Learning genotype-phenotype associations from gaps in multi-species sequence alignments.

ABSTRACT: Understanding the genetic basis of phenotypic variation is fundamental to biology. Here we introduce GAP, a novel machine learning framework for predicting binary phenotypes from gaps in multi-species sequence alignments. GAP employs a neural network to predict the presence or absence of phenotypes solely from alignment gaps, contrasting with existing tools that require additional and often inaccessible input data. GAP can be applied to three distinct problems: predicting phenotypes in species from known associated genomic regions, pinpointing positions within such regions that are important for predicting phenotypes, and extracting sets of candidate regions associated with phenotypes. We showcase the utility of GAP by exploiting the well-known association between the L-gulonolactone oxida

SUBMITTER: Islam UI 

PROVIDER: S-EPMC11840556 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature