Linkage, whole genome sequence, and biological data implicate variants in RAB10 in Alzheimer’s disease resilience
Ridge et al. Genome Medicine
Linkage, whole genome sequence, and biological data implicate variants in RAB10 in Alzheimer's disease resilience
Perry G. Ridge 0
Celeste M. Karch 0
Craig C. Teerlink
Mark T. W. Ebbert
Josue D. Gonzalez Murcia
James M. Farnham
Anna R. Damato
Victoria M. Fernandez
Steven G. Younkin
Dennis W. Dickson
Todd E. Golde
Nathan D. Price
Alison M. Goate
Lisa A. Cannon-Albright
John S. K. Kauwe 1
0 Equal contributors
1 Departments of Biology and Neuroscience, Brigham Young University , Provo, UT 84602 , USA
Background: While age and the APOE ε4 allele are major risk factors for Alzheimer's disease (AD), a small percentage of individuals with these risk factors exhibit AD resilience by living well beyond 75 years of age without any clinical symptoms of cognitive decline. Methods: We used over 200 “AD resilient” individuals and an innovative, pedigree-based approach to identify genetic variants that segregate with AD resilience. First, we performed linkage analyses in pedigrees with resilient individuals and a statistical excess of AD deaths. Second, we used whole genome sequences to identify candidate SNPs in significant linkage regions. Third, we replicated SNPs from the linkage peaks that reduced risk for AD in an independent dataset and in a gene-based test. Finally, we experimentally characterized replicated SNPs. Results: Rs142787485 in RAB10 confers significant protection against AD (p value = 0.0184, odds ratio = 0.5853). Moreover, we replicated this association in an independent series of unrelated individuals (p value = 0.028, odds ratio = 0.69) and used a gene-based test to confirm a role for RAB10 variants in modifying AD risk (p value = 0.002). Experimentally, we demonstrated that knockdown of RAB10 resulted in a significant decrease in Aβ42 (p value = 0.0003) and in the Aβ42/ Aβ40 ratio (p value = 0.0001) in neuroblastoma cells. We also found that RAB10 expression is significantly elevated in human AD brains (p value = 0.04). Conclusions: Our results suggest that RAB10 could be a promising therapeutic target for AD prevention. In addition, our gene discovery approach can be expanded and adapted to other phenotypes, thus serving as a model for future efforts to identify rare variants for AD and other complex human diseases.
Alzheimer's disease; Protective variants; Whole genome sequencing; Utah Population Database; Linkage analyses
A majority of Alzheimer’s disease (AD) genetic discoveries
have been made using cutting edge study designs and large
international collaborations [
]. However, despite these
successes, the genetics of AD are still largely unsolved: 1)
the majority of genetic variance is not explained by known
AD markers [
]; 2) known AD markers are not helpful for
predicting or diagnosing disease [
]; 3) a majority of
remaining AD variants are likely to be rare [
]; 4) and
the functional consequences of known AD markers, or
surrounding genetic variants, are unknown. These
observations demonstrate the complexity of AD genetics and
underscore the importance of developing new and targeted
study designs capable of identifying rare genetic variants.
Recently, several possibly functional, rare variants with
large protective [
] and risk effects [
been identified for AD in APP, APOE, PLD3, and
TREM2 using novel study designs. The TREM2 variant
R47H, for example, was discovered using a study design
that preserved statistical power by focusing solely on
genetic variants that were likely to affect protein
], whereas the PLD3 variant, V232M, was
identified using a family-based study design [
Identifying functional variants, such as the variants in APP,
APOE, PLD3, and TREM2 provide key insights into
disease mechanisms [
]. Since functional variants are
more likely to represent tractable drug targets than other
types of variants, they should be a major focus of AD
genetics research [
We report the development and use of an innovative,
powerful approach to identify functional variants that
provide AD resilience to high-risk individuals. First, we
identified pedigrees with a statistical excess of AD
mortality that also include at least four AD high-risk
resilient individuals. Next, we performed linkage analysis in
these families and used whole genome sequence (WGS)
data from resilient individuals to interrogate identified
linkage regions for candidate variants. We found
promising variants in RAB10 and SAR1A. Our RAB10
findings were replicated in two independent series of
unrelated individuals and in a gene-based test. Both
RAB10 and SAR1A are differentially expressed in human
AD brains. Finally, we tested RAB10 and SAR1A for
biological impact in vitro. Our results suggest that RAB10
variants impact risk for AD and that RAB10 may
represent a promising therapeutic target for AD prevention.
In addition, our approach can be expanded and adapted
to other phenotypes and serves as a model for future
efforts to identify rare functional variants for AD and
other complex human diseases.
We focused on understanding the underlying biology
that protects certain high-risk individuals against AD.
We term these individuals “AD resilient individuals” and
define them as individuals who are at least 75 years old,
cognitively normal, and carry at least one APOE ε4
allele. Our approach consists of three key parts: linkage
analysis and fine mapping, genetic analyses, and
experimental biological validations. For simplicity, an overview
of each step, datasets used, specific criteria applied, and
high level results are presented in Fig. 1.
We used the Utah Population Database (UPDB) to
identify large pedigrees with an evidence of excess AD
mortality (i.e., families with a higher number of AD deaths
than expected). The UPDB is a population-based
resource linking the computerized genealogy of the Utah
pioneers, and their descendants, to various electronic
health data repositories for the state, including Utah
death certificates [
]. The UPDB includes over seven
million individuals, 2.5 million of which have at least
three generations of genealogical data and are
descendants of the original founders of Utah; over one million
of these individuals have at least 12 of their 14
immediate ancestors in the database.
