data. The FDR statistic was used
to account for the error in multiple comparisons.
The demographic characteristics of the NIALOAD and
Caribbean Hispanic datasets are shown in table 1. In analyses of
the NIALOAD study, one SNP was significantly associated with
Figure 2. a. Linkage disequilibrium (LD) pattern in NIA-LOAD study. b. Linkage disequilibrium (LD) pattern in Caribbean Hispanic study.
Table 2. Results from single marker association analyses.
CHRSNPBP A1 F_AF_UA2P ORSE
16rs649964052327178G0.41 0.39A0.05 1.14 0.07
16rs1085252152362466T 0.51 0.48C 0.091.11 0.06
16rs16945088 52370025G 0.080.09A 0.090.820.11
16rs804476952396636T 0.490.47C 0.09 1.110.07
Caribbean Hispanics (AD)
CHRSNP BPA1 F_A F_UA2P ORSE
16 rs9931164 52,382,739G 0.02 0.04A 0.090.66 0.25
16 rs1721908452,413,101G 0.39 0.34A 0.011.25 0.09
16rs11075996 52,415,525T 0.490.44C 0.0091.25 0.09
16rs1107599752,416,413T 0.500.45C 0.011.240.09
A1=minor allele; A2=wild type allele; p=p-value; OR=odds ratio, SE=standard error: F_A=frequency of minor allele in affecteds, F_U=frequency of minor allele in
FTO and Alzheimer’s Disease
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AD and three additional markers were close to significance
0.05,p,0.09,table 2). Out
(rs10852521, rs16945088, rs8044769) are in tight LD with the
previously reported SNPs (D9.0.9; Figure 2a). In the Caribbean
rs11075996, rs11075997, p-value: 0.009,p,0.01) that were
significantly associated with AD. In addition, rs9931164 was close
to significance (table 2). This SNP is in the same LD block as the
previously reported SNPs (Figure 2b), and is independently in LD
with the other four significant SNPs (Figure 2b). In haplotype
analyses, several of these SNPs were also significant (table 3). In
addition, the GTA
rs9931164|rs9941349|rs7199182 was significantly associated with
AD in the Caribbean Hispanic dataset. rs9941349 is a proxy SNP
for rs9939609 previously reported (http://www.broadinstitute.
org/mpg/snap/ldsearch.php) . In metaanalyses of the Cau-
casian NIALOAD and ADNI datasets, three SNPs (rs6499640,
rs16945088, rs6499646) were significantly associated with AD
(table 4). Out of these, two were in the same LD block as the
previously reported SNPs. When in addition the Caribbean
Hispanic dataset was included, five SNPs (rs16945088, rs9931164,
rs17219084, rs11075996, rs11075997) were significantly associat-
ed with AD. Adjustment for APOE genotype did not change these
results, and there was no interactive effect of SNPs in FTO and
APOE genotype on AD risk in either dataset.
Microarray gene expression analyses
While there were no differences in expression levels in tissue
derived from the cerebellum or occipital lobe, microarray
Table 3. Results from haplotype analyses.
SNPS HAPLOTYPE F_AF_U CHISQDFP
rs6499646|rs1421090|rs17219084TCA 0.04 0.173.931 0.04
SNPS HAPLOTYPEF_A F_U CHISQ DFP
rs12597786|rs7201850|rs9931164CTG0.020.04 2.801 0.09
rs7201850|rs9931164|rs9941349TGT 0.02 0.042.731 0.09
rs9931164|rs9941349|rs7199182GTA 0.02 0.042.731 0.09
rs8044769|rs6499646|rs1421090 TTC0.060.08 4.511 0.03
rs6499646|rs1421090|rs17219084TTG 0.240.19 6.261 0.01
rs6499646|rs1421090|rs17219084TCA 0.080.12 6.331 0.01
rs1421090|rs17219084|rs11075996TGT 0.32 0.268.101 0.004
rs17219084|rs11075996|rs11075997 ACC 0.500.55 6.741 0.009
F_A=frequency of minor allele in affecteds, F_U=frequency of minor allele in unaffecteds; CHISQ=x2 test statistic; DF=degrees of freedom; p=p-value.
Table 4. Results from Metaanalyses.
Metaanalysis NIALOAD+ +ADNI
CHR SNPBP A1 A2P P(R)OROR(R)Q
16 rs6499640 52327178GA 0.05 0.054011.1148 1.1148 0.6
16rs1694508852370025GA 0.0060.01041 0.76850.76490.3
16rs649964652401034CT0.03 0.11220.815 0.7918 0.2
Metaanalysis NIALOAD+ +ADNI+ +Caribbean Hispanics
CHR SNPBP A1A2PP(R) OROR(R)Q
16 rs1694508852370025GA0.010.03477 0.83660.82680.2
16rs9931164 52382739GA 0.030.029350.7198 0.7198 0.8
16rs17219084 52413101GA0.03 0.084341.11021.12840.2
16 rs1107599652415525TC0.01 0.071571.118 1.1376 0.1
16 rs1107599752416413TC0.02 0.076551.11171.12780.2
A1=minor allele; A2=wild type allele; P=Fixed-effects meta-analysis p-value; P(R)=random-effects meta-analysis p-value; OR=Fixed-effects meta-analysis odds ratio;
OR(R)=random-effects meta-analysis odds ratio; Q=p-value for Cochrane’s Q statistic.
