SUPPLEMENTAL INFORMATION
SUPPLEMENTAL METHODS
IGAP Cohort
International Genomics of Alzheimer's Project (IGAP)1 is a large two-stage study based
upon genome-wide association studies (GWAS) on individuals of European ancestry.
In stage 1, IGAP used genotyped and imputed data on 7,055,881 single nucleotide
polymorphisms (SNPs) to meta-analyse four previously-published GWAS datasets
consisting of 17,008 Alzheimer's disease cases and 37,154 controls (The European
Alzheimer's disease Initiative – EADI the Alzheimer Disease Genetics Consortium –
ADGC The Cohorts for Heart and Aging Research in Genomic Epidemiology
consortium – CHARGE The Genetic and Environmental Risk in AD consortium –
GERAD). In stage 2, 11,632 SNPs were genotyped and tested for association in an
independent set of 8,572 Alzheimer's disease cases and 11,312 controls. Finally, a
meta-analysis was performed combining results from stages 1 & 2.
ADNI cohort
Data used in the preparation of this article were obtained from the Alzheimer’s Disease
Neuroimaging Initiative (ADNI) database (adni.loni.usc.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).
Each participant was formally evaluated using eligibility criteria that are
described in detail elsewhere
(http://www.adni-info.org/index.php?option=com_content&task=view&id=9&Itemid
=43). The institutional review boards of all participating institutions approved the
procedures for this study. Written informed consent was obtained from all participants
or surrogates. Experienced clinicians conducted independent semi-structured
interviews with the participant and a knowledgeable collateral source that included a
health history, neurological examination, and a comprehensive neuropsychological
battery. We selected participants from the ADNI database if they were clinically
diagnosed at baseline as cognitively normal, amnestic mild cognitive impairment
(MCI) as defined using the revised MCI criteria or probable AD.
Deriving Polygenic Hazard Scores
Selecting set of previously implicated SNPs
We utilized previously published summary statistics (p-values and odds ratios) for AD
associated SNPs from the IGAP consortium.1 Instead of using the estimated effect
sizes as the polygenic risk scores, we used the SNP list to constrain our search space.
We set the significance threshold to p-value < 10-5 as the criteria for association with
AD status. Among all IGAP stage 1 SNPs, we found 1854 SNPs with p-values < 10-5.
From this list, we then used a forward stepwise regression to select SNPs for the final
Cox proportional model. The final SNP selection process was conducted using the
ADGC phase 1 cohort, excluding samples from NACC and ADNI.
Forward stepwise Cox regression with conditional partial likelihood
To build the predictive model for AD age of onset, we adopted an approach that
reduced the effect of linkage disequilibrium and further examined the effect of genetic
loci on survival time. In each step, the algorithm includes one SNP that most
minimized the Martingale regression residuals, and halts if no SNPs can further
minimize the residuals. The Cox regressions were fitted with conditional partial
likelihood:
𝑑
exp(𝛽 ′ 𝑥𝑖 )
𝐿(𝛽) = ∏
∑𝑘∈𝑅̃(𝑡𝑗 ) exp(𝛽 ′ 𝑥𝑘 )
𝑗=1
Here, d represents each distinct time-event and R is the sampled risk set in the event
time tj. Time-ties were solved with Efron’s method. Throughout the fitting process, we
controlled for the effects of gender, APOE variants, and the top five genetic principal
components. The selection procedures were conducted on the ADGC phase 1 cohort.
In the final model, we identified 31 SNPs, in addition to the two APOE variants, that
were associated with AD age of onset.
Calculating individualized absolute hazards
Instead of estimating the baseline hazard from the ADGC cohort, which is biased due
to over-sampling of cases, we used the previously reported annualized incidence rates
by age2 and then converted this into disease onset rates, R, as:
𝑎𝑔𝑒
𝑅(𝑎𝑔𝑒) = 1 − 𝑆(𝑎𝑔𝑒) ≈ 1 − exp(− ∑ 𝐼𝑘 )
𝑘=60
S is the survival function, k is the age strata from 60 years old to age in years and the
Ik is the annualized incidence rates given the age strata k. Combining this baseline
hazard with the hazard ratios estimates from the previous section, we then derived the
corrected survival function that predicts an individual’s risk of developing AD, given
their polygenic profile and age. The incidence rates were also predicted given the
individuals’ genetic background and their population baseline, as the annualized rate
is the instantaneous hazard function converted from the corrected survival function:
𝐴𝑛𝑛𝑢𝑎𝑙𝑖𝑧𝑒𝑑 𝐼𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 𝑟𝑎𝑡𝑒𝑠 = 𝜆0 exp(𝑋𝛽)
For λ0 is the instantaneous hazard function from previous reports2, the 𝛽 is the Cox
coefficients estimated in previous steps, assuming the genotypes X were centered on
population mean. We used allele frequencies from 503 European descendants from
the 1000 Genome Project to ensure proper centering and correct for sampling bias.
