Abstract
We compare and contrast case-only designs for detecting gene x gene (G x G) interaction in rheumatoid arthritis (RA) using the genome-wide data provided by Genetic Analysis Workshop 16 Problem 1. Logistic as well as novel multinomial and proportional odds models that do not depend on the specification of additive or dominant models for susceptibility loci were applied to the case-only sample. We identified 519 significant interactions (p < 1 x 10-4 in at least one test). All methods detected unique significant interactions; 169 were common to more than one model and only 21 were common to all models. Results emphasize that categorization of the genetic variables and choice of regression model are critical and hugely influential in the identification of G x G. Porportional odds and multinomial methods provide new tools for identification of G x G interactions.
Highlights
Various strategies have been proposed to test hypotheses related to gene × gene (G × G) interaction in case-control data
Levels of GRP in blood and Conclusion When comparing the imposition of binary constraints on the genetic locus selected as the response variable in the logistic models with the additive constraint imposed on the genetic locus selected as predictor, the logistic models show the most dependence on choice of single-nucleotide polymorphism (SNP) as response variable
Similar findings hold for the multinomial models, with their nominal constraint on the response variable compared with the additive constraint on the predictor variable
Summary
Various strategies have been proposed to test hypotheses related to gene × gene (G × G) interaction in case-control data. Motivated by differences in the magnitude of genetic effects associated with rheumatoid arthritis (RA) observed at genes PTPN22, CTLA4, and PADI4 across samples of common ancestry [1], we concentrate on interactions between each of these genes and a genomewide subset of markers selected to be in approximate (page number not for citation purposes). We propose to compare case-only designs that test for single-nucleotide polymorphism (SNP)-by-SNP interactions in RA between alleles at loci in candidate genes PTPN22, PADI4, and CTLA4, each known to have a previous putative marginal association with RA, and alleles at a selected subset of markers in the GAW16 data from the North American Rheumatoid Arthritis Consortium (NARAC). Yang et al demonstrated their results assuming binary genotype variables; here we consider case-only designs that allow for disease susceptibility genes with multiple genetic variants
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
