Improving genetic risk modeling of dementia from real-world data in underrepresented populations
Year: 2024
Type: article
Study design: Observational Study
Abstract: Genetic risk modeling for dementia offers significant benefits, but studies based on real-world data, particularly for underrepresented populations, are limited. We employ an Elastic Net model for dementia risk prediction using single-nucleotide polymorphisms prioritized by functional genomic data from multiple neurodegenerative disease genome-wide association studies. We compare this model with APOE and polygenic risk score models across genetic ancestry groups (Hispanic Latino American sample: 610 patients with 126 cases; African American sample: 440 patients with 84 cases; East Asian American sample: 673 patients with 75 cases), using electronic health records from UCLA Health for discovery and the All of Us cohort for validation. Our model significantly outperforms other models across multiple ancestries, improving the area-under-precision-recall curve by 31-84% (Wilcoxon signed-rank test p-value <0.05) and the area-under-the-receiver-operating characteristic by 11-17% (DeLong test p-value <0.05) compared to the APOE and the polygenic risk score models. We identify shared and ancestry-specific risk genes and biological pathways, reinforcing and adding to existing knowledge. Our study highlights the benefits of integrating functional mapping, multiple neurodegenerative diseases, and machine learning for genetic risk models in diverse populations. Our findings hold potential for refining precision medicine strategies in dementia diagnosis.
Source: Communications Biology
Institution: University of California, Los Angeles
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,890
Funders: U.S. Department of Health and Human Services, Larry L. Hillblom Foundation, California Department of Public Health, UCLA Health System, National Institutes of Health, National Institute on Aging, National Institute of Mental Health, National Human Genome Research Institute, National Institute of Neurological Disorders and Stroke, David Geffen School of Medicine, University of California, Los Angeles, Clinical and Translational Science Institute, University of California, Los Angeles
Awards: G1550106813, G165366312, Healthy Americas: All of Us Research Program, G1969433312, G2058372706, G2187930703, G2539055055, G3639549833, G4431766622, G4552016925, Leveraging functional data to predict disease risk in multi-ethnic populations, Leveraging the Electronic Health Record and Integrating Social and Biological Data to Expand Dementia Research in Understudied Populations in Los Angeles County, University of Arizona-Banner Health All of Us Research Program , G5726870544, In Vivo Studies of the Epileptic Hippocampus, G608601981, Rare Genetic Risk and Gene Networks in Tauopathy, G723570823, SouthEast Enrollment Center (SEEC) , G7646871776, G7648458258, G7789722571, G8355735039, G9000000878, G9000002594, G9000004253, G9000006085, G9000011423, G9000011771, G9000013175, G9000020649, G9000021463, G9000021538, G9000032397, G9000035207, G9000040823, G9000041091, G9000041420, G9000042288, G9000046817, G9000051044, G9000055272, G9000058670, G9000063695, G9000063797, G9000071344, G9000072038, G9000077829, G9000085573, G9000092963, G9000116929, G9000128449, G9000145785, G9000147789, G9000158488, G9000169788, G9000200762, G9000202298, G9000206094, G9000219966, G9000279044, G9000290334, G9000303384, Identifying Undiagnosed Alzheimer’s Disease in Understudied Populations
OpenAlex ID: W4401863342