Genome-wide association study of Striga resistance in early maturing white tropical maize inbred lines

Abstract

Background: Striga hermonthica (Benth.) parasitism militates against increased maize production and productivity in savannas of sub-Saharan Africa (SSA). Identification of Striga resistance genes is important in developing genotypes with durable resistance. So far, there is only one report on the existence of QTL for Striga resistance on chromosome 6 of maize. The objective of this study was to identify genomic regions significantly associated with grain yield and other agronomic traits under artificial Striga field infestation. A panel of 132 early-maturing maize inbreds were phenotyped for key agronomic traits under Striga-infested and Striga-free conditions. The inbred lines were also genotyped using 47,440 DArTseq markers from which 7224 markers were retained for population structure analysis and genome-wide association study (GWAS). Results: The inbred lines were grouped into two major clusters based on structure analysis as well as the neighborjoining hierarchical clustering. A total of 24 SNPs significantly 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. Under Striga-free conditions, 11 SNPs significantly associated with grain yield, number of ears per plant and ear aspect were identified. Three markers physically located close to the putative genes GRMZM2G164743 (bin 10.05), GRMZM2G060216 (bin 3.06) and GRMZM2G103085 (bin 5.07) were detected, linked to grain yield, Striga damage at 8 and 10 WAP and number of ears per plant under Striga infestation, explaining 9 to 42% of the phenotypic variance. Furthermore, the S9_154,978,426 locus on chromosome 9 was found at 2.61 Mb close to the ZmCCD1 gene known to be associated with the reduction of strigolactone production in the maize roots. Conclusions: Presented in this study is the first report of the identification of significant loci on chromosomes 9 and 10 of maize that are closely linked to ZmCCD1 and amt5 genes, respectively and may be related to plant defense mechanisms against Striga parasitism. After validation, the identified loci could be targets for breeders for marker-assisted selection (MAS) to accelerate genetic enhancement of maize for Striga resistance in the tropics, particularly in SSA, where the parasitic weed is endemic.

Genomic Analysis of Selected Maize Landraces from Sahel and Coastal West Africa Reveals Their Variability and Potential for Genetic Enhancement

“…These results agreed with previous findings that tropical maize germplasm is highly diverse with H e > 0.3 [37][38][39]. The mean PIC obtained in the present study, 0.29 using 5974 DArTseq SNPs for the 208 maize accessions was higher than the 0.19 and 0.26 reported for tropical early-maturing maize inbred lines using 15,047 [30] and 7224 SNPs for a sample size of 94 and 134, respectively [20,27]. The discrepancies between the results of our study and those of earlier researchers may be due to the use of different genetic materials, the sample sizes, and the number of SNPs used.…”

“…The results of the estimated diversity indices revealed ample genetic diversity within the maize panel indicated by average H e (0.36) and H o (0.5). The He obtained in this study was comparable to the 0.36 reported for provitamin A (PVA) quality protein maize (QPM) germplasm from IITA-MIP [21] but was higher than that reported for maize landraces from Eastern Africa (H e = 0.25), Western Africa (H e = 0.18), and Sahel Africa (H e = 0.24) [9] as well as tropical maize breeding populations (H e = 0.22) [27] including IITA early-maturing white inbred lines [20]. Characterization of the Burkinabe, Ghanaian, and Togolese maize pools showed different values for the estimated diversity indices.…”

“…The population structure of the maize panel was inferred using the Admixture model-based clustering algorithm implemented STRUCTURE 2.3.4 [26]. The adhoc number of clusters (k) was varied from 1 to 12, with 10,000 burn-in steps, followed by 10,000 Markov chain Monte Carlo simulations, as previously described [20,27]. For each k, ten independent iterations were implemented.…”

Identification of QTLs for grain yield and other traits in tropical maize under Striga infestation

“…Seven of the fourteen QTLs identified across environments had their marker intervals greater than 4cM from their linked markers, implying that it is necessary to further fine map these chromosomal regions to reduce the marker intervals [ 33 ]. The QTL qsd-3 and qsd-9 identified in the present study were found to be in the same region with those reported by Adewale et al [ 38 ] on chromosomes 3 and 9, respectively. In contrast, QTL identified in the present study for Striga resistance/tolerance traits are different from those earlier reported by Badu-Apraku et al [ 39 ] in an extra-early maturing yellow F 2:3 mapping population.…”

“…These two QTLs accounted for 55 per cent of the phenotypic variation (PV) with predominance of dominance genetic effects over additive genetic effects in the expression of the two Striga resistant QTLs. Similarly, Adewale et al [ 38 ] identified significant loci on chromosomes 9 and 10 of maize, closely linked to ZmCCD1 and amt5 genes, respectively which could be related to plant defense mechanisms against Striga parasitism. In another study, Badu-Apraku et al [ 39 ] identified twelve QTLs for four Striga resistance/tolerance adaptive traits in an F 2:3 mapping population involving the Striga resistant inbred line TZEEI 79 and the Striga susceptible inbred TZdEEI 11.…”