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RNA Sequencing and Genetic Disease

  • Clinical Genetics (J Stoler, Section Editor)
  • Published:
Current Genetic Medicine Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

Next-generation sequencing is a revolutionary approach for highly accurate identification of gene associations with specific human disease phenotypes. RNA sequencing (RNA-seq) holds great promise for identifying distinct gene expression “signatures” for the detection, prognosis, and chemosensitivity of human disease. However, this technique has yet to be adopted as a standard medical practice.

Recent Findings

The recent emergence of high-throughput, next-generation sequencing technology has facilitated significant advancements in understanding evolutionary diversity and disease. RNA-seq in particular is an invaluable tool for profiling transcription across the entire human genome (i.e., the “transcriptome”) at high resolution, providing the means to quantitatively study links among genotypes, transcript abundance, and other transcript-based features and human phenotypes. RNA-seq technology provides an essential layer of molecular information providing investigators and clinicians a systems genetics prospective of human disease. Integrated analysis of DNA- and RNA-seq data allows elucidation of the causal and regulatory mechanisms of the complex traits of human disease.

Summary

Here, we review RNA-seq technology and present a summary of different methods and their application to the study of human genetic disease. We further illustrate the utility of RNA-seq technology as an important tool for identifying novel transcriptomic biomarkers, and how these link to the pathological phenotypes of human disease initiation and progression. Advanced machine learning techniques for RNA-seq data analysis are also discussed.

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    Authors and Affiliations

    1. WuXi NextCODE Genomics, 101 Main St, 14th Floor, Cambridge, MA, 02142, USA

      Zehua Chen, Ryan P. Abo, Shannon T. Bailey, Jike Cui, Jeffrey R. Gulcher & Thomas W. Chittenden

    2. Bioscience Advising, Ypsilanti, MI, 48198, USA

      Curt Balch

    3. Complex Biological Sciences Alliance, North Andover, MA, 01845, USA

      Curt Balch & Thomas W. Chittenden

    4. Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA, 02115, USA

      Thomas W. Chittenden

    5. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02142, USA

      Thomas W. Chittenden

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    1. Zehua Chen
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    Conflict of Interest

    Zehua Chen, Ryan P. Abo, Shannon T. Bailey, Jike Cui, Curt Balch, Jeffrey R. Gulcher, and Thomas W. Chittenden declare that they have no conflict of interest.

    Human and Animal Rights and Informed Consent

    This article does not contain any studies with human or animal subjects performed by any of the authors.

    Additional information

    Zehua Chen, Ryan P. Abo, Shannon T. Bailey and Jike Cui have contributed equally to this work.

    This article is part of the Topical collection on Clinical Genetics.

    Glossary

    eQTL

    Expression quantitative trait loci. Loci within a genome that affect the level of expression of particular mRNAs.

    edQTL

    Editing quantitative trait loci. Loci within a genome that are associated with alterations in RNA editing levels.

    Deep learning

    Deep learning is a branch of machine learning based on modeling data with biologically-inspired algorithms, such as artificial neural networks.

    Machine learning

    Machine learning is a form of artificial intelligence (AI) that allows computers to iteratively learn hidden patterns within data without being explicitly programmed to do so.

    rdQTL

    RNA decay quantitative trait loci. Loci with a genome that are associated with differences in the rate of mRNA decay across individuals.

    sQTL

    Splicing quantitative trait loci. Loci within a genome that affect splicing events.

    Transcriptome

    The comprehensive set of expressed mRNAs within a particular cell, tissue, or organism.

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    Chen, Z., Abo, R.P., Bailey, S.T. et al. RNA Sequencing and Genetic Disease. Curr Genet Med Rep 4, 49–56 (2016). https://doi.org/10.1007/s40142-016-0098-x

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    • DOI: https://doi.org/10.1007/s40142-016-0098-x

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