Since 1904, Utah death certificates have been coded and
linked to individuals in the UPDB, allowing us to identify
all individuals where AD is included as a cause-of-death.
AD as a specific cause-of-death was first introduced to the
International Classification of Disease (ICD) in revision 9
and retained in revision 10. Deaths were only considered
an AD death if the death certificate included AD ICD
codes (ICD9 331.0; ICD10 F00 or G30) as a primary or
contributing cause-of-death. This study used a uniform,
consistent source for all diagnoses (AD that contributed
to cause of death as evidenced by presence on a death
certificate) and is not limited by bias introduced by study
designs with inconsistent methods of diagnoses, or family
recall of disease symptoms. The most significant limitation
of this analysis is that coding for AD diagnosis has been
present since 1979 (ICD versions 9 and 10). Given the
breadth of our data, this limits our ability to identify cases
that might be related across multiple generations (e.g.,
great grandparent/great grandchild), but our requirement
for three generations of genealogy means that very distant
relationships within the same generation are possible
(Additional file 1: Figures S1 and S2). The most likely
misclassification is that a death certificate for an individual
who died with AD did not include AD as a cause of death.
This would result in an underestimate of the number of
AD deaths in a pedigree. Although individuals dying from
AD may have been censored from our observation in this
resource, the assumption can be made that cases are
uniformly censored within cohorts across the resource,
leading to conservative, but unbiased, estimates of relative AD
mortality within pedigrees.
We used a method previously described by Kauwe et al.
] to identify large pedigrees with a statistical excess of
AD mortality. Briefly, each pedigree in the UPDB consists
of all descendants of a set of UPDB founders. We
identified pedigrees with an excess of AD deaths by comparing
the observed (i.e., number of affected individuals in the
pedigree) to the expected numbers of AD-affected
individuals within the pedigree. The expected number of AD
deaths was estimated using population-based,
cohortspecific rates of AD death estimated from all Utah death
certificates for individuals in the UPDB genealogy. To
calculate the expected number of AD-affected individuals in
a pedigree, first we divided all individuals in UPDB into
cohorts based on birth year (5-year blocks), sex, and birth
state (Utah or somewhere else), and normalized expected
AD incidence to adjust for cohort-specific variation in
death certificate information. All individuals were assigned
to one of the resulting 132 cohorts. The proportion of
individuals with AD in a cohort is the cohort-specific AD
death rate for the UPDB genealogy population. This
approach controls for differences in diagnosis and use of
ICD codes for AD over time and space.
Next, we assessed each pedigree individually. To
calculate the expected number of AD-affected individuals in a
pedigree, we divided all pedigree descendants into
cohorts, as described above, and multiplied the number of
total descendants from the pedigree within the cohort by
the cohort-specific AD rate previously calculated (i.e.,
proportion of AD individuals in the cohort) and summed the
values across all cohorts within the pedigree. Therefore,
the expected number of AD-affected individuals in a
pedigree is the sum of the expected number of AD-affected
individuals from each cohort in the pedigree. Finally, the
observed number of AD descendants for a pedigree is
calculated by counting individuals in the pedigree with an
ICD code that indicates AD as a cause-of-death.
We estimated the relative risk (RR) for AD for each
pedigree as the observed number of AD-affected descendants
divided by the expected number of AD descendants.
Onesided probabilities for the alternative hypothesis testing an
RR > 1.0 were calculated under the null hypothesis RR =
1.0, with the assumption that the number of observed cases
follows a Poisson distribution (an approximation to a sum
of multiple binomial distributions representing the number
of expected cases per cohort) with mean equal to the
expected number of cases. This Poisson approximation is
statistically appropriate for both rare and common
phenotypes, being more conservative for a common disease.
Pedigrees exhibiting excess AD descendants over expected were
defined as high-risk.
DNA and clinical phenotype data for AD cases and AD
resilient samples for the linkage analysis were obtained
from the Cache County Study on Memory Health and
Aging (CCS), which has been described in more detail
]. Briefly, the CCS was initiated in 1994 to
investigate the association of APOE genotype and
environmental exposures on cognitive function and dementia.
This cohort of 5092 Cache County, Utah, residents (90%
of those aged 65 years or older in 1994), has been followed
continuously for over 15 years, with four triennial waves
of data collection and additional clinical assessments for
those at high-risk for dementia. DNA samples were
obtained from 97.6% of participants. The Cache County
population is exceptionally long-lived and ranked number
one in life expectancy among all counties in the 1990 US
]. All but one of the members of the CCS have
been linked to the UPDB and their extended genealogies
are known. This population was the source of most of the
Centre d’Etude du Polymorphisme Humain (CEPH)
families that have been used to represent Caucasians in many
genetic studies worldwide, including the HapMap project.
Recent analyses confirm that these data are representative
of the general European-American population [
this study, we needed both AD cases and resilient
individuals identified in the same pedigrees.
First, we identified 232 AD resilient individuals (defined
as those over age 75, cognitively healthy, and carrying at
least one APOE ε4 allele) from the CCS with a strong
family history of AD. The set consists of 135 females and 97
males, with mean age of 81 years. As previously
mentioned, each of these individuals carries at least one APOE
ε4 allele, and nine were homozygous for APOE ε4. We
obtained WGS for 212 of these CCS samples using the
Illumina HiSeq sequencer to an average depth of 40× and
mapped the resulting reads with the Burrows-Wheeler
Aligner (BWA) [
]. We performed variant calling using
the Genome Analysis Toolkit (GATK) best practices (i.e.,
]. We also genotyped each sample
using the Illumina 2.5 M SNP Array for quality control
and for use in linkage analyses.
