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On the identification of potential regulatory variants within genome wide association candidate SNP sets.

ABSTRACT: Genome wide association studies (GWAS) are a population-scale approach to the identification of segments of the genome in which genetic variations may contribute to disease risk. Current methods focus on the discovery of single nucleotide polymorphisms (SNPs) associated with disease traits. As there are many SNPs within identified risk loci, and the majority of these are situated within non-coding regions, a key challenge is to identify and prioritize variants affecting regulatory sequences that are likely to contribute to the phenotype assessed.We focused investigation on SNPs within lung and breast cancer GWAS loci that reached genome-wide significance for potential roles in gene regulation with a specific focus on SNPs likely to disrupt transcription factor binding sites. Within risk loci, the regulatory potential of sub-regions was classified using relevant open chromatin and epigenetic high throughput sequencing data sets from the ENCODE project in available cancer and normal cell lines. Furthermore, transcription factor affinity altering variants were predicted by comparison of position weight matrix scores between disease and reference alleles. Lastly, ChIP-seq data of transcription associated factors and topological domains were included as binding evidence and potential gene target inference.The sets of SNPs, including both the disease-associated markers and those in high linkage disequilibrium with them, were significantly over-represented in regulatory sequences of cancer and/or normal cells; however, over-representation was generally not restricted to disease-relevant tissue specific regions. The calculated regulatory potential, allelic binding affinity scores and ChIP-seq binding evidence were the three criteria used to prioritize candidates. Fitting all three criteria, we highlighted breast cancer susceptibility SNPs and a borderline lung cancer relevant SNP located in cancer-specific enhancers overlapping multiple distinct transcription associated factor ChIP-seq binding sites.Incorporating high throughput sequencing epigenetic and transcription factor data sets from both cancer and normal cells into cancer genetic studies reveals potential functional SNPs and informs subsequent characterization efforts.

SUBMITTER: Chen CY

PROVIDER: S-EPMC4066296 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

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On the identification of potential regulatory variants within genome wide association candidate SNP sets.

Chen Chih-Yu CY Chang I-Shou IS Hsiung Chao A CA Wasserman Wyeth W WW

BMC medical genomics 20140611

<h4>Background</h4>Genome wide association studies (GWAS) are a population-scale approach to the identification of segments of the genome in which genetic variations may contribute to disease risk. Current methods focus on the discovery of single nucleotide polymorphisms (SNPs) associated with disease traits. As there are many SNPs within identified risk loci, and the majority of these are situated within non-coding regions, a key challenge is to identify and prioritize variants affecting regula ...[more]

PMID: 24920305

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Project description:ImportanceSubstantial genome-wide association study (GWAS) work in Parkinson disease (PD) has led to the discovery of an increasing number of loci shown reliably to be associated with increased risk of disease. Improved understanding of the underlying genes and mechanisms at these loci will be key to understanding the pathogenesis of PD.ObjectiveTo investigate what genes and genomic processes underlie the risk of sporadic PD.Design and settingThis genetic association study used the bioinformatic tools Coloc and transcriptome-wide association study (TWAS) to integrate PD case-control GWAS data published in 2017 with expression data (from Braineac, the Genotype-Tissue Expression [GTEx], and CommonMind) and methylation data (derived from UK Parkinson brain samples) to uncover putative gene expression and splicing mechanisms associated with PD GWAS signals. Candidate genes were further characterized using cell-type specificity, weighted gene coexpression networks, and weighted protein-protein interaction networks.Main outcomes and measuresIt was hypothesized a priori that some genes underlying PD loci would alter PD risk through changes to expression, splicing, or methylation. Candidate genes are presented whose change in expression, splicing, or methylation are associated with risk of PD as well as the functional pathways and cell types in which these genes have an important role.ResultsGene-level analysis of expression revealed 5 genes (WDR6 [OMIM 606031], CD38 [OMIM 107270], GPNMB [OMIM 604368], RAB29 [OMIM 603949], and TMEM163 [OMIM 618978]) that replicated using both Coloc and TWAS analyses in both the GTEx and Braineac expression data sets. A further 6 genes (ZRANB3 [OMIM 615655], PCGF3 [OMIM 617543], NEK1 [OMIM 604588], NUPL2 [NCBI 11097], GALC [OMIM 606890], and CTSB [OMIM 116810]) showed evidence of disease-associated splicing effects. Cell-type specificity analysis revealed that gene expression was overall more prevalent in glial cell types compared with neurons. The weighted gene coexpression performed on the GTEx data set showed that NUPL2 is a key gene in 3 modules implicated in catabolic processes associated with protein ubiquitination and in the ubiquitin-dependent protein catabolic process in the nucleus accumbens, caudate, and putamen. TMEM163 and ZRANB3 were both important in modules in the frontal cortex and caudate, respectively, indicating regulation of signaling and cell communication. Protein interactor analysis and simulations using random networks demonstrated that the candidate genes interact significantly more with known mendelian PD and parkinsonism proteins than would be expected by chance.Conclusions and relevanceTogether, these results suggest that several candidate genes and pathways are associated with the findings observed in PD GWAS studies.

Dataset Information

On the identification of potential regulatory variants within genome wide association candidate SNP sets.

Publications

On the identification of potential regulatory variants within genome wide association candidate SNP sets.

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