Human whole genome genotype and transcriptome data for Alzheimer’s and other neurodegenerative diseases
SCIENTIFIC DATA |
Data Descriptor: Human whole genome genotype and transcriptome data for Alzheimer's and other neurodegenerative diseases
Mariet Allen 0
Minerva M. Carrasquillo 0
Cory Funk 0
Benjamin D. Heavner 0
Fanggeng Zou 0
Curtis S. Younkin 0
Jeremy D. Burgess 0
High-Seng Chai 0
Julia Crook 0
James A. Eddy 0
Hongdong Li 0
Ben Logsdon 0
Mette A. Peters 0
Kristen K. Dang 0
Xue Wang 0
Daniel Serie 0
Chen Wang 0
Thuy Nguyen 0
Sarah Lincoln 0
Kimberly Malphrus 0
Gina Bisceglio 0
Ma Li 0
Todd E. Golde 0
Lara M. Mangravite 0
Yan Asmann 0
Nathan D. Price 0
Ronald C. Petersen 0
Neill R. Graff-Radford 0
Dennis W. Dickson 0
Steven G. Younkin 0
Nilüfer Ertekin-Taner 0
Design Type 0
Measurement Type 0
Technology Type 0
Whole Genome Association Study 0
Factor Type 0
0 Homo sapiens
OPEN Previous genome-wide association studies (GWAS), conducted by our group and others, have identified loci that harbor risk variants for neurodegenerative diseases, including Alzheimer's disease (AD). Human disease variants are enriched for polymorphisms that affect gene expression, including some that are known to associate with expression changes in the brain. Postulating that many variants confer risk to neurodegenerative disease via transcriptional regulatory mechanisms, we have analyzed gene expression levels in the brain tissue of subjects with AD and related diseases. Herein, we describe our collective datasets comprised of GWAS data from 2,099 subjects; microarray gene expression data from 773 brain samples, 186 of which also have RNAseq; and an independent cohort of 556 brain samples with RNAseq. We expect that these datasets, which are available to all qualified researchers, will enable investigators to explore and identify transcriptional mechanisms contributing to neurodegenerative diseases.
» Genetics of the nervous
» RNA sequencing
Background & Summary
In the past decade GWAS identified risk loci for human diseases, including AD1–7 and other
neurodegenerative diseases8,9. Despite this progress, a comprehensive understanding of the molecular
mechanisms underlying these complex conditions remains elusive. This is partly due to the inability of
the disease GWAS approach to identify the actual disease gene and the functional disease risk variants.
We10 and others11,12 utilized combined gene expression GWAS (eGWAS) and disease GWAS to identify
loci which harbor regulatory variants that confer disease risk and to nominate the actual disease genes at
these loci. The underlying premise of these studies is that genetic variants that modulate expression levels
of genes, which encode critical members of disease molecular pathways, will also influence disease risk13.
If this is correct, then there should be significant overlap between disease GWAS and eGWAS variants,
especially if assessed in the disease-relevant tissue. Indeed, in an eGWAS of brain tissue from subjects
with AD and non-AD, comprised largely of other neurodegenerative diagnoses, we identified significant
enrichment for disease GWAS variants for AD and other diseases10. We14–18 and others8,19–22 determined
that many of the risk variants for AD and other neurodegenerative diseases influence brain levels of genes
that are nearby in the genome. These studies implicate the genes that are likely to be involved in disease
pathways, nominate regulatory variants as the functional disease risk factors and provide testable
hypotheses for their downstream effects.
Most large-scale gene expression studies in human brains published to date10,19,20,23 utilize
microarray-based gene or exon arrays. Despite the versatility, cost-effectiveness and large-scale utility,
this approach has limitations, including restricted dynamic range, lack of probes for all known gene
isoforms and confinement of assays to known transcripts. RNA sequencing (RNAseq) provides an
attractive alternative that can surpass these limitations and provide much more in-depth information
about the human transcriptome in a high-throughput manner24. To expand our prior work on the
human transcriptome based on microarray approaches and to evaluate gene/exon/isoform levels in a
comparative fashion between AD and other neurodegenerative diseases, we have generated RNAseq data
on brain samples from both a subset of the subjects that underwent microarray transcriptome studies18
and also an independent cohort. These datasets will be of utility in performing expression quantitative
trait loci (eQTL), expression profiling and network analyses to facilitate interpretation of genetic
associations and further understanding of disease-mediated changes in transcriptional regulation.