Next, we identified 581 AD cases from the CCS, 492 of
whom were followed from diagnosis to death. Since 2002,
CCS participants with incident dementia have been
followed prospectively in the Cache County Dementia
Progression Study. An expert panel of neurologists,
neuropsychologists, neuropsychiatrists, and a cognitive
neuroscientist assigned final diagnoses of dementia following
standard research protocols (e.g., NINCDS-ADRDA
criteria for AD [
] or NINCDS-AIREN criteria for vascular
]). Each case was genotyped for the variants
of interest using Taqman assays.
ADNI data used in the preparation of this article were
obtained from the ADNI database (http://adni.loni.us
c.edu/). The ADNI was launched in 2003 as a public–
private partnership, led by Principal Investigator Michael
W. Weiner, MD. 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). For up-to-date information, see http://
Linkage analyses were conducted using pedigrees that
included at least four AD resilient individuals and four AD
cases. To identify key regions associated with AD
resilience, we identified shared chromosomal segments among
our AD resilient samples within each pedigree using
]. The set of OmniExpress SNPs considered
were reduced to a set of high-heterozygosity markers with
low or no pairwise linkage disequilibrium to enable
unbiased linkage analysis. Pedigrees were analyzed using a
general dominant model that assumed a disease gene
frequency of 0.005 with penetrance estimates for carriers and
non-carriers of 0.5 and 0.0005, respectively, and we
considered different modes of inheritance and corrected for
multiple tests [
]. We extracted inheritance information
for each pedigree by reconstructing haplotypes using a
Monte Carlo Markov Chain methodology with blocked
Gibbs sampling [
]. For parametric analyses,
MCLINK calculates robust multi-point linkage scores
(theta LODs or TLODs) [
]. We consider TLOD scores >
1.86 (corresponding to a false-positive rate of one per
genome) as suggestive evidence for linkage, and scores >
3.30 as significant, as defined by Lander and Kruglyak
]. Using a conservative cutoff further allowed us to
explore biological evidence for the maximal number of
genes and variants, which are few by nature for this type
Once linkage evidence was established via these
methods, we utilized all SNP markers in the region to
provide fine mapping localization evidence. Linkage
evidence from each pedigree was considered independently.
WGS variant filtering
Variants within the one LOD interval of the maximum
linkage score were analyzed using the Ingenuity Variant
Analysis and Tute Genomics Analysis programs (https://
nt-analysis/). For the Ingenuity Variant Analysis we used
version 3.0.20140422 with content versions as follows:
Ingenuity Knowledge Base (Arrakis 140408.002),
COSMIC (v68) [
], dbSNP (build 138 (08/09/2013)), 1000
Genome Frequency (v3) [
], TargetScan (v6.2) [
EVS (ESP6500 0.0.21), JASPAR (10/12/2009) [
PhyloP hg18 (11/2009), PhyloP hg19 (01/2009) [
Vista Enhancer hg18 (10/27/2007), Vista Enhancer
hg19 (12/26/2010) [
], CGI Genomes (11/2011), SIFT
], BSIFT (01/2013), The Cancer Genome
Atlas (09/05/2013), PolyPhen-2 (HumVar Training set
], Clinvar (02/11/2014).
All variants from the linkage regions were filtered as
follows (see Additional file 1: Supplementary Note 1 for
the effect each filter had on the number of variants):
Included variants that are shared by resilient
Included variants with call quality at least 20.0 in
AD cases or resilient samples, outside the top 0.2%
of the most exonically variable 100-base-pair
windows in healthy public genomes (based on the 1000
Genomes Project), and outside the top 1% of the
most exonically variable genes in healthy public
genomes (based on the 1000 Genomes Project)
Excluded variants if the allele frequency was at least
3% in the 1000 Genomes Project, the public
Complete Genomics genomes, or the NHLBI ESP
Included variants associated with gain-of-function,
or were heterozygous, hemizygous, haploinsufficient,
or compound heterozygous
Included variants experimentally observed to be
associated with a phenotype by any of the following
criteria: 1) pathogenic, possibly pathogenic,
established gain-of-function in the literature, or
inferred activating mutations by Ingenuity; 2)
predicted gain-of-function by BSIFT; 3) located in a
known microRNA binding site, or frameshift,
inframe indel, stop loss, missense, and not predicted
to be benign by SIFT, or disrupt a splice site up to
two bases into an intron; 4) deleterious to a
microRNA or structural variant; 5) located in a known
promoter binding or enhancer site; 6) located in an
evolutionarily conserved region, determined by a
phyloP p value ≥ 0.01, or 7) in an untranslated
Included variants absent in AD cases in the pedigree
and present in a gene within two protein interaction
connections upstream, or one connection
downstream, of genes that are known, or predicted,
to affect susceptibility to late-onset familial or
Genetic validation analyses
We used three independent datasets for genetic
validation analyses. First, all SNPs that met the filtering
criteria (described above) were evaluated in a set of
samples with sequence data. Then, significant markers
from those analyses were genotyped and assessed for
association in samples from the CCS. Finally, WGS data
from the ADNI were analyzed. Our initial validation
analysis was conducted using data from an augmented
version of the Alzheimer Genetic Analysis Group dataset
]. These data consist of whole exome sequences
(WES) and WGS for 427 AD cases and 798 elderly
controls originating from the United Kingdom and North
America. The assembly and use of this dataset have been
described in several studies (e.g., [
]). Briefly, since our
dataset consisted of a mix of exomes captured using
different kits, and whole genome sequences, we employed
a highly conservative approach to variant selection to
increase our confidence that analyzed variants are true
positives. We limited our dataset of variants to only those
genomic regions we expected to have been sequenced in
each of the exomes (based on capture probes used for
exome library preparation) and whole genomes. Next, we
compiled a list of all the variants present in at least a
single sample. We examined each of the variants from the list
of total variants in each sample, whether or not the variant
was called by the Genome Analysis Toolkit (GATK) best
practices, and reassigned the genotype for that variant
according to the following criteria. (1) If the variant was
called by the GATK and passed all quality filters
recommended by the GATK, we used the GATK genotype. (2) If
no variant was called at the genomic position in question,
we returned to the raw VCF file and if there were reads
containing the variant, but the variant was not called
because of failing filters or because only a small number of
reads contain the variant, we set the genotype to missing
for the sample. (3) Finally, if all the reads at this position
for the sample indicated reference alleles, we set the
genotype to homozygous reference.
