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Related Concept Videos

Genetic Screens02:46

Genetic Screens

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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Pedigree-based random effect tests to screen gene pathways.

Marcio Almeida1, Juan M Peralta1,2, Vidya Farook1

  • 1Department of Genetics, Texas Biomedical Research Institute. 7620 NW Loop 410, San Antonio, TX 78245, USA.

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|December 19, 2014
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Summary
This summary is machine-generated.

New whole genome sequencing methods identify genetic variants associated with traits. A novel pathway-specific genetic relationship model reduces statistical tests, detecting true signals in causal gene pathways.

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Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Next-generation sequencing generates vast genetic data, posing analytical challenges.
  • Identifying genetic variants associated with phenotypes requires efficient statistical methods.
  • Whole genome sequencing in large cohorts like the San Antonio Family Study reveals millions of variants.

Purpose of the Study:

  • To develop a statistical method for analyzing genetic variants within biological pathways.
  • To reduce the number of statistical tests required for whole genome association studies.
  • To detect associations between gene pathways and phenotypic traits.

Main Methods:

  • Developed a single degree-of-freedom test using a random effect model.
  • Employed an empirical pathway-specific genetic relationship matrix.
  • Utilized SOLAR's pedigree-based variance components modeling and likelihood ratio tests.
  • Applied the method to all KEGG pathways in the Genetic Analysis Workshop 18 simulation data for systolic blood pressure.

Main Results:

  • The random effect approach successfully detected true association signals in causal gene pathways.
  • The method effectively reduced the analytical burden of whole genome sequencing data.
  • Identified specific gene pathways associated with systolic blood pressure in the simulation.

Conclusions:

  • The developed method is effective for identifying biologically relevant gene pathways associated with traits.
  • This approach facilitates the dissection of complex genetic architectures by analyzing variants within pathways.
  • Offers a powerful tool for genetic association studies utilizing whole genome sequencing data.