Abstract
BackgroundGenome-wide association studies (GWAS) provide an increasing number of single nucleotide polymorphisms (SNPs) associated with diseases. Our aim is to exploit those closely spaced SNPs in candidate regions for a deeper analysis of association beyond single SNP analysis, combining the classical stepwise regression approach with haplotype analysis to identify risk haplotypes for complex diseases.MethodsOur proposed multi-locus stepwise regression starts with an evaluation of all pair-wise SNP combinations and then extends each SNP combination stepwise by one SNP from the region, carrying out haplotype regression in each step. The best associated haplotype patterns are kept for the next step and must be corrected for multiple testing at the end. These haplotypes should also be replicated in an independent data set. We applied the method to a region of 259 SNPs from the epidermal differentiation complex (EDC) on chromosome 1q21 of a German GWAS using a case control set (1,914 individuals) and to 268 families with at least two affected children as replication.ResultsA 4-SNP haplotype pattern with high statistical significance in the case control set (p = 4.13 × 10-7 after Bonferroni correction) could be identified which remained significant in the family set after Bonferroni correction (p = 0.0398). Further analysis revealed that this pattern reflects mainly the effect of the well-known FLG gene; however, a FLG-independent haplotype in case control set (OR = 1.71, 95% CI: 1.32-2.23, p = 5.6 × 10-5) and family set (OR = 1.68, 95% CI: 1.18-2.38, p = 2.19 × 10-3) could be found in addition.ConclusionOur approach is a useful tool for finding allele combinations associated with diseases beyond single SNP analysis in chromosomal candidate regions.
Highlights
Genome-wide association studies (GWAS) provide an increasing number of single nucleotide polymorphisms (SNPs) associated with diseases
In order to deal with a larger number of SNPs in candidate regions given for instance in the case of GWAS data, our method aims at the extension and outcome of a preset number of best haplotypes by using a different search strategy for case-control or family data, respectively, which have to be corrected for multiple testing and should be confirmed in an independent data set
We identified 94 tagSNPs using a linkage disequilibrium (LD) criterion r2 > 0.8 for compressing the genotype information, avoiding haplotype patterns containing the same information through high LD, and minimizing computational time
Summary
Genome-wide association studies (GWAS) provide an increasing number of single nucleotide polymorphisms (SNPs) associated with diseases. The advent of the gene chip technology has resulted in a multitude of genome-wide association studies (GWAS). These are in general based upon large numbers of single nucleotide polymorphisms (SNPs) genotyped along the genome for large numbers of individuals. Due to the multitude of tests along the genome, only substantial single locus associations withstand the Bonferroni correction. Another possibility to exploit these high quality data of closely spaced SNPs is to concentrate on certain candidate regions and use these closely spaced SNPs for haplotype estimation. A more thorough association analysis of genetic traits can be performed
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
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 call
