Using cases to strengthen inference on the association between single nucleotide polymorphisms and a secondary phenotype in genome-wide association studies

Huilin Li, Mitchell H. Gail, Sonja Berndt, Nilanjan Chatterjee

Research output: Contribution to journalArticle

Abstract

Case-control genome-wide association studies provide a vast amount of genetic information that may be used to investigate secondary phenotypes. We study the situation in which the primary disease is rare and the secondary phenotype and genetic markers are dichotomous. An analysis of the association between a genetic marker and the secondary phenotype based on controls only (CO) is valid, whereas standard methods that also use cases result in biased estimates and highly inflated type I error if there is an interaction between the secondary phenotype and the genetic marker on the risk of the primary disease. Here we present an adaptively weighted (AW) method that combines the case and control data to study the association, while reducing to the CO analysis if there is strong evidence of an interaction. The possibility of such an interaction and the misleading results for standard methods, but not for the AWor CO approaches, are illustrated by data from a case-control study of colorectal adenoma. Simulations and asymptotic theory indicate that the AW method can reduce the mean square error for estimation with a prespecified SNP and increase the power to discover a new association in a genome-wide study, compared to CO analysis. Further experience with genome-wide studies is needed to determine when methods that assume no interaction gain precision and power, thereby can be recommended, and when methods such as the AW or CO approaches are needed to guard against the possibility of nonzero interactions.

Original languageEnglish (US)
Pages (from-to)427-433
Number of pages7
JournalGenetic Epidemiology
Volume34
Issue number5
DOIs
StatePublished - Jul 2010
Externally publishedYes

Fingerprint

Genome-Wide Association Study
Single Nucleotide Polymorphism
Phenotype
Genetic Markers
Genome
Rare Diseases
Adenoma
Case-Control Studies

Keywords

  • Adaptively weighted
  • Case-control study
  • Genome-wide association study
  • Maximum likelihood
  • Secondary phenotype

ASJC Scopus subject areas

  • Genetics(clinical)
  • Epidemiology

Cite this

Using cases to strengthen inference on the association between single nucleotide polymorphisms and a secondary phenotype in genome-wide association studies. / Li, Huilin; Gail, Mitchell H.; Berndt, Sonja; Chatterjee, Nilanjan.

In: Genetic Epidemiology, Vol. 34, No. 5, 07.2010, p. 427-433.

Research output: Contribution to journalArticle

@article{e06fcb143cf34d58bd3861089fb35869,
title = "Using cases to strengthen inference on the association between single nucleotide polymorphisms and a secondary phenotype in genome-wide association studies",
abstract = "Case-control genome-wide association studies provide a vast amount of genetic information that may be used to investigate secondary phenotypes. We study the situation in which the primary disease is rare and the secondary phenotype and genetic markers are dichotomous. An analysis of the association between a genetic marker and the secondary phenotype based on controls only (CO) is valid, whereas standard methods that also use cases result in biased estimates and highly inflated type I error if there is an interaction between the secondary phenotype and the genetic marker on the risk of the primary disease. Here we present an adaptively weighted (AW) method that combines the case and control data to study the association, while reducing to the CO analysis if there is strong evidence of an interaction. The possibility of such an interaction and the misleading results for standard methods, but not for the AWor CO approaches, are illustrated by data from a case-control study of colorectal adenoma. Simulations and asymptotic theory indicate that the AW method can reduce the mean square error for estimation with a prespecified SNP and increase the power to discover a new association in a genome-wide study, compared to CO analysis. Further experience with genome-wide studies is needed to determine when methods that assume no interaction gain precision and power, thereby can be recommended, and when methods such as the AW or CO approaches are needed to guard against the possibility of nonzero interactions.",
keywords = "Adaptively weighted, Case-control study, Genome-wide association study, Maximum likelihood, Secondary phenotype",
author = "Huilin Li and Gail, {Mitchell H.} and Sonja Berndt and Nilanjan Chatterjee",
year = "2010",
month = "7",
doi = "10.1002/gepi.20495",
language = "English (US)",
volume = "34",
pages = "427--433",
journal = "Genetic Epidemiology",
issn = "0741-0395",
publisher = "Wiley-Liss Inc.",
number = "5",

}

TY - JOUR

T1 - Using cases to strengthen inference on the association between single nucleotide polymorphisms and a secondary phenotype in genome-wide association studies

AU - Li, Huilin

AU - Gail, Mitchell H.

AU - Berndt, Sonja

AU - Chatterjee, Nilanjan

PY - 2010/7

Y1 - 2010/7

N2 - Case-control genome-wide association studies provide a vast amount of genetic information that may be used to investigate secondary phenotypes. We study the situation in which the primary disease is rare and the secondary phenotype and genetic markers are dichotomous. An analysis of the association between a genetic marker and the secondary phenotype based on controls only (CO) is valid, whereas standard methods that also use cases result in biased estimates and highly inflated type I error if there is an interaction between the secondary phenotype and the genetic marker on the risk of the primary disease. Here we present an adaptively weighted (AW) method that combines the case and control data to study the association, while reducing to the CO analysis if there is strong evidence of an interaction. The possibility of such an interaction and the misleading results for standard methods, but not for the AWor CO approaches, are illustrated by data from a case-control study of colorectal adenoma. Simulations and asymptotic theory indicate that the AW method can reduce the mean square error for estimation with a prespecified SNP and increase the power to discover a new association in a genome-wide study, compared to CO analysis. Further experience with genome-wide studies is needed to determine when methods that assume no interaction gain precision and power, thereby can be recommended, and when methods such as the AW or CO approaches are needed to guard against the possibility of nonzero interactions.

AB - Case-control genome-wide association studies provide a vast amount of genetic information that may be used to investigate secondary phenotypes. We study the situation in which the primary disease is rare and the secondary phenotype and genetic markers are dichotomous. An analysis of the association between a genetic marker and the secondary phenotype based on controls only (CO) is valid, whereas standard methods that also use cases result in biased estimates and highly inflated type I error if there is an interaction between the secondary phenotype and the genetic marker on the risk of the primary disease. Here we present an adaptively weighted (AW) method that combines the case and control data to study the association, while reducing to the CO analysis if there is strong evidence of an interaction. The possibility of such an interaction and the misleading results for standard methods, but not for the AWor CO approaches, are illustrated by data from a case-control study of colorectal adenoma. Simulations and asymptotic theory indicate that the AW method can reduce the mean square error for estimation with a prespecified SNP and increase the power to discover a new association in a genome-wide study, compared to CO analysis. Further experience with genome-wide studies is needed to determine when methods that assume no interaction gain precision and power, thereby can be recommended, and when methods such as the AW or CO approaches are needed to guard against the possibility of nonzero interactions.

KW - Adaptively weighted

KW - Case-control study

KW - Genome-wide association study

KW - Maximum likelihood

KW - Secondary phenotype

UR - http://www.scopus.com/inward/record.url?scp=77954202193&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77954202193&partnerID=8YFLogxK

U2 - 10.1002/gepi.20495

DO - 10.1002/gepi.20495

M3 - Article

C2 - 20583284

AN - SCOPUS:77954202193

VL - 34

SP - 427

EP - 433

JO - Genetic Epidemiology

JF - Genetic Epidemiology

SN - 0741-0395

IS - 5

ER -