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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Statistical tests for detecting rare variants using variance-stabilising transformations.

Kai Wang1, John H Fingert

  • 1Department of Biostatistics, College of Public Health, The University of Iowa, Iowa City, IA 52242, USA. kai-wang@uiowa.edu

Annals of Human Genetics
|June 26, 2012
PubMed
Summary
This summary is machine-generated.

Next generation sequencing (NGS) can detect rare variants for complex traits. Variance-stabilising transformations offer a powerful alternative to the conservative Fisher

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical genomics

Background:

  • Next-generation sequencing (NGS) is crucial for identifying rare genetic variants associated with complex human traits.
  • Traditional statistical methods, like normality approximation for proportions, are unreliable for extremely low allele frequencies.
  • The Fisher's exact test is a suitable alternative but often overly conservative, potentially reducing statistical power.

Purpose of the Study:

  • To evaluate the effectiveness of variance-stabilising transformations in single marker association analyses for rare variants.
  • To compare the power and type I error rates of transformed proportion tests against the Fisher's exact test.

Main Methods:

  • Investigated several variance-stabilising transformations applied to proportions of rare variants.
  • Conducted simulation studies to assess statistical performance under conditions of low allele frequencies.
  • Compared the power and type I error rates of tests utilizing transformed proportions versus the Fisher's exact test.

Main Results:

  • Variance-stabilising transformations ensure that the variance of transformed proportions is independent of the proportion itself, ideal for rare variants.
  • Simulation results showed that tests employing these transformations are more powerful than the Fisher's exact test.
  • All tested transformations effectively controlled the type I error rate.

Conclusions:

  • Variance-stabilising transformations provide a powerful and reliable approach for association analysis of rare variants in complex traits.
  • The Anscombe transformation demonstrated superior performance compared to other transformations and the Fisher's exact test.
  • The Anscombe transformation is recommended for single marker association analysis involving rare variants detected by NGS.