The present report is a description of the large-scale human genetic, and both microarray- and
RNAseq-based transcriptome datasets we generated. The datasets described in this report have been
made available to the research community through the Accelerating Medicines Partnership in
Alzheimer’s Disease (AMP-AD) Knowledge Portal (Data Citation 1). The portal is hosted in the
Synapse software platform25 from Sage Bionetworks as part of a series of datasets developed in support of
the AMP-AD Target Identification and Preclinical Validation Project. The AMP-AD consortium includes
six academic teams that will be generating genomic data from human brain or blood samples collected
from more than 10 cohorts. Datasets are hosted in a common environment with standardized meta-data
and annotations to facilitate cross-cohort query, access, and analysis. Each dataset provides a unique
perspective on AD; therefore, datasets differ in types, generation protocols, and underlying patient
characteristics. Together, this collection represents to date the most comprehensive collection of human
genomic data in the field and, as such, it will be invaluable to a broad set of researchers.
The datasets described herein include the following: (
) late-onset AD GWAS1 (Mayo LOAD GWAS)
on 2,099 subjects (Data Citation 2); (
) Mayo eGWAS10 on 773 samples from the cerebellum (CER) and
temporal cortex (TCX) brain regions from a subset of Mayo LOAD GWAS participants (Data
Citations 3,4); (
) Mayo Pilot RNAseq18 generated on a subset of 186 TCX samples from the Mayo
eGWAS (Data Citation 5); (
) Mayo RNAseq on an independent cohort of 556 TCX26 (Data Citation 6)
and CER (Data Citation 7) samples from subjects with AD, progressive supranuclear palsy (PSP),
pathologic aging and elderly controls without neurodegenerative diseases. This report provides a
comprehensive understanding of these cohorts, a detailed description of subjects, samples, data
generation, and quality control (QC) as well as instructions to access these rich datasets by the scientific
The repository of human whole genome genotype and transcriptome data described herein (Table 1,
Fig. 1) consist of the following resources some of which have previously been published: Previously
published datasets include whole genome genotype data from the Mayo LOAD GWAS1 (Data Citation 2)
and microarray-based whole transcriptome data from the Mayo eGWAS10 (Data Citations 3,4).
Next-generation RNA-sequencing (RNAseq) data from a subset of the patients from the Mayo Clinic
eGWAS, referred to as the ‘Mayo Pilot RNAseq’ (Data Citation 5), was published in part18.
A non-overlapping cohort with RNAseq-based transcriptome data named ‘Mayo RNAseq’ (Data
Citations 6,7) has also been published in part26. For a comprehensive description of the overall repository,
the data from the published studies are also described herein, albeit in an abbreviated fashion. These four
study cohorts will be referred to by their names as mentioned above, preceded by letters A-D (Table 1)
level of neurofibrillary tangle pathology between Braak score of 2 and 3; but most controls had
neuropathologies unrelated to AD, including vascular dementia, frontotemporal dementia, dementia with
Lewy bodies, multi-system atrophy, amyotrophic lateral sclerosis, and progressive supranuclear palsy.
Ages, APOE ε4 genotype and sex distribution for the Mayo LOAD GWAS cohort are shown in Table 2.
This study only included subjects with ages between 60 and 80 years, based on the assumption that much
of the genetic risk for LOAD will be concentrated in this age group, especially given the
age-dependent effects of the strongest AD risk variant apolipoprotein E ε4 (APOE4)28. Age for the
clinically diagnosed LOAD cases is defined as age at first diagnosis of AD, since age at onset is not always
available. Age at entry into the study is used for the clinically diagnosed controls. Age at death is utilized
for the cases and controls in the postmortem Mayo Clinic Brain Bank series, given that for this
cohort, age at clinical diagnosis/ evaluation is not always available. Illumina Hap300 microarray genotypes
from the subjects in these three case-control series were utilized to conduct a GWAS of LOAD risk1.
Mayo eGWAS. This cohort was previously described in detail10. All subjects in the Mayo eGWAS
(Data Citations 3,4) are a subset of the Mayo Clinic Brain Bank series from the Mayo LOAD GWAS
A. Mayo LOAD GWAS (n=2,099)
(Data Citation 2)
2 ante-mortem cohorts:
Mayo Clinic Jacksonville
(n=684) and Rochester
1 post-mortem cohort:
Mayo Clinic Brain Bank
B. Mayo eGWAS (n=773)
(Data Citation 3,4)
CER samples with
WGDASL gene expression
(n=202 AD, 197 non-AD)
TCX samples with
WGDASL gene expression
(n=197 AD, 177 non-AD)
C. Mayo Pilot RNAseq
(n=94 AD, 92 PSP)
(Data Citation 5)
D. Mayo RNAseq (n=556)
(Data Citation 6,7)
(n=86 AD, 84 PSP, 28
pathologic aging, 80
(n=84 AD, 84 PSP, 30
pathologic aging, 80
A. Mayo LOAD GWAS (Data Citation 2)
B. Mayo eGWAS (Data Citations 3,4)
C. Mayo Pilot RNAseq (Data Citation
Mayo RNAseq. The subjects from this cohort are non-overlapping with the cohorts described above.
