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 discovery and molecular marker development. Comparative sequence analyses across quantitative trait loci 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: The Rice Galaxy resource has shared datasets that include high-density genotypes from the 3,000 Rice Genomes project and sequences with corresponding annotations from 9 published rice genomes. The Rice Galaxy web server and deployment installer includes tools for designing single-nucleotide polymorphism 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

Identification of orthologous regions associated with tissue growth under water-limited conditions

Plant recovery from early season drought is related to the amount of biomass retained during stress and biomass production after the end of stress. Reduction in leaf expansion is one of the first responses to water deficit. It is assumed that the control of tissue development under water deficit contributes to traits such as early vigor, as well as maintenance of growth of reproductive organs. To dissect the underlying mechanisms controlling tissue expansion under water-limited conditions, we used a multilevel approach combining quantitative genetics and genomics. To identify orthologous genetic regions controlling tissue growth under water-limited conditions a series of QTL mapping and microarray gene expression studies were conducted in rice and maize. Results of differentially expressed genes from microarray experiments, QTLs and candidate genes related to growth in the different species are compared on consensus maps (within species) and then on synteny maps (between species), to identify common genetic regions between rice and maize

Molecular dissection of connected rice populations revealed important genomic regions for agronomic and biofortification traits

Breeding staple crops with increased micronutrient concentration is a sustainable approach to address micronutrient malnutrition. We carried out Multi-Cross QTL analysis and Inclusive Composite Interval Mapping for 11 agronomic, yield and biofortification traits using four connected RILs populations of rice. Overall, MC-156 QTLs were detected for agronomic (115) and biofortification (41) traits, which were higher in number but smaller in effects compared to single population analysis. The MC-QTL analysis was able to detect important QTLs viz: qZn5.2, qFe7.1, qGY10.1, qDF7.1, qPH1.1, qNT4.1, qPT4.1, qPL1.2, qTGW5.1, qGL3.1, and qGW6.1, which can be used in rice genomics assisted breeding. A major QTL (qZn5.2) for grain Zn concentration has been detected on chromosome 5 that accounted for 13% of R2. In all, 26 QTL clusters were identified on different chromosomes. qPH6.1 epistatically interacted with qZn5.1 and qGY6.2. Most of QTLs were co-located with functionally related candidate genes indicating the accuracy of QTL mapping. The genomic region of qZn5.2 was co-located with putative genes such as OsZIP5, OsZIP9, and LOC_OS05G40490 that are involved in Zn uptake. These genes included polymorphic functional SNPs, and their promoter regions were enriched with cis-regulatory elements involved in plant growth and development, and biotic and abiotic stress tolerance. Major effect QTL identified for biofortification and agronomic traits can be utilized in breeding for Zn biofortified rice varieties

Genome-Wide Association Mapping in a Rice MAGIC Plus Population Detects QTLs and Genes Useful for Biofortification

The development of rice genotypes with micronutrient-dense grains and disease resistance is one of the major priorities in rice improvement programs. We conducted Genome-wide association studies (GWAS) using a Multi-parent Advanced Generation Inter-Cross (MAGIC) Plus population to identify QTLs and SNP markers that could potentially be integrated in biofortification and disease resistance breeding. We evaluated 144 MAGIC Plus lines for agronomic and biofortification traits over two locations for two seasons, while disease resistance was screened for one season in the screen house. X-ray fluorescence technology was used to measure grain Fe and Zn concentrations. Genotyping was carried out by genotype by sequencing and a total of 14,242 SNP markers were used in the association analysis. We used Mixed linear model (MLM) with kinship and detected 57 significant genomic regions with a -log10 (P-value) ≥ 3.0. The PH1.1 and Zn7.1 were consistently identified in all the four environments, ten QTLs qDF3.1, qDF6.2qDF9.1qPH5.1qGL3.1, qGW3.1, qGW11.1, and qZn6.2 were detected in two environments, while two major loci qBLB11.1 and qBLB5.1 were identified for Bacterial Leaf Blight (BLB) resistance. The associated SNP markers were found to co-locate with known major genes and QTLs such as OsMADS50 for days to flowering, osGA20ox2 for plant height, and GS3 for grain length. Similarly, Xa4 and xa5 genes were identified for BLB resistance and Pi5(t), Pi28(t), and Pi30(t) genes were identified for Blast resistance. A number of metal homeostasis genes OsMTP6, OsNAS3, OsMT2D, OsVIT1, and OsNRAMP7 were co-located with QTLs for Fe and Zn. The marker-trait relationships from Bayesian network analysis showed consistency with the results of GWAS. A number of promising candidate genes reported in our study can be further validated. We identified several QTLs/genes pyramided lines with high grain Zn and acceptable yield potential, which are a good resource for further evaluation to release as varieties as well as for use in breeding programs

