Prospective genomic regions with superior alleles for seedling stage salt tolerance identified via GWAS in the USDA rice mini-core collection

Authors: Rohila, Jai; Edwards, Jeremy; TRAN, GIOI - Cuu Long Delta Rice Research Institute; Jackson, Aaron; McClung, Anna

Submitted to: Rice Technical Working Group Meeting Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 12/4/2019
Publication Date: 1/6/2021
Citation: Rohila, J.S., Edwards, J., Tran, G.D., Jackson, A.K., McClung, A.M. 2021. Prospective genomic regions with superior alleles for seedling stage salt tolerance identified via GWAS in the USDA rice mini-core collection. Proceedings of 38th Rice Technical Working Group Meeting, February 24-27, 2020, Orange Beach, Alabama. p 80. Electronic Publication.

Interpretive Summary:

Technical Abstract: Rice seedlings are sensitive to salt when the electrical conductivity (EC) of growing media reaches higher than 2.0 dS m-1. Saltol, a major QTL from the rice variety Pokkali, when deployed in various mega varieties in Asia was found insufficient in providing the desired level of salt tolerance in rice seedlings. Search for novel germplasm with different salt tolerance mechanisms and identification of associated QTL/SNPs could help rice breeding programs in developing salt tolerant varieties by the gene pyramiding approach. The objectives of this study were to (i) evaluate the natural genetic variation available in 123 accessions from the USDA rice mini-core (URMC) for early vigor traits under EC 6.0 dS m-1 salt stress, (ii) identify genomic regions for seedling-stage salt tolerance using genome-wide association (GWAS) approach, and (iii) to compare salt tolerance levels of URMC germplasm with varieties developed from a salt-tolerance rice breeding program in Vietnam that has incorporated Saltol QTL effectively. A total of 162 accessions representing four rice subpopulations: tropical japonica (TRJ), temperate japonica (TEJ), indica (IND), and aus (AUS) were evaluated using a hydroponic system under greenhouse conditions. The salt stress was applied at 3-4 leaf stage for 16 days and measurements were taken on eight traits at two time points 10 (d10) and 16d (d16) after treatment including: salt stress injury (SSI) score-d10, SSI score-d16, ' plant height (PHT)-d10, ' PHT-d16, ' green leaf number-d14, total biomass plant-1, shoot biomass plant-1, and root biomass plant-1. Six cultivars were used as salt-sensitive (IR29, Nerica 6, A69-1) and salt-tolerant (Pokkali, IR45427-2B-2-2B-1-1, IR65196-3B-5-2-2) checks. GWAS was performed utilizing 3.2 million SNPs available for the URMC accessions. SNPs were grouped into chromosomal regions by processing GWAS results with custom Perl scripts. Based on p-values and allele effects, chromosomal regions and corresponding peak SNPs that were highly predictive of SSI scores and related phenotypes were selected for detailed analysis. Allele effects, significance, and R2 values for each of the selected SNPs were calculated separately for each subpopulation by ANOVA in JMP 14.0.0. A distribution of similar alleles among a larger panel of rice lines than the URMC alone was also determined. The allele frequency information was computed by using RiceVarMap, which contains genotype information on 4,726 rice accessions. Among the four subpopulations in the URMC, TRJ was identified as the most sensitive, and the IND subpopulation (and two accessions in the TEJ) was the most tolerant to salt stress. Among the tested entries, 59.4% of the accessions were identified as sensitive, 23.9% were identified as moderately tolerant, and 16.7% were identified as highly tolerant. Pokkali was the most tolerant variety, while Nerica-6 was the most sensitive. Based on SSI score, plant height gains, green leaf number gains and total biomass plant-1 after 16 days of salt stress, several accessions (e.g. 4484, A36-3, Bogarigbeli, Chin Chin, CM1_HAIPONG, Doble Carolina, K8C-262-3, Krachek Chap, M202, SOC NAU, SORNAVARI) were identified in the URMC with comparatively high salt-tolerance. Pearson’s correlation coefficient revealed that all traits were highly correlated with each other and contributed to the growth and senescence factors that influence the SSI scores. The ' green leaf number had a strong negative correlation with SSI scores (r = –0.86, p < 0.0001) and a strong positive correlation with the other measured traits such as shoot biomass (r = 0.71, p < 0.0001) and root biomass (r = 0.72, p < 0.0001). From the GWAS analysis, nine genomic regions of interest were identified, mapped to five different chromosomes, and nine significant SNPs associated with superior alleles were identified as useful for marker-assiste