The Mayo RNAseq cohort was utilized to generate RNAseq-based whole transcriptome data from 278
TCX26 (Data Citation 6) and 278 CER (Data Citation 7) samples. Two hundred thirty-eight subjects had
both CER and TCX RNAseq and the rest had either CER or TCX RNAseq measurements based on tissue
availability. CER samples were from the following diagnostic categories: 86 AD, 84 PSP, 28 pathologic
aging and 80 controls without neurodegenerative diagnoses. TCX samples had the following diagnostic
groups: 84 AD, 84 PSP, 30 pathologic aging and 80 controls. Control subjects each had Braak28 NFT stage
of 3.0 or less, CERAD31 neuritic and cortical plaque densities of 0 (none) or 1 (sparse) and lacked any of
the following pathologic diagnoses: AD, Parkinson’s disease (PD), DLB, VaD, PSP, motor neuron disease
(MND), CBD, Pick’s disease (PiD), Huntington’s disease (HD), FTLD, hippocampal sclerosis (HipScl) or
dementia lacking distinctive histology (DLDH). Subjects with pathologic aging also lacked the above
diagnoses and had Braak NFT stage of 3.0 or less, but had CERAD neuritic and cortical plaque densities
of 2 or more. None of the pathologic aging subjects had a clinical diagnosis of dementia or mild cognitive
impairment. Given the presence of amyloid plaques, but not tangles and the absence of dementia,
pathologic aging is considered to be either a prodrome of AD or a condition, in which there is resistance
to the development of NFT and/or dementia32.
Within the Mayo RNAseq cohort (Data Citations 6,7), all AD and PSP subjects were from the Mayo
Clinic Brain Bank, and all pathologic aging subjects were obtained from the Banner Sun Health Institute.
Thirty-four control CER and 31 control TCX samples were from the Mayo Clinic Brain Bank, and the
remaining control tissue was from the Banner Sun Health Institute. All subjects were North American
Caucasians. All but control subjects, had ages at death ≥60, and a more relaxed lower age cutoff of ≥50
was applied for normal controls to achieve sample sizes similar to that of AD and PSP subjects. No upper
age limit was imposed on this cohort, however when subjects had ages at death of ≥90, their ages were
recorded as ‘90_or_above’ and shown as ‘90’ in Table 2 to protect patient confidentiality.
Table 2 details the demographic characteristics of the Mayo RNAseq cohort (Data Citations 6,7).
PSP subjects tended to be younger than the other diagnostic groups. As expected, there was a greater
frequency of APOE4 positive subjects in the AD group, followed by pathologic aging, then PSP and
control subjects. AD and pathologic aging subjects had greater female sex frequency (57%), followed by
controls (49%), then PSP subjects (39%). RIN for all samples were selected to be ≥5.0. Pathologic aging
and control samples had slightly lower RINs than AD and PSP samples, due to limitations in availability
of samples in these former diagnostic categories.
Sample collection and processing. For the Mayo LOAD GWAS (A) (Data Citation 2), DNA samples
were collected and processed as previously described1. For the antemortem Mayo Clinic Jacksonville and
Mayo Clinic Rochester series, whole blood samples were collected in 10 ml EDTA tubes followed by DNA
extraction using AutoGenFlex STAR instrument (AutoGen), whereas cerebellar tissue was used for DNA
extraction from the postmortem Mayo Clinic Brain Bank series using the Wizard Genomic DNA
purification kit (Promega). Given limited amounts of DNA from samples in the Mayo Clinic Rochester
series and Mayo Clinic Brain Bank series, whole genome amplification (WGA) was applied using the
Illustra GenomiPhi V2 DNA Amplification Kit (GE Healthcare Bio-Sciences), in four 5 ml reactions that
utilized 5–15 ng genomic DNA as a template. Subsequent to the pooling of these reaction products, WGA
DNA was subjected to quality control (QC) using SNP genotyping as previously described.
RNA extraction methods for the Mayo eGWAS10 (B) (Data Citations 3,4) and Mayo Pilot RNAseq18
(C) (Data Citation 5) were previously described. Total RNA was extracted from frozen brain samples
using the Ambion RNAqueous kit (Life Technologies, Grand Island, NY) according to the manufacturer’s
instructions. Brain samples for the Mayo RNAseq (D) (Data Citations 6,7) study underwent RNA
extractions via the Trizol/chloroform/ethanol method, followed by DNase and Cleanup of RNA using
Qiagen RNeasy Mini Kit and Qiagen RNase -Free DNase Set. The quantity and quality of all RNA
samples were determined by the Agilent 2100 Bioanalyzer using the Agilent RNA 6000 Nano Chip
(Agilent Technologies, Santa Clara, CA). Samples had to have an RNA Integrity Number (RIN) ≥5.0 for
inclusion in either study (Table 2).
