Combining powers of linkage and association mapping for precise dissection of QTL controlling resistance to gray leaf spot disease in ... View Full Text


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Article Info

DATE

2015-11-10

AUTHORS

Jafar Mammadov, Xiaochun Sun, Yanxin Gao, Cherie Ochsenfeld, Erica Bakker, Ruihua Ren, Jonathan Flora, Xiujuan Wang, Siva Kumpatla, David Meyer, Steve Thompson

ABSTRACT

BackgroundGray Leaf Spot (GLS causal agents Cercospora zeae-maydis and Cercospora zeina) is one of the most important foliar diseases of maize in all areas where the crop is being cultivated. Although in the USA the situation with GLS severity is not as critical as in sub-Saharan Africa or Brazil, the evidence of climate change, increasing corn monoculture as well as the narrow genetic base of North American resistant germplasm can turn the disease into a serious threat to US corn production. The development of GLS resistant cultivars is one way to control the disease. In this study we combined the high QTL detection power of genetic linkage mapping with the high resolution power of genome-wide association study (GWAS) to precisely dissect QTL controlling GLS resistance and identify closely linked molecular markers for robust marker-assisted selection and trait introgression.ResultsUsing genetic linkage analysis with a small bi-parental mapping population, we identified four GLS resistance QTL on chromosomes 1, 6, 7, and 8, which were validated by GWAS. GWAS enabled us to dramatically increase the resolution within the confidence intervals of the above-mentioned QTL. Particularly, GWAS revealed that QTLGLSchr8, detected by genetic linkage mapping as a locus with major effect, was likely represented by two QTL with smaller effects. Conducted in parallel, GWAS of days-to-silking demonstrated the co-localization of flowering time QTL with GLS resistance QTL on chromosome 7 indicating that either QTLGLSchr7 is a flowering time QTL or it is a GLS resistance QTL that co-segregates with the latter. As a result, this genetic linkage – GWAS hybrid mapping system enabled us to identify one novel GLS resistance QTL (QTLGLSchr8a) and confirm with more refined positions four more previously mapped QTL (QTLGLSchr1, QTLGLSchr6, QTLGLSchr7, and QTLGLSchr8b). Through the novel Single Donor vs. Elite Panel method we were able to identify within QTL confidence intervals SNP markers that would be suitable for marker-assisted selection of gray leaf spot resistant genotypes containing the above-mentioned GLS resistance QTL.ConclusionThe application of a genetic linkage – GWAS hybrid mapping system enabled us to dramatically increase the resolution within the confidence interval of GLS resistance QTL by-passing labor- and time-intensive fine mapping. This method appears to have a great potential to accelerate the pace of QTL mapping projects. It is universal and can be used in the QTL mapping projects in any crops. More... »

PAGES

916

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12864-015-2171-3

DOI

http://dx.doi.org/10.1186/s12864-015-2171-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1041954401

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/26555731


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28 schema:description BackgroundGray Leaf Spot (GLS causal agents Cercospora zeae-maydis and Cercospora zeina) is one of the most important foliar diseases of maize in all areas where the crop is being cultivated. Although in the USA the situation with GLS severity is not as critical as in sub-Saharan Africa or Brazil, the evidence of climate change, increasing corn monoculture as well as the narrow genetic base of North American resistant germplasm can turn the disease into a serious threat to US corn production. The development of GLS resistant cultivars is one way to control the disease. In this study we combined the high QTL detection power of genetic linkage mapping with the high resolution power of genome-wide association study (GWAS) to precisely dissect QTL controlling GLS resistance and identify closely linked molecular markers for robust marker-assisted selection and trait introgression.ResultsUsing genetic linkage analysis with a small bi-parental mapping population, we identified four GLS resistance QTL on chromosomes 1, 6, 7, and 8, which were validated by GWAS. GWAS enabled us to dramatically increase the resolution within the confidence intervals of the above-mentioned QTL. Particularly, GWAS revealed that QTLGLSchr8, detected by genetic linkage mapping as a locus with major effect, was likely represented by two QTL with smaller effects. Conducted in parallel, GWAS of days-to-silking demonstrated the co-localization of flowering time QTL with GLS resistance QTL on chromosome 7 indicating that either QTLGLSchr7 is a flowering time QTL or it is a GLS resistance QTL that co-segregates with the latter. As a result, this genetic linkage – GWAS hybrid mapping system enabled us to identify one novel GLS resistance QTL (QTLGLSchr8a) and confirm with more refined positions four more previously mapped QTL (QTLGLSchr1, QTLGLSchr6, QTLGLSchr7, and QTLGLSchr8b). Through the novel Single Donor vs. Elite Panel method we were able to identify within QTL confidence intervals SNP markers that would be suitable for marker-assisted selection of gray leaf spot resistant genotypes containing the above-mentioned GLS resistance QTL.ConclusionThe application of a genetic linkage – GWAS hybrid mapping system enabled us to dramatically increase the resolution within the confidence interval of GLS resistance QTL by-passing labor- and time-intensive fine mapping. This method appears to have a great potential to accelerate the pace of QTL mapping projects. It is universal and can be used in the QTL mapping projects in any crops.
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35 schema:keywords Africa
36 Brazil
37 ConclusionThe application
38 GLS resistance
39 QTL
40 QTL detection power
41 QTL mapping projects
42 SNP markers
43 Saharan Africa
44 US corn production
45 USA
46 analysis
47 applications
48 area
49 association mapping
50 association studies
51 base
52 bi-parental mapping populations
53 changes
54 chromosome 1
55 chromosome 7
56 climate change
57 confidence intervals
58 corn monoculture
59 corn production
60 crops
61 cultivars
62 days
63 detection power
64 development
65 disease
66 dissection
67 donors
68 effect
69 evidence
70 fine mapping
71 foliar diseases
72 four
73 genetic base
74 genetic linkage analysis
75 genetic linkage mapping
76 genome-wide association studies
77 genotypes
78 germplasm
79 gray leaf spot disease
80 great potential
81 high resolution power
82 highest QTL detection power
83 important foliar disease
84 interval
85 introgression
86 labor
87 leaf spot
88 leaf spot disease
89 linkage
90 linkage analysis
91 linkage mapping
92 loci
93 maize
94 major effect
95 mapping
96 mapping population
97 mapping project
98 mapping system
99 marker-assisted selection
100 markers
101 method
102 molecular markers
103 monoculture
104 narrow genetic base
105 pace
106 panel method
107 parallel
108 population
109 position four
110 potential
111 power
112 power of linkage
113 precise dissection
114 production
115 project
116 resistance
117 resistance QTL
118 resistant cultivars
119 resistant genotypes
120 resistant germplasm
121 resolution
122 resolution power
123 results
124 selection
125 serious threat
126 severity
127 silking
128 single donor
129 situation
130 small effect
131 spot disease
132 spots
133 study
134 system
135 threat
136 time QTL
137 way
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