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Sequence robust association test for familial data.

Wei Dai1, Ming Yang1, Chaolong Wang2

  • 1Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, U.S.A.

Biometrics
|March 9, 2017
PubMed
Summary

We introduce the Sequence Robust Association Test (SRAT), a new rank-based method for analyzing genetic data in families. SRAT improves accuracy and power in identifying genetic associations, especially with complex family structures and skewed data.

Keywords:
Genetic association testingMarginal modelNon-parametric transformation modelPerturbation resamplingRobust procedures

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Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Family studies are crucial for genetic association studies (GWAS, NGSS) but analyzing familial data is complex.
  • Existing methods like GEE and LME models may have issues with type I errors due to unmet assumptions.
  • Accurate analysis is needed to identify genetic variants linked to clinical phenotypes within families.

Purpose of the Study:

  • To propose a novel, robust statistical method for genetic association testing in family studies.
  • To develop an approach that accounts for within-family correlations and covariate adjustments.
  • To offer a flexible alternative to existing methods with weaker distributional assumptions.

Main Methods:

  • Developed the Sequence Robust Association Test (SRAT), a fully rank-based statistical test.
  • SRAT accommodates unknown correlation structures and is robust to various outcome distributions.
  • Theoretical justifications provided, showing SRAT encompasses the Wilcoxon rank sum test.

Main Results:

  • SRAT demonstrates superior control of type I error rates compared to existing methods.
  • Simulations indicate SRAT offers increased statistical power, particularly for skewed outcome distributions.
  • Application to Framingham Heart Study data illustrated SRAT's utility in real-world genetic analyses.

Conclusions:

  • SRAT is a flexible and robust method for genetic association studies in families.
  • The rank-based approach enhances reliability when standard model assumptions are violated.
  • SRAT provides a valuable tool for identifying genetic variants influencing clinical traits in familial cohorts.