Richard Mayeux1,2,3,4,5, Giuseppe Tosto1,2,3, Sanjeev Sariya1,2,3, Daniel Felsky6, Dolly Reyes-Dumeyer1,2,3, Ricky Lali7, Rafael A Lantigua1,4, Badri Vardarajan1,2,3, Ivonne Z Jiménez-Velázquez8, Jonathan L Haines9, Gerard D Shellenberg10, Margaret A Pericak-Vance11, Guillaume Paré7. 1. Taub Institute for Research on Alzheimer's Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, New York, NY. 2. The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY. 3. Department of Neurology, College of Physicians and Surgeons, Columbia University and the New York Presbyterian Hospital, New York, NY. 4. Department of Medicine, College of Physicians and Surgeons, Columbia University, and the New York Presbyterian Hospital, New York, NY. 5. Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, NY. 6. Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada. 7. Population Health Research Institute, McMaster University, Hamilton, ON, Canada. 8. Department of Medicine, University of Puerto Rico School of Medicine, Medical Sciences Campus, San Juan, PR. 9. Department of Population & Quantitative Health SciencesCleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH. 10. University of Pennsylvania Perelman School of Medicine, Philadelphia, PA. 11. John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL.
Abstract
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.
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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