|Limeng, Jia - Zhejiang University|
|Chengsong, Zhu - Kansas State University|
|Xiaobai, Li - Zhejiang University|
|Bihu, Huang - University Of Arkansas|
|Biaolin, Hu - Jiangxi Academy Of Agricultural Sciences|
|Dianxing, Wu - Zhejiang University|
Submitted to: Rice Technical Working Group Meeting Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 2/27/2012
Publication Date: 2/1/2013
Citation: Limeng, J., Yan, W., Chengsong, Z., Jackson, A.K., Yeater, K.M., Xiaobai, L., Bihu, H., Biaolin, H., Mcclung, A.M., Dianxing, W. 2013. Sheath blight resistance increases with an increase of putative resistant alleles in rice. Rice Technical Working Group Meeting Proceedings. Feb. 27-Mar. 1, 2012. Hot Springs, AR. pg. 58.
Technical Abstract: Rice sheath blight (ShB), caused by the soil-borne fungal pathogen Rhizoctonia solani Kühn, is an economically important rice disease worldwide, especially in intensive production systems. Over the past two decades, great efforts have been made to explore ShB quantitative trait loci (QTLs) by the traditional cross mapping. Over thirty ShB QTLs have been mapped under field conditions. For the first time, we adopted the advanced strategy of association mapping (AM) to map ShB QTLs in a global germplasm collection with diversity background. A mini-core collection of 217 accessions for the mapping was derived from the United States Department of Agriculture (USDA) core collection of 1,794 accessions, representing the most genetic diversity of the USDA rice world collection of more than 18,000 accessions. The 217 accessions were evaluated for ShB resistance using the micro-chamber method (MCM) with checks, ‘Lemont’ (susceptible) and ‘Jasmine85’ (resistant), in a randomized complete block design, six replications between 2008 and 2009. The phenotypic data used in association mapping were the least square means (LSMs) of ShB severity ratings. Meanwhile, the 217 accessions were genotyped using 154 SSR markers and one indel that covered the entire rice genome with an average genetic distance of 10 cM. Structure analysis divided the mapping panel into five subgroups and classified each accession to an appropriate subgroup. Inferred by our previous study, the five subgroups were denoted as temperate japonica (TEJ), aus (AUS), and aromatic (ARO), indica (IND), and tropical japonica (TRJ). This conclusion was also supported by principal component analysis (PCA) and cluster analysis. Among the 217 accessions, IND had the most (86), followed by TRJ (49), AUS (39), TEJ (36), and ARO (7). Among 24 accessions having greater resistance to ShB than the resistant check ‘Jasmine85’, 20 belonged to IND, two to AUS and one each to TRJ and admix (TRJ-AUS-IND). Using the best fit model in AM based on Bayesian Information Criterion (BIC) value, ten marker loci were identified to be significantly associated with ShB resistance at the probability level of 5% or lower, three on chromosome (Chr) 11, two on Chr1, and one each on Chr2, 4, 5, 6 and 8. Ten alleles, each from the identified marker loci, were noted as the ‘putative resistant allele’ because of their greatest effect in decreasing ShB rating among all the alleles for their respective loci. Further analysis indicated a strong and negative correlation between the ShB severity rating and number of putative resistant alleles (r = -0.535, p<0.0001), indicating the greater number of putative resistant alleles for an accession to have, the lower ShB rating and greater resistance it would have. Combined with the observation in structure analysis, we found the IND group contained the most accessions that have a large number of putative resistant alleles. The extreme case was the entry GSOR 310389 from IND having the most resistant alleles, eight out of ten. Our study concluded that 1) marker-assisted breeding for ShB resistance could be conducted on an allelic level by pyramiding resistant alleles in a cultivar, 2) indica rice contained most resistant alleles, which is consistent with a general observation that indica is more resistant than japonica and 3) the AM showed the great statistical power and efficiency by confirming eight out of ten identified ShB QTLs in our mapping with previous studies.