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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Predicting Gene Regulatory Interactions Using Natural Genetic Variation.

Maura John1, Dominik Grimm1, Arthur Korte2

  • 1Technical University of Munich & Weihenstephan-Triesdorf University of Applied Sciences, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, Straubing, Germany.

Methods in Molecular Biology (Clifton, N.J.)
|September 8, 2023
PubMed
Summary
This summary is machine-generated.

Genome-wide association studies (GWAS) reveal genotype-phenotype connections. This chapter details advanced GWAS methods for inferring gene regulatory networks and their variations.

Keywords:
GWASGene regulatory networksTWAS

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Area of Science:

  • Genetics
  • Bioinformatics
  • Systems Biology

Background:

  • Genome-wide association studies (GWAS) are crucial for understanding the relationship between genetic variations and observable traits.
  • Traditional GWAS focus on univariate associations, but advanced methods can uncover complex genetic architectures.

Purpose of the Study:

  • To present the latest methods and tools for performing genome-wide association studies (GWAS).
  • To extend GWAS to complex models for inferring gene regulatory networks and their dynamics.

Main Methods:

  • Utilizing permutation-based significance thresholds for robust GWAS analysis.
  • Applying univariate GWAS analyses as a foundation for more complex modeling.
  • Developing and describing methods for inferring gene regulatory networks from GWAS data.

Main Results:

  • Demonstrated the capability of GWAS to go beyond simple associations.
  • Provided a framework for inferring gene regulatory interactions and network structures.
  • Illustrated how gene regulatory networks can vary and be analyzed.

Conclusions:

  • Advanced GWAS methodologies offer powerful insights into genetic architecture and gene regulation.
  • The described methods enable the inference and analysis of complex gene regulatory networks.
  • This work enhances the utility of GWAS for systems biology approaches.