Assessment of Rice Sheath Blight Resistance Including Associations with Plant Architecture, as Revealed by Genome-Wide Association Studies

Abstract

Background: Sheath blight (ShB) disease caused by Rhizoctonia solani Kühn, is one of the most economically damaging rice (Oryza sativa L.) diseases worldwide. There are no known major resistance genes, leaving only partial resistance from small-effect QTL to deploy for cultivar improvement. Many ShB-QTL are associated with plant architectural traits detrimental to yield, including tall plants, late maturity, or open canopy from few or procumbent tillers, which confound detection of physiological resistance.

Results: To identify QTL for ShB resistance, 417 accessions from the Rice Diversity Panel 1 (RDP1), developed for association mapping studies, were evaluated for ShB resistance, plant height and days to heading in inoculated field plots in Arkansas, USA (AR) and Nanning, China (NC). Inoculated greenhouse-grown plants were used to evaluate ShB using a seedling-stage method to eliminate effects from height or maturity, and tiller (TN) and panicle number (PN) per plant. Potted plants were used to evaluate the RDP1 for TN and PN. Genome-wide association (GWA) mapping with over 3.4 million SNPs identified 21 targeted SNP markers associated with ShB which tagged 18 ShB-QTL not associated with undesirable plant architecture traits. Ten SNPs were associated with ShB among accessions of the Indica subspecies, ten among Japonica subspecies accessions, and one among all RDP1 accessions. Across the 18 ShB QTL, only qShB4-1 was not previously reported in biparental mapping studies and qShB9 was not reported in the GWA ShB studies. All 14 PN QTL overlapped with TN QTL, with 15 total TN QTL identified. Allele effects at the five TN QTL co-located with ShB QTL indicated that increased TN does not inevitably increase disease development; in fact, for four ShB QTL that overlapped TN QTL, the alleles increasing resistance were associated with increased TN and PN, suggesting a desirable coupling of alleles at linked genes.

Conclusions: Nineteen accessions identified as containing the most SNP alleles associated with ShB resistance for each subpopulation were resistant in both AR and NC field trials. Rice breeders can utilize these accessions and SNPs to develop cultivars with enhanced ShB resistance along with increased TN and PN for improved yield potential.

Keywords: Genome-wide association mapping; Oryza sativa; Rhizoctonia solani; Rice; Sheath blight disease; Tillering.

Fig. 1

Quantile plots comparing sheath blight disease ratings (0 = no disease to 9 = 90% or more infected) between the Rice Diversity Panel 1 (All-RDP1) and the O. sativa subpopulations, aromatic (ARO), aus (AUS), indica (IND), temperate japonica (TEJ) and tropical japonica (TRJ). The ratings were from the field (a, b) and microchamber disease index (DI) (c, d) studies conducted in Arkansas, USA (a, c) and Nanning, China (b, d). Dots indicate full ranges of observed data; green diamonds indicate means ± se; lower and upper sides of red quantile boxes indicate the 25th and 75th percentiles, respectively; horizontal red lines in boxes are medians, vertical lines indicate the 5th and 95th percentiles. Different lowercase letters indicate differences between subpopulation means for that trait based on Tukey-Kramer multiple mean comparison tests and α = 0.05

Fig. 2

The physical position of the sheath blight QTL (qShB), panicle number QTL (qPN) and tiller number QTL (qTN) identified by genome-wide association (GWA) mapping in the Rice Diversity Panel-1 (RDP1) with 3,463,224 SNP markers across the entire rice genome. The 21 SNPs ascertaining the 18 ShB-QTL, 18 SNPs defining the 14 PN-QTL and 19 SNPs delineating the 15 TN-QTL are identified by “SNP”, chromosome and megabase (Mb) position based on the Os-Nipponbare-Reference-IRGSP-1.0 assembly (Kawahara et al. 2013). The Mb position of the centromere, and the beginning and end of each chromosome, is given. The details of the ShB-QTL and distinguishing SNPs are in Table 3, PN-QTL and TN-QTL in Table 4, and the QTL are combined in Table S5. [The figure was created with MapChart 2.32 (Voorrips 2002).]