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Machine learning discovery of variants that alter gene regulation in cardiovascular development and disease

Award Number: R01HL187295
ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 08/01/2026
PERIOD OF PERFORMANCE END DATE: 04/30/2030

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Machine learning discovery of variants that alter gene regulation in cardiovascular development and disease - PROJECT SUMMARY / ABSTRACT Congenital heart disease (CHD) affects ~1% of live births in the US and runs in families. Genome sequencing of children with CHD has identified damaging variants in dozens of heart development genes. These are enriched for transcription factors and chromatin regulators. Despite this progress, the majority of children with CHD lack a genetic diagnosis, and the full spectrum of causal pathways remains unknown. Emerging evidence suggests a role for rare and de novo noncoding variants. Unfortunately, it is challenging to guess which noncoding sequence changes will perturb cardiovascular development and via what mechanisms. This limits our ability to test candidate causal variants outside of protein-coding genes, despite possessing the necessary tools for engineering patient alleles into human cells. To overcome these obstacles, we propose to create a deep learning toolkit for heart development that can predict diverse functional effects of noncoding variants. We will apply these methods to different types of rare and de novo variants detected in the genomes of children with CHD, ranging from single nucleotide changes to large structural variants. Our overarching hypothesis is that some rare and de novo noncoding variants cause CHD by changing three-dimensional chromatin interactions, local nucleosome positioning, and/or transcription factor binding sites. Each aim of this project develops a machine learning model for one of these genetic mechanisms, enabling us to predict candidate causal variants along with a hypothesis about how each high-scoring variant alters gene regulation in heart development. Importantly, our methods will identify candidate target genes, upstream regulators, and cell types for these variants, which would be impossible using standard bioinformatics analyses or genetic association tests. The top hits for cardiomyocytes will be engineered into isogenic cell lines and evaluated for the predicted effects using advanced genomics techniques, including Hi-C, single-molecule nucleosome occupancy, and massively parallel reporter assays, plus RNA sequencing. We anticipate that this machine-guided strategy will reveal new genetic mechanisms, causal variants, and CHD associated loci. Our trained models and code will be freely shared to enable others to predict the effects of sequence variation on gene regulation in heart development.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $925,796 )
2026	2026	J.DAVID GLADSTONE INSTITUTES	1650 OWENS ST	SAN FRANCISCO	CA	94158	SAN FRANCISCO	USA	Cardiovascular Diseases Research	000	1	8/5/2026	NEW	$925,796
														Subtotal = $925,796

Grand Total All Awards = $925,796

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Machine learning discovery of variants that alter gene regulation in cardiovascular development and disease

Award Number: R01HL187295

ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 08/01/2026

PERIOD OF PERFORMANCE END DATE: 04/30/2030

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