Validating the performance and inclusivity of a novel functionally-informed predictive genetic test method for polygenic disease

PROJECT SUMMARY We now know that over 90% of causal variants for common diseases, including cardiovascular disease and many cancers, lie in non-coding regions of the genome. Therefore, a complete picture of common disease risk requires analyzing variants across the whole genome, not just the coding genome. Currently available methods to screen for common disease risk focus on monogenic coding variants and are limited in accuracy, inclusivity, and interpretability by not incorporating functional whole-genome variants into their training methods and output interpretation. Thus, there is still an unmet need for the use of systematic whole-genome functional mapping in building predictive risk models. Drawing on prior research and experience from their time at Harvard, Stanford, MIT, the Broad Institute, and Mount Sinai, the Martingale Labs, Inc. team has developed the first comprehensive functionally informed polygenic model for disease risk. This new polygenic prediction model combines the power of novel genome-wide functional variant annotations with a Bayesian supervised machine learning (ML) prediction method to improve the accuracy, ethnic inclusivity, and interpretability of predictive genetic tests. The goal of this Phase I project is to ready Martingale Labs’ whole-genome predictive machine learning platform for scale by validating and quantifying the model’s predictive accuracy and ethnic inclusivity as compared to current clinically available methods. These models can be implemented in clinical settings as predictive genetic tests to help stratify individuals by risk and tailor preventative strategies such as screenings and preventative medications to minimize disease risk. In this project, we focus on the example of cardiovascular disease. We will expand the quantification of our model performance to other common diseases in Phase II, starting with breast, prostate, and colorectal cancer. At the current rate of testing, even with conservative reimbursement by current health insurers, our proposed genetic testing product could capture an $8 Billion annual revenue opportunity. Our project involves the development of a new type of deep technology by creating the first supervised learning models that incorporate functional annotations of the whole genome. This technology’s use extends beyond medical applications to other novel and useful applications, such as animal models or veterinary medicine.