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ADAPTIVE-WEIGHT BURDEN TEST FOR ASSOCIATIONS BETWEEN QUANTITATIVE TRAITS AND GENOTYPE DATA WITH COMPLEX CORRELATIONS.

Xiaowei Wu1, Ting Guan1, Dajiang J Liu2

  • 1Department of Statistics, Virginia Tech, 250 Drillfield Drive, MC0439, Blacksburg, VA 24061, USA.

The Annals of Applied Statistics
|September 15, 2018
PubMed
Summary

We developed the Adaptive-weight Burden Test (ABT) to analyze genetic association for quantitative traits using genotype data with complex correlations. ABT improves power by utilizing data-driven weights and accounting for familial relationships and linkage disequilibrium.

Keywords:
Genetic association testPrimary 62F03adaptive weightbi-directional genotypic correlationburden testkernel testsecondary 62P10

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • High-throughput sequencing generates genotype data with complex correlations from familial relationships and linkage disequilibrium.
  • Accurate assessment of variant contributions by gene or pathway requires accounting for these genotypic correlations.
  • Existing association testing methods have limitations in handling complex genotypic correlations.

Purpose of the Study:

  • To propose a novel statistical method, the Adaptive-weight Burden Test (ABT), for genetic association analysis of quantitative traits.
  • To develop a retrospective, mixed-model test that effectively utilizes genotypic correlations across samples and variants.
  • To enhance statistical power through data-driven weight adaptation.

Main Methods:

  • Developed the Adaptive-weight Burden Test (ABT), a retrospective, mixed-model association test.
  • Derived the ABT statistic and its null distribution.
  • Employed simulation studies to compare ABT with existing methods like fixed-weight burden tests and family-based SKAT.
  • Investigated the connection of ABT with kernel tests and the adaptability of its weights.

Main Results:

  • ABT demonstrated superior power compared to fixed-weight burden tests and family-based SKAT across various simulation scenarios.
  • The method effectively controlled type I error rates.
  • Simulations confirmed the adaptability of ABT's weights to the direction of genetic effects.
  • ABT successfully analyzed whole-genome data for genes associated with fasting glucose, incorporating both common and rare variants.

Conclusions:

  • The Adaptive-weight Burden Test (ABT) is a powerful and robust method for genetic association studies with complex correlation structures.
  • ABT offers an improvement over existing methods, particularly in scenarios involving familial relationships and linkage disequilibrium.
  • The method's data-driven weighting scheme enhances its ability to detect genetic associations for quantitative traits.