B-G.39: Optimizing genetic association tests for small cohorts
Authors
Tomasz Gambin
Warsaw University of Technology
Keywords
GWAS RVAS Nextflow QC
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Running genetic association tests on small cohorts, which is common for rare diseases, present unique challenges. We have to be especially careful during data filtering stages, commonly executed before actual tests. It also makes it more challenging to correct for population stratification. For such scenarios, when parameter values selected using researcher experience and generally recommended default values are not precise enough, we propose a data-driven approach to select optimal settings, maximizing efficacy and providing trust in the results. We built a comprehensive, high performance Nextflow workflow for genetic association testing, which includes all commonly performed data filtering and preparation steps, controlled by a rich set of modifiable parameters. We also built another workflow, which generates synthetic testing datasets with realistic characteristics, and a versatile tool that orchestrates the execution of mentioned workflows for the purpose of optimizing their parameters. We have run our software on Thousand Genomes project data, executing more than 11000 association testing workflow runs with various parameters and datasets, and we obtained an optimized set of parameters. Results proved that the parameters are highly interdependent and rules for selecting them in isolation fail to capture that complexity. We also found that using dosage is superior to traditional hard genotypes association tests. To enhance the interpretability of our results we applied the Accumulated Local Effects XAI technique. Our tools can also be used to tune the parameters to an individual dataset characteristics, enabling robust association testing for small cohorts.
Co-authors: Tomasz Gambin
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