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Designing Algorithms for Determining Significance of DNA Missense Changes

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Clinical Bioinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1168))

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Abstract

Humans differ from each other in their genomes by <1 %. This determines the difference in susceptibility to disease, phenotypes, and traits. Predominantly, when looking for causal disease mutations, protein-coding sequences are screened first since those have the highest probability of affecting the function of a protein. Recent technological advances have seen a rise in the number of experiments being conducted to study a variety of diseases from monogenic to complex traits. Several computational approaches have been developed to extract putative functional missense variants. In this chapter we review some of these approaches and describe a standard step-by-step procedure that can be used to classify variants for the purpose of clinical care. We also provide two examples demonstrating this approach, one for a patient with a dilated cardiomyopathy diagnosis, and the other for a patient with an unknown etiology undergoing whole-genome sequencing (WGS).

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Abbreviations

ESP:

Exome Sequencing Project

HGMD:

Human Gene Mutation Database

LOF:

Loss of function

OMIM:

Online Mendelian inheritance in man

SNV:

Single nucleotide variation

VUS:

Variant of unknown significance

WES:

Whole-exome sequencing

WGS:

Whole-genome sequencing

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Author information

Authors and Affiliations

  1. Novartis Institutes for Biomedical Research, 500 Technology Square, 840-14, Cambridge, MA, 02139, USA

    Sivakumar Gowrisankar

  2. Partners HealthCare Center for Personalized Genetic Medicine, Cambridge, MA, USA

    Matthew S. Lebo

  3. Department of Pathology, Harvard Medical School, Boston, MA, USA

    Matthew S. Lebo

Authors
  1. Sivakumar Gowrisankar
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  2. Matthew S. Lebo
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Corresponding author

Correspondence to Sivakumar Gowrisankar .

Editor information

Editors and Affiliations

  1. Department of Medical Genomics, Royal Prince Alfred Hospital and Sydney Medical School, University of Sydney, Camperdown, Australia

    Ronald Trent

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Gowrisankar, S., Lebo, M.S. (2014). Designing Algorithms for Determining Significance of DNA Missense Changes. In: Trent, R. (eds) Clinical Bioinformatics. Methods in Molecular Biology, vol 1168. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-0847-9_14

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  • DOI: https://doi.org/10.1007/978-1-4939-0847-9_14

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-0846-2

  • Online ISBN: 978-1-4939-0847-9

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