Identification of QTL for Striga hermonthica Resistance Using Backcross Population Derived from a Cross between Oryza sativa (cv. Nipponbare) and O. rufipogon

Atera, Evans Atuti; Onyango, J. C.; Thanh, Pham Thien; Ishii, Takashige; Itoh, Kazuyuki

doi:10.5539/jas.v7n2p99

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…for Striga resistance in sorghum and rice (Atera et al, 2015;Yasir & Abdalla, 2013;Yohannes et al, 2015;Ali et al, 2016). Using genomic association wide (GWA), 24 SNPS markers associated with grain yield, Striga damage at 8 and 10 weeks after planting (WAP), ears per plant and ear aspect under Striga infestation were detected in early maturing maize inbred (Adewale et al, 2020).…”

Section: Marker-assisted Breeding For Striga Resistancementioning

confidence: 99%

Breeding maize (Zea mays) for Striga resistance: Past, current and prospects in sub‐saharan africa

et al. 2021

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Striga hermonthica, causes up to 100% yield loss in maize production in Sub-Saharan Africa. Developing Striga-resistant maize cultivars could be a major component of integrated Striga management strategies. This paper presents a comprehensive overview of maize breeding activities related to Striga resistance and its management.Scientific surveys have revealed that conventional breeding strategies have been used more than molecular breeding strategies in maize improvement for Striga resistance. Striga resistance genes are still under study in the International Institute for Tropical Agriculture (IITA) maize breeding programme. There is also a need to discover QTL and molecular markers associated with such genes to improve Striga resistance in maize. Marker Assistance Breeding is expected to increase maize breeding efficiency with complex traits such as resistance towards Striga because of the complex nature of the host-parasite relationship and its intersection with other environmental factors. Conventional alongside molecular tools and technical controls are promising methods to effectively assess Striga in Sub-Saharan Africa.

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Section: Marker-assisted Breeding For Striga Resistancementioning

confidence: 99%

Breeding maize (Zea mays) for Striga resistance: Past, current and prospects in sub‐saharan africa

et al. 2021

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“…Also, host plant resistance could be deployed as a vital component of an integrated control strategy for mitigating the menace of the parasitic weed. In Striga research, a maize plant is characterized as resistant when the parasite is unable to penetrate through the endodermis of the host plant, thus disallowing xylem-xylem connections for continuity after attachment of the plant to the host ( Atera et al, 2015 ), whereas tolerance refers to the extent to which the Striga damage effects on the maize plants are mitigated ( Rodenburg et al, 2017 ; Badu-Apraku et al, 2020b ).…”

Section: Introductionmentioning

confidence: 99%

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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Generation Means Analysis for Estimation of Genetic Parameters for Striga hermonthica Resistance in Maize (Zea mays L.)

Mbogo¹,

Dida²,

Owuor³

2015

JAS

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Striga hermonthica is a parasitic weed species that causes considerable yield loss in susceptible maize cultivars in western Kenya. A generation means analysis was conducted to estimate gene effects influencing resistance/tolerance to Striga hermonthica in maize cultivars adapted to western Kenya. A resistant inbred line developed by Maseno University, MSMP1/P2 was crossed to Striga susceptible inbred line 5057. Experimental units comprised of resistant parent P 1 , susceptible parent P 2 , their F 1 and F 2 generations, and BC 1 P 1 and BC 1 P 2 generations. The six generations were evaluated under Striga sick plots at Nyahera during the short rains of 2012 and long rains of 2013. Field evaluation for entries were grown in a randomized complete block design with three replicates. The results showed significant differences among generations for emerged Striga, Striga damage rating and maize growth including Plant and Ear height. Most of the additive, dominance, additive x additive, additive x dominance and dominance x dominance effects were significant indicating the importance of the additive, dominance and epistatic modes of gene actions in controlling emerged Striga, Striga damage rating, plant and ear height. Most components conferring Striga tolerance behaved in a qualitative manner. Dominance effects had higher mean values over additive effects. Significant dominant genetic effects (d) for Striga emergence and Striga damage rating suggested preponderance of non-additive genetic effects in the inheritance of Striga tolerance. The results also reveal the involvement of duplicate epistasis where the dominance estimate and dominance x dominance interaction had opposite signs. The presence of duplicate type of gene interaction confirms the importance of dominance gene effects.

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Identification of QTL for Striga hermonthica Resistance Using Backcross Population Derived from a Cross between Oryza sativa (cv. Nipponbare) and O. rufipogon

Cited by 3 publications

References 18 publications

Breeding maize (Zea mays) for Striga resistance: Past, current and prospects in sub‐saharan africa

Breeding maize (Zea mays) for Striga resistance: Past, current and prospects in sub‐saharan africa

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

Generation Means Analysis for Estimation of Genetic Parameters for Striga hermonthica Resistance in Maize (Zea mays L.)

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