04d0c4d7__characterisation_of_risk_loci__0
[1] High throughput next-generation sequencing of cDNA libraries generated either from the entire set of cellular steady-state RNA or its subsets. [2] Applications: (i) Discovery of novel RNA species including long non-coding RNA and small regulatory RNA, as well as new RNA isoforms originating from known loci; (ii) whole-transcriptome expression profiling including eQTL discovery; (iii) targeted transcription profiling within GWAS regions. [3] Advantages: Eliminates dependence on probes used in Microarray applications; enables quantitative profiling of the entire transcriptome and its subsets. Higher dynamic range and sensitivity. Higher power to distinguish smaller changes in gene expression. Capacity for detecting allele-specific expression. Enables direct detection of RNA splice isoforms. Genetic variations including SNPs, CNVs and translocations canbe detected in the same experiment. Disadvantages: High costs prohibits large scale eQTL projects. [4] Although RNA-seq allows for potentially limitless dynamic range there is a strong effect of diminishing return for detection of RNA with low abundance, as very large portion of sequence reads is taken up by most abundant RNA transcripts. Transcriptome partitioning utilizing either enrichment or depletion techniques (e.g. rRNA depletion or small RNA enrichment) largely alleviates this problem. Data analysis methodologies are rapidly maturing but still represent a significant challenge. [170] [87] [171] [172] [0] 
RNA-Seq
WikiGenes - Principles for the post-GWAS functional characterisation of risk loci
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