Genotype-environment interactions reveal causal pathways that mediate genetic effects on phenotype

Area of Science:

• Genetics
• Systems Biology
• Molecular Biology

Background:

• Understanding genotype-phenotype links is vital for treating genetic diseases.
• Expression quantitative trait loci (eQTL) studies help map genetic influences on gene expression but struggle to distinguish causation from correlation.
• Causal intermediates between genotype and phenotype are key targets for therapeutic intervention.

Purpose of the Study:

• To identify truly causal intermediate genes that influence fitness in specific environments.
• To assess the effectiveness of expression quantitative trait loci (eQTL) studies in predicting causal intermediates.
• To develop a robust statistical model for predicting causal intermediates.

Main Methods:

• Extensive, multi-environment gene expression and fitness profiling of hundreds of genetically diverse yeast strains.
• Functional genomics assays to validate predicted causal intermediates.
• Development of a statistical model leveraging shared genetic effects on expression across environments.

Main Results:

• The predictive power of eQTL studies for causal intermediates is poor when conducted in a single environment.
• Causal intermediates are most reliably predicted from genetic effects on gene expression that are consistent across all tested environments.
• A novel statistical model identified over 400 transcripts as causal intermediates, with experimental validation confirming their role in conditioning fitness.
• A mechanism was proposed where shared molecular consequences of genetic variation across environments lead to environment-dependent phenotypic effects.

Conclusions:

• Multi-environment profiling is essential for accurately inferring causal relationships in eQTL studies.
• Genetic effects on gene expression shared across environments are reliable indicators of causal intermediates.
• The developed statistical model and findings offer a framework for discovering personalized therapeutic targets in clinical omics studies.