STIGMA: Single-cell tissue-specific gene prioritization using machine learning

Saranya Balachandran, Cesar A. Prada-Medina, Martin A. Mensah, Naseebullah Kakar, Inga Nagel, Jelena Pozojevic, Enrique Audain, Marc Phillip Hitz, Martin Kircher, Varun K.A. Sreenivasan*, Malte Spielmann*

*Corresponding author for this work

Abstract

Clinical exome and genome sequencing have revolutionized the understanding of human disease genetics. Yet many genes remain functionally uncharacterized, complicating the establishment of causal disease links for genetic variants. While several scoring methods have been devised to prioritize these candidate genes, these methods fall short of capturing the expression heterogeneity across cell subpopulations within tissues. Here, we introduce single-cell tissue-specific gene prioritization using machine learning (STIGMA), an approach that leverages single-cell RNA-seq (scRNA-seq) data to prioritize candidate genes associated with rare congenital diseases. STIGMA prioritizes genes by learning the temporal dynamics of gene expression across cell types during healthy organogenesis. To assess the efficacy of our framework, we applied STIGMA to mouse limb and human fetal heart scRNA-seq datasets. In a cohort of individuals with congenital limb malformation, STIGMA prioritized 469 variants in 345 genes, with UBA2 as a notable example. For congenital heart defects, we detected 34 genes harboring nonsynonymous de novo variants (nsDNVs) in two or more individuals from a set of 7,958 individuals, including the ortholog of Prdm1, which is associated with hypoplastic left ventricle and hypoplastic aortic arch. Overall, our findings demonstrate that STIGMA effectively prioritizes tissue-specific candidate genes by utilizing single-cell transcriptome data. The ability to capture the heterogeneity of gene expression across cell populations makes STIGMA a powerful tool for the discovery of disease-associated genes and facilitates the identification of causal variants underlying human genetic disorders.

Original languageEnglish
JournalAmerican Journal of Human Genetics
Volume111
Issue number2
Pages (from-to)338-349
Number of pages12
ISSN0002-9297
DOIs
Publication statusPublished - 01.2024

Research Areas and Centers

  • Research Area: Medical Genetics

DFG Research Classification Scheme

  • 205-03 Human Genetics
  • 201-05 General Genetics and Functional Genomics

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