Causal modelling of gene effects from regulators to programs to traits

Area of Science:

• Genetics
• Systems Biology
• Computational Biology

Background:

• Genetic association studies identify links between genes and traits but struggle to elucidate biological mechanisms.
• Genome-scale approaches for inferring causal pathways from genes to cellular functions and traits are lacking.

Purpose of the Study:

• To bridge the gap between genetic associations and biological mechanisms.
• To develop genome-scale approaches for inferring causal mechanistic pathways.
• To build causal graphs explaining gene-trait relationships.

Main Methods:

• Combined quantitative estimates of gene-trait relationships from loss-of-function burden tests.
• Inferred gene-regulatory connections from Perturb-seq experiments in relevant cell types.
• Constructed causal graphs integrating these data sources.

Main Results:

• Developed a proof-of-concept causal graph for the gene-regulatory hierarchy controlling three blood traits.
• Demonstrated the feasibility of combining loss-of-function burden tests and Perturb-seq data.
• Showcased how regulatory effects explain gene-trait associations.

Conclusions:

• Perturbation studies in trait-relevant cell types, combined with gene-level effect sizes, can link genetic association to biological mechanism.
• This integrated approach advances understanding of causal pathways from genes to traits.
• Enables genome-scale inference of mechanistic explanations for genetic associations.