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STIGMA: Single-cell tissue-specific gene prioritization using machine learning
Saranya Balachandran1, Cesar A Prada-Medina2, Martin A Mensah3
1Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck and Kiel University, Lübeck, Germany.
American Journal of Human Genetics
|January 16, 2024
Summary
We developed STIGMA, a machine learning tool using single-cell RNA sequencing data to identify genes linked to rare congenital diseases. STIGMA analyzes gene expression across cell types during development to pinpoint disease-causing variants.
Area of Science:
- Genomics
- Developmental Biology
- Computational Biology
Background:
- Clinical exome and genome sequencing advance disease genetics, but many genes remain uncharacterized, hindering variant interpretation.
- Existing gene prioritization methods do not account for cell-specific gene expression heterogeneity within tissues.
Purpose of the Study:
- To introduce STIGMA (single-cell tissue-specific gene prioritization using machine learning), a novel framework for prioritizing candidate genes in rare congenital diseases.
- To leverage single-cell RNA sequencing (scRNA-seq) data to capture cell-type-specific gene expression dynamics during organogenesis.
Main Methods:
- Applied STIGMA to mouse limb and human fetal heart scRNA-seq datasets.
- Trained machine learning models to learn temporal gene expression patterns across cell types.
- Prioritized candidate genes and variants associated with congenital malformations.
Main Results:
- STIGMA identified 469 variants in 345 genes for congenital limb malformations, highlighting UBA2.
- For congenital heart defects, 34 genes with nonsynonymous de novo variants (nsDNVs) were detected in 7,958 individuals, including the Prdm1 ortholog.
- Demonstrated STIGMA's ability to prioritize tissue-specific genes by analyzing cell population heterogeneity.
Conclusions:
- STIGMA effectively prioritizes candidate genes for rare congenital diseases by integrating scRNA-seq data.
- The framework's capacity to model cell-specific expression heterogeneity enhances the discovery of disease-associated genes and causal variants.
