Data for: "Generative prediction of causal gene sets responsible for complex traits"

The relationship between genotype and phenotype remains an outstanding
question for organism-level traits because these traits are generally
complex. The challenge arises from complex traits being determined by a
combination of multiple genes (or loci), which leads to an explosion of
possible genotype-phenotype mappings. The primary techniques to resolve
these mappings are genome/transcriptome-wide association studies, which
are limited by their lack of causal inference and statistical
power.  Here, we develop an approach that leverages
transcriptional data endowed with causal information and a generative
machine learning model to strengthen statistical power.  Our
implementation of the approach-- dubbed TWAVE---includes a variational
autoencoder trained on human transcriptional data, which is incorporated
into an optimization framework. TWAVE generates trait expression profiles,
which we dimensionally reduce by identifying independently varying
generalized pathways (eigengenes). We then conduct constrained
optimization to find causal gene sets that are the gene perturbations
whose measured transcriptomic responses best explain trait differences. By
considering several complex traits, we show that the approach identifies
causal genes that cannot be detected by the primary existing techniques.
Moreover, the approach identifies complex diseases caused by distinct sets
of genes, meaning that the disease is polygenic and exhibits distinct
subtypes driven by different genotype-phenotype mappings.  We
suggest that the approach will enable the design of tailored experiments
to identify multi-genic targets to address complex diseases.