Hamzeh, Abdul Rezzak, Andrews, T. Daniel, and Field, Matt A. (2021) Detecting Causal Variants in Mendelian Disorders Using Whole-Genome Sequencing. In: Noam, Shomron, (ed.) Deep Sequencing Data Analysis. Methods in Molecular Biology, 2243 . Humana Press, New York, NY, USA, pp. 1-25.

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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.

Item ID:	72407
Item Type:	Book Chapter (Research - B1)
ISBN:	978-1-0716-1102-9
Copyright Information:	© Springer Science+Business Media, LLC, part of Springer Nature 2021.
Date Deposited:	20 Apr 2022 02:22
FoR Codes:	31 BIOLOGICAL SCIENCES > 3102 Bioinformatics and computational biology > 310208 Translational and applied bioinformatics @ 100%
SEO Codes:	20 HEALTH > 2001 Clinical health > 200101 Diagnosis of human diseases and conditions @ 100%
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