Optimal DNA pooling-based two-stage designs in case-control association studies.
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ABSTRACT:
SUBMITTER: Zhao Y
PROVIDER: S-EPMC2868915 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Large-scale whole genome association studies are increasingly common, due in large part to recent advances in genotyping technology. With this change in paradigm for genetic studies of complex diseases, it is vital to develop valid, powerful, and efficient statistical tools and approaches to evaluate such data. Despite a dramatic drop in genotyping costs, it is still expensive to genotype thousands of individuals for hundreds of thousands single nucleotide polymorphisms (SNPs) for large-scale whole genome association studies. A multi-stage (or two-stage) design has been a promising alternative: in the first stage, only a fraction of samples are genotyped and tested using a dense set of SNPs, and only a small subset of markers that show moderate associations with the disease will be genotyped in later stages. Multi-stage designs have also been used in candidate gene association studies, usually in regions that have shown strong signals by linkage studies. To decide which set of SNPs to be genotyped in the next stage, a common practice is to utilize a simple test (such as a chi2 test for case-control data) and a liberal significance level without corrections for multiple testing, to ensure that no true signals will be filtered out. In this paper, I have developed a novel SNP selection procedure within the framework of multi-stage designs. Based on data from stage 1, the method explicitly explores correlations (linkage disequilibrium) among SNPs and their possible interactions in determining the disease phenotype. Comparing with a regular multi-stage design, the approach can select a much reduced set of SNPs with high discriminative power for later stages. Therefore, not only does it reduce the genotyping cost in later stages, it also increases the statistical power by reducing the number of tests. Combined analysis is proposed to further improve power, and the theoretical significance level of the combined statistic is derived. Extensive simulations have been performed, and results have shown that the procedure can reduce the number of SNPs required in later stages, with improved power to detect associations. The procedure has also been applied to a real data set from a genome-wide association study of the sporadic amyotrophic lateral sclerosis (ALS) disease, and an interesting set of candidate SNPs has been identified.
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Project description:BACKGROUND: We previously reported risk haplotypes for two genes related with serotonin and dopamine metabolism: MAOA in migraine without aura and DDC in migraine with aura. Herein we investigate the contribution to migraine susceptibility of eight additional genes involved in dopamine neurotransmission. METHODS: We performed a two-stage case-control association study of 50 tag single nucleotide polymorphisms (SNPs), selected according to genetic coverage parameters. The first analysis consisted of 263 patients and 274 controls and the replication study was composed by 259 cases and 287 controls. All cases were diagnosed according to ICHD-II criteria, were Spanish Caucasian, and were sex-matched with control subjects. RESULTS: Single-marker analysis of the first population identified nominal associations of five genes with migraine. After applying a false discovery rate correction of 10%, the differences remained significant only for DRD2 (rs2283265) and TH (rs2070762). Multiple-marker analysis identified a five-marker T-C-G-C-G (rs12363125-rs2283265-rs2242592-rs1554929-rs2234689) risk haplotype in DRD2 and a two-marker A-C (rs6356-rs2070762) risk haplotype in TH that remained significant after correction by permutations. These results, however, were not replicated in the second independent cohort. CONCLUSION: The present study does not support the involvement of the DRD1, DRD2, DRD3, DRD5, DBH, COMT, SLC6A3 and TH genes in the genetic predisposition to migraine in the Spanish population.
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Project description:Detecting genetic markers with biologically relevant effects remains a challenge due to multiple testing. Standard analysis methods focus on evidence against the null and protect primarily the type I error. On the other hand, the worthwhile alternative is specified for power calculations at the design stage. The balanced test as proposed by Moerkerke and others (2006) and Moerkerke and Goetghebeur (2006) incorporates this alternative directly in the decision criterion to achieve better power. Genetic markers are selected and ranked in order of the balance of evidence they contain against the null and the target alternative. In this paper, we build on this guiding principle to develop 2-stage designs for screening genetic markers when the cost of measurements is high. For a given marker, a first sample may already provide sufficient evidence for or against the alternative. If not, more data are gathered at the second stage which is then followed by a binary decision based on all available data. By optimizing parameters which determine the decision process over the 2 stages (such as the area of the "gray" zone which leads to the gathering of extra data), the expected cost per marker can be reduced substantially. We also demonstrate that, compared to 1-stage designs, 2-stage designs achieve a better balance between true negatives and positives for the same cost.
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