StarBASE-GP: Biologically-Guided Automated Machine Learning for Genotype-to-Phenotype Association Analysis
Journal:
arXiv
Published Date:
May 28, 2025
Abstract
We present the Star-Based Automated Single-locus and Epistasis analysis tool
- Genetic Programming (StarBASE-GP), an automated framework for discovering
meaningful genetic variants associated with phenotypic variation in large-scale
genomic datasets. StarBASE-GP uses a genetic programming-based multi-objective
optimization strategy to evolve machine learning pipelines that simultaneously
maximize explanatory power (r2) and minimize pipeline complexity. Biological
domain knowledge is integrated at multiple stages, including the use of nine
inheritance encoding strategies to model deviations from additivity, a custom
linkage disequilibrium pruning node that minimizes redundancy among features,
and a dynamic variant recommendation system that prioritizes informative
candidates for pipeline inclusion. We evaluate StarBASE-GP on a cohort of
Rattus norvegicus (brown rat) to identify variants associated with body mass
index, benchmarking its performance against a random baseline and a
biologically naive version of the tool. StarBASE-GP consistently evolves Pareto
fronts with superior performance, yielding higher accuracy in identifying both
ground truth and novel quantitative trait loci, highlighting relevant targets
for future validation. By incorporating evolutionary search and relevant
biological theory into a flexible automated machine learning framework,
StarBASE-GP demonstrates robust potential for advancing variant discovery in
complex traits.
