Dataset Information

Integrative Genomic Analysis Predicts Causative Cis-Regulatory Mechanisms of the Breast Cancer-Associated Genetic Variant rs4415084.

ABSTRACT: Previous genome-wide association studies (GWAS) have identified several common genetic variants that may significantly modulate cancer susceptibility. However, the precise molecular mechanisms behind these associations remain largely unknown; it is often not clear whether discovered variants are themselves functional or merely genetically linked to other functional variants. Here, we provide an integrated method for identifying functional regulatory variants associated with cancer and their target genes by combining analyses of expression quantitative trait loci, a modified version of allele-specific expression that systematically utilizes haplotype information, transcription factor (TF)-binding preference, and epigenetic information. Application of our method to a breast cancer susceptibility region in 5p12 demonstrates that the risk allele rs4415084-T correlates with higher expression levels of the protein-coding gene mitochondrial ribosomal protein S30 (MRPS30) and lncRNA RP11-53O19.1 We propose an intergenic SNP rs4321755, in linkage disequilibrium (LD) with the GWAS SNP rs4415084 (r2 = 0.988), to be the predicted functional SNP. The risk allele rs4321755-T, in phase with the GWAS rs4415084-T, created a GATA3-binding motif within an enhancer, resulting in differential GATA3 binding and chromatin accessibility, thereby promoting transcription of MRPS30 and RP11-53O19.1. MRPS30 encodes a member of the mitochondrial ribosomal proteins, implicating the role of risk SNP in modulating mitochondrial activities in breast cancer. Our computational framework provides an effective means to integrate GWAS results with high-throughput genomic and epigenomic data and can be extended to facilitate rapid functional characterization of other genetic variants modulating cancer susceptibility.Significance: Unification of GWAS results with information from high-throughput genomic and epigenomic profiles provides a direct link between common genetic variants and measurable molecular perturbations. Cancer Res; 78(7); 1579-91. ©2018 AACR.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC5882544 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Integrative Genomic Analysis Predicts Causative <i>Cis</i>-Regulatory Mechanisms of the Breast Cancer-Associated Genetic Variant rs4415084.

Zhang Yi Y Manjunath Mohith M Zhang Shilu S Chasman Deborah D Roy Sushmita S Song Jun S JS

Cancer research 20180119 7

Previous genome-wide association studies (GWAS) have identified several common genetic variants that may significantly modulate cancer susceptibility. However, the precise molecular mechanisms behind these associations remain largely unknown; it is often not clear whether discovered variants are themselves functional or merely genetically linked to other functional variants. Here, we provide an integrated method for identifying functional regulatory variants associated with cancer and their targ ...[more]

PMID: 29351903

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Project description:Genetic variants in cis-regulatory elements or trans-acting regulators frequently influence the quantity and spatiotemporal distribution of gene transcription. Recent interest in expression quantitative trait locus (eQTL) mapping has paralleled the adoption of genome-wide association studies (GWAS) for the analysis of complex traits and disease in humans. Under the hypothesis that many GWAS associations tag non-coding SNPs with small effects, and that these SNPs exert phenotypic control by modifying gene expression, it has become common to interpret GWAS associations using eQTL data. To fully exploit the mechanistic interpretability of eQTL-GWAS comparisons, an improved understanding of the genetic architecture and causal mechanisms of cell type specificity of eQTLs is required. We address this need by performing an eQTL analysis in three parts: first we identified eQTLs from eleven studies on seven cell types; then we integrated eQTL data with cis-regulatory element (CRE) data from the ENCODE project; finally we built a set of classifiers to predict the cell type specificity of eQTLs. The cell type specificity of eQTLs is associated with eQTL SNP overlap with hundreds of cell type specific CRE classes, including enhancer, promoter, and repressive chromatin marks, regions of open chromatin, and many classes of DNA binding proteins. These associations provide insight into the molecular mechanisms generating the cell type specificity of eQTLs and the mode of regulation of corresponding eQTLs. Using a random forest classifier with cell specific CRE-SNP overlap as features, we demonstrate the feasibility of predicting the cell type specificity of eQTLs. We then demonstrate that CREs from a trait-associated cell type can be used to annotate GWAS associations in the absence of eQTL data for that cell type. We anticipate that such integrative, predictive modeling of cell specificity will improve our ability to understand the mechanistic basis of human complex phenotypic variation.

Project description:Recent development in high throughput proteomics and genomics profiling enable one to study regulations of genome alterations on protein activities in a systematic manner. In this article, we propose a new statistical method, ProMAP, to systematically characterize the regulatory relationships between proteins and DNA copy number alterations (CNA) in breast and ovarian tumors based on proteogenomic data from the CPTAC-TCGA studies. Because of the dynamic nature of mass spectrometry instruments, proteomics data from labeled mass spectrometry experiments usually have non-ignorable batch effects. Moreover, mass spectrometry based proteomic data often possesses high percentages of missing values and non-ignorable missing-data patterns. Thus, we use a linear mixed effects model to account for the batch structure and explicitly incorporate the abundance-dependent-missing-data mechanism of proteomic data in ProMAP. In addition, we employ a multivariate regression framework to characterize the multiple-to-multiple regulatory relationships between CNA and proteins. Further, we use proper statistical regularization to facilitate the detection of master genetic regulators, which affect the activities of many proteins and often play important roles in genetic regulatory networks. Improved performance of ProMAP over existing methods were illustrated through extensive simulation studies and real data examples. Applying ProMAP to the CPTAC-TCGA breast and ovarian cancer data sets, we identified many genome regions, including a few novel ones, whose CNA were associated with protein and or phosphoprotein abundances. For example, in breast tumors, a small region in 8p11.21 was recognized as the second biggest hub in the CNA-phosphoprotein regulatory map, and further investigation of the regulatory targets suggests the potential role of 8p11.21 CNA in perturbing oxygen binding and transport activities in tumor cells. This and other findings from our analyses help to characterize the impacts of CNAs on protein activity landscapes and cast light on the genetic regulation mechanisms underlying these tumors.

Dataset Information

Integrative Genomic Analysis Predicts Causative Cis-Regulatory Mechanisms of the Breast Cancer-Associated Genetic Variant rs4415084.

Publications

Integrative Genomic Analysis Predicts Causative <i>Cis</i>-Regulatory Mechanisms of the Breast Cancer-Associated Genetic Variant rs4415084.

Similar Datasets

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