Data generation. The genotype data for the Mayo LOAD GWAS (A) (Data Citation 2) was generated
using HumanHap300-Duo Genotyping BeadChips1, which were processed with an Illumina BeadLab
station at the Mayo Clinic Genotyping Shared Resource (currently Mayo Clinic Medical Genome
Facility = MGF, Rochester, Minnesota) according to the manufacturer’s protocols. Two samples were
genotyped per chip for 318,237 SNPs across the genome. Genotype calls were made using the auto-calling
algorithm in Illumina’s BeadStudio 2.0 software.
For the Mayo eGWAS study (B) (Data Citations 3,4), transcript levels were measured using the Whole
Genome DASL assay (Illumina, San Diego, CA) as previously described10. Probe annotations were done
based on NCBI RefSeq, Build 36.2. The RNA samples were randomized across the chips and plates using
a stratified approach to ensure balance with respect to diagnosis, age, gender, RIN and APOE genotype.
Raw probe mRNA expression data were exported from GenomeStudio software (Illumina Inc.) and
preprocessed for background correction, variance stabilizing transformation, quantile normalization and
probe filtering using the lumi package of BioConductor33.
Samples for both Mayo Pilot RNAseq (C) (Data Citation 5) and Mayo RNAseq (D) (Data
Citations 6,7) studies were randomized prior to transfer to the Mayo Clinic MGF Gene Expression Core
for library preparation and then the Sequencing Core for RNA sequencing. Mayo Pilot RNAseq (C)
(Data Citation 5) AD and PSP samples were randomized across flowcells, taking into account age at
death, sex and RIN. These samples underwent library preparation and sequencing at different times and
therefore should be considered as separate datasets. Likewise, Mayo RNAseq (D) of TCX26 and CER
samples (Data Citations 6,7, respectively) underwent RNAseq at different times. These samples were
randomized across flowcells, taking into account age at death, sex, RIN, Braak stage and diagnosis. The
TruSeq RNA Sample Prep Kit (Illumina, San Diego, CA) was used for library preparation from all
samples. The library concentration and size distribution was determined on an Agilent Bioanalyzer DNA
1000 chip. All samples were run in triplicates using barcoding (3 samples per flowcell lane). For Mayo
Pilot RNAseq (C) (Data Citation 5) samples, 50 base-pair, paired-end sequencing was done, whereas
Mayo RNAseq (D) (Data Citations 6,7) samples underwent 101 bp, paired-end sequencing.
Data Processing. Mayo LOAD GWAS (A) (Data Citation 2) genotypes from Illumina BeadStudio 2.0
software were utilized to generate lgen, map and fam files that were imported into PLINK34 and
converted to binary ped (.bed) and map (.bim) files, which are deposited together with PLINK format fam
and covariate files (DOI and descriptions for each these files are provided in Table 3 (available online
The Mayo eGWAS WG-DASL microarray expression dataset from TCX and CER (B) includes
covariates and probe expression levels (Data Citation 3), which are preprocessed as published10 and
described above. The Mayo eGWAS ‘eSNP Results’ (Data Citation 4) are the eQTL results from the test of
association between the Mayo LOAD GWAS (Data Citation 2) genotypes and the WG-DASL gene
expression measures analyzed by multivariable linear regression using an additive model in PLINK34, as
published previously10 (DOI and descriptions for each these files are provided in Table 3 (available online
only)). These analysis used preprocessed probe transcript levels as traits, SNP minor allele dosage as the
independent variable, and adjusted for the following covariates: APOE ε4 dosage (
0, 1, 2
), age at death,
sex, PCR plate, RIN and adjusted RIN squared (RIN-RINmean)2. Analyses were limited to SNP-probe
pairs that were in-cis, defined as +/ − 100 kb of the targeted gene according to NCBI Build 36. The ADs
and non–ADs were analyzed both separately and jointly. The joint analyses included diagnosis as an
additional covariate (AD = 1, non–AD = 0). Results of analyses for both the genotyped SNPs as well as
genotypes imputed to HapMap2 reference are provided. HapMap2 imputations were done as described10.
The eGWAS results were previously made available through the NIAGADS repository (https://www.
The Mayo Pilot RNAseq18 (Data Citation 5), Mayo RNAseq TCX26 and CER data (Data Citations 6,7,
respectively) were processed using the same analytic pipeline. Read alignments were done using the
SNAPR software35, an RNA sequence aligner based on SNAP, using GRCh38 reference and Ensembl v77
gene models. Outputs include per-sample gene and transcript counts, which are merged into a single file
per data type (gene or transcript) that contains data for all samples across all genes/transcripts (DOI and
descriptions for each these files are provided in Table 3 (available online only)). Alignment with SNAPR
starts with the creation of hash indices built from both a reference genome GRCh38 and transcriptome
GRCh38.77. SNAPR filters fastq reads by Phred score (>80% of the read must have a Phred score
> = 20) and simultaneously aligns each read (or read pair) to both the genome and transcriptome. The
best alignment is written to a sorted BAM file with read counts simultaneously tabulated and written for
each sample. Read counts are given by gene ID and transcript ID (two separate files). We have previously
tested the read counts generated by SNAPR to the read counts generated by HT-Seq and found them to
be very comparable.
