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Detecting Causal Variants in Mendelian Disorders Using Whole-Genome Sequencing

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Deep Sequencing Data Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2243))

Abstract

Increasingly affordable sequencing technologies are revolutionizing the field of genomic medicine. It is now feasible to interrogate all major classes of variation in an individual across the entire genome for less than $1000 USD. While the generation of patient sequence information using these technologies has become routine, the analysis and interpretation of this data remains the greatest obstacle to widespread clinical implementation. This chapter summarizes the steps to identify, annotate, and prioritize variant information required for clinical report generation. We discuss methods to detect each variant class and describe strategies to increase the likelihood of detecting causal variant(s) in Mendelian disease. Lastly, we describe a sample workflow for synthesizing large amount of genetic information into concise clinical reports.

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Authors and Affiliations

  1. John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia

    Abdul Rezzak Hamzeh, T. Daniel Andrews & Matt A. Field

  2. Centre for Tropical Bioinformatics and Molecular Biology, Australian Institute of Tropical Health and Medicine, James Cook University, Cairns, QLD, Australia

    Matt A. Field

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  1. Abdul Rezzak Hamzeh
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  2. T. Daniel Andrews
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  1. Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

    Noam Shomron

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Hamzeh, A.R., Andrews, T.D., Field, M.A. (2021). Detecting Causal Variants in Mendelian Disorders Using Whole-Genome Sequencing. In: Shomron, N. (eds) Deep Sequencing Data Analysis. Methods in Molecular Biology, vol 2243. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1103-6_1

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