Genotype-environment interactions reveal causal pathways that mediate genetic effects on phenotype.
Unraveling the molecular processes that lead from genotype to phenotype is crucial for the understanding and effective treatment of genetic diseases. Knowledge of the causative genetic defect most often does not enable treatment; therefore, causal intermediates between genotype and phenotype constitute valuable candidates for molecular intervention points that can be therapeutically targeted. Mapping genetic determinants of gene expression levels (also known as expression quantitative trait loci or eQTL studies) is frequently used for this purpose, yet distinguishing causation from correlation remains a significant challenge. Here, we address this challenge using extensive, multi-environment gene expression and fitness profiling of hundreds of genetically diverse yeast strains, in order to identify truly causal intermediate genes that condition fitness in a given environment. Using functional genomics assays, we show that the predictive power of eQTL studies for inferring causal intermediate genes is poor unless performed across multiple environments. Surprisingly, although the effects of genotype on fitness depended strongly on environment, causal intermediates could be most reliably predicted from genetic effects on expression present in all environments. Our results indicate a mechanism explaining this apparent paradox, whereby immediate molecular consequences of genetic variation are shared across environments, and environment-dependent phenotypic effects result from downstream integration of environmental signals. We developed a statistical model to predict causal intermediates that leverages this insight, yielding over 400 transcripts, for the majority of which we experimentally validated their role in conditioning fitness. Our findings have implications for the design and analysis of clinical omics studies aimed at discovering personalized targets for molecular intervention, suggesting that inferring causation in a single cellular context can benefit from molecular profiling in multiple contexts.
Gene Center, Ludwig-Maximilians-Universität München, Munich, Germany.
Julien
J
Gagneur
Oliver
O
Stegle
Chenchen
C
Zhu
Petra
P
Jakob
Manu M
MM
Tekkedil
Raeka S
RS
Aiyar
Ann-Kathrin
AK
Schuon
Dana
D
Pe'er
Lars M
LM
Steinmetz
D001499:Bayes Theorem; D004777:Environment; D015870:Gene Expression; D059647:Gene-Environment Interaction; D005838:Genotype; D006801:Humans; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D015233:Models, Statistical; D010641:Phenotype; D040641:Quantitative Trait Loci / Q000235:genetics*; D012441:Saccharomyces cerevisiae / Q000235:genetics