Post-processing was also performed using the same pipeline for these three RNAseq datasets as follows:
The individual read count files produced by SNAPR are merged into a single file using two scripts:
merge_count_files.R and a dataset-specific read-count merge script. These scripts generate the
corresponding _counts.txt.gz files. The merged count files are normalized with the normalize_
readcounts.R script, which uses the edgeR implementation of the trimmed mean of M-values (TMM)
normalization method to calculate counts per million (CPM). These normalized counts are saved for both
gene and transcript levels (DOI and descriptions for each these files are provided in Table 3 (available
Code Availability. The R script called merge_count_files.R36 was used to merge the RNAseq read
count files produced by SNAPR into a single file, and can be found at https://github.com/CoryFunk/
AMP-AD-scripts/blob/master/combine_count_files.pl. Also, the R script used to normalize the merged
RNAseq read counts, called normalize_readcounts.R36, can be found at https://github.com/CoryFunk/
Data available for studies A-D (Data Citations 2–7; Table 3 (available online only)) consists of a set of
files that contain genomic, genetic or covariate data for a defined set of samples; analytic results are also
provided when available. Data files can be found in the Sage Bionetworks AMP-AD Knowledge Portal
(Data Citation 1) in study specific folders (and subfolders). Users can identify and search for data files
and data descriptions using the unique Synapse ID and corresponding DOI provided in Table 3 (available
online only). Each sample within a study has a unique sample ID, this sample ID is consistent across all
files within the study, and files in other studies where applicable. The relationship between studies
and sample overlaps is illustrated in Fig. 1. The samples in study C (Data Citation 5) are a subset of
the samples in study B (Data Citation 3) which are likewise a subset of the samples in study A (Data
Citation 2); the samples in study D (Data Citations 6,7) are independent of those in studies A-C. The
Usage Notes section describes the data accession conditions, and the steps for requesting access.
Mayo LOAD GWAS (A) (Data Citation 2) QC methods were previously published1. Briefly, using
PLINK34, subjects with genotyping call rates of o 90%, duplicate genotyping and/or sex-mismatches
between recorded and deduced sex were eliminated from the dataset. All SNPs with genotyping call rates
o 90%, minor allele frequencies o 0.01, and/or Hardy-Weinberg p values o 0.001 were also eliminated.
Prior to QC, 318,237 SNPs were genotyped in 2,465 subjects. The available data includes the 313,504 SNP
genotypes from 2,099 subjects that passed these QC parameters.
The Mayo eGWAS10 (B) (Data Citations 3,4) data was generated as follows: We annotated probes for
presence of genetic variants by comparing their positions according to NCBI RefSeq, Build 36.3 to those
of all variants within dbSNP131 and identified the list of probes that have ≥1 variants within their
sequence. We depict this information in the files for the Mayo eGWAS, ‘eSNP Results’ (Data Citation 4)
(Table 3 (available online only)), by including ‘SNP-In-Probe’ column, which has ‘TRUE’ if the probe
sequence harbors ≥1 SNP, and ‘FALSE’, otherwise. We also calculated for each probe within each analytic
group, percent detection rate above background. Probes that are detected in >12.5%, >25%, >50% and
>75% of the subjects in each analytic group are annotated by four separate columns within the ‘eSNP
Results’ (Data Citation 4) from the eGWAS that included HapMap2 imputed genotypes, described below.
The purpose of these annotation columns is to enable others the flexibility to impose cutoffs based on
presence/absence of variants within probe sequence and/or probe detection rates while providing the full
dataset for completeness. The Mayo eGWAS (Data Citation 3,Data Citation 4) also included replicate
samples as described for QC and to estimate intraclass coefficients (ICC), which is the between-subject
variance, as a percentage of the total variance in probe expression10. There were 4 AD and 4 non-AD
temporal cortex samples that were measured in 5 replicates; and 10 AD and 5 non-AD cerebellar sample
replicates across five plates. Universal human RNA (UHR) samples were also run on each PCR plate as
part of QC. The expression phenotypes include results from only one of the replicate subjects selected
randomly and exclude UHR results. It should be noted that 3 AD and 9 non-AD subjects for TCX, and
4 AD subjects for CER, do not have associated GWAS genotypes as they did not pass ≥ 1 GWAS QC
parameter described above.
