Classification and Visualization of Genotype x Phenotype Interactions in Biomass Sorghum

TL;DR

This paper presents a deep learning-based method to classify and visualize how specific genetic variations influence plant phenotypes in biomass sorghum using image data.

Contribution

It introduces a pipeline combining CNN classification and visualization to explore genotype-phenotype relationships in plants.

Findings

Deep CNNs effectively classify SNP-related plant images.

Visualization highlights key image features linked to genetic variations.

Approach demonstrates potential for uncovering genotype-phenotype interactions.

Abstract

We introduce a simple approach to understanding the relationship between single nucleotide polymorphisms (SNPs), or groups of related SNPs, and the phenotypes they control. The pipeline involves training deep convolutional neural networks (CNNs) to differentiate between images of plants with reference and alternate versions of various SNPs, and then using visualization approaches to highlight what the classification networks key on. We demonstrate the capacity of deep CNNs at performing this classification task, and show the utility of these visualizations on RGB imagery of biomass sorghum captured by the TERRA-REF gantry. We focus on several different genetic markers with known phenotypic expression, and discuss the possibilities of using this approach to uncover genotype x phenotype relationships.