# Development and validation of a computable knowledge framework for genomic medicine

> **NIH NIH R35** · RESEARCH INST NATIONWIDE CHILDREN'S HOSP · 2022 · $323,325

## Abstract

Project Summary/Abstract
Genomic medicine is the discipline of interpreting genomic information about an individual as part of their
clinical care, for diagnosis, prognosis, or therapeutic decision-making. Integral to the practice of genome
interpretation is the collection of multiple lines of evidence from knowledgebases to support or refute the
clinical significance of evaluated variants. Modern clinical variant knowledgebases maintain literature and
variant coverage that is mostly non-overlapping. This diversity of content causes a known problem in genome
interpretation: analysts tasked with assembling a clinical variant report choose to spend considerable time
navigating multiple resources and collating evidence, or risk missing critical information by selectively
evaluating fewer resources. The resulting effort needed for an analyst to clinically interpret a variant list is
known as the interpretation bottleneck, for its rate-limiting role in the clinical evaluation of patient genomes.
Data integrators from public and private genomic medicine organizations work to alleviate this bottleneck by
developing integrative clinical interpretation applications for use by genome analysts. As new knowledgebases
are created, each of these public and private data integrators is left with the task of designing and maintaining
another interface for each new resource, leading to combinatorial growth of data harmonization effort across
the entire system. This approach is not scalable.
The parent R35 is enabling a transition to a scalable, interoperable, and federated genomic data ecosystem
from the data integrators and knowledgebases already in existence today through development and validation
of a computable knowledge framework for genomic medicine. This objective is being carried out through
coordination of research activities with the Variant Interpretation for Cancer Consortium, ClinGen, and the
Global Alliance for Genomics and Health. This administrative supplement extends the activities of the parent
R35 by applying the developing genomic knowledge framework to the Genome Aggregation Database
(gnomAD) a dataset of great value to clinical decision support systems and AI/ML tools used to support clinical
variant interpretation. This is achieved through a new collaboration with the gnomAD team to bring the
developments of the framework to the gnomAD dataset. Utility of the gnomAD dataset in applied AI/ML tools
for genomic medicine will be demonstrated in an augmented intelligence variant classification system.
As a result of this administrative supplement, new AI/ML applications dependent upon Population Frequency
Evidence will be made possible without need for data harmonization efforts. This will provide a foundation for
scalable, AI-assisted classification of variants in genomic medicine pipelines.

## Key facts

- **NIH application ID:** 10594234
- **Project number:** 3R35HG011949-02S1
- **Recipient organization:** RESEARCH INST NATIONWIDE CHILDREN'S HOSP
- **Principal Investigator:** Alex Handler Wagner
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $323,325
- **Award type:** 3
- **Project period:** 2021-09-08 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10594234

## Citation

> US National Institutes of Health, RePORTER application 10594234, Development and validation of a computable knowledge framework for genomic medicine (3R35HG011949-02S1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10594234. Licensed CC0.

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