We estimated the uncertainty of predicted incidence rates solely on the model
estimates from ADGC cohorts because the previous published US population baseline
annualized incidence rate estimates2 did not provide confidence intervals. Assuming
̂ is asymptotically normal, centering on 0, the variance of predicted incidence
𝑋𝛽
proportions were derived using the delta method.
Assessing Prediction Accuracy in Independent Cohorts
We used one independent case-control sample, ADGC phase 2, and one longitudinal
cohort, NACC, to evaluate the performance of PHS. First we used the PHS to
generate the predicted age at onset in ADGC phase 2, and compared that to the
corresponding empirical age at onset observed in ADGC phase 2. The observations
were binned into 100 percentile bins to ensure the reliability of the empirical age at
onset. Pearson correlations were calculated for the linear relationships between
predicted and empirical age at onset in the ADGC phase 2. To take the censoring
process into consideration, we also examined whether the PHS provides information
for differentiating risk strata in the survival context. We used the likelihood ratio test
to determine whether the PHS predicted the proportional risk in ADGC phase 2, while
visualizing the stratification using Kaplan-Meier curves given strata in quartiles.
We then used the NACC longitudinal cohort to determine whether the PHS
predicted progression from normal aging to AD. We focused on cognitively normal
individuals who were followed for at least two years. Given the risk strata defined by
PHS, we assessed whether the rate of AD onset is proportional to the increment of
PHS. We used the Cochrane-Armitage test for trends to evaluate the association
between PHS and empirical progression rate of AD.
MR Image Processing
All ADNI MRI scans were acquired at multiple sites using either a GE, Siemens,
or Philips 1.5T system. Parameter values vary depending on scanning site and can be
found http://adni.loni.usc.edu/. Multiple high-resolution T1- weighted volumetric
MRI scans were collected for each subject and the raw DICOM images were
downloaded from the public ADNI site
(http://adni.loni.usc.edu/data-samples/access-data/). All MRI scans were analyzed
using a modified version of the FreeSurfer software package
(http://surfer.nmr.mgh.harvard.edu). These analysis procedures have been applied,
validated, and described in detail in a number of publications. 3 In brief, the MRI
scans were reviewed for quality, automatically corrected for spatial distortion due to
gradient nonlinearity 4, registered and averaged to improve the signal to noise ratio.
The cortical surface was automatically reconstructed 5,6 and gray matter thickness
measurements were obtained at each point across the cortical mantle. 7
In this study, we primarily focused on the entorhinal cortex and hippocampus
because AD-specific pathology is evident in this region in the earliest stages of the
disease process. 8-10 The entorhinal cortex was delineated using an automated,
surface-based parcellation atlas. 11 The hippocampus was identified using an
automated, subcortical, segmentation atlas. 12 For the analysis of the longitudinal
volume change, gray matter thickness change was examined using Quarc (quantitative
anatomical regional change), a recently developed method from our laboratory. 13-14
Briefly, each participant's follow-up image was affine-aligned to the baseline scan and
locally intensity-normalized. Using nonlinear registration, a deformation field was
then calculated to locally register the images with high fidelity for both large- and
small-scale structures, including those with low boundary contrast. From the
deformation field, a volume-change field (atrophy) can directly be calculated. Using
the baseline subcortical and cortical labels, the volume-change field can be sampled at
points across the cortical surface or averaged over subcortical regions to give the
percent volume change for those regions of interest.
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Hyman BT. Profound loss of layer II entorhinal cortex neurons occurs
in very mild Alzheimer's disease. J Neurosci. 1996;16:4491-500.
11. Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker
D, et al. An automated labeling system for subdividing the human
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Subregional neuroanatomical change as a biomarker for Alzheimer's
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SUPPLEMENTAL FIGURE
Supplemental Figure 1. Frequency distribution of APOE ε 2/3/4 alleles as a function
of PHS and years to AD onset (Age). As illustrated, the risk of developing AD
corresponds to increasing PHS. Positive age values represent years free of AD and
negative age values represent age to AD onset.
