Introducing CHiDO a No Code Genomic Prediction Software implementation for the Characterization & Integration of Driven Omics

Abstract

Climate change represents a significant challenge to global food security by altering environmental conditions critical to crop growth. Plant breeders can play a key role in mitigating these challenges by developing more resilient crop varieties; however, these efforts require significant investments in resources and time. In response, it is imperative to use current technologies that assimilate large biological and environmental datasets into predictive models to accelerate the research, development, and release of new improved varieties. Leveraging large and diverse data sets can improve the characterization of phenotypic responses due to environmental stimuli and genomic pulses. A better characterization of these signals holds the potential to enhance our ability to predict trait performance under changes in weather and/or soil conditions with high precision. This paper introduces CHiDO, an easy-to-use, no-code platform designed to integrate diverse omics datasets and effectively model their interactions. With its flexibility to integrate and process data sets, CHiDOs intuitive interface allows users to explore historical data, formulate hypotheses, and optimize data collection strategies for future scenarios. The platforms mission emphasizes global accessibility, democratizing statistical solutions for situations where professional ability in data processing and data analysis is not available. Core ideasO_LIThe authors developed CHiDO, a platform for breeders to build predictive models integrating multi-omics data. C_LIO_LICHiDO is a no-code tool that leverages the reaction norm model proposed by Jarquin et al. (2014). C_LIO_LIThe platform aims to increase access to predictive analytics lowering relevant technical and financial barriers. C_LI