The Relative Power of Structural Genomic Variation versus SNPs in Explaining the Quantitative Trait Growth in the Marine Teleost Chrysophrys auratus

Abstract

Predicting a complex polygenic trait such as growth using data collected from a low number of individuals remains challenging. While we demonstrate that both SNPs and structural variants provide important information to help understand the genetic basis of phenotypic traits such as fish growth, the full complexities that exist within a genome cannot be easily captured by classical machine learning techniques. When using high-dimensional data, feature selection shows some increase in the prediction accuracy of classification models and provides the potential to identify unknown genomic correlates with growth. Our results show that both SNPs and structural variants significantly impact growth, and we therefore recommend that researchers interested in the genotype-phenotype map should strive to go beyond SNPs and incorporate structural variants in their studies as well. We discuss how our machine learning models can be further expanded to serve as a test bed to inform evolutionary studies and the applied management of species.