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Efficient testing and effect size estimation for set-based genetic association inference via semiparametric

Shonosuke Sugasawa1,2, Hisashi Noma2,3

  • 1Center for Spatial Information Science, The University of Tokyo, Chiba, Japan.

Biometrical Journal. Biometrische Zeitschrift
|May 11, 2022
PubMed
Summary

This study introduces a new statistical framework to improve the power of genetic association tests for complex traits. The method enhances the detection of rare variants and their effect sizes, particularly for diseases like coronary artery disease.

Keywords:
effect size estimationempirical Bayesgenome-wide association studyoptimal discovery procedure

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Rare variants with low allele frequencies are important in complex traits.
  • Existing set-based testing methods for single nucleotide polymorphisms (SNPs) have insufficient power and difficulty estimating individual SNP effect sizes.
  • There is a need for improved statistical inference frameworks in genetic association studies.

Purpose of the Study:

  • To develop an efficient set-based statistical inference framework to simultaneously increase association test power and enable precise effect size estimation.
  • To address limitations in current methods for analyzing rare variants in complex traits.

Main Methods:

  • Utilized an empirical Bayes method with semiparametric multilevel mixture modeling.
  • Incorporated a hierarchical model for set-specific effects.
  • Applied the optimal discovery procedure (ODP) for multiple significance testing and developed an optimal set-based estimator for effect size distributions.

Main Results:

  • Demonstrated efficiency through a genome-wide association study of coronary artery disease (CAD) and simulation studies.
  • Identified numerous rare variants with large effect sizes associated with CAD.
  • The ODP detected significantly more significant sets compared to existing methods.

Conclusions:

  • The proposed framework efficiently enhances statistical power in genetic association studies.
  • The method successfully identifies rare variants and estimates their effect sizes, offering improved insights into complex traits like CAD.
  • The ODP provides a superior approach for detecting significant genetic sets.