Identification of quantitative trait loci associated with maize resistance to bacterial leaf streak

Abstract

Bacterial leaf streak (BLS), a foliar disease of maize (Zea mays L.) caused by Xanthomonas vasicola pv. vasculorum, recently emerged in the Americas as a disease of major importance. Little is known about the disease cycle, and consequently, management is difficult. No chemical control is available. Host resistance will likely play a major role in controlling the disease, but to date, no data regarding the resistance of maize germplasm to X. vasicola pv. vasculorum have been published. The objective of this study was to examine the genetic architecture of resistance to BLS. We conducted quantitative trait locus (QTL) mapping for BLS resistance in three maize populations: the Z022 (B73 × Oh43 recombinant inbred line) nested association mapping (NAM) population, the Z023 (B73 × Oh7B recombinant inbred line) NAM population, and the DRIL78 (NC344 × Oh7B chromosome segment substitution line) population. A total of five QTL were detected across two of the mapping populations. Of the QTL detected, one conferred a moderate effect, whereas the others conferred small effects. We also examined the relationship between resistance to BLS and resistance to three foliar diseases of maize, which had been mapped previously. The only significant correlation we found for BLS was with northern corn leaf blight [caused by Exserohilum turcicum (Pass.) K. J. Leonard & Suggs] in one of the populations, although two of the five BLS regions were involved in resistance to other diseases. These data will be useful for developing maize varieties resistant to BLS to mitigate the impact of bacterial leaf streak on maize production.

Identification of Loci That Confer Resistance to Bacterial and Fungal Diseases of Maize

“…Disease resistance introgression line population DRIL78 is an ideal CSSL population for multiple disease evaluation, as the donor parent (NC344) is multiple disease resistant and the recurrent parent (Oh7B) is multiple disease susceptible ( Lopez-Zuniga et al 2019 ; Cooper et al 2019 ; Qiu et al 2020 ; Wisser et al 2011 ). The population was developed by a cross between NC344 and Oh7B, three generations of backcrosses, and four subsequent generations of self-pollinating via single-seed descent to obtain BC 3 F 4:5 lines ( Lopez-Zuniga et al 2019 ).…”

“…We calculated LSMeans for the BLS data based on the raw measurements from Qiu et al (2020) . The model included genotype as a fixed factor, and replication and block nested within replication as random factors.…”

“…Subsequent studies identified regions, and even genes, that confer resistance to the three most significant fungal foliar diseases – southern corn leaf blight (SCLB), northern corn leaf blight (NCLB), and gray leaf spot (GLS) ( Lopez-Zuniga et al 2019 ; Zwonitzer et al 2010 ; Belcher et al 2012 ; Yang et al 2017b ). Relatively few regions have been identified that confer resistance to both a fungal and a bacterial pathogen in maize ( Chung et al 2010 ; Chung et al 2011 ; Qiu et al 2020 ; Jamann et al 2014 ; Jamann et al 2016 ; Hu et al 2018 ). In this study, we focused on two bacterial diseases bacterial leaf streak (BLS) and Goss’s bacterial wilt and blight (GW), as well as three fungal diseases: SCLB, NCLB, and GLS.…”

“…We selected a DRIL population because it was developed to study multiple disease resistance ( Lopez-Zuniga et al 2019 ). Data for BLS ( Qiu et al 2020 ), SCLB, NCLB, and GLS ( Lopez-Zuniga et al 2019 ) were combined with the GW data to examine MDR. We evaluated the DRIL78 population, an ideal population for this study, as the donor line NC344 is resistant and the recurrent parent Oh7B is susceptible for all the diseases studied ( Cooper et al 2019 ; Qiu et al 2020 ; Lopez-Zuniga et al 2019 ).…”

“…The overall objective of this study was to compare the genomic basis of resistance to fungal and bacterial diseases in maize. Mapping was conducted for GW using phenotypic data collected in three environments and combined with previously published studies for BLS, NCLB, SCLB, and GLS ( Lopez-Zuniga et al 2019 ; Qiu et al 2020 ). Here, we: 1) identify novel QTL associated with GW through linkage mapping; 2) explore the relationship between the five diseases in this population; and 3) estimate the effect of potential MDR haplotypes on the five diseases.…”