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Genetic Diversity and Association Mapping for Salinity Tolerance in Bangladeshi Rice Landraces

Reza M. Emon
Chinese National Center for Rice Improvement/State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 310006,China; Bangladesh Institute of Nuclear Agriculture, Mymensingh 2202, Bangladesh

Mirza M. Islam
Bangladesh Institute of Nuclear Agriculture, Mymensingh 2202, Bangladesh

Jyotirmoy Halder
Bangladesh Institute of Nuclear Agriculture, Mymensingh 2202, Bangladesh

Yeyang Fan
Chinese National Center for Rice Improvement/State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 310006,China

Abstract/ Executive Summary:

Breeding for salinity tolerance using Bangladeshi rice landraces and understand genetic diversity has been limited by the complex and polygenic nature of salt tolerance in rice genotypes. A genetic diversity and association mapping analysis was conducted using 96 germplasm accessions with variable response to salt stress at the seedling stage. These included 86 landraces and 10 indica varieties and lines including Nona Bokra, from southern Bangladesh. A total of 220 alleles were detected at 58 Simple Sequence Repeat (SSR) marker loci randomly distributed on all 12 rice chromosomes and 8 Sequence Tagged Site (STS) markers developed for genes SKC1, DST, and SalT. The average gene diversity was 0.5075 and polymorphism information content value was 0.4426, respectively. Cluster analysis revealed that 68 and 21 accessions were clustered into 2 distinct groups, possibly corresponding to indica and japonica groups, respectively and the remaining 7 landraces were classified as an admixed group. Inaddition to Wn11463, the STS marker forSKC1, RM22418 on Chr. 8 was significantly associated with salinity tolerance, at the location of a QTL detected in previous studies. Our findings offavorable alleles associated with salinity tolerance in Bangladeshi rice landraces, as well as the development of STS markers for salt tolerance genes, will be helpful in future efforts to breed salinity tolerance in rice.

Keywords: Oryza sativaL, Salinity tolerance, Seedling stage, Simple sequence repeat, Sequence tagged site

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End Date:

Program Area: Quality and Nutrition

Commodity: Soil salinity

Objectives:

In the present study, 96 rice accessions from southern Bangladesh were subjected to a genetic diversity and association mapping study using SSR and STS markers. The mainobjective of the present study was to: 1) characterize thegenetic diversity and population structure of Bangladesh ricelandraces; 2) develop novel STS markers for salt-tolerancegenes and confirm their effect; and 3) identify loci significant-ly associated with salinity tolerance in rice.

Methodology:

Rice materials A total of 96 rice accessions were collected by Bangladesh Institute of Nuclear Agriculture (BINA) and used in this study. They included 86 landraces from southern Bangladesh, 9 indica varieties and lines, and salt tolerant Nona Bokra, the donor of SKC1, was used as the tolerant control. Screening for salinity tolerance Hydroponic system based on the IRRI protocol was used inthe glasshouse at BINA to evaluate the salt tolerance responses of rice genotypes at the seedling stage. Three replications of 20 plants were tested under salt stress of 12 dS m⁻¹. Themodified standard evaluation score (SES) of IRRI was usedto assess visual symptoms of salt toxicity 21 days after sowing. Binadhan-8 was used as a second tolerant control and Binadhan-7 was the susceptible control. Marker genotyping DNA was extracted from 6–8 individuals in each accession following the method of Zheng et al. To facilitate marker-assisted selection (MAS), sequence tagged site (STS) markers, rather than single nucleotide polymorphism (SNP) markers, were developed based on Insertion/Deletions (InDels) between the Nipponbare and 9311 genome sequences at the SKC1 (Chr.1), SalT (Chr. 1) and DST(Chr. 3 loci. Primers were designed using Oligo 7.0 software. Eight STSmarkers were developed, 2 forSKC1,3 for SalTand 3 for DST. Wn11463 and Wn11466 were designed based on4 bp and 17 bp In Dels downstream of SKC1(LOC_Os01g20160);Wn13900 was based on a 4 bp In Del upstream of SalT (LOC_Os01g24710); Wn13902 and Wn13903 were based on 7 bpand 8 bp In Dels in the Salcoding region; Th32637 was based ona 3 bp InDel upstream inDST(LOC_Os03g57240); and Th32638 and Th32369 were based on 12 bp and 18 bp InDels in thecoding region of DST.One hundred and ninety-four SSR markers randomlydistributed across 12 chromosomes of rice were selected from Gramene (http://www.gramene.org/) and used to screen for polymorphisms in 4 DNA pools bulked by 24 genotypes each. Finally, 58 polymorphic markers were selected to genotype eachaccession, of which 8, 5, 8, 3, 4, 7, 3, 7, 3, 4, 1, and 5 were locatedon each of 12 chromosomes, respectively. Chromosome 11 was represented by only one marker. PCR was carried out in a 20μL reaction mixtures containing 10μLof2×TaqMasterMix II (Beijing CowinBiotech Co., Ltd.), 0.5μmol L−1SSR primers and 1.0μLof template DNA. Amplifications were performed with pre-denaturation of 2 min at 94 °c, 30 cycles of 30 s at 94 °c, 30 s at 50–55 °c, 30 s at 72 °C and extension of 2 min at 72 °c.PCR products were visualized on 2% agarose gels using GelRedstaining or on 6% non-denaturing polyacrylamide gel usingsilver staining.2.4. Statistical analysesGenetic diversity was assessed using Power Marker version 3.25, and was measured by the number of alleles per locus,major allele frequency, gene diversity, and polymorphism information content (PIC). Nei's distance was calculated andused for the unrooted phylogeny reconstruction though the neighbor joining method implemented in Power Marker with Tree view using MEGA 4.0. Population structure of the ricegermplasm was analyzed using STRUCTURE v2.0. Modelswith putative numbers of sub-populations (K) from 1 to 10 withadmixture and correlated allele frequencies were considered.Seven independent runs with burn-in of 10,000, and run lengthof 100,000 iterations for eachKwere implemented. Both lnP(D)value and Evanno'sΔKwere used to determine theK-value. lnP(D) is the log likelihood of the observed genotype distribution inKclusters and was found by STRUCTURE simulation. Evanno'sΔKtakes into consideration the variance of lnP(D)among repeated runs and indicates the idealK. The optimumvalue ofKwas then used to determine inferred ancestries. Anindividual was assigned to a specific population if it had morethan 0.8 membership in that population, whereas individualswith membership probabilities less than 0.8 were assigned to anadmixed group.Association between marker alleles and salinity tolerancedata was performed using a mixed linear model (MLM) function based on population structure (Q)+relative kinship (K) in TASSEL 3.0. For each locus, rare alleles (frequency <5%) weretreated as null alleles. The relative kinship matrix was calculatedby Tassel. Significant marker-trait associations were declared byP≤0.05 with relative magnitudes represented by theR2value asthe portion of variation explained by the marker.

Source of Information: THE CROP JOURNAL 3(2015) 440–444

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Achievement/Findings:

In summary, of 86 southern Bangladeshi rice landraces, 11(12.6%) were identified as having seedling tolerance (SES score3.0) to salt stress, and 25 (28.7%) were moderately tolerant.These accessions could be suitable sources of salinity tolerance in breeding programs.

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