Supplemental Figure 2. (A) Survival functions stratified by polygenic hazard scores
(PHS) and APOE 4 status. The survival functions were predicted based on the
population baseline and the estimated hazard ratios from the Cox regression. The
survival functions are stratified according to the PHS percentiles and APOE ε4 carrier
status, where APOE ε4+ represents ‘carriers’ and APOE ε4- represents ‘non-carriers’.
(B) Predicted AD age of onset distribution. Based on the population baseline
corrected survival function, the distribution of AD age of onset is illustrated.
Supplemental Figure 1
Supplemental Figure 2A.
Supplemental Figure 2B.
SUPPLEMENTAL ACKNOWLEDGEMENTS
IGAP: We thank the International Genomics of Alzheimer's Project (IGAP) for
providing summary results data for these analyses. The investigators within IGAP
contributed to the design and implementation of IGAP and/or provided data but did not
participate in analysis or writing of this report. IGAP was made possible by the
generous participation of the control subjects, the patients, and their families. The i–
Select chips was funded by the French National Foundation on Alzheimer's disease and
related disorders. EADI was supported by the LABEX (laboratory of excellence
program investment for the future) DISTALZ grant, Inserm, Institut Pasteur de Lille,
Université de Lille 2 and the Lille University Hospital. GERAD was supported by the
Medical Research Council (Grant n° 503480), Alzheimer's Research UK (Grant n°
503176), the Wellcome Trust (Grant n° 082604/2/07/Z) and German Federal Ministry
of Education and Research (BMBF): Competence Network Dementia (CND) grant n°
01GI0102, 01GI0711, 01GI0420. CHARGE was partly supported by the NIH/NIA
grant R01 AG033193 and the NIA AG081220 and AGES contract N01–AG–12100,
the NHLBI grant R01 HL105756, the Icelandic Heart Association, and the Erasmus
Medical Center and Erasmus University. ADGC was supported by the NIH/NIA grants:
U01 AG032984, U24 AG021886, U01 AG016976, and the Alzheimer's Association
grant ADGC–10–196728.
ADGC: The National Institutes of Health, National Institute on Aging (NIH-NIA)
supported this work through the following grants: ADGC, U01 AG032984, RC2
AG036528; NACC, U01 AG016976; NCRAD, U24 AG021886; NIA LOAD, U24
AG026395, U24 AG026390; Banner Sun Health Research Institute P30 AG019610;
Boston University, P30 AG013846, U01 AG10483, R01 CA129769, R01 MH080295,
R01 AG017173, R01 AG025259, R01AG33193; Columbia University, P50
AG008702, R37 AG015473; Duke University, P30 AG028377, AG05128; Emory
University, AG025688; Group Health Research Institute, UO1 AG06781, UO1
HG004610; Indiana University, P30 AG10133; Johns Hopkins University, P50
AG005146, R01 AG020688; Massachusetts General Hospital, P50 AG005134; Mayo
Clinic, P50 AG016574; Mount Sinai School of Medicine, P50 AG005138, P01
AG002219; New York University, P30 AG08051, MO1RR00096, UL1 RR029893,
5R01AG012101, 5R01AG022374, 5R01AG013616, 1RC2AG036502,
1R01AG035137; Northwestern University, P30 AG013854; Oregon Health & Science
University, P30 AG008017, R01 AG026916; Rush University, P30 AG010161, R01
AG019085, R01 AG15819, R01 AG17917, R01 AG30146; TGen, R01 NS059873;
University of Alabama at Birmingham, P50 AG016582, UL1RR02777; University of
Arizona, R01 AG031581; University of California, Davis, P30 AG010129; University
of California, Irvine, P50 AG016573, P50, P50 AG016575, P50 AG016576, P50
AG016577; University of California, Los Angeles, P50 AG016570; University of
California, San Diego, P50 AG005131; University of California, San Francisco, P50
AG023501, P01 AG019724; University of Kentucky, P30 AG028383, AG05144;
University of Michigan, P50 AG008671; University of Pennsylvania, P30 AG010124;
University of Pittsburgh, P50 AG005133, AG030653; University of Southern
California, P50 AG005142; University of Texas Southwestern, P30 AG012300;
University of Miami, R01 AG027944, AG010491, AG027944, AG021547,
AG019757; University of Washington, P50 AG005136; Vanderbilt University, R01
AG019085; and Washington University, P50 AG005681, P01 AG03991. The
Kathleen Price Bryan Brain Bank at Duke University Medical Center is funded by
NINDS grant # NS39764, NIMH MH60451 and by Glaxo Smith Kline. Genotyping
of the TGEN2 cohort was supported by Kronos Science. The TGen series was also
funded by NIA grant AG034504 to AJM, The Banner Alzheimer’s Foundation, The
Johnnie B. Byrd Sr. Alzheimer’s Institute, the Medical Research Council, and the
state of Arizona and also includes samples from the following sites: Newcastle Brain
Tissue Resource (funding via the Medical Research Council, local NHS trusts and
Newcastle University), MRC London Brain Bank for Neurodegenerative Diseases
(funding via the Medical Research Council),South West Dementia Brain Bank
(funding via numerous sources including the Higher Education Funding Council for
England (HEFCE), Alzheimer’s Research Trust (ART), BRACE as well as North
Bristol NHS Trust Research and Innovation Department and DeNDRoN), The
Netherlands Brain Bank (funding via numerous sources including Stichting MS
Research, Brain Net Europe, Hersenstichting Nederland Breinbrekend Werk,
International Parkinson Fonds, Internationale Stiching Alzheimer Onderzoek), Institut
de Neuropatologia, Servei Anatomia Patologica, Universitat de Barcelona. ADNI
Funding for ADNI is through the Northern California Institute for Research and
Education by grants from Abbott, AstraZeneca AB, Bayer Schering Pharma AG,
Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation,
Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli
Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F.
Hoffman-La Roche, Schering-Plough, Synarc, Inc., Alzheimer's Association,
Alzheimer's Drug Discovery Foundation, the Dana Foundation, and by the National
Institute of Biomedical Imaging and Bioengineering and NIA grants U01 AG024904,
RC2 AG036535, K01 AG030514. We thank Drs. D. Stephen Snyder and Marilyn
Miller from NIA who are ex-officio ADGC members. Support was also from the
Alzheimer’s Association (LAF, IIRG-08-89720; MP-V, IIRG-05-14147) and the US
Department of Veterans Affairs Administration, Office of Research and Development,
Biomedical Laboratory Research Program. P.S.G.-H. is supported by Wellcome Trust,
Howard Hughes Medical Institute, and the Canadian Institute of Health Research.
Data for this study were prepared, archived, and distributed by the National Institute on
Aging Alzheimer’s Disease Data Storage Site (NIAGADS) at the University of
Pennsylvania (U24-AG041689-01), funded by the National Institute on Aging.
NACC: The NACC database is funded by NIA/NIH Grant U01 AG016976. NACC
data are contributed by the NIAfunded ADCs: P30 AG019610 (PI Eric Reiman, MD),
P30 AG013846 (PI Neil Kowall, MD), P50 AG008702 (PI Scott Small, MD), P50
AG025688 (PI Allan Levey, MD, PhD), P50 AG047266 (PI Todd Golde, MD, PhD),
P30 AG010133 (PI Andrew Saykin, PsyD), P50 AG005146 (PI Marilyn Albert, PhD),
P50 AG005134 (PI Bradley Hyman, MD, PhD), P50 AG016574 (PI Ronald Petersen,
MD, PhD), P50 AG005138 (PI Mary Sano, PhD), P30 AG008051 (PI Steven Ferris,
PhD), P30 AG013854 (PI M. Marsel Mesulam, MD), P30 AG008017 (PI Jeffrey Kaye,
MD), P30 AG010161 (PI David Bennett, MD), P50 AG047366 (PI Victor Henderson,
MD, MS), P30 AG010129 (PI Charles DeCarli, MD), P50 AG016573 (PI Frank
LaFerla, PhD), P50 AG016570 (PI Marie-Francoise Chesselet, MD, PhD), P50
AG005131 (PI Douglas Galasko, MD), P50 AG023501 (PI Bruce Miller, MD), P30
AG035982 (PI Russell Swerdlow, MD) , P30 AG028383 (PI Linda Van Eldik, PhD),
P30 AG010124 (PI John Trojanowski, MD, PhD), P50 AG005133 (PI Oscar Lopez,
MD), P50 AG005142 (PI Helena Chui, MD), P30 AG012300 (PI Roger Rosenberg,
MD), P50 AG005136 (PI Thomas Montine, MD, PhD), P50 AG033514 (PI Sanjay
Asthana, MD, FRCP), P50 AG005681 (PI John Morris, MD), and P50 AG047270 (PI
Stephen Strittmatter, MD, PhD).
ADNI: Data collection and sharing for this project was funded by the Alzheimer's
Disease Neuroimaging Initiative (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.; 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 (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
Southern
California.