Identification of Loci Through Genome-Wide Association Studies to Improve Tolerance to Sulfur Deficiency in Rice

Abstract

Sulfur (S) is an essential nutrient for plant growth and development; however, S supply for crop production is decreasing due to reduced inputs from atmospheric deposition and reduced application of S-containing fertilizers. Sulfur deficiency in soil is therefore becoming a widespread cause of reduced grain yield and quality in rice (Oryza sativa L). We therefore assessed the genotypic variation for tolerance to S deficiency in rice and identified loci associated with improved tolerance. Plants were grown in nutrient solution with either low (0.01 mM) or high (1.0 mM) supply of S. Plants grown under lowS treatment showed a reduction in total biomass, mainly due to a marked reduction in shoot biomass, while root biomass and root-to-shoot ratio increased, relative to plants under highS treatment. Genome-wide association studies (GWAS) identified loci associated with root length (qSUE2-3, qSUE4, and qSUE9), and root (qSUE1, qSUE2-1, and qSUE3-1 and qSUE3-2) or total dry matter (qSUE2, qSUE3-1, and qSUE11). Candidate genes identified at associated loci coded for enzymes involved in secondary S metabolic pathways (sulfotransferases), wherein the sulfated compounds play several roles in plant responses to abiotic stress; cell wall metabolism including wall loosening and modification (carbohydrate hydrolases: beta-glucosidase and beta-gluconase) important for root growth; and cell detoxification (glutathione S-transferase). This study confirmed the existence of genetic variation conferring tolerance to S deficiency among traditional aus rice varieties. The advantageous haplotypes identified could be exploited through marker assisted breeding to improve tolerance to S-deficiency in modern cultivars in order to achieve sustainable crop production and food security.

Phenotyping of a rice (Oryza sativa L.) association panel identifies loci associated with tolerance to low soil fertility on smallholder farm conditions in Madagascar

“…The model was run using an in-house R script and the rrBLUP package returned a quantile-quantile plot and a Manhattan plot with a significant threshold set to a 5% FDR (false discovery rate) [ 20 , 23 ]. Loci were considered significantly associated with a trait based on a threshold of–log(10)(p) > 5 for those peaks characterized by at least three consecutives Quantitative Trait Nucleotide (QTN) [ 19 , 20 ].…”

“…For the purpose of confirming detected associations, a second analysis was conducted with the software program Trait Analysis by association, Evolution and Linkage 5.0 (TASSEL) [ 25 ]. Prior to association analysis, the 404K coreset SNP genotype dataset was filtered as follows: heterozygotes and indels were set as missing values, and SNP having more than 5% missing data or minor allele frequency (MAF) below 0.03 were excluded [ 19 ]. The association mapping was then analyzed using the mixed linear model (MLM) procedure, with three principal components (PCA) and a kinship matrix.…”

“…GWAS could identify loci, genes and alleles that contribute to specific traits, and therefore could accelerate breeding for targeted traits. For example, GWAS had been successfully applied in rice to dissect the genetic basis of nutrient-related traits such as aluminum tolerance [ 17 ], phosphorus utilization efficiency [ 12 ], manganese toxicity [ 18 ], sulfur deficiency [ 19 ], and of traits related to root development [ 20 ], and root efficiency [ 12 ].…”

Pinpointing genomic regions associated with root system architecture in rice through an integrative meta-analysis approach

“…GWAS studies for root morphological traits in rice were reviewed (Bettembourg et al 2017;Biscarini et al 2016;Courtois et al 2013;Kadam et al 2017;Li et al 2017;Mai et al 2020;Pariasca-Tanaka et al 2020;Phung et al 2016;Wang et al 2018;Xu et al 2020), and the reported SNP peak positions were collected to find the overlaps between their positions with MQTLs. The genes located in the SNP peak positions (+/-25 kb) were extracted from Oryza sativa Japonica group (IRGSP-1.0) reference genome according to their physical positions.…”

Mineral nutrients in plants under changing environments: A road to future food and nutrition security

“…Biofortification using molecular plant breeding approaches has also been undertaken to address the issues of malnutrition and ensure nutrition and food security (Calayugan et al., 2020). To this end, efforts are made for producing crops with enhanced nutritional values by identifying quantitative trait loci (QTLs) using the traditional QTL mapping approach (Calayugan et al., 2020; Jeong et al., 2020), or by employing the genome wide association studies (GWAS) for identifying high value genes for enriched minerals and vitamins in grains (Lopes et al., 2015; Pariasca‐Tanaka et al., 2020). Crops, such as pearl millet ( Cenchrus americanus ) with enriched Fe, maize ( Zea mays ), and rice ( Oryza sativa ) with increased vitamin A, wheat ( Triticum aestivum ), and rice varieties with elevated grain Zn, have been developed through biofortification approaches using biotechnological and breeding approaches to address nutritional security (Bouis & Saltzman, 2017).…”