# Broadly applicable high throughput variant interpretation and validation for MYH7 > **NIH NIH R01** · BAYLOR COLLEGE OF MEDICINE · 2024 · $719,462 ## Abstract Project Summary The advancement in sequencing technology has enabled the practice of clinical diagnostic sequencing, however, the bottle neck remains to be the correct interpretation of millions of variants identified. Despite of the recent advances in artificial intelligence assisted variant interpretation, this is a particularly prominent problem when the pathogenicity is associated with gain-of-function missense mutations. The overall objective of this application is to develop a high throughput variant interpretation approach for MYH7, which will be broadly applicable to other cardiomyopathy and genetic diseases. The central hypothesis is that MYH7 variants will lead to gene expression profile changes, which will serve as a sensitive marker to predicting pathogenicity. In this proposal, we will test the central hypothesis with three specific aims: 1) Develop an integrase-based system for high throughput variant molecular phenotyping in induced pluripotent stem cell differentiated cardiomyocytes (iPSC-CM) using single cell RNAseq and contrastive machine learning algorithms; 2) Functional validation in iPSC-CM using cell size and contractility; 3) Detailed myofilament structure/function studies of the selected variants for in-depth understanding of genotype-phenotype relationship. The research proposed in this application is innovative because it takes advantage of single cell sequencing data for robust molecular phenotyping, it will develop novel machine learning variant interpretation tools specific for structure proteins like MYH7, and it will generate unprecedented functional experimental data of MYH7 variants in iPSC-CM, which not only is informative in variants interpretation, but also provides amino acid level functional data on myosin that can inform our understanding of sarcomere biology. The proposed research is significant because it will develop a platform that enables high-throughput functional assessment of all possible variants of MYH7, which could ultimately help to solve all current variant of unknown significance and future ones to be identified in MYH7. This strategy is readily adaptable to all other inherited cardiomyopathies and other genetic diseases. ## Key facts - **NIH application ID:** 10997854 - **Project number:** 1R01HL175964-01 - **Recipient organization:** BAYLOR COLLEGE OF MEDICINE - **Principal Investigator:** JONATHAN A KIRK - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $719,462 - **Award type:** 1 - **Project period:** 2024-07-01 → 2028-06-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10997854 ## Citation > US National Institutes of Health, RePORTER application 10997854, Broadly applicable high throughput variant interpretation and validation for MYH7 (1R01HL175964-01). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10997854. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*