Variants that were significant in the first validation
analyses were genotyped in 523 AD cases and 3560
controls from the CCS (after exclusion of samples that were
included in the linkage analysis). WGS from 191 AD
cases and 279 controls from ADNI were used to conduct
gene-based tests for association. These samples are
described in detail on the ADNI website (http://adni.loni.us
c.edu/data-samples/genetic-data/wgs/). Finally, there were
no variants in these genes passed quality control in the
Alzheimer’s Disease Sequencing Project samples.
We performed association analyses, using PLINK [
between AD status and the top SNP in each linkage
region (based on Ingenuity analyses), using a logistic
regression and controlling for age, sex, and site. Given the
linkage results, all tests were conducted assuming we
were searching for a SNP with a protective effect against
AD. We tested a single SNP from the linkage region in
each family. As such, the alpha for the single SNP
analyzed in each family is 0.05. Next, we used the sequence
kernel association test (SKAT)-O to perform gene-based
association tests in the ADNI samples to test whether
each gene was a potential AD resilience gene [
SKAT-O was designed to combine both a burden test
and a non-burden sequence kernel association test. It
maximizes power from both test types, where burden
tests are more powerful when the majority of variants in
a region are both causal and in the same direction, and
SKAT is adapted to regions with largely non-causal
variants or causal variant effects are in different directions
]. Thus, SKAT-O is ideal when the percentage of
causal variants and their directions within a region are
not known beforehand.
Gene expression studies
We examined levels of RAB10 and SAR1A expression in
the temporal cortex of 80 brains with neuropathologic
diagnosis of AD vs. 76 elderly control brains which
lacked any diagnosis of neurodegenerative diseases.
These brains were part of the Mayo Clinic RNA
sequencing (RNAseq) cohort, described previously [
subjects underwent RNAseq using Illumina HiSeq 2000,
101-base-pair, paired-end sequencing at the Mayo Clinic
Genomic Core Facility. All the AD and some of the
control brains were from the Mayo Clinic Brain Bank;
whereas other control brains were from the Banner Sun
Health Institute. Following quality control, raw read
counts normalized according to conditional quantile
normalization (CQN) using the Bioconductor package
were used in the analyses. For differential gene
expression (DGE) comparing AD vs. controls using the “Simple
Model”, multi-variable linear regression analyses were
conducted in R, using CQN normalized gene expression
measures and including age-at-death, gender, RNA
integrity number (RIN), brain tissue source, and flowcell
as biological and technical covariates. We also
performed DGE including cell-specific gene levels as
covariates in addition to all covariates in the “Simple Model”,
using the expression levels for the five central nervous
system (CNS)-specific genes as follows: ENO2 for
neurons, GFAP for astrocytes, CD68 for microglia, OLIG2
for oligodendrocytes, and CD34 for endothelial cells.
The rationale for the “Comprehensive Model” is to
account for any CNS cell-population changes that occur
due to disease pathology. Significance accounting for
multiple testing was assigned using q values which are
based on false discovery rates [
Additionally, RAB10 and SAR1A expression levels
were evaluated in publicly available datasets from human
AD and age-matched control brains (GSE5281 and
syn3159438). The GSE5281 dataset was obtained from
laser microdissected neurons from AD and control
]. The syn3159438 dataset was obtained from
anterior prefrontal cortex (APC), superior temporal
gyrus (STG), parahippocampal gyrus (PHG), and pars
opercularis (PO) [
]. RNA expression values were log
transformed to achieve a normal distribution. An
analysis of covariance, including age and sex as covariates,
was used to determine association with disease status as
previously described [
Biological validation studies
To further investigate the connection between RAB10
and SAR1A and AD risk, we assessed the impact of gene
overexpression and silencing on APP and ß-amyloid
levels in N2A695 cells.
We used the following plasmids for this study:
pCMV6Rab10 (Origene), pCMV6-Sar1A (Origene),
pGFP-V-RSRab10 shRNA (Origene), pGFP-V-RS-Sar1A shRNA
(Origene), pCMV-GFP, and pGFP-V-RS-scrambled shRNA
(Origene). The optimal shRNA for each gene was selected
from four possible shRNAs based on the plasmid
producing the most robust knockdown in vitro.