For the Mayo Pilot RNAseq18 (C) (Data Citation 5) data principal components analysis (PCA)
identified 2 outliers in the AD and 4 in the PSP cohort. The covariates for these subjects were set to
missing ( = NA) in the respective covariate files (DOI and descriptions for these files are provide in
Table 3 (available online only)). Hence, although 96 AD and 96 PSP subjects underwent sequencing in
the Mayo Pilot RNAseq study, 94 AD and 92 PSP subjects were retained for analyses. It should be noted
that of these subjects, 1 AD and 7 PSP subjects lack GWAS data due to either having genotype counts
o 90% or failing sex checks. PCA identified no outliers in the Mayo RNAseq (D) of TCX26 samples (Data
Citation 6) but 2 such subjects in the CER analyses (Data Citation 7). The covariate data in the relevant
CER files for these two subjects were set to missing. We likewise assessed the RNASeq data for sex
discrepancies based on Y chromosome gene expression and documented sex and identified 2 subjects
with mis-matched sex for both TCX and CER, plus a third subject in the CER cohort. These were also set
to missing in the covariate files. At the time of this publication, the Mayo RNAseq subjects did not have
GWAS genotypes deposited on Synapse.
The data described herein is available for use by the research community and has been deposited in the
AMP-AD Knowledge Portal (Data Citation 1). Table 3 (available online only) provides a detailed
description of the files deposited for the four studies, their specific Synapse identifiers (IDs), DOIs, the
types of files and definitions of the column headers. These files (Data Citations 2–7), and their assigned
DOIs will be maintained in perpetuity in the AMP-AD Knowledge Portal (Data Citation 1). Access to all
of these files is enabled through the Sage Bionetworks, Synapse repository; and a subset of the files for the
Mayo LOAD GWAS (Data Citation 2) and the Mayo eGWAS (Data Citations 3,4) are also available via
The AMP-AD Knowledge Portal hosts data derived from multiple cohorts that were generated as part
of or used in support of the AMP-AD Target Identification and Preclinical Validation project
(Data Citation 1). The portal uses the Synapse software platform25 for backend support, providing users
with both web-based and programmatic access to data files. All data files in the portal are annotated using
a standard vocabulary to enable users to search for relevant content across the AMP-AD datasets using
programmatic queries. Data is stored in a cloud based manner hosted by Amazon web services (AWS),
which enables user to execute cloud-based compute. Detailed descriptions including data processing,
QC metrics, and assay and cohort specific variables are provided for each file as applicable.
Access for the data described herein is controlled in a manner set forth by the institutional review
board (IRB) at the Mayo Clinic. All data use terms include: (
) maintenance of data in a secure and
confidential manner, (
) respect for the privacy of study participants, (
) citation of the data contributors
in any publications resulting from data use, and (
) informing data contributors of resultant publications.
Specific data use terms are provided for each dataset (Data Citations 3–6) under the header ‘Terms of
use’; users must register for a Synapse account and provide electronic agreement to these terms prior to
accessing the study files. Access to the Mayo LOAD GWAS data (A) (Data Citation 2) requires a data use
certificate (doi:10.7303/syn2954402.2). User approvals are managed by the Synapse Access and
Compliance Team (ACT).
Data on the AMP-AD Knowledge Portal are annotated with a common dictionary of terms
(doi:10.7303/syn5478487.2) to enable querying of the data using the Synapse analytical clients (R client:
syn1834618, python client: syn1768504, command line client: syn2375225). Fields, their allowable values
specific to the datasets described herein and the dictionary of annotations are shown in Table 3 (available
online only). These annotations can be used to identify files of interest within the available datasets and to
filter on any of the fields using the allowable values from the dictionary (an example is shown here:
We thank the patients and their families for the sample and tissue donations. Without their generosity,
this research would not be possible. The Mayo Clinic Alzheimer's Disease Genetic Studies were led by
Dr Nilüfer Ertekin-Taner and Dr Steven G. Younkin, Mayo Clinic, Jacksonville, FL using samples from
the Mayo Clinic Study of Aging, the Mayo Clinic Alzheimer's Disease Research Center, and the Mayo
Clinic Brain Bank. Data collection was supported through funding by NIA grants P50 AG016574, R01
AG032990, U01 AG046139, R01 AG018023, U01 AG006576, U01 AG006786, R01 AG025711, R01
AG017216, R01 AG003949, NINDS grant R01 NS080820, the GHR foundation, CurePSP Foundation,
