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Updated: May 15, 2026

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
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Published on: November 3, 2010

Sequence kernel association test for quantitative traits in family samples.

Han Chen1, James B Meigs, Josée Dupuis

  • 1Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA. hanchen@bu.edu

Genetic Epidemiology
|January 3, 2013
PubMed
Summary
This summary is machine-generated.

Family-based sequence kernel association test (famSKAT) accurately analyzes rare genetic variants in families. This method improves upon existing techniques by correctly handling correlated family data and increasing statistical power for disease association studies.

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08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

  • Genetics
  • Biostatistics
  • Genomic Medicine

Background:

  • Advancements in sequencing technology have led to the discovery of numerous rare genetic variants.
  • Rare variant analysis is crucial for identifying novel genes linked to diseases and quantitative traits, enhancing our understanding of heritability.
  • Existing statistical methods for rare variant analysis often rely on restrictive assumptions or are computationally intensive.

Purpose of the Study:

  • To extend the Sequence Kernel Association Test (SKAT) for application to family-based genetic data.
  • To develop a method that accounts for familial correlation without compromising statistical accuracy.
  • To improve the power of rare variant association testing in family studies.

Main Methods:

  • Development of family-based SKAT (famSKAT) with a distinct test statistic and null distribution.
  • Comparison of famSKAT with the original SKAT under scenarios with and without familial correlation.
  • Simulation studies to evaluate type I error rates and statistical power.
  • Application of famSKAT to analyze rare genetic variants associated with glycemic traits in the Framingham Heart Study.

Main Results:

  • SKAT shows inflated type I error when familial correlation is ignored but has appropriate error rates when applied to unrelated subsets.
  • famSKAT maintains correct type I error rates when analyzing correlated family observations.
  • famSKAT demonstrates higher statistical power compared to competing methods across various scenarios.
  • The analysis of glycemic traits in the Framingham Heart Study illustrates the practical application of famSKAT.

Conclusions:

  • famSKAT is a robust statistical method for rare variant association analysis in family studies.
  • Ignoring familial correlation can lead to inaccurate results, whereas famSKAT effectively handles such data.
  • famSKAT offers improved power and accuracy, advancing the study of genetic contributions to complex traits and diseases.