A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms

Xiaoyi Gao, Joshua Starmer, Eden R Martin

Research output: Contribution to journalArticle

358 Citations (Scopus)

Abstract

Multiple testing is a challenging issue in genetic association studies using large numbers of single nucleotide polymorphism (SNP) markers, many of which exhibit linkage disequilibrium (LD). Failure to adjust for multiple testing appropriately may produce excessive false positives or overlook true positive signals. The Bonferroni method of adjusting for multiple comparisons is easy to compute, but is well known to be conservative in the presence of LD. On the other hand, permutation-based corrections can correctly account for LD among SNPs, but are computationally intensive. In this work, we propose a new multiple testing correction method for association studies using SNP markers. We show that it is simple, fast and more accurate than the recently developed methods and is comparable to permutation-based corrections using both simulated and real data. We also demonstrate how it might be used in whole-genome association studies to control type I error. The efficiency and accuracy of the proposed method make it an attractive choice for multiple testing adjustment when there is high intermarker LD in the SNP data set.

Original languageEnglish
Pages (from-to)361-369
Number of pages9
JournalGenetic Epidemiology
Volume32
Issue number4
DOIs
StatePublished - May 1 2008

Fingerprint

Linkage Disequilibrium
Genetic Association Studies
Single Nucleotide Polymorphism
Genome-Wide Association Study

Keywords

  • Composite linkage disequilibrium
  • Eigenvalues
  • Multiple testing correction
  • Principal component analysis
  • Single nucleotide polymorphism

ASJC Scopus subject areas

  • Genetics(clinical)
  • Epidemiology

Cite this

A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms. / Gao, Xiaoyi; Starmer, Joshua; Martin, Eden R.

In: Genetic Epidemiology, Vol. 32, No. 4, 01.05.2008, p. 361-369.

Research output: Contribution to journalArticle

@article{a4bbe05b0f6a43a98478dd3f60c67006,
title = "A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms",
abstract = "Multiple testing is a challenging issue in genetic association studies using large numbers of single nucleotide polymorphism (SNP) markers, many of which exhibit linkage disequilibrium (LD). Failure to adjust for multiple testing appropriately may produce excessive false positives or overlook true positive signals. The Bonferroni method of adjusting for multiple comparisons is easy to compute, but is well known to be conservative in the presence of LD. On the other hand, permutation-based corrections can correctly account for LD among SNPs, but are computationally intensive. In this work, we propose a new multiple testing correction method for association studies using SNP markers. We show that it is simple, fast and more accurate than the recently developed methods and is comparable to permutation-based corrections using both simulated and real data. We also demonstrate how it might be used in whole-genome association studies to control type I error. The efficiency and accuracy of the proposed method make it an attractive choice for multiple testing adjustment when there is high intermarker LD in the SNP data set.",
keywords = "Composite linkage disequilibrium, Eigenvalues, Multiple testing correction, Principal component analysis, Single nucleotide polymorphism",
author = "Xiaoyi Gao and Joshua Starmer and Martin, {Eden R}",
year = "2008",
month = "5",
day = "1",
doi = "10.1002/gepi.20310",
language = "English",
volume = "32",
pages = "361--369",
journal = "Genetic Epidemiology",
issn = "0741-0395",
publisher = "Wiley-Liss Inc.",
number = "4",

}

TY - JOUR

T1 - A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms

AU - Gao, Xiaoyi

AU - Starmer, Joshua

AU - Martin, Eden R

PY - 2008/5/1

Y1 - 2008/5/1

N2 - Multiple testing is a challenging issue in genetic association studies using large numbers of single nucleotide polymorphism (SNP) markers, many of which exhibit linkage disequilibrium (LD). Failure to adjust for multiple testing appropriately may produce excessive false positives or overlook true positive signals. The Bonferroni method of adjusting for multiple comparisons is easy to compute, but is well known to be conservative in the presence of LD. On the other hand, permutation-based corrections can correctly account for LD among SNPs, but are computationally intensive. In this work, we propose a new multiple testing correction method for association studies using SNP markers. We show that it is simple, fast and more accurate than the recently developed methods and is comparable to permutation-based corrections using both simulated and real data. We also demonstrate how it might be used in whole-genome association studies to control type I error. The efficiency and accuracy of the proposed method make it an attractive choice for multiple testing adjustment when there is high intermarker LD in the SNP data set.

AB - Multiple testing is a challenging issue in genetic association studies using large numbers of single nucleotide polymorphism (SNP) markers, many of which exhibit linkage disequilibrium (LD). Failure to adjust for multiple testing appropriately may produce excessive false positives or overlook true positive signals. The Bonferroni method of adjusting for multiple comparisons is easy to compute, but is well known to be conservative in the presence of LD. On the other hand, permutation-based corrections can correctly account for LD among SNPs, but are computationally intensive. In this work, we propose a new multiple testing correction method for association studies using SNP markers. We show that it is simple, fast and more accurate than the recently developed methods and is comparable to permutation-based corrections using both simulated and real data. We also demonstrate how it might be used in whole-genome association studies to control type I error. The efficiency and accuracy of the proposed method make it an attractive choice for multiple testing adjustment when there is high intermarker LD in the SNP data set.

KW - Composite linkage disequilibrium

KW - Eigenvalues

KW - Multiple testing correction

KW - Principal component analysis

KW - Single nucleotide polymorphism

UR - http://www.scopus.com/inward/record.url?scp=43249090541&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=43249090541&partnerID=8YFLogxK

U2 - 10.1002/gepi.20310

DO - 10.1002/gepi.20310

M3 - Article

C2 - 18271029

AN - SCOPUS:43249090541

VL - 32

SP - 361

EP - 369

JO - Genetic Epidemiology

JF - Genetic Epidemiology

SN - 0741-0395

IS - 4

ER -