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Variant association tools for quality control and analysis of large-scale sequence and genotyping array data.

Gao T Wang1, Bo Peng2, Suzanne M Leal1

  • 1Center for Statistical Genetics, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.

American Journal of Human Genetics
|May 6, 2014
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Summary

Variant Association Tools (VAT) offers a robust pipeline for rare-variant association studies. It provides comprehensive quality control, flexible analysis methods, and efficient processing for complex trait genetic research.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Detecting associations between complex traits and rare genetic variants is a significant challenge in current research.
  • Existing tools may lack comprehensive features for rigorous rare-variant association studies.

Purpose of the Study:

  • To introduce Variant Association Tools (VAT) and its associated pipeline.
  • To provide a best-practice framework for rare-variant association studies.

Main Methods:

  • VAT pipeline incorporates variant-site and call-level quality control (QC).
  • Includes phenotype- and genotype-based sample selection, variant annotation, and selection for association analysis.
  • Employs a regression-based framework for flexible association models with covariates and weighting.
  • Supports pathway, conditional, gene-gene, and gene-environment interaction analyses.
  • Utilizes multi-process computation and adaptive permutation for rapid analysis.

Main Results:

  • VAT enables rapid scanning and simultaneous association analysis via multiple methods.
  • Results can be output in text, graphic, or relational database formats.
  • An R language interface allows for user implementation of novel association methods.

Conclusions:

  • The VAT pipeline provides a reliable and reproducible computational environment for analyzing diverse genetic data (sequence, imputed, array).
  • It supports studies of varying scales, from small to large, using the latest genotyping and sequencing technologies.
  • Demonstrated application on 1000 Genomes project data validates its utility.