Characterizing the Role of Environment on Phenotypic Traits using Topological Data Analysis

Abstract

Phenomics is an emerging area within modern biology, which uses high throughput phenotyping tools to capture multiple environment and phenotypic trait measurements, at a massive scale. Due to the relatively nascency of the field, current tools and techniques used for phenomics data analysis are still, at large, the same tools originally designed to decode genotype to phenotype association studies (referred to as Genome Wide Association Studies). However, one of the key contributors to phenotypes (along with genotypes) is the environment. Yet there are presently no tools that allow users to analyze and characterize the role of environment in phenotypic performance. Here, we present a new algorithmic framework to characterize the role of environment on phenotypic traits. Our framework is an application of the Topological Data Analysis (TDA), which is an emerging branch in computational mathematics that deals with shapes and structures of complex data. To the best of our knowledge, this effort represents the first application of topological data analysis on phenomics data.