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Next-generation Sequencing03:00

Next-generation Sequencing

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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
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Privacy and ethical challenges in next-generation sequencing.

Nicole Martinez-Martin1, David Magnus1

  • 1Stanford Center for Biomedical Ethics, Stanford University, Stanford, CA, USA.

Expert Review of Precision Medicine and Drug Development
|August 11, 2020
PubMed
Summary
This summary is machine-generated.

Next-generation sequencing (NGS) offers medical advancements but presents ethical challenges. Addressing privacy, informed consent, and data management is crucial for responsible implementation in healthcare.

Keywords:
Next generation sequencingethicsprivacy

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Area of Science:

  • Genomic Medicine
  • Bioethics
  • Health Informatics

Background:

  • Next-generation sequencing (NGS) promises to transform healthcare with rapid, cost-effective whole-genome sequencing.
  • While NGS enables significant advancements in medical diagnostics and treatments, it introduces complex ethical considerations.

Purpose of the Study:

  • To provide a comprehensive overview of the ethical challenges associated with next-generation sequencing (NGS).
  • To examine the ethical implications of NGS in both research and clinical settings.

Main Methods:

  • Literature review encompassing research ethics, clinical ethics, and next-generation sequencing.
  • Analysis of ethical frameworks in the context of large-scale genomic data.

Main Results:

  • NGS generates vast datasets with varying clinical relevance, challenging traditional ethical protections for participants and patients.
  • Ethical concerns arise from data protection, management, and communication, especially with machine learning applications in genomic data interpretation.
  • Key ethical challenges include ensuring privacy, obtaining informed consent, and managing the return of genomic results.

Conclusions:

  • Traditional ethical frameworks require adaptation to address the unique challenges posed by NGS.
  • Developing robust processes for data handling and communication is essential.
  • Potential biases and fiduciary duty implications associated with machine learning in genomics warrant careful consideration.