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On the follow-up of genome-wide association studies: an overall test for the most promising SNPs.

ABSTRACT: Even in large-scale genome-wide association studies (GWASs), only a fraction of the true associations are detected at the genome-wide significance level. When few or no associations reach the significance threshold, one strategy is to follow up on the most promising candidates, i.e. the single nucleotide polymorphisms (SNPs) with the smallest association-test P-values, by genotyping them in additional studies. In this communication, we propose an overall test for GWASs that analyzes the SNPs with the most promising P-values simultaneously and therefore allows an early assessment of whether the follow-up of the selected SNPs is likely promising. We theoretically derive the properties of the proposed overall test under the null hypothesis and assess its power based on simulation studies. An application to a GWAS for chronic obstructive pulmonary disease suggests that there are true association signals among the top SNPs and that an additional follow-up study is promising.

SUBMITTER: Lipman PJ

PROVIDER: S-EPMC4096304 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature

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On the follow-up of genome-wide association studies: an overall test for the most promising SNPs.

Lipman Peter J PJ Cho Michael H MH Bakke Per P Gulsvik Amund A Kong Xiangyang X Lomas David A DA Anderson Wayne W Silverman Edwin K EK Lange Christoph C

Genetic epidemiology 20110303 5

Even in large-scale genome-wide association studies (GWASs), only a fraction of the true associations are detected at the genome-wide significance level. When few or no associations reach the significance threshold, one strategy is to follow up on the most promising candidates, i.e. the single nucleotide polymorphisms (SNPs) with the smallest association-test P-values, by genotyping them in additional studies. In this communication, we propose an overall test for GWASs that analyzes the SNPs wit ...[more]

PMID: 21374717

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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.

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On the follow-up of genome-wide association studies: an overall test for the most promising SNPs.

Publications

On the follow-up of genome-wide association studies: an overall test for the most promising SNPs.

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