and support from Mayo Foundation. 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. We thank Mrs. Kelly Viola for her assistance with revisions of this
M.A. helped with draft of the manuscript, analyzed data, contributed to the Mayo eGWAS and oversaw
the Mayo Pilot RNAseq and Mayo RNAseq studies; M.M.C. helped with draft of manuscript, analyzed
data, co-led the Mayo LOAD GWAS, and oversaw the Mayo Pilot RNAseq and Mayo RNAseq studies;
C.F. analyzed data for Mayo Pilot RNAseq and Mayo RNAseq; B.D.H. analyzed data for Mayo Pilot
RNAseq and Mayo RNAseq; F.Z. analyzed data and oversaw the Mayo eGWAS; C.S.Y. analyzed and
databased data for all studies; J.D.B. analyzed data for Mayo eGWAS, Mayo Pilot RNAseq and Mayo
RNAseq; H.-S.C. analyzed data for Mayo eGWAS; J.C. provided statistical support; J.A.E. analyzed data
for Mayo Pilot RNAseq and Mayo RNAseq; H.L. analyzed data for Mayo Pilot RNAseq and Mayo
RNAseq; B.L. architected the data repository, deposited these data into the public portal and manage data
dissemination; M.A.P. architected the data repository, deposited these data into the public portal and
manage data dissemination; K.K.D architected the data repository, deposited these data into the public
portal and manage data dissemination; X.W. analyzed data for Mayo Pilot RNAseq and Mayo RNAseq;
D.S. analyzed data for Mayo eGWAS, Mayo Pilot RNAseq and Mayo RNAseq; C.W. analyzed data for
Mayo eGWAS; T.N. generated data; S.L. generated data; K.M. generated data; G.B. generated data;
M.L. generated data; T.E.G. provided comments for the manuscript; L.M.M. architected the data
repository, deposited these data into the public portal and manage data dissemination; Y.A. analyzed data
for Mayo Pilot RNAseq and Mayo RNAseq; N.P. oversaw bioinformatics analysis of Mayo Pilot RNAseq
and Mayo RNAseq; R.C.P. provided patient material and data; N.R.G.-R. provided patient material and
data; D.W.D. provided patient material and data; S.G.Y. analyzed data, designed and led the Mayo
GWAS, wrote the manuscript; N.E.-T. analyzed data, designed and led the Mayo eGWAS, Mayo Pilot
RNAseq and Mayo RNAseq studies and wrote the manuscript.
Table 3 is only available in the online version of this paper.
Competing financial interests: Below are the disclosures for R.C.P.: Pfizer, Inc., and Janssen Alzheimer
Immunotherapy: Chair, Data Monitoring Committee. Hoffman-La Roche, Inc.: Consultant. Merck, Inc.:
Consultant. Genentech, Inc.: Consultant. Biogen, Inc.: Consultant. Eli Lilly & Co.: Consultant. N.R.G.-R.
has multicenter treatment study grants from Lilly and TauRx and consulted for Cytox. N.E.-T. has
consulted for Cytox. The remaining authors declare no competing financial interests.
How to cite this article: Allen, M et al. Human whole genome genotype and transcriptome data for
Alzheimer's and other neurodegenerative diseases. Sci. Data 3:160089 doi: 10.1038/sdata.2016.89 (2016).
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1. Carrasquillo , M. M. et al. Genetic variation in PCDH11X is associated with susceptibility to late-onset Alzheimer's disease . Nat Genet 41 , 192 - 198 ( 2009 ).
2. Harold , D. et al. Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer's disease . Nature genetics 41 , 1088 - 1093 ( 2009 ).
3. Lambert , J. C. et al. Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer's disease . Nature genetics 41 , 1094 - 1099 ( 2009 ).
4. Seshadri , S. et al. Genome-wide analysis of genetic loci associated with Alzheimer disease . Jama 303 , 1832 - 1840 ( 2010 ).
5. Naj , A. C. et al. Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer's disease . Nature genetics 43 , 436 - 441 ( 2011 ).
6. Hollingworth , P. et al. Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer's disease . Nature genetics 43 , 429 - 435 ( 2011 ).
7. Lambert , J. C. et al. Meta-analysis of 74 , 046 individuals identifies 11 new susceptibility loci for Alzheimer's disease . Nature genetics 45 , 1452 - 1458 ( 2013 ).
8. Hoglinger , G. U. et al. Identification of common variants influencing risk of the tauopathy progressive supranuclear palsy . Nature genetics 43 , 699 - 705 ( 2011 ).
9. Simon-Sanchez , J. et al. Genome-wide association study reveals genetic risk underlying Parkinson's disease . Nature genetics 41 , 1308 - 1312 ( 2009 ).
10. Zou , F. et al. Brain expression genome-wide association study (eGWAS) identifies human disease-associated variants . PLoS Genet 8 , e1002707 ( 2012 ).
11. Dixon , A. L. et al. A genome-wide association study of global gene expression . Nature genetics 39 , 1202 - 1207 ( 2007 ).
12. Emilsson , V. et al. Genetics of gene expression and its effect on disease . Nature 452 , 423 - 428 ( 2008 ).
13. Saykin , A. J. et al. Genetic studies of quantitative MCI and AD phenotypes in ADNI: Progress, opportunities, and plans . Alzheimers Dement 11 , 792 - 814 ( 2015 ).
14. Zou , F. et al. Gene expression levels as endophenotypes in genome-wide association studies of Alzheimer disease . Neurology 74 , 480 - 486 ( 2010 ).