Mouse neuroblastoma cells (N2A) expressing human
APP-695 isoform (termed N2A695) were used in this study
]. N2A695 cells were plated and grown in Dulbecco’s
modified Eagle medium (DMEM) and Opti-MEM
supplemented with 1% L-glutamine, 10% FBS and 1%
antibioticanti-microbial solution, and 200 μg/mL G418. Upon
reaching confluency, cells were transiently transfected using
Lipofectamine 2000 (Life Technologies). Culture media was
changed 24 h after transfection. After an additional 24 h,
cell media and cell pellets were collected for subsequent
analysis. Nine independent replicates were performed for
Cell death following overexpression and knockdown
was assessed by measuring LDH release in the cellular
medium (Thermo Scientific) according to the
manufacturer’s instructions. Percentage of cytotoxicity was then
calculated following manufacturer’s recommendations:
% Cytotoxicity ¼ ðTransfected LDH – Spontaneous LDHÞ
ðMaximum LDH – Spontaneous LDHÞ
To assess overexpression and silencing of RAB10 and
SAR1A, total RNA was isolated from N2A695 cells 48 h
after transfection using RNeasy (Qiagen). RNA was
converted to cDNA using the High-capacity cDNA reverse
transcription kit (Thermo Fisher Scientific). Gene
expression was analyzed by real-time PCR using an
ABI7900 real time PCR system. Taqman (Thermo Fisher
Scientific) real time PCR assays were used to measure
the expression of RAB10 (Mm00489481_m1), SAR1A
(Mm01150424_m1), and the housekeeping gene GAPDH
(Hs02758991_g1). Samples were run in triplicate. To
avoid amplification interference, expression assays were
run in separate wells from the housekeeping gene.
Real-time data were analyzed using the comparative
threshold cycle (CT) method [
]. Briefly, the CT is the
PCR cycle at which fluorescence rises above background,
allowing us to calculate the original RNA levels. For the
comparative CT method, the average CT for RAB10 or
SAR1A were normalized to the average CT for GAPDH.
The resulting value was then corrected for assay
efficiency. Samples with a standard error of 20% or less
were analyzed. RAB10 shRNA resulted in a 54%
reduction of endogenous RAB10, and SAR1A shRNA resulted
in a 26% reduction of endogenous SAR1A.
To assess steady-state levels of RAB10, SAR1A, and APP,
cell lysates were extracted in lysis buffer (50 mM Tris
pH7.6, 1 mM EDTA, 150 mM NaCl, 1% TritonX-100,
protease inhibitor cocktail) on ice. Lysates were centrifuged at
14,000xg for 10 minutes at 4 °C and the resulting
supernatant was saved for SDS-PAGE and immunoblotting.
Total protein concentration was measured by BCA assay
according to manufacturer’s protocol (Thermo Scientific).
Standard sodium dodecyl sulfate-polyacrylamide gel
electrophoresis (SDS-PAGE) was performed using 4–12%
Criterion Tris-HCl gels (Bio-Rad). Samples were boiled in
Laemmli sample buffer prior to electrophoresis [
Immunoblots were probed with 9E10 (myc; Sigma), 6E10
(APP, sAPPα; Covance), 22C11 (APP, sAPPtotal; Millipore),
sAPPβ (Clontech), and CT695 (APP, CTF-β and CTF-ɑ;
The levels of human Aβ40 and Aβ42 were measured
from conditioned cell culture media by sandwich ELISA as
described by the manufacturer (Thermo Fisher Scientific).
ELISA values were obtained (pg/mL) and corrected for
total intracellular protein (μg/mL) based on BCA
measurements from cell lysates.
Aβ concentrations are expressed as mean ± standard
deviation obtained from at least three separate
experiments in each group. Data were assessed by one-way
analysis of variance (ANOVA). When ANOVA indicated
significant differences, the Student’s t-test was used with
Bonferroni correction for multiple comparisons. Results
presented are representative and those with p values
< 0.05 were considered significant.
Pedigree selection and linkage analysis
We identified five pedigrees that passed all filtering
criteria: 1) evidence of an excess of AD deaths; 2)
available samples for at least four AD resilient individuals
(i.e., elderly APOE ε4 carriers); and 3) available samples
for at least four AD cases. Two pedigrees reached our
1.86 TLOD cutoff for linkage analysis (Additional file 1:
Figures S1 and S2).
In the first pedigree (Additional file 1: Figure S1), we
detected a linkage region with a TLOD score of 2.21 on
chromosome 2. This peak is located between rs4341893
and rs2252032 (chr2:20935817-36872196; 2p23-22), and
includes 14,898 SNPs and 101 genes. In the second
pedigree (Additional file 1: Figure S2), we detected evidence
of linkage with a TLOD score of 2.10 in two adjacent
regions on chromosome 10, which includes 10,686
variants in 138 genes. These peaks are located between
rs10823229 and rs7900882, and rs7918631 and rs3740382,
respectively, and hereafter are treated as a single peak
(chr10:68572823-103419457; 10q22.1-24.3). We failed to
detect evidence of linkage in the three other pedigrees.
Association with AD risk
We extracted all variants from whole genomes in the
two linkage regions. We identified eight candidate
variants that passed all filters (Table 1; Additional file 1:
Supplementary Note 1), and selected one candidate SNP
from each of the two peaks for further analysis. Each of
these variants, in RAB10 (rs142787485) and SAR1A
(rs7653), respectively, had statistically significant
associations with AD in the Alzheimer’s Genetic Analysis
Group. We deliberately selected our candidate SNPs
from RAB10 and SAR1A because these genes interact
with APP [
]. We observed significant associations
in the Alzheimer’s Genetic Analysis Group in the
protective direction for both SNPs (rs142787485, RAB10, p
value = 0.018, odds ratio (OR) = 0.58; rs7653, SAR1A, p
value = 0.0049, OR = 0.35). Both SNPs are rare, with
MAF (1000 Genomes/ExAC
For each variant, the dbSNP identifier (SNP), chromosome, position, closest gene(s) (Gene), variant type, minor allele frequency (MAF) in controls and cases, and
frequency from the 1000 Genomes Project and Exome Aggregation Consortium (ExAC) are provided (NA if not present in the database)
1000 Genomes minor allele frequencies of 0.0136 and
0.0168, for rs142787485 and rs7653, respectively.
