Quantile Regression Applied to Genome-Enabled Prediction of Traits Related to Flowering Time in the Common Bean

Ana Carolina Nascimento,Cosme Cruz,Leiri Barili,Moyses Nascimento,Naine Vale,José Eustáquio Carneiro,Nick Serão,Pedro Crescencio Carneiro,Fabyano Silva,Camila Azevedo

doi:10.3390/agronomy9120796

Ana Carolina Nascimento, Cosme Cruz + Show 8 more

Open Access

https://doi.org/10.3390/agronomy9120796

Copy DOI

Journal: Agronomy	Publication Date: Nov 23, 2019
Citations: 7	License type: CC BY 4.0

Affiliation: Universidade Federal de Viçosa, Iowa State University

Abstract

Genomic selection (GS) aims to incorporate molecular information directly into the prediction of individual genetic merit. Regularized quantile regression (RQR) can be used to fit models for all portions of a probability distribution of the trait, enabling the conditional quantile that “best” represents the functional relationship between dependent and independent variables to be chosen. The objective of this study was to predict the individual genetic merits of the traits associated with flowering time (DFF—days to first flower; DTF—days to flower) in the common bean using RQR and to compare the predictive abilities obtained from Random Regression Best Linear Unbiased Predictor (RR-BLUP), Bayesian LASSO (BLASSO), BayesB, and RQR for predicting the genetic merit. GS was performed using 80 genotypes of common beans genotyped for 380 single nucleotide polymorphism (SNP) markers. Considering the “best” RQR fit models (RQR0.3 for DFF, and RQR0.2 for DTF), the gains in predictive ability in relation to BLASSO, BayesB, and RR-BLUP were 18.75%, 22.58%, and 15.15% for DFF, respectively, and 15.20%, 24.65%, and 12.55% for DTF, respectively. The potential cultivars selected, considering the RQR “best” models, were among the 5% of cultivars with the lowest genomic estimated breeding value (GEBV) for the DFF and DTF traits—the IAC Imperador, IPR Colibri, Capixaba Precoce, and IPR Andorinha were included in the list of early cycle cultivars.

Highlights

Meuwissen et al [1] introduced genomic selection (GS) as a means of incorporating molecular information directly into the prediction of individual genetic merit
We aimed to (1) predict the individual genetic merits of the traits associated with the flowering time (DFF and days to flowering (DTF)) in the common bean using regularized quantile regression (RQR), and (2) to compare the predictive abilities obtained for RQR, Random Regression Best Linear Unbiased Predictor (RR-BLUP), BayesB [1], and Bayesian LASSO (BLASSO) [16] for predicting genetic merit
We predicted the individual genetic merits of the traits associated with the flowering time (DFF and DTF) in the common bean using RQR models

Summary

Introduction

Meuwissen et al [1] introduced genomic selection (GS) as a means of incorporating molecular information directly into the prediction of individual genetic merit. Non-normal distributions can be found for some traits in the fields of plant and animal breeding, for example, traits that measure the time until the occurrence of specific events (such as flowering and parity [6,7]) and hormone concentrations [8]. Another issue is related to the residual heteroscedastic variance. This tends to be neglected by existing methods, which focus on the mean of the conditional distribution, E(X|Y). In the presence of heteroscedastic variance, which is frequently observed in high dimensional data sets such as those found in GS studies, the sets of relevant covariates may differ when the different segments of conditional distribution are considered [9]

Objectives

Methods

Results

Discussion

Conclusion