Malachy Campbell, Alexandre Grondin, Harkamal Walia, Gota Morota
Elucidating genotype-by-environment interactions and partitioning its contribution to phenotypic variation remains a challenge for plant scientists. We propose a framework that utilizes genome-wide markers to model genotype-specific shoot growth trajectories as a function of time and soil water availability. A rice diversity panel was phenotyped daily for twenty-one days using an automated, high-throughput image-based, phenotyping platform that enabled estimation of daily shoot biomass and soil water content. Using these data, we modeled shoot growth as a function of time and soil water content, and were able to determine the time point where an inflection in the growth trajectory occurred. We found that larger, more vigorous plants exhibited an earlier repression in growth compared to smaller, slow-growing plants, indicating a trade-off between early vigor and tolerance to prolonged water deficits. Genomic inference for model parameters and time of inflection (TOI) identified several candidate genes. This study is the first to utilize a genome-enabled growth model to study drought responses in rice, and presents a new approach to jointly model dynamic morpho-physiological responses and environmental covariates.
This repo contains all the code and data used for the manuscript: "Leveraging genome-enabled growth models to study shoot growth responses to water deficit in rice (Oryza sativa)". All genotypic data has been previously published anc can be accessed at RiceDiversity.org. The folder RMarkdown contains all the R scripts used. Raw phenotpic data is in /phenotypes, and 'cleaned' phenotypic and genotypic data is in /Inputs.
Funding for this research was provided by the National Science Foundation (United States) through Award No. 1238125 to HW, and Award No. 1736192 to HW and GM.