IC4R005-GWAS-2015-25627243
Contents
Project Title
Genome-wide association mapping of salinity tolerance in rice (Oryza sativa)
The Background of This Project
- Salinity tolerancein rice is highly desirable to sustainproduction inareasrenderedsaline duetovarious reasons. It is a complex quantitative trait having different components, which can be dissected effectively by genome-wide association study (GWAS).
- Rice, commonly known to be salt sensitive, 1 is greatly affected by soilsalinity. Salinity affects rice growth in varying degree at all stages starting from germination through maturation. 3–6 Excess salts adversely affect all major metabolic activities in rice, and cause overall decline in germination and seedling growth, leading ultimately to reduced growth and diminished grain yield. The reduction in major yield components including tiller numbers in plant and spikelet numbers per panicle has been reported to be the major cause of yield loss (27–50%) in rice cultivars during early reproductive panicle initiation stage under salinity stress. 5,8,9 Millions of hectares in the humid regions of South and Southeast Asia are technically suited for rice pro- duction but are left uncultivated or are grown with very low yields because of salinity and problem soils. Salt-affected soils in arid and semi-arid regionsof Asia, Africaand South Americacause considerable agronomic problems. In Asia, 12 million ha of land area is thought to be salinity affected with India having >50% salinity affected area. Considerable variation for different yield contributing agronomic traits has been observedindiverserice genotypes under salinitystress. 10,11 Therefore, to maximize productivity of rice under saline soils, there is an ur- gent need to look for discovery of genes imparting salt tolerance and their introduction in salt-sensitive rice cultivars.
- The dissectionof salt tolerance traits has beencarried out by quantitative trait loci (QTLs) mapping approach using bi-parental populations. These studies have led to identification of both majorand minor QTLs for various traits on different rice chromosomes. 13–22 For example, Koyama et al. 13 identified 11 QTLs for Na + and K + content related to salinity stress tolerance. Bonilla et al. mapped Saltol locus linked to major QTLs for Na + and K + uptake and Na + /K + ratio on chromosome 1 explaining 64.3% phenotypic variance. Lin et al. mapped QTLs for root and shoot Na + /K + concentration and transport on five rice chromosomes. Ammar et al. reported 25 QTLs for salt ion concentrations (Na + , K + and Cl − measured in the leaf tissues at the reproductive stage) on rice chromosomes 1, 2, 3 and 8. Pandit et al. reported eight QTLs for salt ion concentrations on rice chromosomes 1, 8 and 12, and Cheng et al. 23 reported 12 QTLs for salt ion concentrations on rice chromosomes 1, 2, 3, 4, 7 and 11, respectively.
Plant Culture & Treatment
- The experiment comprised of 220 rice accessions, which were obtained from various national and international institutes to assess the performance in normal and high saline stress conditions. These genotypes were evaluated in randomized complete block design with two replications under two environments, viz., normal (EC iw ∼1.0 dS/m) and high salinity stress (EC iw∼10 dS/m) in micro plots at the Central Soil Salinity Research Institute, Karnal, Haryana, India. This experimental site is situated at 29.43°N latitude and 76.58°N longitude and 245 m above the sea level. The 35-day-old seedlings from wet bed nurseries were transplanted using two seedlings per hill at a spacing of 15×20 cm. Basal fertilizers for the main crop were 120-60-60 kg of NPK/ha. The recommended agronomic practices were followed to get healthy and good crop. Twenty-one days after transplanting, salinity stress was imposed using 7 NaCl: 1Na 2 SO 4 :2 CaCl 2 onequivalent basis. Salinitywasrecorded onceina week and maintained at desired level (EC iw ∼10 dS/m) throughout the croppingseason.Randomlyfiveplantsweretaggedforeachgenotypein each replication, and phenotypic data were recorded for all traits. The response of the genotypes to salt stress was expressed using the stress susceptibility index (SSI) calculated according to Fischer and Maurer 39 using the following formula: SSI=(1−Ys/Yp)/D, where Ys=mean performance of a genotype under stress; Yp=mean performance of the same genotype without stress; D (stress intensity)=1−(mean Ys of all genotypes/mean Yp of all genotypes).
