Polygenic Risk Score for Alzheimer's Disease in Caribbean Hispanics
Sanjeev Sariya1,2,3, Daniel Felsky4, Dolly Reyes-Dumeyer1,2,3
1Taub Institute for Research on Alzheimer's Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, New York, NY.
Objective:
Polygenic risk scores (PRSs) assess the individual genetic propensity to a condition by combining sparse information scattered across genetic loci, often displaying small effect sizes. Most PRSs are constructed in European-ancestry populations, limiting their use in other ethnicities. Here we constructed and validated a PRS for late-onset Alzheimer's Disease (LOAD) in Caribbean Hispanics (CH).
Methods:
We used a CH discovery (n = 4,312) and independent validation sample (n = 1,850) to construct an ancestry-specific PRS ("CH-PRS") and evaluated its performance alone and with other predictors using the area under curve (AUC) and logistic regression (strength of association with LOAD and statistical significance). We tested if CH-PRS predicted conversion to LOAD in a subsample with longitudinal data (n = 1,239). We also tested the CH-PRS in an independent replication CH cohort (n = 200) and brain autopsy cohort (n = 33). Finally, we tested the effect of ancestry on PRS by using European and African American discovery cohorts to construct alternative PRSs ("EUR-PRS", "AA-PRS").
Results:
The full model (LOAD ~ CH-PRS + sex + age + APOE-ɛ4), achieved an AUC = 74% (ORCH-PRS = 1.51 95%CI = 1.36-1.68), raising to >75% in APOE-ɛ4 non-carriers. CH-PRS alone achieved an AUC = 72% in the autopsy cohort, raising to AUC = 83% in full model. Higher CH-PRS was significantly associated with clinical LOAD in the replication CH cohort (OR = 1.61, 95%CI = 1.19-2.17) and significantly predicted conversion to LOAD (HR = 1.93, CI = 1.70-2.20) in the longitudinal subsample. EUR-PRS and AA-PRS reached lower prediction accuracy (AUC = 58% and 53%, respectively).
Interpretation:
Enriching diversity in genetic studies is critical to provide an effective PRS in profiling LOAD risk across populations. ANN NEUROL 2021;90:366-376.
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