Trimming, weighting, and grouping SNPs in human case-control association studies

The search for genes underlying complex traits has been difficult and often disappointing. The main reason for these difficulties is that several gene s, each with rather small effect, might be interacting to produce the trait . Therefore, we must search the whole genome for a good chance to find thes e genes. Doing this with tens of thousands of SNP markers, however, greatly increases the overall probability of false-positive results, and current m ethods limiting such error probabilities to acceptable levels tend to reduc e the power of detecting weak genes. Investigating large numbers of SNPs in evitably introduces errors (e.g., in genotyping), which will distort analys is results. Here we propose a simple strategy that circumvents many of thes e problems. We develop a set-association method to blend relevant sources o f information such as allelic association and Hardy-Weinberg disequilibrium . Information is combined over multiple markers and genes in the genome, qu ality control is improved by trimming, and an appropriate testing strategy limits the overall false-positive rate. In contrast to other available meth ods, Our method to detect association to sets of SNP markers in different g enes in a real data application has shown remarkable success.