Decision tree analysis of genetic risk for clinically heterogeneous Alzheimer’s disease
- Jennifer S Yokoyama1Email author,
- Luke W Bonham†1,
- Renee L Sears†2,
- Eric Klein2,
- Anna Karydas1,
- Joel H Kramer1,
- Bruce L Miller1 and
- Giovanni Coppola2
© Yokoyama et al. 2015
Received: 11 February 2015
Accepted: 12 March 2015
Published: 28 March 2015
Heritability of Alzheimer’s disease (AD) is estimated at 74% and genetic contributors have been widely sought. The ε4 allele of apolipoprotein E (APOE) remains the strongest common risk factor for AD, with numerous other common variants contributing only modest risk for disease. Variability in clinical presentation of AD, which is typically amnestic (AmnAD) but can less commonly involve visuospatial, language and/or dysexecutive syndromes (atypical or AtAD), further complicates genetic analyses. Taking a multi-locus approach may increase the ability to identify individuals at highest risk for any AD syndrome. In this study, we sought to develop and investigate the utility of a multi-variant genetic risk assessment on a cohort of phenotypically heterogeneous patients with sporadic AD clinical diagnoses.
We genotyped 75 variants in our cohort and, using a two-staged study design, we developed a 17-marker AD risk score in a Discovery cohort (n = 59 cases, n = 133 controls) then assessed its utility in a second Validation cohort (n = 126 cases, n = 150 controls). We also performed a data-driven decision tree analysis to identify genetic and/or demographic criteria that are most useful for accurately differentiating all AD cases from controls.
We confirmed APOE ε4 as a strong risk factor for AD. A 17-marker risk panel predicted AD significantly better than APOE genotype alone (P < 0.00001) in the Discovery cohort, but not in the Validation cohort. In decision tree analyses, we found that APOE best differentiated cases from controls only in AmnAD but not AtAD. In AtAD, HFE SNP rs1799945 was the strongest predictor of disease; variation in HFE has previously been implicated in AD risk in non-ε4 carriers.
Our study suggests that APOE ε4 remains the best predictor of broad AD risk when compared to multiple other genetic factors with modest effects, that phenotypic heterogeneity in broad AD can complicate simple polygenic risk modeling, and supports the association between HFE and AD risk in individuals without APOE ε4.
KeywordsAlzheimer’s disease Genetics Decision tree analysis
Alzheimer’s disease (AD) is a devastating neurodegenerative disorder that results in memory impairment and can also involve deterioration of language, visuospatial and/or executive functioning abilities. As the world’s population ages and the number of individuals with AD grows, it will become increasingly important to identify those at highest risk for AD during the earliest stages of—or prior to—disease.
Genetic predictors of AD hold strong potential for identifying those at risk of developing disease. Indeed, a large clinical study will launch in 2015 to assess the utility of AD therapies given to individuals at highest genetic risk for AD but who are still cognitively healthy . These individuals, who carry the ε4 allele of apolipoprotein E (APOE), have a 2-10x increased risk for developing AD compared to non-carriers [2,3], but not all ε4 carriers go on to develop disease [3,4]. Despite the vast number of genetic studies of AD, which is estimated to be 74% heritable , no other common variants have been identified that confer as high a risk as APOE ε4. In rare cases, AD is familial, caused by an autosomal dominant mutation in APP, PSEN1, or PSEN2 [6,7]. For sporadic late-onset AD (LOAD), numerous common variants of very low effect (odds ratio [OR] ~ 1.1-1.3) have been identified through genome-wide association studies (GWAS) and replicated across multiple large , and diverse populations [9,10]. More recently, rare variants (<1% allele frequency) of larger effect size have also been identified as risk conferring (TREM2 0.3% , PLD3 < 0.5% , MAPT 0.3% ) or protective against (APP 0.01%  to 0.62% ) AD.
In addition to genetic heterogeneity, there is also clinical heterogeneity in AD. The majority of patients present with amnestic syndromes (AmnAD) but approximately 6-14% of AD patients demonstrate atypical clinical syndromes (AtAD) . These include 1) posterior cortical atrophy (PCA), characterized by predominant visuospatial deficits ; 2) the logopenic variant of primary progressive aphasia (lvPPA) , characterized by loss in phonologic short-term memory; and 3) dysexecutive/behavioral AD  characterized by loss of executive function and/or behavioral changes with retention of memory function.
