Trimming, weighting, and grouping SNPs in human case-control association studies - PubMed (original) (raw)

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

J Hoh et al. Genome Res. 2001 Dec.

Abstract

The search for genes underlying complex traits has been difficult and often disappointing. The main reason for these difficulties is that several genes, 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 these genes. Doing this with tens of thousands of SNP markers, however, greatly increases the overall probability of false-positive results, and current methods limiting such error probabilities to acceptable levels tend to reduce the power of detecting weak genes. Investigating large numbers of SNPs inevitably introduces errors (e.g., in genotyping), which will distort analysis results. Here we propose a simple strategy that circumvents many of these problems. We develop a set-association method to blend relevant sources of information such as allelic association and Hardy-Weinberg disequilibrium. Information is combined over multiple markers and genes in the genome, quality control is improved by trimming, and an appropriate testing strategy limits the overall false-positive rate. In contrast to other available methods, our method to detect association to sets of SNP markers in different genes in a real data application has shown remarkable success.

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Figures

Figure 1

Figure 1

Flow diagram illustrating the algorithm implemented in the set-association approach.

Figure 2

Figure 2

Significance level of Sn statistic as a function of the number n of SNPs in different genes that are included at each step. The smallest significance level, min_n_ pn, occurs with 10 SNPs included in Sn. The 10 SNPs represent 9 different genes.

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