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Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Published on: June 21, 2018

Experiences with array-based sequence capture; toward clinical applications.

Rowida Almomani1, Jaap van der Heijden, Yavuz Ariyurek

  • 1Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands.

European Journal of Human Genetics : EJHG
|November 25, 2010
PubMed
Summary
This summary is machine-generated.

Targeted gene sequencing using array-based capture is a cost-effective method for identifying genetic variants in genes associated with developmental disorders. This approach reliably detects known variants and copy number variations, aiding in genetic diagnostics.

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Area of Science:

  • Genetics
  • Genomics
  • Molecular Biology

Background:

  • Whole human genome sequencing is becoming more accessible, but targeted sequencing remains a cost-effective strategy for specific research and diagnostic needs.
  • Identifying genetic variants in genes linked to mental retardation and congenital malformation is crucial for understanding disease mechanisms and improving diagnostics.

Purpose of the Study:

  • To evaluate the efficacy of array-based sequence capture combined with next-generation sequencing for targeted analysis of 112 genes implicated in developmental disorders.
  • To develop and validate a data analysis pipeline for variant detection, annotation, and prediction of functional consequences.

Main Methods:

  • Array-based sequence capture was performed using 385K Roche NimbleGen arrays targeting protein-coding and flanking intronic regions of 112 genes.
  • Captured DNA was sequenced using Illumina technology.
  • A comprehensive data analysis pipeline was developed to detect sequence variants, annotate their positions, query public databases (e.g., dbSNP), and predict effects on RNA splicing and protein translation.

Main Results:

  • The method reliably detected all known variants, including pathogenic variants in control samples and single-nucleotide polymorphisms (SNPs).
  • Copy number variations (CNVs), such as deletions and duplications, were detectable, including a partial deletion in the B3GALTL gene in a patient sample.
  • While coverage varied, it was reproducible across regions, enabling CNV detection.

Conclusions:

  • Array-based sequence capture and next-generation sequencing provide a reliable and cost-effective approach for targeted genetic analysis of disease-associated genes.
  • The developed data analysis pipeline effectively identifies sequence variants and predicts their functional impact.
  • Further optimization, including probe design and coverage uniformity, is needed to enhance diagnostic utility.