Badji Arfang, Machida Lewis, Kwemoi Daniel Bomet, Kumi Frank, Okii Dennis, Mwila Natasha, Agbahoungba Symphorien, Ibanda Angele, Bararyenya Astere, Nghituwamhata Selma Ndapewa, Odong Thomas, Wasswa Peter, Otim Michael, Ochwo-Ssemakula Mildred, Talwana Herbert, Asea Godfrey, Kyamanywa Samuel, Rubaihayo Patrick
Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda.
Alliance Bioversity-CIAT, Africa-Office, Kampala P.O. Box 24384, Uganda.
Plants (Basel). 2020 Dec 24;10(1):29. doi: 10.3390/plants10010029.
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.
当针对基因组预测（GP）模型的预测准确性（PA）优化训练集（TS）大小及其与育种集（BS）的关系时，基因组选择（GS）可以加速品种改良。在一个热带玉米群体中，对十六种GP算法进行了运行，这些算法基于对秋粘虫（FAW）和玉米象（MW）抗性的表型最佳线性无偏预测器（BLUP）和估计器（BLUE）。对于MW抗性，群体的37%为TS，其余为BS，而对于FAW，设计了基于随机的训练集（RBTS）和基于系谱的训练集（PBTS）。对于MW抗性性状，使用BLUP获得的PA在0.66至0.82之间，对于FAW抗性，37%的RBTS的PA为0.694至0.714，85%的RBTS的PA为0.843至0.844，这些至少是BLUE获得的PA的两倍。对于PBTS，除了一个数据集外，FAW抗性PA通常高于RBTS。GP模型在各个性状上通常显示出相似的PA，而TS的指定是决定性的，因为对于RBTS，观察到TS大小与PA之间存在正相关（R = 0.92***），而对于PBTS，是负相关（R = 0.44**）。本研究率先在撒哈拉以南非洲将GS用于玉米抗虫害。