Given significant findings in the sequence data, we
genotyped both rs142787485 (RAB10) and rs7653 (SAR1A)
in samples from the Cache County Study on Memory
Health and Aging (CCS), an independent dataset of 544
cases and 3605 controls. While odds ratios for both
markers were in the predicted protective direction
(Table 2), we detected significant association with
rs142787485 (p value = 0.028, OR = 0.69), but not rs7653
(p value = 0.26, OR = 0.87). Gene-based tests conducted
in the CCS and Alzheimer’s Disease Neuroimaging
Initiative (ADNI) samples using SKAT-O resulted in a
significant association for RAB10 (p value = 0.002), but
not SAR1A (p value = 1.00).
Differential expression of RAB10 and SAR1A in AD brains
To determine whether RAB10 and SAR1A expression
are altered in AD brains, we examined transcriptomic
data from 80 AD brains and 76 age-matched control
brains (Mayo Clinic Dataset). RAB10 mRNA levels were
significantly higher (Table 3) in the temporal cortex of
AD brains compared to controls. To replicate our
RAB10 findings, we analyzed a publicly available dataset
containing 260 brains from AD cases and age-matched
controls from the Mount Sinai Brain Bank (syn3159438).
We observed a significant increase in RAB10 expression
in AD brains (STG p value = 0.0285) and a marginal
association between RAB10 expression and plaque load
(STG p value = 0.0579). AD brains are characterized by
extensive neuronal loss. To evaluate whether the effect
on RAB10 expression in AD brains is driven by altered
cell composition within the brain homogenates, we
analyzed RAB10 expression after correcting for cell
composition in the Mayo Clinic Dataset (Comprehensive
Model). After correction for cell composition, RAB10
expression levels remained significantly elevated in the
temporal cortex of AD brains (Table 3). We replicated
this finding by examining RAB10 expression in neurons
isolated from AD brains (GSE5281). We found that
RAB10 expression was higher in AD neurons compared
with controls (p value = 0.0456).
We found that SAR1A expression was significantly
reduced in AD brains compared with age-matched
controls (APC p value = 0.04; STG p value = 0.0005; PO p
value = 0.0000279) and associated with plaque load
For each variant the dbSNP identifier (SNP), closest gene, p value and odds
ratio (OR) in the CCS, and minor allele frequency (MAF) in controls and cases
(APC p value = 0.062; STG p value = 0.0005; PG p value
= 0.00638; PO p value = 0.00000911). This association
was validated in human neurons from AD cases and
controls, where SAR1A levels were significantly lower in
AD neurons compared with age-matched controls (p
value = 0.0008). We observed a trend towards lower
SAR1A levels in AD brains in the Mayo Clinic Dataset;
however, SAR1A levels were not significantly different in
the temporal cortex between AD cases and controls
Over-expression and knockdown of RAB10 and SAR1A
To examine previous reports of biochemical interactions
between RAB10 and APP and between SAR1A and APP,
we examined the effects of overexpressing and silencing
RAB10 and SAR1A on APP processing in mouse
neuroblastoma cells [
]. Overexpression and silencing of
SAR1A and RAB10 did not affect cell viability. SAR1A
overexpression and modest silencing of SAR1A
expression failed to produce a significant change in full-length
intracellular APP, sAPP levels, or in extracellular Aβ
levels (Fig. 2). Interestingly, overexpression of SAR1A
produced an increase in CTF-β and corresponding
decrease in CTF-ɑ relative to GFP-only (p value = 0.0010
and 0.0382, respectively). Overexpressing RAB10
resulted in a significant increase in the Aβ42/Aβ40 ratio (p
value = 0.0133) and CTF-β (p value = 0.0409), while
knockdown of endogenous RAB10 resulted in a
significant decrease in Aβ42 (p value = 0.0003) and in the
Aβ42/Aβ40 ratio (p value = 0.0001) (Fig. 3b; Table 4). Aβ
levels were altered in the absence of an accompanying
change in full-length, intracellular APP, or sAPP levels
(Fig. 3a, c; Table 4).
We exploited strengths in the Utah Population Database
(UPDB) and CCS to identify five pedigrees with a
statistical excess of AD deaths. Using linkage analysis, we
identified linkages in two pedigrees on chromosomes 2
and 10. The linkage region on chromosome 2 is far
(>90 Mb) from known AD genome-wide association
study (GWAS) genes, and no known AD GWAS genes
are on chromosome 10.
