A unified GMDR method for detecting gene-gene interactions in family and unrelated samples with application to nicotine dependence

Guo Bo Chen, Nianjun Liu, Yann C Klimentidis, Xiaofeng Zhu, Degui Zhi, Xujing Wang, Xiang Yang Lou

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Gene-gene and gene-environment interactions govern a substantial portion of the variation in complex traits and diseases. In convention, a set of either unrelated or family samples are used in detection of such interactions; even when both kinds of data are available, the unrelated and the family samples are analyzed separately, potentially leading to loss in statistical power. In this report, to detect gene-gene interactions we propose a generalized multifactor dimensionality reduction method that unifies analyses of nuclear families and unrelated subjects within the same statistical framework. We used principal components as genetic background controls against population stratification, and when sibling data are included, within-family control were used to correct for potential spurious association at the tested loci. Through comprehensive simulations, we demonstrate that the proposed method can remarkably increase power by pooling unrelated and offspring's samples together as compared with individual analysis strategies and the Fisher's combining p value method while it retains a controlled type I error rate in the presence of population structure. In application to a real dataset, we detected one significant tetragenic interaction among CHRNA4, CHRNB2, BDNF, and NTRK2 associated with nicotine dependence in the Study of Addiction: Genetics and Environment sample, suggesting the biological role of these genes in nicotine dependence development.

Original languageEnglish (US)
Pages (from-to)139-150
Number of pages12
JournalHuman Genetics
Volume133
Issue number2
DOIs
StatePublished - Feb 2014
Externally publishedYes

Fingerprint

Tobacco Use Disorder
Genes
Multifactor Dimensionality Reduction
Gene-Environment Interaction
Brain-Derived Neurotrophic Factor
Nuclear Family
Population
Siblings

ASJC Scopus subject areas

  • Genetics(clinical)
  • Genetics

Cite this

A unified GMDR method for detecting gene-gene interactions in family and unrelated samples with application to nicotine dependence. / Chen, Guo Bo; Liu, Nianjun; Klimentidis, Yann C; Zhu, Xiaofeng; Zhi, Degui; Wang, Xujing; Lou, Xiang Yang.

In: Human Genetics, Vol. 133, No. 2, 02.2014, p. 139-150.

Research output: Contribution to journalArticle

Chen, Guo Bo ; Liu, Nianjun ; Klimentidis, Yann C ; Zhu, Xiaofeng ; Zhi, Degui ; Wang, Xujing ; Lou, Xiang Yang. / A unified GMDR method for detecting gene-gene interactions in family and unrelated samples with application to nicotine dependence. In: Human Genetics. 2014 ; Vol. 133, No. 2. pp. 139-150.
@article{e69fd13a9f624367a289f913bac91fbb,
title = "A unified GMDR method for detecting gene-gene interactions in family and unrelated samples with application to nicotine dependence",
abstract = "Gene-gene and gene-environment interactions govern a substantial portion of the variation in complex traits and diseases. In convention, a set of either unrelated or family samples are used in detection of such interactions; even when both kinds of data are available, the unrelated and the family samples are analyzed separately, potentially leading to loss in statistical power. In this report, to detect gene-gene interactions we propose a generalized multifactor dimensionality reduction method that unifies analyses of nuclear families and unrelated subjects within the same statistical framework. We used principal components as genetic background controls against population stratification, and when sibling data are included, within-family control were used to correct for potential spurious association at the tested loci. Through comprehensive simulations, we demonstrate that the proposed method can remarkably increase power by pooling unrelated and offspring's samples together as compared with individual analysis strategies and the Fisher's combining p value method while it retains a controlled type I error rate in the presence of population structure. In application to a real dataset, we detected one significant tetragenic interaction among CHRNA4, CHRNB2, BDNF, and NTRK2 associated with nicotine dependence in the Study of Addiction: Genetics and Environment sample, suggesting the biological role of these genes in nicotine dependence development.",
author = "Chen, {Guo Bo} and Nianjun Liu and Klimentidis, {Yann C} and Xiaofeng Zhu and Degui Zhi and Xujing Wang and Lou, {Xiang Yang}",
year = "2014",
month = "2",
doi = "10.1007/s00439-013-1361-9",
language = "English (US)",
volume = "133",
pages = "139--150",
journal = "Human Genetics",
issn = "0340-6717",
publisher = "Springer Verlag",
number = "2",

}

TY - JOUR

T1 - A unified GMDR method for detecting gene-gene interactions in family and unrelated samples with application to nicotine dependence

AU - Chen, Guo Bo

AU - Liu, Nianjun

AU - Klimentidis, Yann C

AU - Zhu, Xiaofeng

AU - Zhi, Degui

AU - Wang, Xujing

AU - Lou, Xiang Yang

PY - 2014/2

Y1 - 2014/2

N2 - Gene-gene and gene-environment interactions govern a substantial portion of the variation in complex traits and diseases. In convention, a set of either unrelated or family samples are used in detection of such interactions; even when both kinds of data are available, the unrelated and the family samples are analyzed separately, potentially leading to loss in statistical power. In this report, to detect gene-gene interactions we propose a generalized multifactor dimensionality reduction method that unifies analyses of nuclear families and unrelated subjects within the same statistical framework. We used principal components as genetic background controls against population stratification, and when sibling data are included, within-family control were used to correct for potential spurious association at the tested loci. Through comprehensive simulations, we demonstrate that the proposed method can remarkably increase power by pooling unrelated and offspring's samples together as compared with individual analysis strategies and the Fisher's combining p value method while it retains a controlled type I error rate in the presence of population structure. In application to a real dataset, we detected one significant tetragenic interaction among CHRNA4, CHRNB2, BDNF, and NTRK2 associated with nicotine dependence in the Study of Addiction: Genetics and Environment sample, suggesting the biological role of these genes in nicotine dependence development.

AB - Gene-gene and gene-environment interactions govern a substantial portion of the variation in complex traits and diseases. In convention, a set of either unrelated or family samples are used in detection of such interactions; even when both kinds of data are available, the unrelated and the family samples are analyzed separately, potentially leading to loss in statistical power. In this report, to detect gene-gene interactions we propose a generalized multifactor dimensionality reduction method that unifies analyses of nuclear families and unrelated subjects within the same statistical framework. We used principal components as genetic background controls against population stratification, and when sibling data are included, within-family control were used to correct for potential spurious association at the tested loci. Through comprehensive simulations, we demonstrate that the proposed method can remarkably increase power by pooling unrelated and offspring's samples together as compared with individual analysis strategies and the Fisher's combining p value method while it retains a controlled type I error rate in the presence of population structure. In application to a real dataset, we detected one significant tetragenic interaction among CHRNA4, CHRNB2, BDNF, and NTRK2 associated with nicotine dependence in the Study of Addiction: Genetics and Environment sample, suggesting the biological role of these genes in nicotine dependence development.

UR - http://www.scopus.com/inward/record.url?scp=84893185129&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84893185129&partnerID=8YFLogxK

U2 - 10.1007/s00439-013-1361-9

DO - 10.1007/s00439-013-1361-9

M3 - Article

VL - 133

SP - 139

EP - 150

JO - Human Genetics

JF - Human Genetics

SN - 0340-6717

IS - 2

ER -