scholarly journals TIGAR-V2: Efficient TWAS Tool with Nonparametric Bayesian eQTL Weights of 49 Tissue Types from GTEx V8

2021 ◽  
Author(s):  
Randy L. Parrish ◽  
Greg C. Gibson ◽  
Michael P. Epstein ◽  
Jingjing Yang
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Parallel Computation ◽  
Dirichlet Process ◽  
Prediction Models ◽  
Complex Diseases ◽  
Breast And Ovarian Cancer ◽  
Nonparametric Bayesian ◽  
Risk Genes ◽  
Regulated Gene Expression

Standard Transcriptome-Wide Association Study (TWAS) methods first train gene expression prediction models using reference transcriptomic data, and then test the association between the predicted genetically regulated gene expression and phenotype of interest. Most existing TWAS tools require cumbersome preparation of genotype input files and extra coding to enable parallel computation. To improve the efficiency of TWAS tools, we develop TIGAR-V2, which directly reads VCF files, enables parallel computation, and reduces up to 90% computation cost compared to the original version. TIGAR-V2 can train gene expression imputation models using either nonparametric Bayesian Dirichlet Process Regression (DPR) or Elastic-Net (as used by PrediXcan), perform TWAS using either individual-level or summary-level GWAS data, and implements both burden and variance-component test statistics for inference. We trained gene expression prediction models by DPR for 49 tissues using GTEx V8 by TIGAR-V2 and illustrated the usefulness of these nonparametric Bayesian DPR eQTL weights through TWAS of breast and ovarian cancer utilizing public GWAS summary statistics. We identified 88 and 37 risk genes respectively for breast and ovarian cancer, most of which are either known or near previously identified GWAS (~95%) or TWAS (~40%) risk genes of the corresponding phenotype and three novel independent TWAS risk genes with known functions in carcinogenesis. These findings suggest that TWAS can provide biological insight into the transcriptional regulation of complex diseases. TIGAR-V2 tool, trained Bayesian cis-eQTL weights, and LD information from GTEX V8 are publicly available, providing a useful resource for mapping risk genes of complex diseases.

scholarly journals Consensus Recommendations of the German Consortium for Hereditary Breast and Ovarian Cancer

Breast Care ◽  
2021 ◽  
pp. 1-9
Author(s):  
Kerstin Rhiem ◽  
Bernd Auber ◽  
Susanne Briest ◽  
Nicola Dikow ◽  
Nina Ditsch ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Disease Risk ◽  
Interdisciplinary Team ◽  
Individual Risk ◽  
Patient Counseling ◽  
Consensus Process ◽  
Breast And Ovarian Cancer ◽  
Risk Genes ◽  
Therapeutic Measures ◽  
Individual Disease

<b><i>Background:</i></b> The German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC) has established a multigene panel (TruRisk®) for the analysis of risk genes for familial breast and ovarian cancer. <b><i>Summary:</i></b> An interdisciplinary team of experts from the GC-HBOC has evaluated the available data on risk modification in the presence of pathogenic mutations in these genes based on a structured literature search and through a formal consensus process. <b><i>Key Messages:</i></b> The goal of this work is to better assess individual disease risk and, on this basis, to derive clinical recommendations for patient counseling and care at the centers of the GC-HBOC from the initial consultation prior to genetic testing to the use of individual risk-adapted preventive/therapeutic measures.

Network Integration of Genetically Regulated Gene Expression to Study Complex Diseases

2015 ◽  
pp. 88-109 ◽  
Author(s):  
Zhidong Tu ◽  
Bin Zhang ◽  
Jun Zhu ◽  
George C. Tseng ◽  
Debashis Ghosh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Diseases ◽  
Network Integration ◽  
Regulated Gene Expression

scholarly journals Leveraging Methylation Alterations to Discover Potential Causal Genes Associated with the Survival Risk of Cervical Cancer in TCGA through a Two-stage Inference Approach

2020 ◽  
Author(s):  
Jinhui Zhang ◽  
Ting Wang ◽  
Xinghao Yu ◽  
Shuiping Huang ◽  
Huashuo Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Prediction Models ◽  
Two Stage ◽  
Promising Candidate ◽  
Causal Genes ◽  
Regulated Gene Expression ◽  
Survival Risk ◽  
Functional Analyses ◽  
The Relationship

Abstract Background：Multiple genes were previously identified to be associated with cervical cancer; however, the genetic architecture of cervical cancer remains unknown and many causal genes have yet been discovered.Methods: To explore causal genes related to cervical cancer, a two-stage causal inference approach was proposed within the framework of Mendelian randomization, where the gene expression was treated as exposure, with methylations located within that gene serving as instrumental variables. Five prediction models were first utilized to characterize the relationship between the expression and methylations for each gene; then the methylation-regulated gene expression (MReX) was obtained and the association was evaluated via Cox mixed-effects model based on MReX. We further implemented the harmonic mean p-value (HMP) combination to take advantage of respective strengths of these prediction models while accounting for dependency among the p-values.Results: A total of 14 causal genes were discovered to be associated with the survival risk of cervical cancer in TCGA when the five prediction models were separately employed. The total number of causal genes was brought to 23 when conducting HMP. Some of the newly discovered genes may be novel (e.g. YJEFN3, SPATA5L1, IMMP1L, C5orf55, PPIP5K2, ZNF330, CRYZL1, PPM1A, ESCO2, ZNF605, ZNF225, ZNF266, FICD and OSTC). Functional analyses showed these genes were enriched in tumor-associated pathways. Additionally, four genes (i.e. COL6A1, SYDE1, ESCO2 and GIPC1) were differentially expressed.Conclusion: Overall, our study discovered promising candidate genes that are causally associated with the survival risk of cervical cancer and thus provided new insights into the genetic etiology of cervical cancer.

