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Huestis, G.M

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Huestis
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G.M
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Huestis, G.M

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    Identification and fine mapping of a major QTL (qRtsc8-1) conferring resistance to maize tar spot complex and validation of production markers in breeding lines
    (Springer, 2022) Jiaojiao Ren; Penghao Wu; Huestis, G.M; Ao Zhang; Jingtao Qu; Yubo Liu; Hongjian Zheng; Alakonya, A.; Dhliwayo, T.; Olsen, M.; San Vicente Garcia, F.M.; Prasanna, B.M.; Jiafa Chen; Xuecai Zhang
    Publication
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    Molecular assisted breeding in maize
    (CIMMYT, 2017) Olsen, M.; Nair, S.K.; Gedil, M.; Xuecai Zhang; Huestis, G.M; Yunbi Xu; Gowda, M.; Beyene, Y.
    Publication
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    Genome-wide analysis of tar spot complex resistance in maize using genotyping-by-sequencing SNPs and whole-genome prediction
    (Crop Science Society of America, 2017) Shiliang Cao; Loladze, A.; Yibing Yuan; Yongsheng Wu; Ao Zhang; Jiafa Chen; Huestis, G.M; Jingsheng Cao; Chaikam, V.; Olsen, M.; Prasanna, B.M.; San Vicente Garcia, F.M.; Xuecai Zhang
    Tar spot complex (TSC) is one of the most destructive foliar diseases of maize (Zea mays L.) in tropical and subtropical areas of Central and South America, causing significant grain yield losses when weather conditions are conducive. To dissect the genetic architecture of TSC resistance in maize, association mapping, in conjunction with linkage mapping, was conducted on an association-mapping panel and three biparental doubled-haploid (DH) populations using genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs). Association mapping revealed four quantitative trait loci (QTL) on chromosome 2, 3, 7, and 8. All the QTL, except for the one on chromosome 3, were further validated by linkage mapping in different genetic backgrounds. Additional QTL were identified by linkage mapping alone. A major QTL located on bin 8.03 was consistently detected with the largest phenotypic explained variation: 13% in association-mapping analysis and 13.18 to 43.31% in linkage-mapping analysis. These results indicated that TSC resistance in maize was controlled by a major QTL located on bin 8.03 and several minor QTL with smaller effects on other chromosomes. Genomic prediction results showed moderate-to-high prediction accuracies in different populations using various training population sizes and marker densities. Prediction accuracy of TSC resistance was >0.50 when half of the population was included into the training set and 500 to 1,000 SNPs were used for prediction. Information obtained from this study can be used for developing functional molecular markers for marker-assisted selection (MAS) and for implementing genomic selection (GS) to improve TSC resistance in tropical maize.
    Publication
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    Quality Assurance/ Quality Control (QA/QC) in maize breeding and seed production: theory and practice
    (CIMMYT, 2017) Gowda, M.; Regasa, M.W.; Nair, S.K.; Palacios-Rojas, N.; Huestis, G.M; Prasanna, B.M.
    This technical manual introduces the theory and practice of Quality Assurance/Quality Control (QA/QC) of seed quality in maize breeding and seed production. It focuses mainly on the genetic quality related issues of the improved maize seed. The seed quality program typically involves Quality Assurance (QA) which focuses on prevention of low-quality or genetically impure seed to be mixed with high-quality seed, while Quality Control (QC) in seed production chain ensures identification and correction of possible errors or mixtures that might have slipped through QA protocols. The manual outlines in detail the advantages of undertaking QA/QC in maize breeding and seed production by complementing the morphology-based testing (grow-out test) with the molecular markers-based analysis, especially in terms of testing for genetic purity and identity, parent-offspring test, and trait-specific testing using breeder-ready molecular markers. It also highlights the procedure for effectively monitoring the kernel-based nutritional quality traits (e.g., QPM and provitamin A) in maize breeding and seed production.
    Publication
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    Fine mapping a tarspot complex resistance QTL in maize
    (CIMMYT, 2016) Jiafa Chen; Zhang, X.; Hearne, S.; Mahuku, G.; Prasanna, B.M.; Huestis, G.M
    Publication