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Computational Characterization of Osteoporosis Associated SNPs and Genes Identified by Genome-Wide Association Studies.

ABSTRACT: OBJECTIVES:Genome-wide association studies (GWASs) have revealed many SNPs and genes associated with osteoporosis. However, influence of these SNPs and genes on the predisposition to osteoporosis is not fully understood. We aimed to identify osteoporosis GWASs-associated SNPs potentially influencing the binding affinity of transcription factors and miRNAs, and reveal enrichment signaling pathway and "hub" genes of osteoporosis GWAS-associated genes. METHODS:We conducted multiple computational analyses to explore function and mechanisms of osteoporosis GWAS-associated SNPs and genes, including SNP conservation analysis and functional annotation (influence of SNPs on transcription factors and miRNA binding), gene ontology analysis, pathway analysis and protein-protein interaction analysis. RESULTS:Our results suggested that a number of SNPs potentially influence the binding affinity of transcription factors (NFATC2, MEF2C, SOX9, RUNX2, ESR2, FOXA1 and STAT3) and miRNAs. Osteoporosis GWASs-associated genes showed enrichment of Wnt signaling pathway, basal cell carcinoma and Hedgehog signaling pathway. Highly interconnected "hub" genes revealed by interaction network analysis are RUNX2, SP7, TNFRSF11B, LRP5, DKK1, ESR1 and SOST. CONCLUSIONS:Our results provided the targets for further experimental assessment and further insight on osteoporosis pathophysiology.

SUBMITTER: Qin L

PROVIDER: S-EPMC4773152 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Computational Characterization of Osteoporosis Associated SNPs and Genes Identified by Genome-Wide Association Studies.

Qin Longjuan L Liu Yuyong Y Wang Ya Y Wu Guiju G Chen Jie J Ye Weiyuan W Yang Jiancai J Huang Qingyang Q

PloS one 20160301 3

<h4>Objectives</h4>Genome-wide association studies (GWASs) have revealed many SNPs and genes associated with osteoporosis. However, influence of these SNPs and genes on the predisposition to osteoporosis is not fully understood. We aimed to identify osteoporosis GWASs-associated SNPs potentially influencing the binding affinity of transcription factors and miRNAs, and reveal enrichment signaling pathway and "hub" genes of osteoporosis GWAS-associated genes.<h4>Methods</h4>We conducted multiple c ...[more]

PMID: 26930606

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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.

Dataset Information

Computational Characterization of Osteoporosis Associated SNPs and Genes Identified by Genome-Wide Association Studies.

Publications

Computational Characterization of Osteoporosis Associated SNPs and Genes Identified by Genome-Wide Association Studies.

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