Genetic and phenotypic heterogeneity strongly support the notion that multiple genetic variants of small effect contribute to disease susceptibility. A multi-locus approach may increase the ability to identify individuals at highest risk for any AD syndrome. The multi-locus approach has had modest success in LOAD, with polygenic risk scoring approaches associating better with LOAD diagnoses and age of onset than APOE genotype alone [19-21]. However, most studies have focused on clinically homogeneous groups with primary amnestic presentations.
In this study, we investigated two different strategies for polygenic risk assessment of clinically heterogeneous AD. First, we took a traditional approach and developed and assessed the utility of a multi-marker genetic risk score to predict AD. The risk score was based on a Discovery cohort association study that sought to replicate previous AD findings and assess additional candidate variants for their association with disease risk. The risk score was then tested for its predictive ability in a separate Validation cohort. Second, we used a more novel decision tree analysis  to identify genetic and demographic risk factors for AD. This data-driven method has been used in diverse clinical contexts [23-26] to predict binary outcomes, but is largely unutilized in the prediction of AD diagnosis. It allowed us to assess step-wise interactions between variables to identify the factors that best predict AD.
Individuals 65- to 101-years-old (N = 216 males, N = 232 females) were evaluated at the University of California, San Francisco Memory and Aging Center (UCSF MAC) and had genotype data available for analysis. All participants were unrelated Caucasians (confirmed by multi-dimensional scaling (MDS) plots or self-described for those without GWAS data available). Non-Caucasians were excluded due to the insufficient number of participants and potential for confounding background genetics. All aspects of the study were approved by the UCSF Institutional Review Board and written informed consent was obtained from all participants and surrogates (as per UCSF Institutional Review Board protocol).
All participants underwent a multi-step screening process with an in-person visit at the MAC that included a neurologic exam, cognitive assessment , and medical history. Each participant’s study partner was also interviewed regarding functional abilities. A multidisciplinary team composed of a neurologist, neuropsychologist, and nurse then reviewed all potential participants. Participants included in this study had a study partner (i.e., spouse, close friend). The multidisciplinary team established clinical diagnoses for cases according to consensus criteria for AD . Atypical or concomitant diagnoses were established for lvPPA [16,18], PCA syndrome [16,17], primary executive AD , vascular disease , or dementia with Lewy bodies (DLB)  according to consensus criteria. Individuals with primarily amnestic AD presentations were considered “AmnAD” and those with less common clinical syndromes (lvPPA, PCA, primary executive) or comorbidities (vascular disease, DLB) were considered as “AtAD”. All control subjects underwent a similar multi-step screening process, including study partner interview and a consensus team of clinicians then reviewed all potential participants. Controls included in this study had Mini-Mental State Exam (MMSE)  scores ≥26 or a Clinical Dementia Rating Scale (CDR)  of 0, no participant or informant report of cognitive decline in the prior year, and no evidence from their screening visit suggesting a neurodegenerative disorder (per team neurologist’s clinical judgment). Individuals harboring a known disease mutation were excluded from the study.
Genomic DNA was extracted from peripheral blood using standard protocols (Gentra PureGene Blood Kit, QIAGEN, Inc. – USA, Valencia, CA). Genotyping was performed using one of three platforms: TaqMan, Sequenom, or via array genotyping. The method used for each variant is provided in the Supplement (Additional file 1). TaqMan Allelic Discrimination Assay was used for APOE genotyping (rs429358 and rs7412) and others as noted, and was conducted on an ABI 7900HT Fast Real-Time PCR system (Applied Biosystems, Foster City, CA) according to manufacturer's instructions. Sequenom iPLEX Technology (Sequenom, San Diego, CA) was also used for genotyping a subset of variants as per manufacturer’s instructions. The SpectroAquire and MassARRAY Typer Software packages (Sequenom, San Diego, CA) were used for interpretation and Typer analyzer (v126.96.36.199) was used to review and analyze data. Only genotypes with “Conservative” or “Moderate” quality calls were included in analysis. A subset of genotypes was also obtained from the Illumina Omni1-Quad array genotyping platform (Illumina Inc., San Diego, CA), processed using manufacturer’s instructions.
A total of 75 variants were genotyped in all subjects and analyzed for association with AD risk. These variants are a culmination of different, on-going studies to evaluate the effect of genes involved in neurodegenerative disease, neurodevelopment, social function, behavior, neuropsychiatry, and language on diseases like AD and frontotemporal dementia (FTD). These included polymorphisms previously associated with: 1) risk for AD or other neurodegenerative disease; 2) neuropsychiatric phenotypes implicated in dementia risk (e.g., depression [32-34], dyslexia ; 3) cognitive protection . A full list of variants, associated phenotypes, and accompanying references is provided in Additional file 1. Inclusion criteria for analyzed markers were: >80% non-missing genotypes, ≥0.01 minor allele frequency (MAF), and Hardy-Weinberg equilibrium (HWE) P > 0.001. The average call rate was 98% for all variants.
The study cohort was divided into two groups, a first stage “Discovery” cohort for development of the AD risk score and a second stage “Validation” cohort with which to test the risk scoring method developed in the Discovery cohort. We first conducted association analysis of all markers meeting inclusion criteria in the Discovery cohort. Analyses were performed in PLINK as a logistic regression under an additive model .
For scoring, we ranked all findings by p-value and then removed SNPs that were in linkage disequilibrium (LD, r2 > 0.8) in our dataset; the single most strongly associated SNP of a set of linked markers was retained. Using the unlinked markers we created raw scoring files for each top finding, iteratively adding the next most significant finding to each scoring set (i.e., 1st marker in first set, 1st and 2nd markers in second set, etc.). Reference alleles were established in the scoring files such that all effects were in the same direction of conferring risk (e.g., a SNP with an empirical OR 0.1 for the reference minor allele would be switched such that the major allele was the reference allele for scoring). Using this paradigm, we created scoring sets for the top findings that were not in LD.
We implemented the ‘SNP scoring’ algorithm in PLINK to first assess the predictive ability of each score set (A-Z) in the Discovery dataset for evaluative purposes. We compared the risk scores for each set against the true phenotypes using receiver operating characteristic (ROC) curves and used the resulting area under the curve (AUC) values to determine the optimal score set, with higher AUC values representing better sensitivity and specificity. The optimal score set was determined as follows. First, score sets were evaluated in two ways: 1) by simple consecutive comparisons of AUC values to identify the set at which AUC is largest, and 2) by statistical comparisons of a given set’s ROC curve AUC (AUCi) versus the previous set’s ROC curve AUC (AUCi-1) and versus the APOE-only score’s AUC (AUCA). We then iteratively evaluated sets to determine the maximum AUC, stopping when two consecutive sets each resulted in decreases of AUC as compared to the previous set (i.e., AUCi > AUCi+1 & AUCi > AUCi+2). After determining this optimal set, we used the same scoring file to create risk scores for the Validation cohort and assessed the AUC of the resulting ROC curve to determine the generalization of our risk scoring method in an independent dataset. All ROC analyses were performed in Stata10/MP (StataCorp LP, College Station, TX).
Decision tree analysis
To explore and evaluate the diagnostic potential of the genetic variants available with ROC curves, we used the ROC4 software platform (ROC4.22.exe; http://www.stanford.edu/~yesavage/ROC.html). The software utilizes a user-set weight of sensitivity and specificity (kappa) to choose the predictive variable and value that best divides the sample. The sample is then divided on the value of the variable, which is most predictive based on this sensitivity and specificity. Following this, the program performs the same analysis amongst the subgroups created by the previous step. The process continues until a stopping rule is enforced. The output after stopping rules come into place is a “decision tree” which shows the variables and interactions between them in predicting the outcome of interest. We chose a kappa weight of 0.5 in order to balance efficiency (sensitivity and specificity were equally weighted). There were three stopping rules: when subgroup totals were less than 10, when a significance value corresponding to a multiple-testing-corrected Χ2 test greater than P = 0.01 was reached, or when a three way interaction was reached. We performed three ROC analyses: one combined analysis of controls and all types of AD patients, one for the controls and AmnAD, and one for controls and AtAD. The ‘gold standard’ binary score was case/control outcome for any AD clinical diagnosis. Additional predictors included sex (0/1 for male/female), age (in years), and all genetic variants passing quality control (0/1/2 for dose of minor frequency allele).
