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Disease Gene Identification

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Cover of 'Disease Gene Identification'

Table of Contents

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    Book Overview
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    Chapter 1 Identification of Disease Susceptibility Alleles in the Next Generation Sequencing Era
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    Chapter 2 Induced Pluripotent Stem Cells in Disease Modeling and Gene Identification
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    Chapter 3 Development of Targeted Therapies Based on Gene Modification
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    Chapter 4 What Can We Learn About Human Disease from the Nematode C. elegans?
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    Chapter 5 Microbiome Sequencing Methods for Studying Human Diseases
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    Chapter 6 The Emerging Role of Long Noncoding RNAs in Human Disease
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    Chapter 7 Identification of Disease-Related Genes Using a Genome-Wide Association Study Approach
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    Chapter 8 Whole Genome Library Construction for Next Generation Sequencing
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    Chapter 9 Whole Exome Library Construction for Next Generation Sequencing
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    Chapter 10 Optimized Methodology for the Generation of RNA-Sequencing Libraries from Low-Input Starting Material: Enabling Analysis of Specialized Cell Types and Clinical Samples
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    Chapter 11 Using Fluidigm C1 to Generate Single-Cell Full-Length cDNA Libraries for mRNA Sequencing
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    Chapter 12 MiSeq: A Next Generation Sequencing Platform for Genomic Analysis
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    Chapter 13 Methods for CpG Methylation Array Profiling Via Bisulfite Conversion
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    Chapter 14 miRNA Quantification Method Using Quantitative Polymerase Chain Reaction in Conjunction with C q Method
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    Chapter 15 Primary Airway Epithelial Cell Gene Editing Using CRISPR-Cas9
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    Chapter 16 RNA Interference to Knock Down Gene Expression
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    Chapter 17 Using Luciferase Reporter Assays to Identify Functional Variants at Disease-Associated Loci
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    Chapter 18 Physiologic Interpretation of GWAS Signals for Type 2 Diabetes
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    Chapter 19 Identification of Genes for Hereditary Hemochromatosis
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    Chapter 20 Identification of Driver Mutations in Rare Cancers: The Role of SMARCA4 in Small Cell Carcinoma of the Ovary, Hypercalcemic Type (SCCOHT)
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    Chapter 21 The Rise and Fall and Rise of Linkage Analysis as a Technique for Finding and Characterizing Inherited Influences on Disease Expression
Attention for Chapter 3: Development of Targeted Therapies Based on Gene Modification
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Chapter title
Development of Targeted Therapies Based on Gene Modification
Chapter number 3
Book title
Disease Gene Identification
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7471-9_3
Pubmed ID
Book ISBNs
978-1-4939-7470-2, 978-1-4939-7471-9
Authors

Taylor M. Benson, Fatjon Leti, Johanna K. DiStefano

Abstract

With the advent of next-generation sequencing (NGS) and the demand for a personalized healthcare system, the fields of precision medicine and gene therapy are advancing in new directions. There is a push to identify genes that contribute to disease development, either alone or in conjunction with other genes or environmental factors, and then design targeted therapies based on this knowledge, rather than the traditional approach of treating generalized symptoms with pharmaceuticals in a one-size-fits-all manner. Identification of genes that contribute to disease pathogenesis and progression is critical for the maturation of the precision medicine field. Concomitant with a better understanding of disease pathology, precision medicine approaches can be adopted with greater confidence and are expected to lead to a new standard for clinical practice. In this chapter, we provide a brief introduction to precision medicine, discuss the importance of identifying genes and genetic variants that contribute to disease development and progression, offer examples of approaches that can be applied to treat specific diseases, and present some of the current challenges and limitations of precision medicine.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 25%
Student > Postgraduate 2 25%
Researcher 1 13%
Unknown 3 38%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 25%
Economics, Econometrics and Finance 2 25%
Medicine and Dentistry 1 13%
Unknown 3 38%