Combinatorial search methods for multi-SNP disease association.
Recent improvements in the accessibility of high-throughput genotyping have brought a deal of attention to genome-wide association studies for common complex diseases. Although, such diseases can be caused by multi-loci interactions, locus-by-locus studies are prevailing. Recently, two-loci analysis has been shown promising (Marchini et al, 2005), and multi-loci analysis is expected to find even deeper disease-associated interactions. Unfortunately, an exhaustive search among all possible corresponding multi-markers can be unfeasible even for small number of SNPs let alone the complete genome. In this paper we first propose to extract informative (indexing) SNPs that can be used for reconstructing of all SNPs almost without loss (He and Zelikovsky, 2006). In the reduced set of SNPs, we then propose to apply a novel combinatorial method for finding disease-associated multi-SNP combinations (MSCs). Our experimental study shows that the proposed methods are able to find MSCs whose disease association is statistically significant even after multiple testing adjustment. For (Daly et al, 2001) data we found a few unphased MSCs associated with Crohn's disease with multiple testing adjusted p-value below 0.05 while no single SNP or pair of SNPs show any significant association. For (Ueda et al, 2003) data we found a few new unphased and phased MSCs associated with autoimmune disorder.
Brinza
Dumitru
D
Dept. of Comput. Sci., Georgia State Univ., Atlanta, GA 30303, USA. dima@cs.gsu.edu
He
Jingwu
J
Zelikovsky
Alexander
A
D000483:Alleles; D002875:Chromosomes; D019295:Computational Biology / Q000379:methods*; D030342:Genetic Diseases, Inborn / Q000235:genetics*; D015894:Genome, Human; D005838:Genotype; D006239:Haplotypes; D006801:Humans; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide; D012984:Software