Effects of Covariates: A Summary of Group 5 Contributions

Elizabeth R. Hauser, Fang Chi Hsu, Denise Daley, Jane M. Olson, Evadnie Rampersaud, Jing Ping Lin, Andrew D. Paterson, Laila M. Poisson, Gary A. Chase, Gerlinde Dahmen, Andreas Ziegler

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

This report summarizes the contributions of Genetic Analysis Workshop 13 (GAW13) related to the use of covariates in genetic analysis. Seven papers are summarized, five of which analyzed the Framingham Heart Study Data, and two the simulated data. Five papers examined the role of covariates in linkage analysis, using a variety of statistical approaches including affected sibling pair analysis, conditional logistic regression, and variance components methods. One paper examined the impact of covariates on family-based association analysis. In each of these papers, the detection of genetic effects could be influenced by the incorporation of covariates. The final paper examined the role of transmission ratio distortion in the analysis of complex traits and the role of covariates in the variability in transmission ratio distortion. While each paper takes a different approach to the genetic analysis of complex traits, a common thread running through each is that the inclusion of covariates can have a substantial impact on the results of the analysis. Care must be taken to understand how the covariates are being used in each analysis, what assumptions are being made, and how these assumptions might affect the results and their interpretation. Finally, the results of Group 5 studies show that inclusion of covariates can increase the power to detect genes for complex traits, and has the potential to advance an understanding of the role of genes in these complex traits.

Original languageEnglish
JournalGenetic Epidemiology
Volume25
Issue numberSUPPL. 1
DOIs
StatePublished - Dec 12 2003

Fingerprint

Genes
Logistic Models
Education

Keywords

  • Association
  • Covariates
  • Framingham Heart Study
  • Linkage
  • Simulations
  • Statistical methods

ASJC Scopus subject areas

  • Genetics(clinical)
  • Epidemiology

Cite this

Hauser, E. R., Hsu, F. C., Daley, D., Olson, J. M., Rampersaud, E., Lin, J. P., ... Ziegler, A. (2003). Effects of Covariates: A Summary of Group 5 Contributions. Genetic Epidemiology, 25(SUPPL. 1). https://doi.org/10.1002/gepi.10283

Effects of Covariates : A Summary of Group 5 Contributions. / Hauser, Elizabeth R.; Hsu, Fang Chi; Daley, Denise; Olson, Jane M.; Rampersaud, Evadnie; Lin, Jing Ping; Paterson, Andrew D.; Poisson, Laila M.; Chase, Gary A.; Dahmen, Gerlinde; Ziegler, Andreas.

In: Genetic Epidemiology, Vol. 25, No. SUPPL. 1, 12.12.2003.

Research output: Contribution to journalArticle

Hauser, ER, Hsu, FC, Daley, D, Olson, JM, Rampersaud, E, Lin, JP, Paterson, AD, Poisson, LM, Chase, GA, Dahmen, G & Ziegler, A 2003, 'Effects of Covariates: A Summary of Group 5 Contributions', Genetic Epidemiology, vol. 25, no. SUPPL. 1. https://doi.org/10.1002/gepi.10283
Hauser ER, Hsu FC, Daley D, Olson JM, Rampersaud E, Lin JP et al. Effects of Covariates: A Summary of Group 5 Contributions. Genetic Epidemiology. 2003 Dec 12;25(SUPPL. 1). https://doi.org/10.1002/gepi.10283
Hauser, Elizabeth R. ; Hsu, Fang Chi ; Daley, Denise ; Olson, Jane M. ; Rampersaud, Evadnie ; Lin, Jing Ping ; Paterson, Andrew D. ; Poisson, Laila M. ; Chase, Gary A. ; Dahmen, Gerlinde ; Ziegler, Andreas. / Effects of Covariates : A Summary of Group 5 Contributions. In: Genetic Epidemiology. 2003 ; Vol. 25, No. SUPPL. 1.
@article{2b04c12f4cbb43c1aa3e161e266c66f5,
title = "Effects of Covariates: A Summary of Group 5 Contributions",
abstract = "This report summarizes the contributions of Genetic Analysis Workshop 13 (GAW13) related to the use of covariates in genetic analysis. Seven papers are summarized, five of which analyzed the Framingham Heart Study Data, and two the simulated data. Five papers examined the role of covariates in linkage analysis, using a variety of statistical approaches including affected sibling pair analysis, conditional logistic regression, and variance components methods. One paper examined the impact of covariates on family-based association analysis. In each of these papers, the detection of genetic effects could be influenced by the incorporation of covariates. The final paper examined the role of transmission ratio distortion in the analysis of complex traits and the role of covariates in the variability in transmission ratio distortion. While each paper takes a different approach to the genetic analysis of complex traits, a common thread running through each is that the inclusion of covariates can have a substantial impact on the results of the analysis. Care must be taken to understand how the covariates are being used in each analysis, what assumptions are being made, and how these assumptions might affect the results and their interpretation. Finally, the results of Group 5 studies show that inclusion of covariates can increase the power to detect genes for complex traits, and has the potential to advance an understanding of the role of genes in these complex traits.",
keywords = "Association, Covariates, Framingham Heart Study, Linkage, Simulations, Statistical methods",
author = "Hauser, {Elizabeth R.} and Hsu, {Fang Chi} and Denise Daley and Olson, {Jane M.} and Evadnie Rampersaud and Lin, {Jing Ping} and Paterson, {Andrew D.} and Poisson, {Laila M.} and Chase, {Gary A.} and Gerlinde Dahmen and Andreas Ziegler",
year = "2003",
month = "12",
day = "12",
doi = "10.1002/gepi.10283",
language = "English",
volume = "25",
journal = "Genetic Epidemiology",
issn = "0741-0395",
publisher = "Wiley-Liss Inc.",
number = "SUPPL. 1",

