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. 2021 Sep 30;7(1):e12211.
doi: 10.1002/trc2.12211. eCollection 2021.

GenoRisk: A polygenic risk score for Alzheimer's disease

Samuel P Dickson  1 , Suzanne B Hendrix  1 , Bruce L Brown  2 , Perry G Ridge  3 , Jessie Nicodemus-Johnson  1 , Marci L Hardy  4 , Allison M McKeany  4 , Steven B Booth  4 , Ryan R Fortna  4 , John S K Kauwe  2   3 ; NIAGADS
Affiliations

GenoRisk: A polygenic risk score for Alzheimer's disease

Samuel P Dickson et al. Alzheimers Dement (N Y). .

Abstract

Introduction: Recent clinical trials are considering inclusion of more than just apolipoprotein E (APOE) ε4 genotype as a way of reducing variability in analysis of outcomes.

Methods: Case-control data were used to compare the capacity of age, sex, and 58 Alzheimer's disease (AD)-associated single nucleotide polymorphisms (SNPs) to predict AD status using several statistical models. Model performance was assessed with Brier scores and tenfold cross-validation. Genotype and sex × age estimates from the best performing model were combined with age and intercept estimates from the general population to develop a personalized genetic risk score, termed age, and sex-adjusted GenoRisk.

Results: The elastic net model that included age, age x sex interaction, allelic APOE terms, and 29 additional SNPs performed the best. This model explained an additional 19% of the heritable risk compared to APOE genotype alone and achieved an area under the curve of 0.747.

Discussion: GenoRisk could improve the risk assessment of individuals identified for prevention studies.

Keywords: Alzheimer's disease heritability; Alzheimer's disease prevention studies; cross‐validation; genetic risk; model validation; polygenic risk score; regression; risk models.

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Conflict of interest statement

Suzanne Hendrix, as the owner of Pentara, received support from Affirmative Diagnostics for the manuscript and received contract work from AC Immmune, Acumen, ADCS, ADDF, Affirmativ Dignpostics, Alector, Alkahest, Allergan, Alzheon, Amylyx, Apodemus, Athira, Avanir, Banner, Biogen, Biohaven, Cadent, Capricor, Cerecin, Cognito, Cognoptix, Cortexyme, Elusis, Green Valley, Grifols, Ionis, Janssen, Kyowa‐Kirin, Lexeo, LuMind, Lundbeck, NCRI, Nilvad, NovoNordisk, Nutricia, Photopharmics, PTCBio, Regenera, Retrotope, Richard Isaacson, Samus, Soleno, Spaulding, Suven, Takeda, Telocyte, Tetra Discovery, Toyama, United Neuroscience, Vaccinex, Vacinity, vTv Therapeutics, and WGU. Suzanne Hendrix has also received royalties from Wiley in the last 36 months; received honoraria for lectures, presentations, etc. from Biogen; received support in the last 36 months from Grifols and Nutricia for presentations and conference attendance; claims one patent pending Brown BL, Hedges DW, Hendrix SB (2014) Extracting A periodic Components from a Time Series Wave Data Set—Patent Pending with a filing date of October 16, 2013, Application Number PCT/US2013/065327; and participated in Alzheon, Cortexyme, and Janssen DSMB or Advisory Board in the last 36 months. Samuel P. Dickson received support from Pentara, which in turn received support from Affirmativ Diagnostics for the manuscript. S. B. Booth received support from Affirmative Diagnostics for the manuscript; received travel support from her employer for work events; ADX provides profit sharing incentives with additional salary which were broadly applied to any work performed for the company (including manuscript generation). J. S. K. Kauwe received support for the manuscript from Brigham Young university and has received honoraria from UH Hilo. B. L. Brown received support through the following grants: Measuring the Interactive Effects of COVID‐19 and Latent Infections on Patterns of cognitive Timing: A Middle‐Aged to Elderly Sample from the Utah Valley Community. Translational Medicine Award from the BYU Simmons Research Endowment ($31,300). This is an intramural grant from a research endowment within our university to my departmental research fund. Brown also claims one patent pending Brown BL, Hedges DW, Hendrix SB (2014) Extracting A periodic Components from a Time Series Wave Data Set—Patent Pending with a filing date of October 16, 2013, Application Number PCT/US2013/065327. Data used in this manuscript is publically available. Participant consent is not needed, and was obtained from investigators who ran each study. M. L. Hardy has received paid consulting fees to the LLC solely owned by ML Hardy from ixLayer, NW Pathology & Labs, Mt. Baker Imaging, PlumCare, ADx Healthcare, Architectural Elements; received travel support from her employer for work events; and was the past Volunteer Secretary for the Lets Pool Together and Hope Philantropies non‐profit boards. P. G. Ridge had received honoraria from University of Kansas Alzheimer's Disease Center for a seminar presentation. R. R. Fortna is a board member of Northwest Pathology and ADx Healthcare and is co‐owner and holds stock for ADx healthcare. J. Nicodemus Johnson has nothing to disclose.

Figures

FIGURE 1
FIGURE 1
Violin plots comparing the Brier scores derived from 10‐fold cross‐validation for 21 of the 25 models tested. Elastic net, lasso, logistic, and probit are shown in purple, green, red, and blue, respectively. The scores for the other four models are not shown here because their Brier scores were so much higher than the other models that it altered the scale of the figure and made comparison between the remaining models more difficult
FIGURE 2
FIGURE 2
The distribution of GenoRisk scores among the 2504 individuals in the 1000 Genomes Project. The scores are defined so that a score of 20 indicates a 10% risk of having Alzheimer's disease at the age of 85. The 40‐point scale helps reduce the risk of misinterpreting the score as a probability. The vertical dashed lines divide the overall distribution into quintiles
FIGURE 3
FIGURE 3
The estimated odds ratios of the apolipoprotein E genotypes from the final GenoRisk model compared to the estimates from Genin et al. To combine ɛ2/ɛ2 with ɛ2/ɛ3 in GenoRisk to match the format of Genin et al. the effect shown is the weighted mean of the effect of the two genotypes. The ɛ3/ɛ3 genotype was the reference genotype in the Genin et al. comparison and ɛ3 was the reference allele in the GenoRisk model, so the odds ratios for both are 1, by definition. The dashed line indicates the line at which the two estimates are equal
FIGURE 4
FIGURE 4
Lineplots of 1000 Genomes Project age and sex‐adjusted GenoRisk probabilities stratified by age and apolipoprotein E isoform status. The solid line represents the mean value of individuals within each isoform group (+ 95% confidence intervals) across the age ranges present
FIGURE 5
FIGURE 5
Probability by age of having Alzheimer's disease (AD; unconditional risk), or developing AD given that it is not present (solid curves line), in a hypothetical 72‐year‐old male. Comparison of curves for three conditions: given no additional information (blue), given that the individual has the ɛ3/ɛ4 genotype (black), and given the additional information provided by the GenoRisk score (green). Specific risk curves can help provide better metrics with which to compare treatment outcomes in future clinical trials
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