Genome-wide Association Analysis Tracks Bacterial Leaf Blight Resistance Loci In Rice Diverse Germplasm

Genome-wide association analysis of bacterial blight resistance to nine Xoo strains in 198 indica genotypes based on Efficient Mixed-Model Association eXpedited Model (EMMAX). Manhattan plots for nine Xoo strains (a) PXO61, (b) PXO86, (c) PXO79, (d) PXO71, (e) PXO112, (f) PXO99, (g) PXO339, (h) PXO349, and (i) PXO341. X-axis shows the SNPs along each chromosome; y axis is the − log10 (P-value) for the association. Significant SNPs are those beyond the red line having P-value < 1 × 10 −5. Quantile-quantile plots for nine Xoo strains (j) PXO61, (k) PXO86, (l) PXO79, (m) PXO71, (n) PXO112, (o) PXO99, (p) PXO339, (q) PXO349, and (r) PXO341. (PPTX 521 kb

Large-scale deployment of a rice 6 K SNP array for genetics and breeding applications

Abstract Background Fixed arrays of single nucleotide polymorphism (SNP) markers have advantages over reduced representation sequencing in their ease of data analysis, consistently higher call rates, and rapid turnaround times. A 6 K SNP array represents a cost-benefit “sweet spot” for routine genetics and breeding applications in rice. Selection of informative SNPs across species and subpopulations during chip design is essential to obtain useful polymorphism rates for target germplasm groups. This paper summarizes results from large-scale deployment of an Illumina 6 K SNP array for rice. Results Design of the Illumina Infinium 6 K SNP chip for rice, referred to as the Cornell_6K_Array_Infinium_Rice (C6AIR), includes 4429 SNPs from re-sequencing data and 1571 SNP markers from previous BeadXpress 384-SNP sets, selected based on polymorphism rate and allele frequency within and between target germplasm groups. Of the 6000 attempted bead types, 5274 passed Illumina’s production quality control. The C6AIR was widely deployed at the International Rice Research Institute (IRRI) for genetic diversity analysis, QTL mapping, and tracking introgressions and was intensively used at Cornell University for QTL analysis and developing libraries of interspecific chromosome segment substitution lines (CSSLs) between O. sativa and diverse accessions of O. rufipogon or O. meridionalis. Collectively, the array was used to genotype over 40,000 rice samples. A set of 4606 SNP markers was used to provide high quality data for O. sativa germplasm, while a slightly expanded set of 4940 SNPs was used for O. sativa X O. rufipogon populations. Biparental polymorphism rates were generally between 1900 and 2500 well-distributed SNP markers for indica x japonica or interspecific populations and between 1300 and 1500 markers for crosses within indica, while polymorphism rates were lower for pairwise crosses within U.S. tropical japonica germplasm. Recently, a second-generation array containing ~7000 SNP markers, referred to as the C7AIR, was designed by removing poor-performing SNPs from the C6AIR and adding markers selected to increase the utility of the array for elite tropical japonica material. Conclusions The C6AIR has been successfully used to generate rapid and high-quality genotype data for diverse genetics and breeding applications in rice, and provides the basis for an optimized design in the C7AIR

Genetic Dissection of Grain Nutritional Traits and Leaf Blight Resistance in Rice

Colored rice is rich in nutrition and also a good source of valuable genes/quantitative trait loci (QTL) for nutrition, grain quality, and pest and disease resistance traits for use in rice breeding. Genome-wide association analysis using high-density single nucleotide polymorphism (SNP) is useful in precisely detecting QTLs and genes. We carried out genome-wide association analysis in 152 colored rice accessions, using 22,112 SNPs to map QTLs for nutritional, agronomic, and bacterial leaf blight (BLB) resistance traits. Wide variations and normal frequency distributions were observed for most of the traits except anthocyanin content and BLB resistance. The structural and principal component analysis revealed two subgroups. The linkage disequilibrium (LD) analysis showed 74.3% of the marker pairs in complete LD, with an average LD distance of 1000 kb and, interestingly, 36% of the LD pairs were less than 5 Kb, indicating high recombination in the panel. In total, 57 QTLs were identified for ten traits at p &lt; 0.0001, and the phenotypic variance explained (PVE) by these QTLs varied from 9% to 18%. Interestingly, 30 (53%) QTLs were co-located with known or functionally-related genes. Some of the important candidate genes for grain Zinc (Zn) and BLB resistance were OsHMA9, OsMAPK6, OsNRAMP7, OsMADS13, and OsZFP252, and Xa1, Xa3, xa5, xa13 and xa26, respectively. Red rice genotype, Sayllebon, which is high in both Zn and anthocyanin content, could be a valuable material for a breeding program for nutritious rice. Overall, the QTLs identified in our study can be used for QTL pyramiding as well as genomic selection. Some of the novel QTLs can be further validated by fine mapping and functional characterization. The results show that pigmented rice is a valuable resource for mineral elements and antioxidant compounds; it can also provide novel alleles for disease resistance as well as for yield component traits. Therefore, large opportunities exist to further explore and exploit more colored rice accessions for use in breeding

Rice Galaxy: an open resource for plant science

Background Rice molecular genetics, breeding, genetic diversity, and allied research (such as rice-pathogen interaction) have adopted sequencing technologies and high density genotyping platforms for genome variation analysis and gene discovery. Germplasm collections representing rice diversity, improved varieties and elite breeding materials are accessible through rice gene banks for use in research and breeding, with many having genome sequences and high density genotype data available. Combining phenotypic and genotypic information on these accessions enables genome-wide association analysis, which is driving quantitative trait loci (QTL) discovery and molecular marker development. Comparative sequence analyses across QTL regions facilitate the discovery of novel alleles. Analyses involving DNA sequences and large genotyping matrices for thousands of samples, however, pose a challenge to non-computer savvy rice researchers. Findings We adopted the Galaxy framework to build the federated Rice Galaxy resource, with shared datasets, tools, and analysis workflows relevant to rice research. The shared datasets include high density genotypes from the 3,000 Rice Genomes project and sequences with corresponding annotations from nine published rice genomes. Rice Galaxy includes tools for designing single nucleotide polymorphism (SNP) assays, analyzing genome-wide association studies, population diversity, rice-bacterial pathogen diagnostics, and a suite of published genomic prediction methods. A prototype Rice Galaxy compliant to Open Access, Open Data, and Findable, Accessible, Interoperable, and Reproducible principles is also presented. Conclusions Rice Galaxy is a freely available resource that empowers the plant research community to perform state-of-the-art analyses and utilize publicly available big datasets for both fundamental and applied science