}

TY - JOUR

T1 - Effects of Covariates

T2 - A Summary of Group 5 Contributions

AU - Hauser, Elizabeth R.

AU - Hsu, Fang Chi

AU - Daley, Denise

AU - Olson, Jane M.

AU - Rampersaud, Evadnie

AU - Lin, Jing Ping

AU - Paterson, Andrew D.

AU - Poisson, Laila M.

AU - Chase, Gary A.

AU - Dahmen, Gerlinde

AU - Ziegler, Andreas

PY - 2003/12/12

Y1 - 2003/12/12

N2 - This report summarizes the contributions of Genetic Analysis Workshop 13 (GAW13) related to the use of covariates in genetic analysis. Seven papers are summarized, five of which analyzed the Framingham Heart Study Data, and two the simulated data. Five papers examined the role of covariates in linkage analysis, using a variety of statistical approaches including affected sibling pair analysis, conditional logistic regression, and variance components methods. One paper examined the impact of covariates on family-based association analysis. In each of these papers, the detection of genetic effects could be influenced by the incorporation of covariates. The final paper examined the role of transmission ratio distortion in the analysis of complex traits and the role of covariates in the variability in transmission ratio distortion. While each paper takes a different approach to the genetic analysis of complex traits, a common thread running through each is that the inclusion of covariates can have a substantial impact on the results of the analysis. Care must be taken to understand how the covariates are being used in each analysis, what assumptions are being made, and how these assumptions might affect the results and their interpretation. Finally, the results of Group 5 studies show that inclusion of covariates can increase the power to detect genes for complex traits, and has the potential to advance an understanding of the role of genes in these complex traits.

AB - This report summarizes the contributions of Genetic Analysis Workshop 13 (GAW13) related to the use of covariates in genetic analysis. Seven papers are summarized, five of which analyzed the Framingham Heart Study Data, and two the simulated data. Five papers examined the role of covariates in linkage analysis, using a variety of statistical approaches including affected sibling pair analysis, conditional logistic regression, and variance components methods. One paper examined the impact of covariates on family-based association analysis. In each of these papers, the detection of genetic effects could be influenced by the incorporation of covariates. The final paper examined the role of transmission ratio distortion in the analysis of complex traits and the role of covariates in the variability in transmission ratio distortion. While each paper takes a different approach to the genetic analysis of complex traits, a common thread running through each is that the inclusion of covariates can have a substantial impact on the results of the analysis. Care must be taken to understand how the covariates are being used in each analysis, what assumptions are being made, and how these assumptions might affect the results and their interpretation. Finally, the results of Group 5 studies show that inclusion of covariates can increase the power to detect genes for complex traits, and has the potential to advance an understanding of the role of genes in these complex traits.

KW - Association

KW - Covariates

KW - Framingham Heart Study

KW - Linkage

KW - Simulations

KW - Statistical methods

UR - http://www.scopus.com/inward/record.url?scp=17544385374&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=17544385374&partnerID=8YFLogxK

U2 - 10.1002/gepi.10283

DO - 10.1002/gepi.10283

M3 - Article

C2 - 14635168

AN - SCOPUS:17544385374

VL - 25

JO - Genetic Epidemiology

JF - Genetic Epidemiology

SN - 0741-0395

IS - SUPPL. 1

ER -