Age (mean ± SD)
76.2 ± 7.9
73.3 ± 6.5
73.9 ± 6.2
Education (years, mean ± SD)
16.3 ± 2.5
16.5 ± 2.9
17.3 ± 2.1
% APOE4 carrier
Age (mean ± SD)
80.7 ± 8.4
80.9 ± 9.3
76.8 ± 7.4
Education (years, mean ± SD)
15.8 ± 3.1
16.1 ± 4.8
17.4 ± 2.1
% APOE4 carrier
Age (mean ± SD)
79.2 ± 8.5
77.6 ± 9.0
75.4 ± 7.0
Education (years, mean ± SD)
16.0 ± 2.9
16.7 ± 3.1
17.4 ± 2.1
% APOE4 carrier
Confirmation of AD risk variants and establishment of a 17-marker risk assessment
Score set evaluation statistics
AUC ± SE
P-val vs. A
P-val vs. Prev.
0.69 ± 0.04
0.75 ± 0.04
0.79 ± 0.03
0.81 ± 0.03
0.83 ± 0.03
0.84 ± 0.03
0.85 ± 0.03
0.85 ± 0.03
0.86 ± 0.03
0.86 ± 0.03
0.87 ± 0.03
0.86 ± 0.03
0.87 ± 0.03
0.86 ± 0.03
0.87 ± 0.03
0.87 ± 0.03
0.88 ± 0.03
0.87 ± 0.03
0.87 ± 0.03
0.88 ± 0.03
0.87 ± 0.03
0.88 ± 0.03
0.88 ± 0.03
0.89 ± 0.03
0.89 ± 0.03
0.89 ± 0.02
Genetic risk score does not predict AD better than APOE in a separate cohort
Risk scoring results for the Discovery and Validation cohorts
AUC ± SE
P-val vs. A
0.69 ± 0.04
0.88 ± 0.03
0.63 ± 0.03
0.62 ± 0.04
Decision tree analysis identifies genetic heterogeneity in amnestic versus atypical AD
We postulated that the clinical heterogeneity between the Discovery and Validation cohorts might be contributing to the failure of the 17-variant risk score to differentiate AD cases from controls better than APOE genotype alone. Under an alternative model, the genetic risk for AmnAD is different from that for AtAD. In order to identify genetic and/or demographic criteria that are most useful for accurately differentiating all AD cases from controls and to test whether AmnAD and AtAD share disease predictors or are distinct in their risk profiles, we performed data-driven decision tree analyses. We performed three analyses, one in all AD cases (N = 165) versus controls (N = 283), one with only AmnAD (N = 126) versus controls, and one with AtAD (N = 39) versus controls.
In our association study, we found continuing support for APOE, HFE, PICALM, CR1, SLC6A4, CDC42BP, TPH1, and KIAA0319 as genetic risk factors for AD. Using information from 17 variants combined into a genetic risk score allowed us to predict clinically heterogeneous AD cases significantly better than APOE genotype alone, supporting the role of these variants as predictors of AD risk in this primary Discovery group. However, when we attempted to apply this polygenic risk assessment to an independent cohort of clinically heterogeneous AD patients for validation, the utility of analyzing 17 variants was not significantly better than analyzing APOE alone. Taken together, this suggests two things. First, it suggests that APOE ε4 remains the best predictor of AD risk, likely due to its strong effect, when compared to multiple other risk factors with very modest risk effects. Second, it suggests that phenotypic variability in AD complicates simple genetic risk modeling, particularly when co-morbidities are suspected.
The fact that APOE ε4 is the most predictive variant for amnestic AD but does not appear to be associated with risk for atypical AD syndromes such as PCA and lvPPA  likely contributes to the decreased specificity of the genetic risk assessment; namely, carrying an ε4 allele is associated with being affected in amnestic AD but is also associated with not being affected by PCA or lvPPA. Thus, APOE ε4 in the simple context of amnestic AD is quite adept at predicting who will be a case versus control, but is much less specific in the broader context of all AD syndromes, inclusive of atypical presentations and co-morbidities. Indeed, in our entire cohort of Discovery + Validation samples, APOE ε4 was significantly enriched in AmnAD but not AtAD cases when compared to controls (AmnAD vs Control P = 3.08 × 10−7; AtAD vs Control P = 0.1). A similar discrepancy due to clinical heterogeneity may also underlie our association of variants in PICALM and CR1 in the opposite direction of historical findings. An alternate methodology to identify genetic and demographic factors that predict case/control status in AmnAD and AtAD separately was able to improve differentiation. Utilizing a decision tree methodology, we found that APOE best differentiated cases from controls only in AmnAD but not AtAD. In contrast, HFE genotype was the best differentiating factor between AtAD cases and controls; the same variant was also the first genetic risk factor for broad AD in individuals without APOE ε4. These findings are consistent with prior research implicating HFE in AD risk in individuals without APOE ε4 . These results also suggest that atypical presentations could represent a distinct genetic class of AD, although the present study was not designed to specifically address this question. A recent study suggests that AtAD is more heritable than AmnAD , supporting the theory that there are additional genetic risk factors for AtAD that remain to be elucidated. In the future, GWAS of larger, more diverse cohorts of individuals with specific atypical phenotypes (e.g., PCA) could identify novel genetic risk factors specific to these AD syndromes. Phenotypic specificity in studies of amnestic AD may also provide additional statistical power to identify risk factors of small effect size.
In an effort to rule out the possibility of misdiagnosis, particularly in the AtAD group, we performed a post hoc chart review of patients for which pathological data was available (N = 25 AmnAD and N = 8 AtAD). All of these individuals had AD pathology cited as a primary (N = 24 AmnAD, N = 5 AtAD) or major contributing factor (N = 1 AmnAD, N = 3 AtAD) that correlated with each patient’s clinical presentation (Additional file 4). Although not exhaustive, this data suggests that AD pathology was correctly recognized as a major contributor to patients’ clinical syndrome in our patient cohort, and that the differential genetic risk profile of AtAD potentially influences its pathological heterogeneity when compared to AmnAD.
This study benefits from a two-staged discovery-validation study design, inclusion of a broad spectrum of clinical patients representing the phenotypic heterogeneity of AD, well-characterized cognitively normal controls, and inclusion of many of the most replicated genetic loci implicated in AD as well as several, more novel gene candidates. The main limitations of this study include the limited sample size, lack of pathological confirmation in all study participants, and the relatively young age of the controls. In addition, Caucasian individuals were the sole participants in our study, which potentially limits the scope of our findings. Co-morbid depression was not assessed in this analysis and may be a contributing factor to the associations with the depression associated variants. This hypothesis requires direct testing in a separate study.
We implemented a decision tree analysis to identify genetic and demographic criteria most useful for accurately differentiating AD cases from controls. With an iterative, non-parametric approach, we used recursive partitioning to identify individuals according to a binary outcome of interest . This method benefits from limiting the use of restrictive assumptions like linearity, additivity, and homoscedasticity, which are required by most linear models . This approach has been used in a variety of clinical settings to identify variables of interest in predicting binary outcomes such as identification of AD patients who will have rapid cognitive decline , presence of tuberculosis after multiple conflicting tests , and ability to succeed in diabetes self-management programs . Decision trees are amenable for use in a clinical setting, where an individual’s risk for the outcome of interest—in this case, AD—can be estimated based on multiple predictive variables that follow a logical progression. Testing whether the factors identified in our decision tree analyses have predictive value in a larger, independent cohort will be critical for elucidating whether this risk assessment has clinical utility, particularly with the inclusion of pathologically confirmed cases and exclusion of amyloid-positive ‘controls.’
We found that APOE genotype is the best predictor of risk compared to a polygenic risk score when assessing groups of clinically heterogeneous AD patients versus healthy older controls. In decision tree analysis, we found that AmnAD and AtAD have differential genetic risk factors, which may account for the inaccuracy of the traditional polygenic scoring method. Identifying individuals at highest genetic risk for AD could potentially allow for earlier diagnosis and intervention, allowing the opportunity to intervene with pathological processes and/or provide support prior to clinical onset of symptoms. These risk assessments will benefit from future work to characterize genetic risk factors of clinically homogeneous subtypes of AD in large, diverse populations.
J.S.Y. was funded by the Larry L. Hillblom Foundation (2012-A-015-FEL) and a diversity supplement from the NIA-NIH (P50-AG023501-08S1, PI: Miller, BL). Additional support was provided by NIH grants P50-AG023501 (B.L.M.) and RC1 AG035610 and R01 AG26938 (G.C.), the Larry L. Hillblom Foundation (B.L.M.), and the John Douglas French Alzheimer’s Foundation (G.C.). We acknowledge the support of the NINDS Informatics Center for Neurogenetics and Neurogenomics (P30 NS062691). Samples from the National Cell Repository for Alzheimer’s Disease (NCRAD), which receives government support under a cooperative agreement grant (U24 AG21886) awarded by the National Institute on Aging (NIA), were used in this study. We thank Dr. Jerome Yesavage and Art Noda for technical advice on the decision tree analysis. We thank contributors who collected samples used in this study, as well as patients and their families, whose help and participation made this work possible.
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