Locating genes controlling allelopathic effects against barnyardgrass in upland rice

To understand the genetic control of allelopathy in rice (Oryza sativa L.), quantitative trait loci (QTL) mapping was performed using a population of 142 recombinant inbred lines derived from a cross between cultivar IAC 165 (japonica upland variety) and cultivar CO 39 (indica irrigated variety). The map contained 140 DNA markers. The relay seeding technique, which is a laboratory bioassay measuring the inhibition in weed root growth due to the presence of rice seedlings, was used to evaluate the allelopathic effect of the rice lines. Cultivar IAC 165 showed strong and consistent allelopathic activity against barnyardgrass [Echinochloa crus-galli (L.) Beauv.], whereas CO 39 was weakly allelopathic. Transgressive segregation for allelopathic activity in both directions was observed in the population. No significant correlation was found between root morphology of the lines and their allelopathic potential, suggesting that allelopathy in rice was under genetic control independent from root morphology. Four maineffect QTLs located on three chromosomes were identified, which collectively explained 35% of the total phenotypic variation of the allelopathic activity in the population. One pair of digenic epistatic loci, not involving any of the main-effect loci, was also detected. Once confirmed, these QTLs may be useful for genetic improvement of allelopathy in rice using marker-assisted selection. (Résumé d'auteur

Computational data from: Maximising recombination across macadamia populations to generate linkage maps for genome anchoring

Computational files associated with the publication Langdon et al. 2020 Maximising recombination across macadamia populations to generate linkage maps for genome anchoring. Scientific Reports

Computational data for: Chromosome-scale assembly and annotation of the macadamia genome

Establishing an open-source platform for unravelling the genetics of macadamia: integration of linkage and genome maps

Discovery of genomic variants associated with genebank historical traits for rice improvement: SNP and indel data, phenotypic data, and GWAS results

This dataset provides supporting information for Sanciangco et al (submitted) consisting of: A) file list, tables of phenotypes for quantitative and categorical traits and trait descriptions, and tables of SNP/indel numbers for Filtered, LD-pruned and subpopulation datasets (7 files named as "00_*"); B) plink files for Filtered and LD-pruned SNP/indel datasets for all genotypes and for indica, japonica and aus subsets (15 fIles named as "01_*"); C) EMMAX results on Filtered dataset for 12 quantitative traits on All, Aus, Indica, and Japonica genotypes and corresponding Manhattan and QQ plots (144 files named as "0[2345]_*"); D) EMMAX results on LD-pruned dataset for 12 quantitative traits on All, Aus, Indica, and Japonica genotypes and corresponding Manhattan and QQ plots (72 files named as "0[6789]_*"); E) EMMAX results on LD-pruned dataset for 20 categorical traits treated as numeric on All genotypes and corresponding Manhattan and Q-Q plots (60 files named as "10_*"); F) Anova results obtained on numerically transformed LD-pruned dataset for 20 categorical traits on All genotypes and corresponding Manhattan plots (40 files named as "11_*")