Dataset Information

Gene-based GWAS analysis for consecutive studies of GEFOS.

ABSTRACT: By integrating the multilevel biological evidence and bioinformatics analyses, the present study represents a systemic endeavor to identify BMD-associated genes and their roles in skeletal metabolism. INTRODUCTION:Single-nucleotide polymorphism (SNP)-based genome-wide association studies (GWASs) have already identified about 100 loci associated with bone mineral density (BMD), but these loci only explain a small proportion of heritability to osteoporosis risk. In the present study, we performed a gene-based analysis of the largest GWASs in the bone field to identify additional BMD-associated genes. METHODS:BMD-associated genes were identified by combining the summary statistic P values of SNPs across individual genes in the two consecutive meta-analyses of GWASs from the Genetic Factors for Osteoporosis (GEFOS) studies. The potential functionality of these genes to bone was partially assessed by differential gene expression analysis. Additionally, the consistency of the identification of potential bone mineral density (BMD)-associated variants were evaluated by estimating the correlation of the P values of the same single-nucleotide polymorphisms (SNPs)/genes between the two consecutive Genetic Factors for Osteoporosis Studies (GEFOS) with largely overlapping samples. RESULTS:Compared to the SNP-based analysis, the gene-based strategy identified additional BMD-associated genes with genome-wide significance and increased their mutual replication between the two GEFOS datasets. Among these BMD-associated genes, three novel genes (UBTF, AAAS, and C11orf58) were partially validated at the gene expression level. The correlation analysis presented a moderately high between-study consistency of potential BMD-associated variants. CONCLUSIONS:Gene-based analysis as a supplementary strategy to SNP-based genome-wide association studies, when applied here, is shown that it helped identify some novel BMD-associated genes. In addition to its empirically increased statistical power, gene-based analysis also provides a higher testing stability for identification of BMD genes.

SUBMITTER: Zhu W

PROVIDER: S-EPMC6279247 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Gene-based GWAS analysis for consecutive studies of GEFOS.

Zhu W W Xu C C Zhang J-G JG He H H Wu K-H KH Zhang L L Zeng Y Y Zhou Y Y Su K-J KJ Deng H-W HW

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA 20181010 12

By integrating the multilevel biological evidence and bioinformatics analyses, the present study represents a systemic endeavor to identify BMD-associated genes and their roles in skeletal metabolism.<h4>Introduction</h4>Single-nucleotide polymorphism (SNP)-based genome-wide association studies (GWASs) have already identified about 100 loci associated with bone mineral density (BMD), but these loci only explain a small proportion of heritability to osteoporosis risk. In the present study, we per ...[more]

PMID: 30306226

Similar Datasets

Project description:Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by its significant social impact and high heritability. The latest meta-analysis of ASD GWAS (genome-wide association studies) has revealed the association of several SNPs that were replicated in additional sets of independent samples. However, summary statistics from GWAS can be used to perform a gene-based analysis (GBA). GBA allows to combine all genetic information across the gene to create a single statistic (p-value for each gene). Thus, PASCAL (Pathway scoring algorithm), a novel GBA tool, has been applied to the summary statistics from the latest meta-analysis of ASD. GBA approach (testing the gene as a unit) provides an advantage to perform an accurate insight into the biological ASD mechanisms. Therefore, a gene-network analysis and an enrichment analysis for KEGG and GO terms were carried out. GENE2FUNC was used to create gene expression heatmaps and to carry out differential expression analysis (DEA) across GTEx v7 tissues and Brainspan data. dbMDEGA was employed to perform a DEG analysis between ASD and brain control samples for the associated genes and interactors. Results: PASCAL has identified the following loci associated with ASD: XRN2, NKX2-4, PLK1S1, KCNN2, NKX2-2, CRHR1-IT1, C8orf74 and LOC644172. While some of these genes were previously reported by MAGMA (XRN2, PLK1S1, and KCNN2), PASCAL has been useful to highlight additional genes. The biological characterization of the ASD-associated genes and their interactors have demonstrated the association of several GO and KEGG terms. Moreover, DEA analysis has revealed several up- and down-regulated clusters. In addition, many of the ASD-associated genes and their interactors have shown association with ASD expression datasets. Conclusions: This study identifies several associations at a gene level in ASD. Most of them were previously reported by MAGMA. This fact proves that PASCAL is an efficient GBA tool to extract additional information from previous GWAS. In addition, this study has characterized for the first time the biological role of the ASD-associated genes across brain regions, neurodevelopmental stages, and ASD gene-expression datasets.

Dataset Information

Gene-based GWAS analysis for consecutive studies of GEFOS.

Publications

Gene-based GWAS analysis for consecutive studies of GEFOS.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets