Rice Imputation Project
Ontology highlight
ABSTRACT:
INSTRUMENT(S): -
ORGANISM(S): Oryza Sativa
SUBMITTER: Cornell University
PROVIDER: PRJEB26328 | EVA | 2018-05-01
REPOSITORIES: EVA
Similar Datasets
Project description:A High Density Rice Array (HDRA) was developed as an Affymetrix Custom GeneChip Array by the McCouch Rice Lab at Cornell University. The HDRA assays 700,000 SNPs, or approximately one SNP every 0.54 Kb across the rice genome (genome size = 380 Mb). It was designed to capture most of the haplotype variation observed in a discovery panel consisting of 16M SNPs (generated by sequencing 125 rice genomes at ~7X genome coverage) and to maximize the inclusion of non-synonymous SNPs. Six probes per SNP target were designed as 3 A-allele and 3 B-allele probes at offsets from center ranging from -6 to +6. A small fraction of SNPs have only 4 probes (2-A, 2-B). For all SNPs, the “A” allele is the reference allele (Os-Nipponbare-Reference-IRGSP-1.0 assembly). Additionally, we designed 23,656 x 25-bp probes complimentary to invariant regions of the genome that were used to normalize systematic differences between samples. An estimated 45% of HDRA SNPs map within genes, hitting all 39,045 unique, non-TE rice gene models (MSUv7 rice genome annotation, GFF3 file, Feb. 7, 2012, http://rice.plantbiology.msu.edu/), while 55% of SNPs map to intergenic regions. Non-synonymous are found in 91% of unique, non-TE gene models, and 57% of genic SNPs are distributed within exons, 36% within introns, 5% within 5’ UTRs and 2% within 3’ UTRs. Of the intergenic SNPs, 40% are located in putative regulatory regions within 2 Kb of a transcriptional start site.
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Project description:Low-pass sequencing (sequencing a genome to an average depth less than 1× coverage) combined with genotype imputation has been proposed as an alternative to genotyping arrays for trait mapping and calculation of polygenic scores. To empirically assess the relative performance of these technologies for different applications, we performed low-pass sequencing (targeting coverage levels of 0.5× and 1×) and array genotyping (using the Illumina Global Screening Array (GSA)) on 120 DNA samples derived from African and European-ancestry individuals that are part of the 1000 Genomes Project. We then imputed both the sequencing data and the genotyping array data to the 1000 Genomes Phase 3 haplotype reference panel using a leave- one-out design. We evaluated overall imputation accuracy from these different assays as well as overall power for GWAS from imputed data, and computed polygenic risk scores for coronary artery disease and breast cancer using previously derived weights. We conclude that low-pass sequencing plus imputation, in addition to providing a substantial increase in statistical power for genome wide association studies, provides increased accuracy for polygenic risk prediction at effective coverages of ∼ 0.5× and higher compared to the Illumina GSA.
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Project description:Thus, our observations suggested an intriguing possibility that OsCERK1 functions as the receptor of the mycorrhizal signal Myc-LCOs, although the function of Myc-LCOs has been examined only in dicotyledonous plants. To test this hypothesis, we evaluated Myc-LCO responses in rice by transcriptome analysis using next-generation sequencing.
2015-02-26 | E-MTAB-4050 | biostudies-arrayexpress
Project description:In this study, we aim to present a global view of transcriptome dynamics in different rice cultivars (IR64, Nagina 22 and Pokkali) under control and stress conditions. More than 50 million high quality reads were obtained for each tissue sample using Illumina platform. Reference-based assembly was performed for each rice cultivar. The transcriptome dynamics was studied by differential gene expression analyses between stress treatment and control sample. We collected seedlings of three rice cultivars subjected to control (kept in water), desiccation (transferred on folds of tissue paper) and salinity (transferred to beaker containing 200 mM NaCl solution) treatments. Total RNA isolated from these tissue samples was subjected to Illumina sequencing. The sequence data was further filtered using NGS QC Toolkit to obtain high-quality reads. The filtered reads were mapped to Japonica reference genome using Tophat software. Cufflinks was used for reference-based assembly and differential gene expression was studied using cuffdiff software. The differentially expressed genes during various abiotic stress conditions were identified.
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