A Bayesian random regression method using mixture priors for genome‐enabled analysis of time‐series high‐throughput phenotyping data. Issue 3 (29th July 2022)

• Record Type: Journal Article
• Authors: Qu, Jiayi; Morota, Gota; Cheng, Hao
• Is Part Of: œplant genome. Volume 15:Issue 3(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-07-29
• Subjects: Plant genomes -- Periodicals; Plant genome mapping -- Periodicals; 572.862
• DOI: 10.1002/tpg2.20228
• Languages: English
• ISSNs: 1940-3372

Abstract

Abstract: The recent advancement in image‐based phenotyping platforms enables the acquisition of large‐scale nondestructive crop phenotypes measured at frequent intervals. To further understand the underlying genetic basis over a physiological process and improve plant breeding programs, the question of how to efficiently utilize these time‐series measurements in genome‐enabled analysis including genomic prediction and genome‐wide association studies (GWASs) should be considered. In this paper, a Bayesian random regression model with mixture priors is developed to introduce more meaningful biological assumptions to the analysis of longitudinal traits. The mixture prior for marker effects in Bayes C π is implemented in our developed model (RR‐BayesC) for demonstration purpose. The estimation of single‐nucleotide polymorphism–specific effects that are related to the dynamic performance of crops and the accuracy of genomic prediction by RR‐BayesC were studied through both simulated and real rice (Oryza sativa L.) data. For genomic prediction, three predictive scenarios were studied. In the simulated study, RR‐BayesC showed a significantly higher prediction accuracy than that obtained by single‐trait analysis, especially for days when heritability were low. In real data analysis, RR‐BayesC showed relatively high prediction accuracy when forecast is required for phenotypes at later period (e.g., from 0.94 to 0.98 for lines with observations at an earlier period and from 0.65 to 0.67 for lines without any observations). For GWASs, inference of single markers and inference of genomic windows were conducted. In the simulated study, RR‐BayesC showed its promising ability to distinguish quantitative trait loci (QTL) that are invariant to temporal covariates and QTL that interact with time. An association study of real data was also presented to demonstrate the application of RR‐BayesC in real data analysis. In this paper, we develop a Bayesian random regression model that is able to incorporate mixture priors to marker effects and show improved performance of genomic prediction and GWASs for longitudinal data analysis based on both simulated and real data. The software tool JWAS offers routines to perform our proposed random regression analysis. Core Ideas: Large‐scale longitudinal data are obtained from high‐throughput phenotyping platforms. We developed a Bayesian random regression model with mixture priors to analyze such data. Our method is able to detect QTL that are invariant to or that interact with time. Our proposed method can forecast future phenotypes with improved prediction accuracy.