Multiple lines of evidence support a role for RAB10 in
AD. We detected evidence for linkage in RAB10,
significant associations in the Alzheimer’s Genetic Analysis
Group (p value = 0.0184), replication in an independent
set of samples from the CCS (p value = 0.028), and
replication by gene-based tests in WGS data from ADNI
(p value = 0.002). Furthermore, we assessed the effect of
RAB10 expression on Aβ. Approximately 50%
knockdown of RAB10 resulted in a 45% reduction in Aβ42
levels (p value = 0.0003) and a 61% reduction in the
Aβ42/Aβ40 (p value = 0.0001) ratio. These findings are
Gene ID, ENSEMBL gene ID; Tissue, TCX temporal cortex; Dx.Beta, coefficient of effect in AD in comparison to controls; Dx.SE, standard error of effect; Dx.pValue,
significance of effect (uncorrected); Dx.qValue, significance corrected using FDR-based q-values
consistent with previous reports that RAB10 silencing
affects Aβ levels [
] and extend those findings by defining
the effects of RAB10 overexpression and silencing on
APP processing, including Aβ isoforms, APP-CTF, and
sAPP. Based on our results, we hypothesize that Rab10
impacts APP processing through direct interaction with
]. The relationship between RAB10 and Aβ
suggests RAB10 may affect γ-secretase-mediated cleavage of
APP, and the secretion and degradation of cleaved Aβ.
Furthermore, RAB10 is expressed in all cell types in
human and mouse brains [
], trends toward increased
expression in neurons isolated from AD brains , and
has higher brain expression levels in AD cases than
controls. RAB10 plays a role in endocytosis, which has been
implicated in AD [
], and is involved in membrane
trafficking regulation and moving proteins from the Golgi
apparatus to the membrane [
]. It also has a role in
neurotransmitter release, phagosome maturation, and
The p value for the comparison to GFP control (over-expression) and
scrambled shRNA (knockdown) and fold change for each amyloid
measurement are provided
GLUT4 translocation [
]. In neurons, RAB10 is involved
in axonogenesis through the regulation of vesicular
membrane trafficking toward the axonal plasma membrane
]. Our experimental results and previous reports
support our genetic discovery. These functional findings are
consistent with the identification of a rare variant in
RAB10 that is over-represented in cognitively normal,
elderly individuals. Adding further interest to this
discovery, these individuals have high genetic risk for AD, yet
remain healthy. Thus, targeting RAB10 could represent a
novel therapeutic strategy for treating AD.
The variant in SAR1A did not replicate in an
independent set of samples from the CCS, but the effect was in the
expected direction (odds ratio = 0.87, 95% confidence
interval (CI) 0.54–1.31). The exact function of SAR1A, a
GTPase, is unknown, but it is believed to be involved in
membrane trafficking and is part of the endoplasmic
reticulum to Golgi apparatus transport complex [
tested the effect of SAR1A overexpression and knockdown
on Aβ levels and our functional data were inconclusive.
We achieved only modest silencing of SAR1A expression.
This also contributes to the inconclusive nature of our
results. Yet, additional evidence supports a possible role for
SAR1A in AD. SAR1A binds APP [
] and is widely
expressed in all regions of both the human and mouse
], and SAR1A expression is lower in neurons
isolated from AD brains compared to controls .
Rs7653 is located in the 3′ untranslated region of SAR1A
and could possibly be involved in regulation of translation
by modifying microRNA binding, but no definitive data
on functional impact is available and no clear
bioinformatic predictions can be made at this time. To date,
rs7653 is not associated with any phenotypes in the
NHGRI-EBI GWAS Catalog (accessed 18 September
In summary, we used an innovative approach to
identify rare variants that affect risk for AD. Our approach
provides several advantages compared to other study
designs. First, these large and wide pedigrees capture even
distantly related individuals and therefore provide many
informative meioses. Second, each pedigree has a
significant excess of AD mortality over multiple generations
and distant relationships when compared to general
Utah rates, thus provides sets of distantly related
individuals who likely have a strong genetic component to
their AD, which narrows the likely genomic location to a
small window. Third, since we have a set of healthy,
high-risk, elderly individuals, some of which are
members of families with an excess of AD deaths, these
individuals likely share protective genetics and this study
design is ideal to identify protective genetic variants.
Despite the advantages of this approach, there are
several limitations to the design. First, the nature of the
pedigree selection and rarity of the AD resilient samples
led to sampling that made obtaining significant LOD
scores very difficult. As a result, we obtained suggestive
LOD scores in two of the five pedigrees, but no
significant LOD scores in any of the pedigrees. However, any
concerns about genetic results should be at least
somewhat alleviated by the experimental evidence supporting
the genetic discoveries.
Second, in tests for AD excess in the UPDB, we
identified affected individuals based on presence of
International Classification of Disease (ICD)9 or ICD10 codes
for AD on a Utah death certificate. Assignment of
cause-of-death from death certificates is recognized to
be imprecise. Due to the challenge of diagnosing AD,
especially in the past, it is much more likely that AD as
cause-of-death is missing from death certificates where
it belongs, as compared to having been incorrectly
included. This makes our estimates of AD death rates
extra conservative, and any biases that exist, exist across
all UPDB data equally.
Third, genealogy data used to define relationships
might have included some relationships that were not
biological and some relationship data might have been
censored due to failure to link records. Some results
may require validation in other populations and results
based only on Utah data can only be extended to similar
populations of European descent. Despite these potential
limitations in our genetics work, our biological findings
suggest that RAB10 may regulate Aβ levels, thus altering
risk for AD.
Using an innovative study design and unique resources,
we have obtained evidence that rare variation in RAB10
may provide resilience to AD. Linkage and sequence
analyses, replication using both SNP and gene-based
tests, and in vitro functional work suggest that RAB10
may represent effective targets for AD prevention and
therapy. Finally, we have provided a model for an
effective research design for studying complex traits.
Additional file 1: Supplementary Note 1. Variant filtration process.
