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Genome-wide Association Studies-GWAS

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Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

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Published on: February 3, 2013

New correction algorithms for multiple comparisons in case-control multilocus association studies based on haplotypes

Kazuharu Misawa¹, Shoogo Fujii^2,3, Toshimasa Yamazaki²

¹Research Program for Computational Science, Research and Development Group for Next-Generation Integrated Living Matter Simulation, Fusion of Data and Analysis Research and Development Team, RIKEN, 4-6-1 Shirokane-dai, Minato-ku, Tokyo, 108-8639, Japan. kazumisawa@riken.jp.

Journal of Human Genetics

|July 25, 2008

Summary

New algorithms address the multiple comparison problem in genetic association studies. These methods offer more accurate error rates for multilocus genotypes in linkage disequilibrium than traditional Bonferroni correction.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Population genetics
Statistical genetics
Bioinformatics

Background:

Multiple comparison issues complicate genetic association studies examining phenotype-genotype links.
Bonferroni correction, while common, can be overly conservative due to independence assumptions.

Purpose of the Study:

Develop novel correction algorithms for testing phenotype-multilocus genotype associations, especially with linked loci.
Improve the accuracy of type I error rate calculations in multilocus association analyses.

Main Methods:

Developed an exact algorithm to calculate type I error rates for independency tests, feasible for smaller datasets (up to 50 cases/controls) on a PC cluster.
Created Markov-chain Monte Carlo (MCMC) based algorithms for asymptotic type I error rate calculation, approximating exact values.

Main Results:

New algorithms applied to simulated and real data showed significantly lower overall type I error rates (one-third to half) compared to Bonferroni correction for linked loci.
The MCMC method provided a good approximation to the exact calculation results.

Conclusions:

The developed algorithms provide more accurate type I error control for multilocus association studies involving linkage disequilibrium.
These methods are valuable for analyzing case-control and cohort study data in population genetics research.