Multiomics Companion Diagnostic
Develop diagnostic biomarkers for an autoimmune disease. Our goal was to combine genomic, transcriptomic, and proteomic data to find single nucleotide polymorphisms (SNPs) predictive of certain disease traits.
Deployed a library of modules for GWAS, eQTL, qQTL, and Bayesian model averaging in Python. Optimized the runtime efficiency of core QTL methods in Python.
Reduced compute time to analyze 4 million variants from 42 hours to 2 minutes. Reduced the cost of running the workload to run on a single 4-core EC2 machine, rather than running hundreds of Nextflow jobs. Discovered several SNPs as promising candidates for further scientific study.