Factors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm and Weevils

Arfang Badji,Selma Ndapewa Nghituwamhata,Daniel Bomet Kwemoi,Peter Wasswa,Astere Bararyenya,Natasha Mwila,Lewis Machida,Frank Kumi,Dennis Okii,Thomas Odong,Michael Otim,Angele Ibanda,Godfrey Asea,Symphorien Agbahoungba,Patrick Rubaihayo,Mildred Ochwo-Ssemakula,Samuel Kyamanywa,Herbert Talwana

doi:10.3390/plants10010029

Abstract

Genomic selection (GS) can accelerate variety improvement when training set (TS) size and its relationship with the breeding set (BS) are optimized for prediction accuracies (PAs) of genomic prediction (GP) models. Sixteen GP algorithms were run on phenotypic best linear unbiased predictors (BLUPs) and estimators (BLUEs) of resistance to both fall armyworm (FAW) and maize weevil (MW) in a tropical maize panel. For MW resistance, 37% of the panel was the TS, and the BS was the remainder, whilst for FAW, random-based training sets (RBTS) and pedigree-based training sets (PBTSs) were designed. PAs achieved with BLUPs varied from 0.66 to 0.82 for MW-resistance traits, and for FAW resistance, 0.694 to 0.714 for RBTS of 37%, and 0.843 to 0.844 for RBTS of 85%, and these were at least two-fold those from BLUEs. For PBTS, FAW resistance PAs were generally higher than those for RBTS, except for one dataset. GP models generally showed similar PAs across individual traits whilst the TS designation was determinant, since a positive correlation (R = 0.92***) between TS size and PAs was observed for RBTS, and for the PBTS, it was negative (R = 0.44**). This study pioneered the use of GS for maize resistance to insect pests in sub-Saharan Africa.

Highlights

Insect damage on maize plants and stored grains potentially impedes food security in Africa [1,2,3]
The panel consisted of 71 inbred lines developed for various purposes at National Crop Resources Research Institute (NaCRRI); 28 and five stem borer (SB)-resistant inbred lines from CIMMYT [6,13,14] and Institute of Tropical Agriculture (IITA), respectively; 19 storage pest (SP)-resistant inbred lines [7,8]; and a doubled haploid (DH) panel of 235 lines developed at CIMMYT using six parents—three of which were stem borer-resistant, one was a storage pest-resistant inbred line, and two were CML elite lines (Supplementary Materials Table S1)
Higher prediction accuracies (PAs) Achieved for fall armyworm (FAW) and maize weevil (MW)-Resistance Traits with best linear unbiased predictors (BLUPs) when Compared to best linear unbiased estimators (BLUEs) across genomic prediction (GP) Algorithms. Both genotypic BLUEs and BLUPs for resistance to FAW and MW traits such as affected kernels (AK), adult progeny emergence (AP), and grain weight loss (GWL) were used in GPs

Summary

Introduction

Insect damage on maize plants and stored grains potentially impedes food security in Africa [1,2,3]. The fall armyworm (FAW) and stem borers in the field, and the maize weevils (MWs) in storage facilities, are some of the most devastating insect pests on the continent. These insect pests cause yield losses ranging from 10–90% leading to loss of grain marketability, and consumer health concerns due to the possible contamination of the grain with mycotoxins, such as aflatoxins [3,4,5,6]. Several Africa-adapted maize lines were developed and successfully tested for resistance to MW damage on grains [7,8,9,10,11,12]. Efforts to develop FAW resistant varieties are underway at several institutions, including CIMMYT, published reports of FAW resistant varieties are not yet available [18,19]

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