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

Period of Performance: 06/15/2015 - 12/14/2015

$180K

Phase 1 SBIR

Recipient Firm

Parabon NanoLabs, Inc.
11260 Roger Bacon Drive Array
Reston, VA 20190
Principal Investigator

Abstract

DESCRIPTION (provided by applicant): Late-onset Alzheimer's Disease (LOAD) affects millions of elderly people in the United States, yet there are no well-established clinical guidelines for assessing a person's relative risk. Accurate assessment of lifetime risk for LOAD would give high-risk individuals the opportunity to undergo regular biomarker screening for signs of disease and to modify environmental risk factors or participate in prospective clinical trials. This Phase I SBIR project aims to develop a risk prediction model for LOAD that meets or exceeds the accuracy standards established in 1998 by the Working Group for Biochemical and Molecular Markers of Alzheimer's Disease. The recent release of whole-genome sequence data from an extensively phenotyped cohort of the Alzheimer's Disease Neuroimaging Initiative creates a unique opportunity to develop the methodology needed to successfully construct such a risk prediction model. The Parabon team will undertake three specific aims in pursuit of the final goal of producing a risk prediction model that can be used in the clinic. First, a novel methodology will be created for analyzing whole-genome sequence data to discover common SNPs, rare variants, and epistatic interactions that significantly associate with LOAD endophenotypes, the specific physiological changes that underlie disease. This will require innovative algorithm and software development, particularly the implementation of multi-objective optimization in our existing evolutionary search algorithm for detecting epistasis. Second, the discovered significant variants will be built into risk prediction models for each endophenotype using state-of-the-art machine learning methods. These models will then be combined into a single predictive model for lifetime risk, which will be validated in an independent cohort from the Alzheimer's Disease Sequencing Project. Finally, to quantify the confidence associated with each prediction made by the model, algorithms and software for calculating confidence intervals will be developed and implemented. Each new prediction will be presented with a measure of confidence to enable clinicians to determine what, if any, intervention is necessary. When these aims have been completed, Parabon will have produced the first clinically relevant genetic risk prediction model for late-onset Alzheimer's Disease, as well as a suite of software that can be used in the development of other diagnostics. In Phase II, we will move beyond the ADNI-supplied endophenotypes, using image processing and deep learning to infer neuroimaging features most relevant to AD diagnoses, as well as work to validate the predictive models in a larger, more diverse cohort across multiple sites.