Literature DB >> 22739340

RNA-Seq and human complex diseases: recent accomplishments and future perspectives.

Valerio Costa1, Marianna Aprile, Roberta Esposito, Alfredo Ciccodicola.   

Abstract

The availability of the human genome sequence has allowed identification of disease-causing mutations in many Mendelian disorders, and detection of significant associations of nucleotide polymorphisms to complex diseases and traits. Despite these progresses, finding the causative variations for most of the common diseases remains a complex task. Several studies have shown gene expression analyses provide a quite unbiased way to investigate complex traits and common disorders' pathogenesis. Therefore, whole-transcriptome analysis is increasingly acquiring a key role in the knowledge of mechanisms responsible for complex diseases. Hybridization- and tag-based technologies have elucidated the involvement of multiple genes and pathways in pathological conditions, providing insights into the expression of thousand of coding and noncoding RNAs, such as microRNAs. However, the introduction of Next-Generation Sequencing, particularly of RNA-Seq, has overcome some drawbacks of previously used technologies. Identifying, in a single experiment, potentially novel genes/exons and splice isoforms, RNA editing, fusion transcripts and allele-specific expression are some of its advantages. RNA-Seq has been fruitfully applied to study cancer and host-pathogens interactions, and it is taking first steps for studying neurodegenerative diseases (ND) as well as neuropsychiatric diseases. In addition, it is emerging as a very powerful tool to study quantitative trait loci associated with gene expression in complex diseases. This paper provides an overview on gene expression profiling of complex diseases, with emphasis on RNA-Seq, its advantages over conventional technologies for studying cancer and ND, and for linking nucleotide variations to gene expression changes, also discussing its limitations.

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Year:  2012        PMID: 22739340      PMCID: PMC3548270          DOI: 10.1038/ejhg.2012.129

Source DB:  PubMed          Journal:  Eur J Hum Genet        ISSN: 1018-4813            Impact factor:   4.246


  95 in total

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  83 in total

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Review 9.  Analysis of the transcriptome in molecular epidemiology studies.

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