2025/04/14 by Krishna Rijal, Caroline M. Holmes, Rijal, Krishna +9 · 1 voice
Biochemistry, Genetics and Molecular Biology · Computer Science · #Epistasis #FOS: Biological sciences #FOS: Computer and information sciences #Genetic architecture #Genetics, Bioinformatics, and Biomedical Research #Genomics (q-bio.GN) #Machine Learning (cs.LG) #Pairwise comparison #Parameterized complexity #Populations and Evolution (q-bio.PE) #Quantitative trait locus #Range (aeronautics) #Trait #cs.LG #q-bio.GN #q-bio.PE
paper · pdf · doi:10.48550/arxiv.2504.10388
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/04/14 · arxiv published 2025/04/14 · arxiv updated 2025/04/14 · openalex created_date 2025/10/14 · openalex updated_date 2026/08/06
Predicting phenotype from genotype is a central challenge in genetics. Traditional approaches in quantitative genetics typically analyze this problem using methods based on linear regression. These methods generally assume that the genetic architecture of complex traits can be parameterized in terms of an additive model, where the effects of loci are independent, plus (in some cases) pairwise epistatic interactions between loci. However, these models struggle to analyze more complex patterns of epistasis or subtle gene-environment interactions. Recent advances in machine learning, particularly attention-based models, offer a promising alternative. Initially developed for natural language processing, attention-based models excel at capturing context-dependent interactions and have shown exceptional performance in predicting protein structure and function. Here, we apply attention-based models to quantitative genetics. We analyze the performance of this attention-based approach in predicting phenotype from genotype using simulated data across a range of models with increasing epistatic complexity, and using experimental data from a recent quantitative trait locus mapping study in budding yeast. We find that our model demonstrates superior out-of-sample predictions in epistatic regimes compared to standard methods. We also explore a more general multi-environment attention-based model to jointly analyze genotype-phenotype maps across multiple environments and show that such architectures can be used for "transfer learning" - predicting phenotypes in novel environments with limited training data.