Novel whole-genome analysis methods for Alzheimer's risk prediction

Parabon Nanolabs, Inc., Reston VA

Investigators

Linked publications & trials

Abstract

Project Summary/Abstract Alzheimer's Disease (AD) affects millions of Americans, yet there are no treatments that meaningfully affect disease progression once symptoms manifest. This has shifted the focus to early detection and intervention, which is thought by many researchers to offer the best chance of slowing or stopping the progression of AD. However, trials aimed at averting the underlying causes of disease have proven difficult because pathological changes in AD happen well in advance of cognitive decline. A widely-available genetic test for determining AD risk early in life, while prevention might still be possible, would allow early treatment intervention, enrollment in clinical trials, and improved patient stratification for testing treatment effectiveness. However, despite recent advancements, genetic risk prediction models (GRPMs) for late-onset AD (LOAD) lack sufficient discrimination ability to support such applications. Given the lack of effective treatments once symptoms have manifested and the socioeconomic consequences at stake, there is a serious unmet need for a widely-available GRPM able to accurately assess a patient's risk in middle age or earlier, before neurodegeneration begins. To address this need, Parabon has teamed with AD researchers from Washington University and Emory to develop a GRPM able to accurately predict an individual's risk of developing LOAD at various ages. Phase I demonstrated that this GRPM, which exploits diagnostic heterogeneity, non-additive (epistatic) interactions among variants, and machine learning, significantly outperforms traditional risk factors. In Phase II, the GRPM will be further optimized, validated, and commercialized as a direct-to-consumer (DTC) genetic health risk assessment test. In Aim 1, thousands of new and existing case and control subjects with genotypes and detailed phenotypes will be added. In Aim 2, novel approaches to feature selection for epistatic interactions will be implemented to increase the generalizability of selected features. In Aim 3, the selected genomic features will be used to predict imaging and biomarker endophenotypes of LOAD. In Aim 4, the out- of-sample endophenotype predictions will be combined into a final predictive model for AD diagnosis that can be applied to new subjects at any age, which will be validated in an independent replication set. Finally, in Aim 5, data and results will be prepared for scientific publication and submission to the FDA for marketing authorization as a DTC genetic health risk assessment system.