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An integrative analysis of genome-wide association study and regulatory SNP annotation datasets identified candidate genes for bipolar disorder.

ABSTRACT: BACKGROUND:Bipolar disorder (BD) is a complex mood disorder. The genetic mechanism of BD remains largely unknown. METHODS:We conducted an integrative analysis of genome-wide association study (GWAS) and regulatory SNP (rSNP) annotation datasets, including transcription factor binding regions (TFBRs), chromatin interactive regions (CIRs), mature microRNA regions (miRNAs), long non-coding RNA regions (lncRNAs), topologically associated domains (TADs) and circular RNAs (circRNAs). Firstly, GWAS dataset 1 of BD (including 20,352 cases and 31,358 controls) and GWAS dataset 2 of BD (including 7481 BD patients and 9250 controls) were integrated with rSNP annotation database to obtain BD associated SNP regulatory elements and SNP regulatory element-target gene (E-G) pairs, respectively. Secondly, a comparative analysis of the two datasets results was conducted to identify the common rSNPs and also their target genes. Then, gene sets enrichment analysis (FUMA GWAS) and HumanNet-XC analysis were conducted to explore the functional relevance of identified target genes with BD. RESULTS:After the integrative analysis, we identified 52 TFBRs target genes, 44 TADs target genes, 55 CIRs target genes and 21 lncRNAs target genes for BD, such as ITIH4 (Pdataset1?=?6.68?×?10-8, Pdataset2?=?6.64?×?10-7), ITIH3 (Pdataset1?=?1.09?×?10-8, Pdataset2?=?2.00?×?10-7), SYNE1 (Pdataset1?=?1.80?×?10-6, Pdataset2?=?4.33?×?10-9) and OPRM1 (Pdataset1?=?1.80?×?10-6, Pdataset2?=?4.33?×?10-9). CONCLUSION:We conducted a large-scale integrative analysis of GWAS and 6 common rSNP information datasets to explore the potential roles of rSNPs in the genetic mechanism of BD. We identified multiple candidate genes for BD, supporting the importance of rSNP in the development of BD.

SUBMITTER: Qi X

PROVIDER: S-EPMC6995798 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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An integrative analysis of genome-wide association study and regulatory SNP annotation datasets identified candidate genes for bipolar disorder.

Qi Xin X Wen Yan Y Li Ping P Liang Chujun C Cheng Bolun B Ma Mei M Cheng Shiqiang S Zhang Lu L Liu Li L Kafle Om Prakash OP Zhang Feng F

International journal of bipolar disorders 20200203 1

<h4>Background</h4>Bipolar disorder (BD) is a complex mood disorder. The genetic mechanism of BD remains largely unknown.<h4>Methods</h4>We conducted an integrative analysis of genome-wide association study (GWAS) and regulatory SNP (rSNP) annotation datasets, including transcription factor binding regions (TFBRs), chromatin interactive regions (CIRs), mature microRNA regions (miRNAs), long non-coding RNA regions (lncRNAs), topologically associated domains (TADs) and circular RNAs (circRNAs). Fi ...[more]

PMID: 32009227

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Project description: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.

Dataset Information

An integrative analysis of genome-wide association study and regulatory SNP annotation datasets identified candidate genes for bipolar disorder.

Publications

An integrative analysis of genome-wide association study and regulatory SNP annotation datasets identified candidate genes for bipolar disorder.

Similar Datasets

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