- THIS ARTICLE
- Full Text
- Full Text (PDF)
-
All Versions of this Article:
genetics.105.049122v1
172/1/693 most recent - Alert me when this article is cited
- Alert me if a correction is posted
- SERVICES
- Email this article to a friend
- Similar articles in this journal
- Similar articles in PubMed
- Alert me to new issues of the journal
- Download to citation manager
- Reprints & Permissions
- CITING ARTICLES
- Citing Articles via Google Scholar
- GOOGLE SCHOLAR
- Articles by Zhang, H.
- Articles by Ye, Y.
- Search for Related Content
- PUBMED
- PubMed Citation
- Articles by Zhang, H.
- Articles by Ye, Y.
Originally published as Genetics Published Articles Ahead of Print on October 11, 2005.
Genetics, Vol. 172, 693-699, January 2006, Copyright © 2006
doi:10.1534/genetics.105.049122
Detection of Genes for Ordinal Traits in Nuclear Families and a Unified Approach for Association Studies
Heping Zhang1, Xueqin Wang and Yuanqing Ye
Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, Connecticut 06520-8034
1 Corresponding author: Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520-8034.
E-mail: heping.zhang{at}yale.edu
There is growing interest in genomewide association analysis using single-nucleotide polymorphisms (SNPs), because traditional linkage studies are not as powerful in identifying genes for common, complex diseases. Tests for linkage disequilibrium have been developed for binary and quantitative traits. However, since many human conditions and diseases are measured in an ordinal scale, methods need to be developed to investigate the association of genes and ordinal traits. Thus, in the current report we propose and derive a score test statistic that identifies genes that are associated with ordinal traits when gametic disequilibrium between a marker and trait loci exists. Through simulation, the performance of this new test is examined for both ordinal traits and quantitative traits. The proposed statistic not only accommodates and is more powerful for ordinal traits, but also has similar power to that of existing tests when the trait is quantitative. Therefore, our proposed statistic has the potential to serve as a unified approach to identifying genes that are associated with any trait, regardless of how the trait is measured. We further demonstrated the advantage of our test by revealing a significant association (P = 0.00067) between alcohol dependence and a SNP in the growth-associated protein 43.
