scholarly journals Integrative Genome-Wide Association Studies of eQTL and GWAS Data for Gout Disease Susceptibility

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Meng-tse Gabriel Lee ◽  
Tzu-Chun Hsu ◽  
Shyr-Chyr Chen ◽  
Ya-Chin Lee ◽  
Po-Hsiu Kuo ◽  
...  
Keyword(s):  
Disease Susceptibility ◽  
Association Studies ◽  
Gwas Data ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals A practical approach to adjusting for population stratification in genome-wide association studies: principal components and propensity scores (PCAPS)

2018 ◽  
Vol 17 (6) ◽  
Author(s):  
Huaqing Zhao ◽  
Nandita Mitra ◽  
Peter A. Kanetsky ◽  
Katherine L. Nathanson ◽  
Timothy R. Rebbeck
Keyword(s):  
Principal Components ◽  
Population Stratification ◽  
Propensity Scores ◽  
Association Studies ◽  
Germ Cell Tumors ◽  
Gwas Data ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Testicular Germ Cell ◽  
Genome Wide

Abstract Genome-wide association studies (GWAS) are susceptible to bias due to population stratification (PS). The most widely used method to correct bias due to PS is principal components (PCs) analysis (PCA), but there is no objective method to guide which PCs to include as covariates. Often, the ten PCs with the highest eigenvalues are included to adjust for PS. This selection is arbitrary, and patterns of local linkage disequilibrium may affect PCA corrections. To address these limitations, we estimate genomic propensity scores based on all statistically significant PCs selected by the Tracy-Widom (TW) statistic. We compare a principal components and propensity scores (PCAPS) approach to PCA and EMMAX using simulated GWAS data under no, moderate, and severe PS. PCAPS reduced spurious genetic associations regardless of the degree of PS, resulting in odds ratio (OR) estimates closer to the true OR. We illustrate our PCAPS method using GWAS data from a study of testicular germ cell tumors. PCAPS provided a more conservative adjustment than PCA. Advantages of the PCAPS approach include reduction of bias compared to PCA, consistent selection of propensity scores to adjust for PS, the potential ability to handle outliers, and ease of implementation using existing software packages.

scholarly journals Assessing the performance of genome-wide association studies for predicting disease risk

2019 ◽  
Author(s):  
Jonas Patron ◽  
Arnau Serra-Cayuela ◽  
Beomsoo Han ◽  
Carin Li ◽  
David Scott Wishart
Keyword(s):  
Disease Risk ◽  
Association Studies ◽  
Roc Curves ◽  
Gwas Data ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Link Type ◽  
Genome Wide ◽  
Risk Predictors ◽  
Gwa Studies

AbstractTo date more than 3700 genome-wide association studies (GWAS) have been published that look at the genetic contributions of single nucleotide polymorphisms (SNPs) to human conditions or human phenotypes. Through these studies many highly significant SNPs have been identified for hundreds of diseases or medical conditions. However, the extent to which GWAS-identified SNPs or combinations of SNP biomarkers can predict disease risk is not well known. One of the most commonly used approaches to assess the performance of predictive biomarkers is to determine the area under the receiver-operator characteristic curve (AUROC). We have developed an R package called G-WIZ to generate ROC curves and calculate the AUROC using summary-level GWAS data. We first tested the performance of G-WIZ by using AUROC values derived from patient-level SNP data, as well as literature-reported AUROC values. We found that G-WIZ predicts the AUROC with <3% error. Next, we used the summary level GWAS data from GWAS Central to determine the ROC curves and AUROC values for 569 different GWA studies spanning 219 different conditions. Using these data we found a small number of GWA studies with SNP-derived risk predictors that have very high AUROCs (>0.75). On the other hand, the average GWA study produces a multi-SNP risk predictor with an AUROC of 0.55. Detailed AUROC comparisons indicate that most SNP-derived risk predictions are not as good as clinically based disease risk predictors. All our calculations (ROC curves, AUROCs, explained heritability) are in a publicly accessible database called GWAS-ROCS (http://gwasrocs.ca). The G-WIZ code is freely available for download at https://github.com/jonaspatronjp/GWIZ-Rscript/.

scholarly journals Discovering genetic interactions bridging pathways in genome-wide association studies

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Gang Fang ◽  
Wen Wang ◽  
Vanja Paunic ◽  
Hamed Heydari ◽  
Michael Costanzo ◽  
...  
Keyword(s):  
Statistical Power ◽  
Complex Disease ◽  
Association Studies ◽  
Genetic Network ◽  
Genetic Interactions ◽  
Gwas Data ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Cancer Breast ◽  
Genome Wide

Abstract Genetic interactions have been reported to underlie phenotypes in a variety of systems, but the extent to which they contribute to complex disease in humans remains unclear. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions, but existing methods for identifying them from GWAS data tend to focus on testing individual locus pairs, which undermines statistical power. Importantly, a global genetic network mapped for a model eukaryotic organism revealed that genetic interactions often connect genes between compensatory functional modules in a highly coherent manner. Taking advantage of this expected structure, we developed a computational approach called BridGE that identifies pathways connected by genetic interactions from GWAS data. Applying BridGE broadly, we discover significant interactions in Parkinson’s disease, schizophrenia, hypertension, prostate cancer, breast cancer, and type 2 diabetes. Our novel approach provides a general framework for mapping complex genetic networks underlying human disease from genome-wide genotype data.

scholarly journals Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Stefanie Friedrichs ◽  
Juliane Manitz ◽  
Patricia Burger ◽  
Christopher I. Amos ◽  
Angela Risch ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Lung Cancer ◽  
Association Studies ◽  
Kernel Functions ◽  
Gwas Data ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Cancer Dataset ◽  
Prediction Ability ◽  
Genome Wide

