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

Abstract

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.

Combining Ability and Classification of New Thirteen Yellow Maize Inbred Lines (Zea mays L.) Using Line X Tester Mating Design Across Three Locations

“…These results indicating that, the presence of genetic variation among the plant materials for these studied traits. Several researchers were in agreement with these results; Mohamed et al, (2020), Ibrahim et al, (2021), Raihan et al, (2021), Indu et al, (2022), andAbd El-Azeem et al, (2022), Aly (2013) and Badu Apraku et al, (2023). Line x testers analysis for the seven studied traits of 39 maize crosses combined through three locations are shown in Table 3.…”

Estimation of Genetic Parameters, Combining Ability and Heterotic Groups of New Yellow Maize Inbred Lines Using Line X Tester Model Across Two Locations

“…To classify the inbred lines into distinct heterotic groups for yield potential, we employed the Heterospecific Grouping Specific and Combining Ability (HSGCA) method of Fan et al (2008Fan et al ( , 2009. The HSGCA values were calculated following the approach described by both Fan et al (2009) and Badu-Apraku et al (2023).…”