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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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ti-scMR: trajectory-inference-based dynamic single-cell Mendelian randomization identifies causal genes underlying

Jianle Sun1,2, Qun Dong1, Jialu Wei1

  • 1Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.

NAR Genomics and Bioinformatics
|July 9, 2025
PubMed
Summary

We developed trajectory-inference-based dynamic single-cell Mendelian randomization (ti-scMR) to uncover causal genes linking genotypes to phenotypes. This method integrates single-cell data and genetic variants to identify genes influencing cellular development and disease.

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Area of Science:

  • Genomics
  • Transcriptomics
  • Systems Biology

Background:

  • Gene expression underlies cellular and individual phenotypic variation, connecting genotypes to phenotypes.
  • Single-cell differential expression analysis identifies cell types but cannot establish causality due to confounders.
  • Traditional Mendelian randomization methods often ignore cellular heterogeneity and dynamic expression changes.

Purpose of the Study:

  • To develop a novel method, trajectory-inference-based dynamic single-cell Mendelian randomization (ti-scMR), for causal gene discovery.
  • To integrate population genomics and single-cell transcriptomics for exploring causal links between transcriptional features and phenotypes.
  • To identify causal genes influencing cellular development and related diseases.

Main Methods:

  • Leveraged trajectory inference and functional principal component analysis to model cumulative gene expression effects.
  • Selected genetic instrumental variables using single-cell expression quantitative trait locus (eQTL) mapping.
  • Employed transcriptome-level Mendelian randomization to prioritize causal genes for phenotypes.

Main Results:

  • Demonstrated the superior performance of ti-scMR in causal gene identification through simulations.
  • Applied ti-scMR to two real single-cell datasets, revealing potential causal genes.
  • Identified causal genes associated with immune cell differentiation and related diseases.

Conclusions:

  • ti-scMR effectively addresses limitations of existing methods for causal inference in single-cell studies.
  • The integration of trajectory inference, eQTL, and Mendelian randomization offers a powerful approach for elucidating causal mechanisms.
  • This framework advances our understanding of complex traits by uncovering causal genes related to cellular development and disease.