A Meta-Analysis of Quantitative Trait Loci Associated with Multiple Disease Resistance in Rice (Oryza sativa L.)

Rice blast, sheath blight and bacterial leaf blight are major rice diseases found worldwide. The development of resistant cultivars is generally perceived as the most effective way to combat these diseases. Plant disease resistance is a polygenic trait where a combinatorial effect of major and minor genes affects this trait. To locate the source of this trait, various quantitative trait loci (QTL) mapping studies have been performed in the past two decades. However, investigating the congruency between the reported QTL is a daunting task due to the heterogeneity amongst the QTLs studied. Hence, the aim of our study is to integrate the reported QTLs for resistance against rice blast, sheath blight and bacterial leaf blight and objectively analyze and consolidate the location of QTL clusters in the chromosomes, reducing the QTL intervals and thus identifying candidate genes within the selected meta-QTL. A total of twenty-seven studies for resistance QTLs to rice blast (8), sheath blight (15) and bacterial leaf blight (4) was compiled for QTL projection and analyses. Cumulatively, 333 QTLs associated with rice blast (114), sheath blight (151) and bacterial leaf blight (68) resistance were compiled, where 303 QTLs could be projected onto a consensus map saturated with 7633 loci. Meta-QTL analysis on 294 QTLs yielded 48 meta-QTLs, where QTLs with membership probability lower than 60% were excluded, reducing the number of QTLs within the meta-QTL to 274. Further, three meta-QTL regions (MQTL2.5, MQTL8.1 and MQTL9.1) were selected for functional analysis on the basis that MQTL2.5 harbors the highest number of QTLs; meanwhile, MQTL8.1 and MQTL9.1 have QTLs associated with all three diseases mentioned above. The functional analysis allows for determination of enriched gene ontology and resistance gene analogs (RGAs) and other defense-related genes. To summarize, MQTL2.5, MQTL8.1 and MQTL9.1 have a considerable number of R-genes that account for 10.21%, 4.08% and 6.42% of the total genes found in these meta-QTLs, respectively. Defense genes constitute around 3.70%, 8.16% and 6.42% of the total number of genes in MQTL2.5, MQTL8.1 and MQTL9.1, respectively. This frequency is higher than the total frequency of defense genes in the rice genome, which is 0.0096% (167 defense genes/17,272 total genes). The integration of the QTLs facilitates the identification of QTL hotspots for rice blast, sheath blight and bacterial blight resistance with reduced intervals, which helps to reduce linkage drag in breeding. The candidate genes within the promising regions could be utilized for improvement through genetical engineering.

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Mapping Resistance Quantitative Trait Loci for Three Foliar Diseases in a Maize Recombinant Inbred Line Population—Evidence for Multiple Disease Resistance?

Phytopathology ◽

10.1094/phyto-100-1-0072 ◽

2010 ◽

Vol 100 (1) ◽

pp. 72-79 ◽

Cited By ~ 57

Author(s):

John C. Zwonitzer ◽

Nathan D. Coles ◽

Matthew D. Krakowsky ◽

Consuelo Arellano ◽

James B. Holland ◽

...

Keyword(s):

Disease Resistance ◽

Quantitative Trait Loci ◽

Inbred Line ◽

Recombinant Inbred Line ◽

Quantitative Trait ◽

Recombinant Inbred ◽

Leaf Blight ◽

Foliar Diseases ◽

Multiple Disease Resistance ◽

Trait Loci

Southern leaf blight (SLB), gray leaf spot (GLS), and northern leaf blight (NLB) are all important foliar diseases impacting maize production. The objectives of this study were to identify quantitative trait loci (QTL) for resistance to these diseases in a maize recombinant inbred line (RIL) population derived from a cross between maize lines Ki14 and B73, and to evaluate the evidence for the presence genes or loci conferring multiple disease resistance (MDR). Each disease was scored in multiple separate trials. Highly significant correlations between the resistances and the three diseases were found. The highest correlation was identified between SLB and GLS resistance (r = 0.62). Correlations between resistance to each of the diseases and time to flowering were also highly significant. Nine, eight, and six QTL were identified for SLB, GLS, and NLB resistance, respectively. QTL for all three diseases colocalized in bin 1.06, while QTL colocalizing for two of the three diseases were identified in bins 1.08 to 1.09, 2.02/2.03, 3.04/3.05, 8.05, and 10.05. QTL for time to flowering were also identified at four of these six loci (bins 1.06, 3.04/3.05, 8.05, and 10.05). No disease resistance QTL was identified at the largest-effect QTL for flowering time in bin 10.03.

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Maize Introgression Library Provides Evidence for the Involvement of liguleless1 in Resistance to Northern Leaf Blight

G3 Genes|Genome|Genetics ◽

10.1534/g3.120.401500 ◽

2020 ◽

Vol 10 (10) ◽

pp. 3611-3622

Author(s):

Judith M. Kolkman ◽

Josh Strable ◽

Kate Harline ◽

Dallas E. Kroon ◽

Tyr Wiesner-Hanks ◽

...

Keyword(s):

Disease Resistance ◽

Quantitative Trait Loci ◽

Quantitative Trait ◽

Leaf Blight ◽

Near Isogenic Lines ◽

Northern Leaf Blight ◽

Leaf Architecture ◽

Isogenic Lines ◽

Trait Loci ◽

Introgression Library

Plant disease resistance is largely governed by complex genetic architecture. In maize, few disease resistance loci have been characterized. Near-isogenic lines are a powerful genetic tool to dissect quantitative trait loci. We analyzed an introgression library of maize (Zea mays) near-isogenic lines, termed a nested near-isogenic line library for resistance to northern leaf blight caused by the fungal pathogen Setosphaeria turcica. The population was comprised of 412 BC5F4 near-isogenic lines that originated from 18 diverse donor parents and a common recurrent parent, B73. Single nucleotide polymorphisms identified through genotyping by sequencing were used to define introgressions and for association analysis. Near-isogenic lines that conferred resistance and susceptibility to northern leaf blight were comprised of introgressions that overlapped known northern leaf blight quantitative trait loci. Genome-wide association analysis and stepwise regression further resolved five quantitative trait loci regions, and implicated several candidate genes, including Liguleless1, a key determinant of leaf architecture in cereals. Two independently-derived mutant alleles of liguleless1 inoculated with S. turcica showed enhanced susceptibility to northern leaf blight. In the maize nested association mapping population, leaf angle was positively correlated with resistance to northern leaf blight in five recombinant inbred line populations, and negatively correlated with northern leaf blight in four recombinant inbred line populations. This study demonstrates the power of an introgression library combined with high density marker coverage to resolve quantitative trait loci. Furthermore, the role of liguleless1 in leaf architecture and in resistance to northern leaf blight has important applications in crop improvement.

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