Mapping of QTLs associated with recovery resistance to streak virus disease in maize

Ladejobi, Olufunmilayo; Salaudeen, Muhammadu Tajudeen; Kumar, P. Lava; Menkir, Abebe; Adesoye, Adenubi; Atiri, GI; Gedil, Melaku

doi:10.1016/j.aoas.2018.05.006

Cited by 8 publications

(4 citation statements)

References 24 publications

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“…However, 3 lines (2.2%) with the Msv1 resistant allele were susceptible, whereas 10 (55%) that did not carry the resistance allele for MSV resistance were found to be moderately resistant. This could arise from the inability of the SNPs to consistently detect and identify resistance alleles or from the presence of other QTLs contributing to the MSV resistance response [15,20].…”

Section: Msv Symptom Severity Scores and Relationship With Alleles Ofmentioning

confidence: 99%

Validation of Diagnostic Markers for Streak Virus Disease Resistance in Maize

et al. 2021

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Maize streak virus (MSV) is responsible for streak disease of maize and poses a serious threat to maize production in sub-Saharan Africa. Polygenic resistance to MSV has become an essential requirement in modern maize cultivars to mitigate yield losses. Many single nucleotide polymorphism (SNP) markers linked to putative MSV resistance loci have been identified for use in forward breeding. This study aimed to validate, using the high-throughput kompetitive allele specific PCR (KASP) assay, the diagnostic ability of the three SNP markers linked to the loci for the Msv1 resistance trait in 151 early generations inbred lines with diverse genetic backgrounds, together with nine MSV-resistant elite lines and a susceptible check (cv. Pool-16). The phenotypic responses were determined by MSV inoculation using viruliferous leafhoppers (Cicadulina triangular) under screenhouse conditions. Based on an established MSV disease rating system, the maize lines were categorized into resistant, moderately resistant, susceptible, and highly susceptible. The three SNPs associated with MSV resistance were detected in 133 lines, which were categorized as resistant (54), moderately resistant (76), and susceptible (1). The 18 early generation lines without these SNPs were classified as moderately resistant (10), susceptible (5), and highly susceptible (3). This study confirms the strong association of SNPs with MSV resistance and their usefulness for forward breeding in maize while emphasizing the need to identify additional markers to screen lines for MSV resistance without any ambiguity.

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Section: Msv Symptom Severity Scores and Relationship With Alleles Ofmentioning

confidence: 99%

Validation of Diagnostic Markers for Streak Virus Disease Resistance in Maize

et al. 2021

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“…The MIP team began searching for additional genes conditioning resistance to the disease. The use of QTL analysis in bi-parental mapping populations resulted in the identification of additional QTLs (Ladejobi et al, 2018), pointing to additional QTLs on a different chromosome. In addition, a different population was used to identify new loci underlying MSV resistance in maize using 93 F 2:3 families.…”

Section: Application Of Molecular Tools To Accelerate Genetic Gainmentioning

confidence: 99%

An Integrated Molecular and Conventional Breeding Scheme for Enhancing Genetic Gain in Maize in Africa

Getachew

Menkir

2019

Front. Plant Sci.

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Maize production in West and Central Africa (WCA) is constrained by a wide range of interacting stresses that keep productivity below potential yields. Among the many problems afflicting maize production in WCA, drought, foliar diseases, and parasitic weeds are the most critical. Several decades of efforts devoted to the genetic improvement of maize have resulted in remarkable genetic gain, leading to increased yields of maize on farmers’ fields. The revolution unfolding in the areas of genomics, bioinformatics, and phenomics is generating innovative tools, resources, and technologies for transforming crop breeding programs. It is envisaged that such tools will be integrated within maize breeding programs, thereby advancing these programs and addressing current and future challenges. Accordingly, the maize improvement program within International Institute of Tropical Agriculture (IITA) is undergoing a process of modernization through the introduction of innovative tools and new schemes that are expected to enhance genetic gains and impact on smallholder farmers in the region. Genomic tools enable genetic dissections of complex traits and promote an understanding of the physiological basis of key agronomic and nutritional quality traits. Marker-aided selection and genome-wide selection schemes are being implemented to accelerate genetic gain relating to yield, resilience, and nutritional quality. Therefore, strategies that effectively combine genotypic information with data from field phenotyping and laboratory-based analysis are currently being optimized. Molecular breeding, guided by methodically defined product profiles tailored to different agroecological zones and conditions of climate change, supported by state-of-the-art decision-making tools, is pivotal for the advancement of modern, genomics-aided maize improvement programs. Accelerated genetic gain, in turn, catalyzes a faster variety replacement rate. It is critical to forge and strengthen partnerships for enhancing the impacts of breeding products on farmers’ livelihood. IITA has well-established channels for delivering its research products/technologies to partner organizations for further testing, multiplication, and dissemination across various countries within the subregion. Capacity building of national agricultural research system (NARS) will facilitate the smooth transfer of technologies and best practices from IITA and its partners.

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“…With the development of sequencing technology, high-density SNPs are now used in constructing genetic maps for increasing precision ( Zhang et al, 2022 ). Several QTL studies have been conducted to understand the molecular genetics of complex traits under both biotic and abiotic stresses in maize, including drought ( Tuberosa et al, 2002 ; Almeida et al, 2013 ), low soil nitrogen ( Ribeiro et al, 2018 ), maize grey leaf spot disease ( Berger et al, 2014 ), Fusarium ear rot ( Maschietto et al, 2017 ), maize streak virus ( Ladejobi et al, 2018 ), and Striga parasitism ( Amusan et al, 2008 ; Badu-Apraku et al, 2020a ; Badu-Apraku et al, 2020b ). However, information is limited on the molecular genetics of Striga resistance.…”

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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Mapping of QTLs associated with recovery resistance to streak virus disease in maize

Cited by 8 publications

References 24 publications

Validation of Diagnostic Markers for Streak Virus Disease Resistance in Maize

Validation of Diagnostic Markers for Streak Virus Disease Resistance in Maize

An Integrated Molecular and Conventional Breeding Scheme for Enhancing Genetic Gain in Maize in Africa

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

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