FTO and Alzheimer’s Disease
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expression analyses of the amygdala tissue from the 19 AD and 10
control brains showed significantly lower expression of FTO in AD
brains compared to control brains (mean gene expression intensity:
8.9160.36 vs 9.5760.23, p=2.1E-5; Figure 3). These findings
were validated by comparison with publicly available gene
expression results (188 AD cases, 176 controls: mean expression
intensity 594.926148.2 vs. 680.236139.65, p,0.0001, http://
labs.med.miami.edu/myers/) . In this publicly available
dataset, logistic regression analyses relating SNPs in FTO with
FTO gene expression levels suggested that the A allele of
rs9972717 residing in intron 2 may be positively associated with
FTO expression levels (b=44.4, SE 14.61, nominal p=0.002,
FDR p-value: 0.05, Table S2), further providing support for a
functional role of this genetic region.
The findings reported here confirm the association between
genetic variation in Intron 1, Exon 2 or Intron 2 in the FTO gene
and AD. Several SNPs in this region of the gene were associated
with AD in Caucasians of European ancestry as well as in
Caribbean Hispanics. In addition, FTO was significantly lower
expressed in AD cases compared to controls in two independent
datasets and there was an effect of genetic variation in intron 2 on
FTO expression levels.
Figure 3. View of FTO exon expression profile in 19 AD (red triangles) and ten control (blue squares) amygdala tissue. Each triangle
dot represents least squares mean expression of an exon in AD tissue; each square dot represents least squares mean expression of an exon in control
tissue. The mean gene expression intensity of AD vs. Controls was 8.9160.36 vs 9.5760.32 (p=2.1861025) across all exons.
FTO and Alzheimer’s Disease
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These results are consistent with epidemiological studies relating
obesity measures with AD [4,5,8,45]. In addition, they are
consistent with the findings of genetic associations between
variation in FTO and obesity measures with brain volume ,
verbal fluency  and the previous study reporting an association
of the rs9939609 SNP with AD . Of note, consistent with the
previous reports, several of the disease-associated SNPs are located
in the 47 kb LD block that spans Intron1, Exon2 and Intron2, and
are in tight LD with the SNPs previously reported to be associated
with obesity, obesity-related traits, brain volume, verbal fluency
and AD. Other SNPs are located downstream in Intron 2 and
have not been reported before. The occurrence of pathogenic
mutations across multiple domains of disease genes (allelic
heterogeneity) and the absence of these variants in some datasets
or ethnic groups (locus heterogeneity) are frequently observed in
both monogenic and complex traits. As expected, the effect sizes of
associated SNPs were modest (OR 1.1–1.2). This is consistent with
the notion of a complex disease and all recently detected novel AD
susceptibility loci [46,47,48,49,50] and may explain why the FTO
locus has not been reported by the recent large GWAS studies
which may have been underpowered when correcting for total the
number of genome-wide performed tests.
There are several potential mechanisms that could link obesity
and AD. Obesity is a risk factor for hyperinsulinemia and T2D
 and both are risk factors for AD . Obesity is also related
to other vascular risk factors such as hypertension and dyslipide-
mia, heart disease, and stroke, which have also been reported to be
associated with AD in isolation and in aggregate . Finally,
obesity is also related to the production of adipokines and
cytokines , which are correlates of hyperinsulinemia and T2D
although their independent role in LOAD is less clear.
It has to be noted that the SNPs assessed were derived from the
available genome-wide screening in all datasets. Thus, they do not
cover the complete genetic variation in Intron 1, Exon2 and
Intron 2 and it is possible that there are additional disease-
associated markers that have not been genotyped. It is also possible
that there are disease-associated variants in other regions of the
gene, or that we lacked power to detect additional disease-
associated markers with lower allele frequencies or effect sizes.
Taken together, our results suggest that FTO is causally
involved in AD. Future studies should include comprehensive
sequencing analysis to identify the specific causative sequence
variants underlying the detected associations.
Platforms used for APOE genotyping.
Effect of genetic variation in FTO on FTO expression
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_Manuscript_Citations.pdf). Also, a complete listing of ADNI
investigators can be found at: http://adni.loni.ucla.edu/wp-content/
Conceived and designed the experiments: CR GT RM JL. Performed the
experiments: CR GT. Analyzed the data: CR GT. Contributed reagents/
materials/analysis tools: CR GT RM JL. Wrote the paper: CR GT RM
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FTO and Alzheimer’s Disease
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