Applications of Quantitative Genetics and Statistical Analyses in Sugarcane Breeding

Jean-Yves Hoarau,Xianming Wei,Angélique D’Hont,Thomas Dumont,Philip Jackson

doi:10.1007/s12355-021-01012-3

Abstract

Sugarcane breeding programs aim to deliver new high-yielding varieties, resistant to diseases and pests, which contribute to profitability and sustainability of cane industries. These programs generally mobilize significant experimental, technological and human resources on long-term basis. Their efficiency in terms of genetic gains per unit of cost and time and their ability to release new varieties rely on the development of many breeding applications based on quantitative genetics theory and on statistical analyses of numerous experimental data from selection schemes including DNA marker data developed for some genomic breeding applications. New methodological approaches and new technologies that might better guide and support breeding research in cultivars development programs are continually sought. This paper presents an overview of the main applications developed in statistical methodology in support of the efficiency of sugarcane breeding programs. For each type of application, its conceptual and methodological framework is presented. Implementation issues are reviewed as well as the main scientific and practical achievements so far obtained.

Highlights

Profitability and sustainability of sugarcane industries rely on several cornerstones including development of best agronomic practices and efficient breeding programs that regularly deliver improved cultivars
The present review aims to present some useful applications of quantitative genetics and statistical analyses that can support decisions affecting the efficiency of sugarcane breeding programs
This article provides an overall picture of applications of quantitative genetics and statistical analyses developed to support the efficiency of breeding programs dedicated to sugarcane industries

Summary

Introduction

Profitability and sustainability of sugarcane industries rely on several cornerstones including development of best agronomic practices and efficient breeding programs that regularly deliver improved cultivars. Despite many differences in details between selection programs of different countries such as population sizes and duration (Milligan 1994; Cox et al 2000; Scortecci et al 2012; Zhou 2013; Dumont et al 2019, 2021; Santchurn et al 2021; Cursi et al 2021), a sequence of selection trials typically follows the four typical successive stages of Fig. 1 This trial sequence is characterized by a gradually decreasing number of candidate lines tested in progressively more accurate trials (larger plot size more replicates and sites). These and some other basic concepts in statistical analysis of data in sugarcane breeding programs were presented in this Special Issue in the paper of Jackson (2021). Best Linear Unbiased Prediction An acronym for the statistical estimator of random effects in a LMM that has the lowest variance (Best) and (BLUP)

BLUP Methodology

Objective

Findings

Conclusions