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Testing SNPs and sets of SNPs for importance in association studies.

ABSTRACT: A major goal of genetic association studies concerned with single nucleotide polymorphisms (SNPs) is the detection of SNPs exhibiting an impact on the risk of developing a disease. Typically, this problem is approached by testing each of the SNPs individually. This, however, can lead to an inaccurate measurement of the influence of the SNPs on the disease risk, in particular, if SNPs only show an effect when interacting with other SNPs, as the multivariate structure of the data is ignored. In this article, we propose a testing procedure based on logic regression that takes this structure into account and therefore enables a more appropriate quantification of importance and ranking of the SNPs than marginal testing. Since even SNP interactions often exhibit only a moderate effect on the disease risk, it can be helpful to also consider sets of SNPs (e.g. SNPs belonging to the same gene or pathway) to borrow strength across these SNP sets and to identify those genes or pathways comprising SNPs that are most consistently associated with the response. We show how the proposed procedure can be adapted for testing SNP sets, and how it can be applied to blocks of SNPs in linkage disequilibrium (LD) to overcome problems caused by LD.

SUBMITTER: Schwender H

PROVIDER: S-EPMC3006123 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature

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Testing SNPs and sets of SNPs for importance in association studies.

Schwender Holger H Ruczinski Ingo I Ickstadt Katja K

Biostatistics (Oxford, England) 20100702 1

A major goal of genetic association studies concerned with single nucleotide polymorphisms (SNPs) is the detection of SNPs exhibiting an impact on the risk of developing a disease. Typically, this problem is approached by testing each of the SNPs individually. This, however, can lead to an inaccurate measurement of the influence of the SNPs on the disease risk, in particular, if SNPs only show an effect when interacting with other SNPs, as the multivariate structure of the data is ignored. In th ...[more]

PMID: 20601626

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Project description:BackgroundRecently mixed linear models are used to address the issue of "missing" heritability in traditional Genome-wide association studies (GWAS). The models assume that all single-nucleotide polymorphisms (SNPs) are associated with the phenotypes of interest. However, it is more common that only a small proportion of SNPs have significant effects on the phenotypes, while most SNPs have no or very small effects. To incorporate this feature, we propose an efficient Hierarchical Bayesian Model (HBM) that extends the existing mixed models to enforce automatic selection of significant SNPs. The HBM models the SNP effects using a mixture distribution of a point mass at zero and a normal distribution, where the point mass corresponds to those non-associative SNPs.ResultsWe estimate the HBM using Gibbs sampling. The estimation performance of our method is first demonstrated through two simulation studies. We make the simulation setups realistic by using parameters fitted on the Framingham Heart Study (FHS) data. The simulation studies show that our method can accurately estimate the proportion of SNPs associated with the simulated phenotype and identify these SNPs, as well as adapt to certain model mis-specification than the standard mixed models. In addition, we analyze data from the FHS and the Health and Retirement Study (HRS) to study the association between Body Mass Index (BMI) and SNPs on Chromosome 16, and replicate the identified genetic associations. The analysis of the FHS data identifies 0.3% SNPs on Chromosome 16 that affect BMI, including rs9939609 and rs9939973 on the FTO gene. These two SNPs are in strong linkage disequilibrium with rs1558902 (Rsq =0.901 for rs9939609 and Rsq =0.905 for rs9939973), which has been reported to be linked with obesity in previous GWAS. We then replicate the findings using the HRS data: the analysis finds 0.4% of SNPs associated with BMI on Chromosome 16. Furthermore, around 25% of the genes that are identified to be associated with BMI are common between the two studies.ConclusionsThe results demonstrate that the HBM and the associated estimation algorithm offer a powerful tool for identifying significant genetic associations with phenotypes of interest, among a large number of SNPs that are common in modern genetics studies.

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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Testing SNPs and sets of SNPs for importance in association studies.

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Testing SNPs and sets of SNPs for importance in association studies.

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