Figures S1 and S2 The pedigrees for RAB10 and SAR1A. (PDF 815 kb)
(SKAT)-O: Sequence kernel association test; AD: Alzheimer’s disease;
ADNI: Alzheimer’s Disease Neuroimaging Initiative; APC: Anterior prefrontal
cortex; CCS: Cache County Study on Memory Health and Aging;
CEPH: Centre d’Etude du Polymorphisme Humain; CQN: Conditional quantile
normalization; DGE: Differential gene expression; GATK: Genome Analysis
Toolkit; ICD: International Classification of Disease; PHG: Parahippocampal
gyrus; PO: Pars opercularis; RIN: RNA integrity number; RR: Relative risk;
STG: Superior temporal gyrus; UPDB: Utah Population Database; WES: Whole
exome sequence; WGS: Whole genome sequence
For samples collected through the Sun Health Research Institute Brain and
Body Donation Program of Sun City, Arizona: The Brain and Body Donation
Program is supported by the National Institute of Neurological Disorders and
Stroke (U24 NS072026 National Brain and Tissue Resource for Parkinson’s
Disease and Related Disorders), the National Institute on Aging (P30
AG19610 Arizona Alzheimer’s Disease Core Center), the Arizona Department
of Health Services (contract 211002, Arizona Alzheimer’s Research Center),
the Arizona Biomedical Research Commission (contracts 4001, 0011, 05-901
and 1001 to the Arizona Parkinson’s Disease Consortium) and the Michael J.
Fox Foundation for Parkinson’s Research.
Partial support for all data sets within the Utah Population Database (UPDB)
was provided by Huntsman Cancer Institute, University of Utah and the
Huntsman Cancer Institute’s Cancer Center Support grant, P30 CA42014 from
National Cancer Institute. LACA acknowledges support from the George E.
Wahlen Department of Veterans Affairs Medical Center and the Huntsman
Cancer Foundation, Salt Lake City, Utah.
The ADNI Data collection and sharing for this project was funded by ADNI
(National Institutes of Health Grant U01 AG024904) and DOD ADNI
(Department of Defense award number W81XWH-12-2-0012). ADNI is funded
by the National Institute on Aging, the National Institute of Biomedical Imaging
and Bioengineering, and through generous contributions from the following:
AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon
Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.;
Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun;
F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE
Healthcare; IXICO Ltd; Janssen Alzheimer Immunotherapy Research & Development,
LLC; Johnson & Johnson Pharmaceutical Research & Development LLC; Lumosity;
Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research;
Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal
Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. 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 (https://fnih.org/). The grantee organization is the
Northern California Institute for Research and Education, and the study is coordinated
by the Alzheimer’s Therapeutic Research Institute at the University of Southern
California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the
University of Southern California.
Data used in preparation of this article were obtained from the Alzheimer’s
Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.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.usc.edu/wp-content/uploads/how_to_apply/
This work was supported by National Institute on Aging (R01AG042611 and
RF1AG054052 to JSKK; R01AG032990, RFAG051504 to NET; U01AG046139 to
NET, SGY, TEG, NDP; AG046374 to CMK; U01AG052411 to AMG; RO1AG18712
to RM); National Institute of Neurological Disorders and Stroke (R01NS080820
to NET); Mayo Clinic Center for Individualized Medicine. This work was
supported by access to equipment made possible by the Hope Center for
Neurological Disorders, and the Departments of Neurology and Psychiatry at
Washington University School of Medicine.
Availability of data and materials
Several datasets were used in this research. ADNI datasets are available
through the ADNI website (http://adni.loni.usc.edu/data-samples/) at the
“Download genetic data” tab and the “ADNI WGS + Omni2.5 M” section.
Mayo Clinic and Mount Sinai Brain Bank RNAseq data are available on
synapse.org (Mayo – syn6090802; MSBB – syn3157743). Whole genome
sequence from the Cache County subjects is available through the
Alzheimer’s Disease Sequencing Project (https://www.niagads.org/adsp/
content/home). Utah Population Database samples and data, per the IRB,
may not be made publically available due to lack of consent for this type of
JSKK oversaw all aspects of the project; PGR and JSKK conceived the project;
PGR performed initial association tests; PGR, CMK, and JSKK wrote the
manuscript; CMK, SH, and IA performed in vitro analyses; CCT, JMF, and LAC
identified high risk pedigrees from the UPDB and performed linkage
analyses; MTWE performed gene-based tests; JG, OH, VMF, RG, JB, JH, CS, AS,
CC, and AG performed replications (genotyping and statistical tests); CC and
AG helped with project design; MA, XW, SGY, DWD, TEG, NDP, ARD, NE, and
CMK generated and analyzed expression data; RM, MN, CC, and JT developed
and contributed the Cache County dataset; and ADNI provided whole genome
sequence data for > 800 samples. All authors read and approved the final
Ethics approval and consent to participate
All research reported in this manuscript complies with the Declaration of
Helsinki and was approved by the Brigham Young University Institutional
Review Board (approval number E110252) and Utah State University
Institutional Review Board (approval number CCSMHA5). Informed consent
was obtained from all study participants.
Consent for publication
ADNI has received some funding from the following companies: Araclon
Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.;
Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company;
EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.;
Fujirebio; GE Healthcare; IXICO Ltd; Janssen Alzheimer Immunotherapy
Research & Development, LLC; Johnson & Johnson Pharmaceutical Research &
Development LLC; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics,
LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals
Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company;
and Transition Therapeutics. Private sector contributions were facilitated by the
Foundation for the National Institutes of Health (https://fnih.org/). Donors retain no
rights to discoveries and are not included in discussions of results or publication
decisions. As such, these contributions do not represent a conflict of interest. The
remaining authors declare that they have no competing interests
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
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