Research Findings
- The array designed with 5,246 SNPs in our study was successfully used to genotype all the 220 rice accessions. The data generated through Illumina Infinium platform were loaded in Genome Studio software where, after cluster refinement with optimum GenTrain (>0.7) and GenCall score (>0.3), 4,929 polymorphic SNPs (∼94%) were identified. After excluding the SNPs with MAF< 0.02, finally, 4,191 (∼80%) high-quality SNPs genotyped across 220 rice accessions were utilized for GWA mapping.
- Using PCA, most of the genetic variation (66%) in the accessions was explained by first two PCs. Based on 4,191 SNPs, the first PC explained almost 52% of genetic variation, whereas PC II explained 14%. The scree plot generated through GAPIT recommended the first three components as informative, where descent changes gradually (Figure. 1A). When we plotted the first two components against each other, three subpopulations were identified. A total of 130 accessions were clustered as a single large subpopulation (I) (Figure. 1B). For inferring the most likely number of populations among 220 accessions in STRUCTURE, the transformation method 51 was used, and similar to PCA, three subpopulations were identified (Figure. 1C). Forty-four percent of the accessions (97/220) did not show any admixture, 46% accessions (101/220) showed up to 20% admixture, while the remaining 10% (22/220) were found to be highly admixed.
- The LD decay rate was measured as the chromosomal distance (kb) at which the average pairwise correlation coefficient (r 2 ) dropped to half its maximum value. 36 The binned r 2 values were mapped against the physical distance (kb) across the genome. Overall, the LD for SNPs at 20 kbdistancefromeach otherwas 0.50(r 2 ), whichdecayed toits half value (∼0.25) at around 300 kb. However, further decay in r 2 was found very slow and paralleled at 0.15 up to 2 Mb (Figure. 4). Out of total pairwise LD events, only 14% pairs showed r 2 above 0.5. When LD was calculated population-wise, the maximum average (r 2 <0.70) was observed for Subpopulation I, which was maintained at r 2 >0.21 up to2 Mb.Theaverage LDfor SubpopulationIII was observed comparatively low (0.44), anddecayed halfto its initial valueat 240 kb. The average minimum LD was also found very low (0.12) for Subpopulation III. The maximum and minimum LD for Subpopulation II were 0.50 and 0.15, respectively. PLINK was also used to calculate chromosome-wise LD between SNP pairs, which was found maximum (0.40) for chromosomes 3 and 7, and minimum (r 2 <0.23)
Figure 2 Comparison of LD patterns and LD decay in the whole panel and subgroups. The whole genome r 2 values from PLINK are first sorted considering distance, and then divided into 100 blocks of 20 kb. The r 2 values in each block are averaged and plotted against the genetic distance for different subgroups.
- All the 220 rice accessions in our study were phenotyped for 12 agronomic traits, and two assays of Na + and K + accumulation were also conducted. Using 4,191 SNPs, GAPIT produced association signals for various traits under control as well as stress condition. The association signals were more in number and potent under stress condition. SNPs were considered significant onlyafteradjusting for multiple testing at 0.05 threshold level. The associated loci in this study for different traits were interspersed on all the chromosomes. Using the CMLM approach, we successfully identified both known associations (for example, enrichment in a priori candidate genes and previously reported QTLs from rice) as well as new candidate loci in the rice genome. The results of our genome-wide association scans are summarized in Supplementary Figs S4–11 where we showed the SNP trait associations discovered in the association panel using CMLM approaches.
Labs working on this Project
- National Research Centre on Plant Biotechnology, New Delhi 110012, India
- Central Soil Salinity Research Institute, Karnal, Haryana 132001, India
- Indian Agricultural Statistics Research Institute, New Delhi 110012, India
- University of Delhi South Campus, New Delhi 110021, India
- Central Rice Research Institute, Cuttack, Odisha 753006, India
Corresponding Author
- Trilochan Mohapatra(tmnrcpb@gmail.com)
