Genome-wide association studies of <i>Striga</i> resistance in extra-early maturing quality protein maize inbred lines

Okunlola, Gbemisola; Badu‐Apraku, B.; Ariyo, O. J.; Agre, Paterne A.; Offernedo, Queen; Ayo-Vaughan, M. A.

doi:10.1093/g3journal/jkac237

Cited by 8 publications

(7 citation statements)

References 61 publications

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“…The authors identified significant SNP markers S5_70442824 and S2_188120710 to be associated with Striga damage ratings as well as S2_135038935 and S2_208978140 associated with number of emerged Striga plants in a genome-wide association study to detect SNP markers linked with Striga resistance traits in late maturing maize germplasm. Similarly, the QTL qGY -8 has been previously reported by Badu-Apraku et al (2020a) ; qSD -10 by Adewale et al (2020) and Okunlola et al (2022) . Six major effect QTLs qGY 4, qGY 5, qSD 5, qSC 2.2, qsc7.1 and qsc9 were identified on chromosomes 2, 4, 5, 7, and 9, explaining phenotypic variance varying from 10.2% to 14.4%.…”

Section: Discussionsupporting

confidence: 74%

Mapping quantitative trait loci and predicting candidate genes for Striga resistance in maize using resistance donor line derived from Zea diploperennis

et al. 2023

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The parasitic weed, Striga is a major biological constraint to cereal production in sub-Saharan Africa (SSA) and threatens food and nutrition security. Two hundred and twenty-three (223) F2:3 mapping population involving individuals derived from TZdEI 352 x TZEI 916 were phenotyped for four Striga-adaptive traits and genotyped using the Diversity Arrays Technology (DArT) to determine the genomic regions responsible for Striga resistance in maize. After removing distorted SNP markers, a genetic linkage map was constructed using 1,918 DArTseq markers which covered 2092.1 cM. Using the inclusive composite interval mapping method in IciMapping, twenty-three QTLs influencing Striga resistance traits were identified across four Striga-infested environments with five stable QTLs (qGY4, qSC2.1, qSC2.2, qSC5, and qSC6) detected in more than one environment. The variations explained by the QTLs ranged from 4.1% (qSD2.3) to 14.4% (qSC7.1). Six QTLs each with significant additive × environment interactions were also identified for grain yield and Striga damage. Gene annotation revealed candidate genes underlying the QTLs, including the gene models GRMZM2G077002 and GRMZM2G404973 which encode the GATA transcription factors, GRMZM2G178998 and GRMZM2G134073 encoding the NAC transcription factors, GRMZM2G053868 and GRMZM2G157068 which encode the nitrate transporter protein and GRMZM2G371033 encoding the SBP-transcription factor. These candidate genes play crucial roles in plant growth and developmental processes and defense functions. This study provides further insights into the genetic mechanisms of resistance to Striga parasitism in maize. The QTL detected in more than one environment would be useful for further fine-mapping and marker-assisted selection for the development of Striga resistant and high-yielding maize cultivars.

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Section: Discussionsupporting

confidence: 74%

Mapping quantitative trait loci and predicting candidate genes for Striga resistance in maize using resistance donor line derived from Zea diploperennis

et al. 2023

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“…Few studies have reported on QTLs and genes linked to Striga resistance in maize. Using GWAS, Okunlola et al. (2023) identified 22 SNP markers significantly associated with GY and Striga damage rating at 10 weeks after planting, the number of emerged Striga plants at 8 and 10 weeks after planting, and ear aspect.…”

Section: Genomic-assisted Techniques To Accelerate Striga ...mentioning

confidence: 99%

Genetic resources and breeding of maize for Striga resistance: a review

et al. 2023

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The potential yield of maize (Zea mays L.) and other major crops is curtailed by several biotic, abiotic, and socio-economic constraints. Parasitic weeds, Striga spp., are major constraints to cereal and legume crop production in sub-Saharan Africa (SSA). Yield losses reaching 100% are reported in maize under severe Striga infestation. Breeding for Striga resistance has been shown to be the most economical, feasible, and sustainable approach for resource-poor farmers and for being environmentally friendly. Knowledge of the genetic and genomic resources and components of Striga resistance is vital to guide genetic analysis and precision breeding of maize varieties with desirable product profiles under Striga infestation. This review aims to present the genetic and genomic resources, research progress, and opportunities in the genetic analysis of Striga resistance and yield components in maize for breeding. The paper outlines the vital genetic resources of maize for Striga resistance, including landraces, wild relatives, mutants, and synthetic varieties, followed by breeding technologies and genomic resources. Integrating conventional breeding, mutation breeding, and genomic-assisted breeding [i.e., marker-assisted selection, quantitative trait loci (QTL) analysis, next-generation sequencing, and genome editing] will enhance genetic gains in Striga resistance breeding programs. This review may guide new variety designs for Striga-resistance and desirable product profiles in maize.

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“…in maize and sorghum, most association mapping studies were based on population sizes not very far from our population size, e.g. n=132 ( Adewale et al., 2020 ), n=150 ( Stanley et al., 2021 ), n=169 ( Okunola et al., 2022 ), or n=173 (Kavuloko et al., 2021), in most cases using a single Striga population. Other studies used larger population sizes, e.g.…”

Section: Discussionmentioning

confidence: 99%

Association mapping for broomrape resistance in sunflower

et al. 2023

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IntroductionSunflower breeding for resistance to the parasitic plant sunflower broomrape (Orobanche cumana Wallr.) requires the identification of novel resistance genes. In this research, we conducted a genome-wide association study (GWAS) to identify QTLs associated with broomrape resistance.MethodsThe marker-trait associations were examined across a germplasm set composed of 104 sunflower accessions. They were genotyped with a 600k AXIOM® genome-wide array and evaluated for resistance to three populations of the parasite with varying levels of virulence (races EFR, FGV, and GTK) in two environments.Results and DiscussionThe analysis of the genetic structure of the germplasm set revealed the presence of two main groups. The application of optimized treatments based on the general linear model (GLM) and the mixed linear model (MLM) allowed the detection of 14 SNP markers significantly associated with broomrape resistance. The highest number of marker-trait associations were identified on chromosome 3, clustered in two different genomic regions of this chromosome. Other associations were identified on chromosomes 5, 10, 13, and 16. Candidate genes for the main genomic regions associated with broomrape resistance were studied and discussed. Particularly, two significant SNPs on chromosome 3 associated with races EFR and FGV were found at two tightly linked SWEET sugar transporter genes. The results of this study have confirmed the role of some QTL on resistance to sunflower broomrape and have revealed new ones that may play an important role in the development of durable resistance to this parasitic weed in sunflower.

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Genome-wide association studies of Striga resistance in extra-early maturing quality protein maize inbred lines

Cited by 8 publications

References 61 publications

Mapping quantitative trait loci and predicting candidate genes for Striga resistance in maize using resistance donor line derived from Zea diploperennis

Mapping quantitative trait loci and predicting candidate genes for Striga resistance in maize using resistance donor line derived from Zea diploperennis

Genetic resources and breeding of maize for Striga resistance: a review

Association mapping for broomrape resistance in sunflower

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