15. Allen , M. et al. Novel late-onset Alzheimer disease loci variants associate with brain gene expression . Neurology 79 , 221 - 228 ( 2012 ).
16. Allen , M. et al. Glutathione S-transferase omega genes in Alzheimer and Parkinson disease risk, age-at-diagnosis and brain gene expression: an association study with mechanistic implications . Mol Neurodegener 7 , 13 ( 2012 ).
17. Allen , M. et al. Association of MAPT haplotypes with Alzheimer's disease risk and MAPT brain gene expression levels . Alzheimers Res Ther 6 , 39 ( 2014 ).
18. Allen , M et al. Late-onset Alzheimer disease risk variants mark brain regulatory loci . Neurology: Genetics 1 , e15 ( 2015 ).
19. Myers , A. J. et al. A survey of genetic human cortical gene expression . Nature genetics 39 , 1494 - 1499 ( 2007 ).
20. Webster , J. A. et al. Genetic control of human brain transcript expression in Alzheimer disease . Am J Hum Genet 84 , 445 - 458 ( 2009 ).
21. Chapuis , J. et al. Increased expression of BIN1 mediates Alzheimer genetic risk by modulating tau pathology . Mol Psychiatry 18 , 1225 - 1234 ( 2013 ).
22. Hazrati , L. N. et al. Genetic association of CR1 with Alzheimer's disease: a tentative disease mechanism . Neurobiol Aging 33 , 2949 e5 - 2949 e12 ( 2012 ).
23. Ramasamy , A. et al. Genetic variability in the regulation of gene expression in ten regions of the human brain . Nat Neurosci 17 , 1418 - 1428 ( 2014 ).
24. Montgomery , S. B. et al. Transcriptome genetics using second generation sequencing in a Caucasian population . Nature 464 , 773 - 777 ( 2010 ).
25. Derry , J. M. et al. Developing predictive molecular maps of human disease through community-based modeling . Nature genetics 44 , 127 - 130 ( 2012 ).
26. Allen , M. et al. Gene expression, methylation and neuropathology correlations at progressive supranuclear palsy risk loci . Acta Neuropathol 132 , 197 - 211 ( 2016 ).
27. McKhann , G et al. Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease . Neurology 34 , 939 - 944 ( 1984 ).
28. Farrer , LA et al. Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. A meta-analysis . APOE and Alzheimer Disease Meta Analysis Consortium. Jama 278 , 1349 - 1356 ( 1997 ).
29. Braak , H. & Braak , E. Neuropathological stageing of Alzheimer-related changes . Acta Neuropathol (Berl) 82 , 239 - 259 ( 1991 ).
30. Hauw , J. J. et al. Preliminary NINDS neuropathologic criteria for Steele-Richardson-Olszewski syndrome (progressive supranuclear palsy) . Neurology 44 , 2015 - 2019 ( 1994 ).
31. Mirra , S. S. et al. Interlaboratory comparison of neuropathology assessments in Alzheimer's disease: a study of the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) . J Neuropathol Exp Neurol 53 , 303 - 315 ( 1994 ).
32. Wang , J. , Dickson , D. W. , Trojanowski , J. Q. & Lee , V. M. The levels of soluble versus insoluble brain Abeta distinguish Alzheimer's disease from normal and pathologic aging . Exp Neurol 158 , 328 - 337 ( 1999 ).
33. Du , P. , Kibbe , W. A. & Lin , S. M. lumi: a pipeline for processing Illumina microarray . Bioinformatics 24 , 1547 - 1548 ( 2008 ).
34. Purcell , S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses . Am J Hum Genet 81 , 559 - 575 ( 2007 ).
35. Magis , A. T. , Funk , C. C. & Price , N. D. SNAPR: A Bioinformatics Pipeline for Efficient and Accurate RNA-Seq Alignment and Analysis . IEEE Life Sciences Letters 1 , 22 - 25 ( 2015 ).
36. Funk , C. AMP-AD-scripts: AMP-AD Fl-Mayo-ISB . in Zenodo https://dx.doi.org/10.5281/zenodo.56828 ( 2016 ).
1. Synapse http://dx.doi.org/10.7303/syn2580853 ( 2016 ).
2. Carrasquillo , M. M. et al. Synapse http://dx.doi.org/10.7303/syn2910256 ( 2016 ).
3. Zou , F. et al. Synapse http://dx.doi.org/10.7303/syn3157225 ( 2016 ).
4. Zou , F. et al. Synapse http://dx.doi.org/10.7303/syn3157249 ( 2016 ).
5. Allen , M. et al. Synapse http://dx.doi.org/10.7303/syn3157268 ( 2016 ).
6. Allen , M. et al. Synapse http://dx.doi.org/10.7303/syn3163039 ( 2016 ).
7. Allen , M. et al. Synapse http://dx.doi.org/10.7303/syn5049298 ( 2016 ).