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Two-stage study designs for analyzing disease-associated covariates: linkage thresholds and case-selection strategies.

Publication ,  Journal Article
Schmidt, M; Qin, X; Martin, ER; Hauser, ER; Schmidt, S
Published in: BMC Proc
2007

The incorporation of disease-associated covariates into studies aiming to identify susceptibility genes for complex human traits is a challenging problem. Accounting for such covariates in genetic linkage and association analyses may help reduce the genetic heterogeneity inherent in these complex phenotypes. For Genetic Analysis Workshop 15 (GAW15) Problem 3 simulated data, our goal was to compare the power of several two-stage study designs to identify rheumatoid arthritis-related genes on chromosome 9 (disease severity), 11 (IgM), and 18 (anti-cyclic citrinullated protein), with knowledge of the answers. Five study designs incorporating an initial linkage step, followed by a case-selection scheme and case-control association analysis by logistic regression, were considered. The linkage step was either qualitative-trait linkage analysis as implemented in MERLIN-nonparametric linkage (NPL), or quantitative-trait locus analysis as implemented in MERLIN-REGRESS. A set of cases representing either one case from each available family, one case per linked family (NPL >/= 0), or one case from each family identified by ordered-subset analysis was chosen for comparison with the full set of 2000 simulated controls. As expected, the performance of these study designs depended on the disease model used to generate the data, especially the simulated allele frequency difference between cases and controls. The quantitative trait loci analysis performed well in identifying these loci, and the power to identify disease-associated alleles was increased by using ordered-subset analysis as a case selection tool.

Duke Scholars

Published In

BMC Proc

DOI

EISSN

1753-6561

Publication Date

2007

Volume

1 Suppl 1

Issue

Suppl 1

Start / End Page

S138

Location

England
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Schmidt, M., Qin, X., Martin, E. R., Hauser, E. R., & Schmidt, S. (2007). Two-stage study designs for analyzing disease-associated covariates: linkage thresholds and case-selection strategies. BMC Proc, 1 Suppl 1(Suppl 1), S138. https://doi.org/10.1186/1753-6561-1-s1-s138
Schmidt, Mike, Xuejun Qin, Eden R. Martin, Elizabeth R. Hauser, and Silke Schmidt. “Two-stage study designs for analyzing disease-associated covariates: linkage thresholds and case-selection strategies.BMC Proc 1 Suppl 1, no. Suppl 1 (2007): S138. https://doi.org/10.1186/1753-6561-1-s1-s138.
Schmidt M, Qin X, Martin ER, Hauser ER, Schmidt S. Two-stage study designs for analyzing disease-associated covariates: linkage thresholds and case-selection strategies. BMC Proc. 2007;1 Suppl 1(Suppl 1):S138.
Schmidt, Mike, et al. “Two-stage study designs for analyzing disease-associated covariates: linkage thresholds and case-selection strategies.BMC Proc, vol. 1 Suppl 1, no. Suppl 1, 2007, p. S138. Pubmed, doi:10.1186/1753-6561-1-s1-s138.
Schmidt M, Qin X, Martin ER, Hauser ER, Schmidt S. Two-stage study designs for analyzing disease-associated covariates: linkage thresholds and case-selection strategies. BMC Proc. 2007;1 Suppl 1(Suppl 1):S138.
Journal cover image

Published In

BMC Proc

DOI

EISSN

1753-6561

Publication Date

2007

Volume

1 Suppl 1

Issue

Suppl 1

Start / End Page

S138

Location

England