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Related Experiment Video

Updated: Mar 14, 2026

Identification of Homologous Recombination Events in Mouse Embryonic Stem Cells Using Southern Blotting and Polymerase Chain Reaction
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Guidelines for Large-Scale Sequence-Based Complex Trait Association Studies: Lessons Learned from the NHLBI Exome

Paul L Auer1, Alex P Reiner2, Gao Wang3

  • 1Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI 53205, USA; Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.

American Journal of Human Genetics
|September 27, 2016
PubMed

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Summary

Whole-genome sequencing (WGS) advances human genetics research, but analysis challenges persist. Lessons from the Exome Sequencing Project (ESP) guide large-scale WGS studies for complex traits and precision medicine.

Area of Science:

  • Human genetics
  • Genomic medicine
  • Bioinformatics

Background:

  • Massively parallel whole-genome sequencing (WGS) generates vast data for human genetics.
  • WGS data are crucial for understanding rare variants in complex traits and advancing precision medicine.
  • Technological advances in WGS have outpaced rigorous analytical methodologies.

Purpose of the Study:

  • To share lessons learned from the NHLBI Exome Sequencing Project (ESP) for WGS initiatives.
  • To provide guidance and a framework for conducting large-scale association studies using WGS data.
  • To address challenges in WGS data analysis, including quality control, rare variant analysis, and study design.

Main Methods:

  • Analysis of NHLBI Exome Sequencing Project (ESP) data.

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  • Review of collective experiences and challenges in WGS data analysis.
  • Development of a framework for large-scale WGS association studies.

    • Main Results:

    • Identification of disease and complex trait associations from WES data.
    • Highlighted caveats in automated pipelines for WGS data quality control and rare variant analysis.
    • Emphasized the importance of minority population studies, sample size, efficient study designs, and managing incidental findings.

    Conclusions:

    • The ESP provides valuable lessons for future WGS initiatives.
    • Rigorous analytical approaches are essential for harnessing the potential of WGS data.
    • A structured framework is needed to guide large-scale WGS association studies in human genetics.