DOI: 10.5061/dryad.s4mw6m9hf
title: Data for: "Generative prediction of causal gene sets responsible for complex traits"

description: 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. 
description: Data were collected from GEO.
description: # Generative prediction of causal gene sets responsible for complex traits
 [https://doi.org/10.5061/dryad.s4mw6m9hf](https://doi.org/10.5061/dryad.s4mw6m9hf) ## Description of the data and file structure This is the data repository for the project 'Generative prediction of causal gene sets responsible for complex traits'. This repository contains data to run Jupyter notebooks and a Python script in the associated Zenodo code repository (doi: 10.5281/zenodo.12955283). Data: 1) single-cell RNAseq data on the human complex disease traits featured in the manuscript (labeled by GEO series, see Table 1 in main text). In the files below, the traits are labeled by their abbreviations and GEO series: * tep = Non-small cell lung cancer (GSE89843) * t1d = Type-1 diabetes (GSE182870) * MODY3 = Maturity-onset diabetes of the young type 3 (GSE129653) * ib = Inflammatory bowel (GSE193677) * cancermeta = Cancer metastasis (GSE202695) * asthma = Allergic asthma (GSE96783) * allergy = Food allergy (GSE114065) * amd = Macular degerneration (GSE135092) * depmap = Pan-cancer metastasis (no GEO i.d.) `'traits'` files are binary 1-hot encoded arrays labeling baseline and variant phenotypes for each trait (labeled by abbreviation and GEO i.d., see above) `'matching_indices'` files contain matching indices to match genes between perturbation and trait datasets, labeled by abbreviation (see above)  mart_export.txt-2.gz, gene_pert_dict.npy and gene_perturbations_for_ben.gctx contain gene perturbations `'expression'` files are gene expression arrays (cells by genes) containing gene expression for each trait (by abbreviation, see above) in transcripts-per-million sample optimization data for allergic asthma trait to run the second Jupyter notebook in the Zenodo repository TWAVE2 below: data_aa_lam_3.zip. Jupyter notebooks in the associated Zenodo repository: 1\) TWAVE1: a notebook implementing the variational autoencoder TWAVE, as well as dimensionality reduction via selection of causal eigengenes 2\) TWAVE2: a notebook implementing post-optimization analysis by the maximum entropy graph null model and construction of gene perturbation co-occurrence networks. Python script in the associated Zenodo repository: TWAVE_optimization: implementing constrained optimization to find relevant genes that drive the transition between baseline and variant clusters in causal eigengene space. his script (see Zenodo) likely needs to be run on the cluster (2500 X 2 optimizationseach taking ~15-30 minutes). Please reach out to Ben Kuznets-Speck at [biophysben@gmail.com](mailto:biophysben@gmail.com) with any questions. ### Files and variables #### File: traits\_tep\_GSE89843\_series\_matrix.npy **Description:** binary trait matrix for Non-small cell lung cancer trait  #### File: traits\_t1d\_GSE182870\_series\_matrix.npy **Description:** binary trait matrix for Type-1 diabetes #### File: traits\_MODY3\_GSE129653\_series\_matrix.npy **Description:** binary trait matrix for Maturity onset diabetes of the young type-3 #### File: traits\_ib\_GSE193677\_series\_matrix.npy **Description:** binary trait matrix for Inflammatory bowel #### File: traits\_cancermeta\_GSE202695\_series\_matrix.npy **Description:** binary trait matrix for cancer metastasis #### File: traits\_asthma\_GSE96783\_series\_matrix.npy **Description:** binary trait matrix for allergic asthma #### File: traits\_allergy\_GSE114065\_series\_matrix.npy **Description:** binary trait matrix for food allergy  #### File: traits\_amd\_GSE135092\_series\_matrix.npy **Description:** binary trait matrix for macular degeneration #### File: matching\_indices\_tep\_rnaseq.npy **Description:** matching gene indices between traits and perturbations (Non-small cell lung cancer) #### File: matching\_indices\_ib\_rnaseq.npy **Description:** matching gene indices between traits and perturbations (inflammatory bowel) #### File: matching\_indices\_t1d\_rnaseq.npy **Description:** matching gene indices between traits and perturbations (type-1 diabetes) #### File: matching\_indices\_depmap\_rnaseq.npy **Description:** matching gene indices between traits and perturbations (pan-cancer metastasis) #### File: matching\_indices\_allergy\_rnaseq.npy **Description:** matching gene indices between traits and perturbations (food allergy) #### File: matching\_indices\_asthma\_rnaseq.npy **Description:** matching gene indices between traits and perturbations (allergic asthma) #### File: matching\_indices\_MODY3\_rnaseq.npy **Description:** matching gene indices between traits and perturbations (maturity onset diabetes of the young type-3) #### File: matching\_indices\_cancermeta\_rnaseq.npy **Description:** matching gene indices between traits and perturbations (cancer metastasis) #### File: matching\_indices\_amd\_rnaseq.npy **Description:** matching gene indices between traits and perturbations (macular degeneration) #### File: depmap\_y.npy **Description:** depmap binary trait matrix (pan-cancer metastasis) #### File: mart\_export.txt-2.gz **Description:** file needed for perturbations #### File: gene\_pert\_dict.npy **Description:** gene perturbation dictionary  #### File: expression\_GSE114065\_series\_matrix.npy **Description:** trait expression data (food allergy) #### File: expression\_GSE129653\_series\_matrix.npy **Description:** trait expression data (MODY3) #### File: expression\_GSE202695\_series\_matrix.npy **Description:** trait expression data (cancer metastasis) #### File: expression\_GSE89843\_series\_matrix.npy **Description:** trait expression data (Non-small cell lung cancer aka tep) #### File: gene\_perturbations\_for\_ben.gctx **Description:** gene perturbation expression #### File: expression\_GSE96783\_series\_matrix.npy **Description:** trait expression data (allergic asthma) #### File: expression\_GSE135092\_series\_matrix.npy **Description:** trait expression data (macular degeneration amd) #### File: expression\_GSE182870\_series\_matrix.npy **Description:** trait expression data (type-1 diabetes t1d) #### File: depmap\_expression.npy **Description:** depmap expression data (pan-cancer metastasis) #### File: expression\_GSE193677\_series\_matrix.npy **Description:** trait expression data (inflammatory bowel ib) #### File: data\_aa\_lam\_3.zip **Description:** data from allergic asthma optimization ## Code/software See Zenodo Generative-prediction-of-causal-gene-sets-responsible-for-complex-traits doi: [10.5281/zenodo.12955283](https://doi.org/10.5281/zenodo.12955283) ## Access information Other publicly accessible locations of the data: * Gene Expression Omnibus Data was derived from the following sources: * n/a