scholarly journals PrediXcan: Trait Mapping Using Human Transcriptome Regulation

2015 ◽  
Author(s):  
Eric R Gamazon ◽  
Heather E Wheeler ◽  
Kaanan Shah ◽  
Sahar V Mozaffari ◽  
Keston Aquino-Michaels ◽  
...  
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Prediction Models ◽  
Association Studies ◽  
Regulatory Function ◽  
Genetic Profile ◽  
Genome Wide Association Studies ◽  
Reverse Causality ◽  
Reference Transcriptome ◽  
Regulated Gene Expression

Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in general, not well understood. We propose a gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individual's genetic profile and correlates the “imputed” gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. The genetically regulated gene expression is estimated using whole-genome tissue-dependent prediction models trained with reference transcriptome datasets. PrediXcan enjoys the benefits of gene- based approaches such as reduced multiple testing burden, more comprehensive annotation of gene function compared to that derived from single variants, and a principled approach to the design of follow-up experiments while also integrating knowledge of regulatory function. Since no actual expression data are used in the analysis of GWAS data - only in silico expression - reverse causality problems are largely avoided. PrediXcan harnesses reference transcriptome data for disease mapping studies. Our results demonstrate that PrediXcan can detect known and novel genes associated with disease traits and provide insights into the mechanism of these associations.

scholarly journals Exploration of Potential miRNA Biomarkers and Prediction for Ovarian Cancer Using Artificial Intelligence

2021 ◽  
Vol 12 ◽  
Author(s):  
Farzaneh Hamidi ◽  
Neda Gilani ◽  
Reza Arabi Belaghi ◽  
Parvin Sarbakhsh ◽  
Tuba Edgünlü ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Gene Expression Data ◽  
Prediction Models ◽  
Gynecologic Cancer ◽  
External Validation ◽  
Small Sample ◽  
Validation Dataset ◽  
Expression Data ◽  
Embedded Algorithms

Ovarian cancer is the second most dangerous gynecologic cancer with a high mortality rate. The classification of gene expression data from high-dimensional and small-sample gene expression data is a challenging task. The discovery of miRNAs, a small non-coding RNA with 18–25 nucleotides in length that regulates gene expression, has revealed the existence of a new array for regulation of genes and has been reported as playing a serious role in cancer. By using LASSO and Elastic Net as embedded algorithms of feature selection techniques, the present study identified 10 miRNAs that were regulated in ovarian serum cancer samples compared to non-cancer samples in public available dataset GSE106817: hsa-miR-5100, hsa-miR-6800-5p, hsa-miR-1233-5p, hsa-miR-4532, hsa-miR-4783-3p, hsa-miR-4787-3p, hsa-miR-1228-5p, hsa-miR-1290, hsa-miR-3184-5p, and hsa-miR-320b. Further, we implemented state-of-the-art machine learning classifiers, such as logistic regression, random forest, artificial neural network, XGBoost, and decision trees to build clinical prediction models. Next, the diagnostic performance of these models with identified miRNAs was evaluated in the internal (GSE106817) and external validation dataset (GSE113486) by ROC analysis. The results showed that first four prediction models consistently yielded an AUC of 100%. Our findings provide significant evidence that the serum miRNA profile represents a promising diagnostic biomarker for ovarian cancer.

scholarly journals Criteria of the German Consortium for Hereditary Breast and Ovarian Cancer for the Classification of Germline Sequence Variants in Risk Genes for Hereditary Breast and Ovarian Cancer

2020 ◽  
Vol 80 (04) ◽  
pp. 410-429 ◽  
Author(s):  
Barbara Wappenschmidt ◽  
Jan Hauke ◽  
Ulrike Faust ◽  
Dieter Niederacher ◽  
Lisa Wiesmüller ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Genetic Variants ◽  
Task Force ◽  
Individual Risk ◽  
Extensive Literature ◽  
Breast And Ovarian Cancer ◽  
Risk Genes ◽  
New Information ◽  
Central Database ◽  
Set Up

AbstractMore than ten years ago, the German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC) set up a panel of experts (VUS Task Force) which was tasked with reviewing the classifications of genetic variants reported by individual centres of the GC-HBOC to the central database in Leipzig and reclassifying them, where necessary, based on the most recent data. When it evaluates variants, the VUS Task Force must arrive at a consensus. The resulting classifications are recorded in a central database where they serve as a basis for ensuring the consistent evaluation of previously known and newly identified variants in the different centres of the GC-HBOC. The standardised VUS evaluation by the VUS Task Force is a key element of the recall system which has also been set up by the GC-HBOC. The system will be used to pass on information to families monitored and managed by GC-HBOC centres in the event that previously classified variants are reclassified based on new information. The evaluation algorithm of the VUS Task Force was compiled using internationally established assessment methods (IARC, ACMG, ENIGMA) and is presented here together with the underlying evaluation criteria used to arrive at the classification decision using a flow chart. In addition, the characteristics and special features of specific individual risk genes associated with breast and/or ovarian cancer are discussed in separate subsections. The URLs of relevant databases have also been included together with extensive literature references to provide additional information and cover the scope and dynamism of the current state of knowledge on the evaluation of genetic variants. In future, if criteria are updated based on new information, the update will be published on the website of the GC-HBOC (https://www.konsortium-familiaerer-brustkrebs.de/).

Sign in / Sign up

Export Citation Format

Share Document