Researchers have developed and validated a new multi-ancestry polygenic risk score for late-onset Alzheimer’s disease that significantly improves risk prediction across diverse population groups compared to previous models built predominantly on European ancestry, according to a study published in Nature Genetics. Led by investigators at the Boston University Chobanian & Avedisian School of Medicine, the ancestry-aware tool showed enhanced predictive accuracy for African American, Hispanic, and East Asian populations, addressing a long-standing demographic bias in genetic research.
Addressing Ancestral Disparities in Alzheimer’s Genetics
Late-onset Alzheimer’s disease is a progressive disorder affecting an estimated 6.9 million people in the U.S., marked by memory impairment and cognitive decline over many years. While the APOE ε4 allele remains the strongest known genetic risk factor, genome-wide association studies have historically concentrated heavily on individuals of European ancestry. Farrer, PhD, chief of biomedical genetics at Boston University Chobanian & Avedisian School of Medicine.
Did you know? Polygenic risk scores provide a numerical estimate of an individual’s genetic susceptibility to a specific disease by combining information from multiple genetic markers across the genome.
Methodology and Validation Across Diverse Populations
To build a more equitable model, the research team utilized summarized genomic data from a multi-ancestry group comprising more than 63,000 Alzheimer’s disease cases and 484,000 age-matched controls. The resulting multi-ancestry polygenic risk score was initially tested in an independent sample of 10,612 cases and 16,625 elderly controls. Subsequent validation was performed in a mixed-ancestry group containing 1,500 cases and 75,500 elderly controls, according to the published findings.
Data analyses demonstrated that the new risk score successfully predicted clinical diagnoses of Alzheimer’s disease across all examined populations. The model performed particularly well in groups that have traditionally been underrepresented in genetic studies, including African American participants, Hispanic individuals from both continental U.S. and Caribbean populations, and East Asian cohorts.
Connecting Genetic Risk to Biological and Cognitive Markers
Beyond clinical diagnosis, the investigators evaluated how the polygenic risk score correlates with physical and cognitive biomarkers using participants from the Alzheimer’s Disease Sequencing Project, the Framingham Heart Study, the Alzheimer’s Disease Neuroimaging Initiative, and the Korean Brain Aging Study for the Early Diagnosis and Prediction of AD. Results showed that high risk scores associated significantly with poorer memory, language, and executive function performance.
Brain imaging assessments utilizing MRI scans revealed that individuals with elevated risk scores exhibited reduced hippocampal volume—the brain region impacted earliest by Alzheimer’s disease. Furthermore, cerebrospinal fluid analyses detected abnormal levels of hallmark proteins amyloid-beta and phosphorylated Tau, with women showing a greater deviation in phosphorylated Tau levels. Longitudinal evaluations also indicated that participants with very high risk scores experienced the steepest cognitive declines prior to developing overt dementia, according to co-corresponding author Xiaoling Zhang, MD, PhD, associate professor of medicine at Boston University Chobanian & Avedisian School of Medicine.
Frequently Asked Questions
What is a polygenic risk score for Alzheimer’s disease?
A polygenic risk score is a numerical estimate that calculates an individual’s overall genetic susceptibility to a disease by analyzing summarized information from numerous genetic markers across the entire genome.
Why was a multi-ancestry risk score necessary?
Previous genetic risk scores relied heavily on datasets from individuals of European ancestry, which limited their accuracy and transferability when applied to African American, Hispanic, and East Asian populations.
How does this new score assist clinicians and researchers?
According to researchers, the ancestry-aware model improves long-range risk prediction, aids in selecting appropriate subjects for clinical trials, and supports personalized intervention and prevention strategies.
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