The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility.

scholarly journals Learning from Fifteen Years of Genome-Wide Association Studies in Age-Related Macular Degeneration

Cells ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 2267
Author(s):  
Tobias Strunz ◽  
Christina Kiel ◽  
Bastian L. Sauerbeck ◽  
Bernhard H. F. Weber
Keyword(s):  
Macular Degeneration ◽  
Association Studies ◽  
Age Related Macular Degeneration ◽  
Gwas Data ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Age Related ◽  
Genome Wide ◽  
Follow Up Studies

Over the last 15 years, genome-wide association studies (GWAS) have greatly advanced our understanding of the genetic landscape of complex phenotypes. Nevertheless, causal interpretations of GWAS data are challenging but crucial to understand underlying mechanisms and pathologies. In this review, we explore to what extend the research community follows up on GWAS data. We have traced the scientific activities responding to the two largest GWAS conducted on age-related macular degeneration (AMD) so far. Altogether 703 articles were manually categorized according to their study type. This demonstrates that follow-up studies mainly involve “Review articles” (33%) or “Genetic association studies” (33%), while 19% of publications report on findings from experimental work. It is striking to note that only three of 16 AMD-associated loci described de novo in 2016 were examined in the four-year follow-up period after publication. A comparative analysis of five studies on gene expression regulation in AMD-associated loci revealed consistent gene candidates for 15 of these loci. Our random survey highlights the fact that functional follow-up studies on GWAS results are still in its early stages hampering a significant refinement of the vast association data and thus a more accurate insight into mechanisms and pathways.

scholarly journals Reworking GWAS Data to Understand the Role of Nongenetic Factors in MS Etiopathogenesis

Genes ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 97
Author(s):  
Rosella Mechelli ◽  
Renato Umeton ◽  
Grazia Manfrè ◽  
Silvia Romano ◽  
Maria Chiara Buscarinu ◽  
...  
Keyword(s):  
Disease Risk ◽  
Association Studies ◽  
Gwas Data ◽  
Genome Wide Association ◽  
Future Perspective ◽  
Polygenic Risk Score ◽  
Genome Wide Association Studies ◽  
Disease Etiology ◽  
Genome Wide ◽  
The Impact

Genome-wide association studies have identified more than 200 multiple sclerosis (MS)-associated loci across the human genome over the last decade, suggesting complexity in the disease etiology. This complexity poses at least two challenges: the definition of an etiological model including the impact of nongenetic factors, and the clinical translation of genomic data that may be drivers for new druggable targets. We reviewed studies dealing with single genes of interest, to understand how MS-associated single nucleotide polymorphism (SNP) variants affect the expression and the function of those genes. We then surveyed studies on the bioinformatic reworking of genome-wide association studies (GWAS) data, with aggregate analyses of many GWAS loci, each contributing with a small effect to the overall disease predisposition. These investigations uncovered new information, especially when combined with nongenetic factors having possible roles in the disease etiology. In this context, the interactome approach, defined as “modules of genes whose products are known to physically interact with environmental or human factors with plausible relevance for MS pathogenesis”, will be reported in detail. For a future perspective, a polygenic risk score, defined as a cumulative risk derived from aggregating the contributions of many DNA variants associated with a complex trait, may be integrated with data on environmental factors affecting the disease risk or protection.

scholarly journals Conditions for the validity of SNP-based heritability estimation

2014 ◽  
Author(s):  
James J Lee ◽  
Carson C Chow
Keyword(s):  
Lower Bound ◽  
Association Studies ◽  
Gwas Data ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Population Variance ◽  
Genome Wide ◽  
Heritability Estimation ◽  
Novel Method ◽  
The Given

The heritability of a trait ($h^2$) is the proportion of its population variance caused by genetic differences, and estimates of this parameter are important for interpreting the results of genome-wide association studies (GWAS). In recent years, researchers have adopted a novel method for estimating a lower bound on heritability directly from GWAS data that uses realized genetic similarities between nominally unrelated individuals. The quantity estimated by this method is purported to be the contribution to heritability that could in principle be recovered from association studies employing the given panel of SNPs ($h^2_\textrm{SNP}$). Thus far the validity of this approach has mostly been tested empirically. Here, we provide a mathematical explication and show that the method should remain a robust means of obtaining $h^2_\textrm{SNP}$ under circumstances wider than those under which it has so far been derived.

scholarly journals Discovering genetic interactions bridging pathways in genome-wide association studies

2017 ◽  
Author(s):  
Gang Fang ◽  
Wen Wang ◽  
Vanja Paunic ◽  
Hamed Heydari ◽  
Michael Costanzo ◽  
...  
Keyword(s):  
Statistical Power ◽  
Complex Disease ◽  
Association Studies ◽  
Genetic Networks ◽  
Genetic Interactions ◽  
Gwas Data ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Cancer Breast ◽  
Genome Wide

AbstractGenetic interactions have been reported to underlie phenotypes in a variety of systems, but the extent to which they contribute to complex disease in humans remains unclear. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions, but existing methods for identifying them from GWAS data tend to focus on testing individual locus pairs, which undermines statistical power. Importantly, the global genetic networks mapped for a model eukaryotic organism revealed that genetic interactions often connect genes between compensatory functional modules in a highly coherent manner. Taking advantage of this expected structure, we developed a computational approach called BridGE that identifies pathways connected by genetic interactions from GWAS data. Applying BridGE broadly, we discovered significant interactions in Parkinson’s disease, schizophrenia, hypertension, prostate cancer, breast cancer, and type 2 diabetes. Our novel approach provides a general framework for mapping complex genetic networks underlying human disease from genome-wide genotype data.

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