# New quantitative approaches to interpret variant pathogenicity

> **NIH NIH R00** · UNIVERSITY OF FLORIDA · 2024 · $242,449

## Abstract

Project Summary
Insufficient knowledge and throughput to interpret pathogenicity of genetic variants identified by next
generation sequencing (NGS) is a major bottleneck for genomic medicine implementation. The American
College of Medical Genetics and Genomics and Association for Molecular Pathology (ACMG/AMP) guidelines
identify high-confidence pathogenic and likely pathogenic variants but are limited in scalability. Many variants
are classified as variants of uncertain significance by the ACMG/AMP guidelines without an indication of which
of these variants are more or less likely to be pathogenic, leading to inappropriate medical treatment. Hence, I
propose to develop standardized quantitative approaches to improve our ability to interpret genomic variations
accurately at high-throughput. In-silico tools are commonly used to assign variant pathogenicity based on
conservation, but their predictive accuracy is limited. The current methods have not been calibrated across
genes, and the same pathogenicity score does not infer the same likelihood of pathogenicity across different
genes. In this proposal, 1) I aim to recalibrate the pathogenicity scores incorporating gene-specific features
making the pathogenicity scores more comparable across genes, and improve the accuracy of pathogenicity
predictions using advanced deep neural network models and functional data from saturation mutagenesis
studies. 2) I aim to quantify the ACMG/AMP variant classification and provide probability of variant
pathogenicity for clinically relevant genes using advanced supervised learning and leveraging a large case-
control cohort. The improved computational predictions (Aim 1) will refine variant prioritization for downstream
analyses and strengthen the computational evidence used in the ACMG/AMP guidelines. The estimated
probability of variant pathogenicity based on ACMG/AMP guideline (Aim 2) will improve communication
between laboratories, health care providers and patients about genetic test results.

## Key facts

- **NIH application ID:** 10766846
- **Project number:** 5R00HG011490-04
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Xiao Fan
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $242,449
- **Award type:** 5
- **Project period:** 2021-09-17 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10766846

## Citation

> US National Institutes of Health, RePORTER application 10766846, New quantitative approaches to interpret variant pathogenicity (5R00HG011490-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10766846. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*