Genomic prediction and quantitative trait locus discovery in a cassava training population constructed from multiple breeding stages

Mohamed Somo,Chiedozie Egesi,Ismail Y Rabbi,Alfred Ozimati,Heneriko Kulembeka,Jean‐Luc Jannink,Marnin D Wolfe,Kiddo Mtunda,Kasele Salum,Roberto Lozano,Robert Kawuki,Emmanuel Mrema

doi:10.1002/csc2.20003

Abstract

AbstractAssembly of a training population (TP) is an important component of effective genomic selection‐based breeding programs. In this study, we examined the power of diverse germplasm assembled from two cassava (Manihot esculenta Crantz) breeding programs in Tanzania at different breeding stages to predict traits and discover quantitative trait loci (QTL). This is the first genomic selection and genome‐wide association study (GWAS) on Tanzanian cassava data. We detected QTL associated with cassava mosaic disease (CMD) resistance on chromosomes 12 and 16; QTL conferring resistance to cassava brown streak disease (CBSD) on chromosomes 9 and 11; and QTL on chromosomes 2, 3, 8, and 10 associated with resistance to CBSD for root necrosis. We detected a QTL on chromosome 4 and two QTL on chromosome 12 conferring dual resistance to CMD and CBSD. The use of clones in the same stage to construct TPs provided higher trait prediction accuracy than TPs with a mixture of clones from multiple breeding stages. Moreover, clones in the early breeding stage provided more reliable trait prediction accuracy and are better candidates for constructing a TP. Although larger TP sizes have been associated with improved accuracy, in this study, adding clones from Kibaha to those from Ukiriguru and vice versa did not improve the prediction accuracy of either population. Including the Ugandan TP in either population did not improve trait prediction accuracy. This study applied genomic prediction to understand the implications of constructing TP from clones at different breeding stages pooled from different locations on trait accuracy.

Highlights

Cassava is an important source of dietary calories for millions of people in tropical regions (Howeler, Lutaladio, & Thomas, 2013; Salvador, Steenkamp, McCrindle, & Ethelwyn, 2014)
The Kibaha clones were at the preliminary (PYTKIB) and advanced yield trial (AYTKIB) breeding stages, whereas those from Ukiriguru were at the clonal evaluation trial stage (CETUKG) and the preliminary yield trial stage (PYTUKG)
Genomic prediction and selection have been touted as tools that could greatly modernize plant breeding and accelerate genetic gain

Summary

Introduction

Cassava is an important source of dietary calories for millions of people in tropical regions (Howeler, Lutaladio, & Thomas, 2013; Salvador, Steenkamp, McCrindle, & Ethelwyn, 2014). Cassava breeders have used phenotypes for selection but this strategy has not always generated adequate genetic gain for yield, especially in Africa (Ceballos, Kulakow, & Hershey, 2012). The use of marker-assisted selection for complex traits in cassava has remained largely ineffective because of multiple minor loci, which are difficult to detect and deploy (Ceballos et al, 2012). The adoption of GS is expected to increase the rate of genetic gain and reduce selection cycle time. Recent implementation of GS in three breeding programs in Africa have improved trait prediction accuracy. Wolfe et al (2017) reported a prediction accuracy increase of 57% for CMD in a Nigerian cassava population. Kayondo et al (2018) reported improvements in prediction accuracy of 0.42 for both CBSD severity in leaves and roots in two Ugandan cassava populations

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Results

Conclusion