Genome-wide association mapping of salinity tolerance in rice (Oryza sativa)

Abstract

Salinity tolerance in rice is highly desirable to sustain production in areas rendered saline due to various reasons. It is a complex quantitative trait having different components, which can be dissected effectively by genome-wide association study (GWAS). Here, we implemented GWAS to identify loci controlling salinity tolerance in rice. A custom-designed array based on 6,000 single nucleotide polymorphisms (SNPs) in as many stress-responsive genes, distributed at an average physical interval of <100 kb on 12 rice chromosomes, was used to genotype 220 rice accessions using Infinium high-throughput assay. Genetic association was analysed with 12 different traits recorded on these accessions under field conditions at reproductive stage. We identified 20 SNPs (loci) significantly associated with Na+/K+ ratio, and 44 SNPs with other traits observed under stress condition. The loci identified for various salinity indices through GWAS explained 5–18% of the phenotypic variance. The region harbouring Saltol, a major quantitative trait loci (QTLs) on chromosome 1 in rice, which is known to control salinity tolerance at seedling stage, was detected as a major association with Na+/K+ ratio measured at reproductive stage in our study. In addition to Saltol, we also found GWAS peaks representing new QTLs on chromosomes 4, 6 and 7. The current association mapping panel contained mostly indica accessions that can serve as source of novel salt tolerance genes and alleles. The gene-based SNP array used in this study was found cost-effective and efficient in unveiling genomic regions/candidate genes regulating salinity stress tolerance in rice.

Introgression the <i>SalTol</i> QTL into the Elite Rice Variety of Russia by Marker-Assisted Selection<br>

“…It had mapped more than 70 QTLs which were linked to the salt tolerance and two salt tolerance genes of rice (SKC1 and DST) have already been cloned (Hu et al, 2012;Cotsaftis et al, 2012;Kumar et al, 2015). Introduction of the tolerance QTLs into the elite varieties adapted to certain agro-climatic conditions, as well as pyramiding of several tolerance QTLs in one genotype are considered to be the most perspective ways of selection of varieties tolerant to the salt stress (Ashraf et al, 2012;Das et al, 2015;Hoang et al, 2015).…”

“…Application of MAS provides clear understanding of the targeted gene inheritance from parental lines (Kumar et al, 2015). Therefore, it may be effectively used to correct the breeding of plants in order to obtain of new varieties less cost and time consuming (Wang et al, 2011;Kumar et al, 2015).…”

“…One of these approaches is the technology of molecular markers (Hospital, 2009;Kumar et al, 2015). DNA marker analyses provide several significant advantages in comparison with analysis of morphological and physiological traits (Collard et al, 2008;Usatov et al, 2014a).…”

Identification of candidate genes for dissecting complex branch number trait in chickpea

“…The population genetic structure among accessions was determined by a model-based program STRUCTURE v2.3.4 following Kujur et al [24,25] and Saxena et al [34]. The genome-wide and candidate gene-based genotyping data of SNPs physically mapped on eight kabuli chromosomes were analysed by PLINK and the full-matrix approach of TASSELv5.0 [34,37]. Based on these, the genome-wide LD patterns (r 2 , frequency correlation among pair of alleles across a pair of SNP loci) and LD decay (by plotting average r 2 against 50 and 20 kb uniform physical intervals across chromosomes) in population was determined.…”

“…For genetic association mapping, the genome-wide and candidate gene-derived SNP genotyping and BN phenotyping data of 60 Cicer accessions was integrated with their ancestry coefficient (Q matrix of population structure), relative kinship matrix (K) and PCA (principal component analysis) information based on P3D/compressed mixed linear model (CMLM) interfaces of GAPIT [37][38][39]. The quantile-quantile plot of GAPIT was employed to compare individually the relative distribution of observed and expected −log 10 (P)-value for each SNP marker-trait association.…”