scholarly journals Assessing Disease Risk in Genome-wide Association Studies Using Family History

Epidemiology ◽  
2012 ◽  
Vol 23 (4) ◽  
pp. 616-622 ◽  
Author(s):  
Arpita Ghosh ◽  
Patricia Hartge ◽  
Mark P. Purdue ◽  
Stephen J. Chanock ◽  
Laufey Amundadottir ◽  
...  
Keyword(s):  
Family History ◽  
Disease Risk ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

Genomics Overview

2010 ◽  
Author(s):  
W Gregory Feero
Keyword(s):  
Genetic Testing ◽  
Family History ◽  
Disease Risk ◽  
Association Studies ◽  
Clinical Care ◽  
Family Health ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Sequencing Technologies ◽  
Genome Wide

New genomic applications are affecting internal medicine subspecialties and will soon affect the practices of all physicians. This chapter discusses the fields of genetics versus genomics and details the fundamentals of a genomic approach to health care. It includes special considerations such as the intersection between genomics and evidence-based medicine, genetic discrimination, the regulation of genetic testing, and the marketing of genetic testing directly to consumers. The chapter looks at genome-wide association studies and clinical care, as well as sequencing technologies. Tables offer examples of patterns of inheritance, clinical recommendations and red flags raised by family history, and intended uses for genetic tests. One figure shows an example pedigree obtained by using the US surgeon general's My Family Health Portrait family history tool, while the other shows the chromosomal locations of genetic markers associated with disease risk discovered in genome-wide association studies between 2005 and 2009. This chapter contains 41 references.

Genomics Overview

2010 ◽  
Author(s):  
W Gregory Feero
Keyword(s):  
Genetic Testing ◽  
Family History ◽  
Disease Risk ◽  
Association Studies ◽  
Clinical Care ◽  
Family Health ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Sequencing Technologies ◽  
Genome Wide

New genomic applications are affecting internal medicine subspecialties and will soon affect the practices of all physicians. This chapter discusses the fields of genetics versus genomics and details the fundamentals of a genomic approach to health care. It includes special considerations such as the intersection between genomics and evidence-based medicine, genetic discrimination, the regulation of genetic testing, and the marketing of genetic testing directly to consumers. The chapter looks at genome-wide association studies and clinical care, as well as sequencing technologies. Tables offer examples of patterns of inheritance, clinical recommendations and red flags raised by family history, and intended uses for genetic tests. One figure shows an example pedigree obtained by using the US surgeon general's My Family Health Portrait family history tool, while the other shows the chromosomal locations of genetic markers associated with disease risk discovered in genome-wide association studies between 2005 and 2009. This chapter contains 41 references.

scholarly journals Challenges and progress in interpretation of non-coding genetic variants associated with human disease

2017 ◽  
Vol 242 (13) ◽  
pp. 1325-1334 ◽  
Author(s):  
Yizhou Zhu ◽  
Cagdas Tazearslan ◽  
Yousin Suh
Keyword(s):  
Molecular Mechanisms ◽  
Target Genes ◽  
Disease Risk ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Functional Interpretation ◽  
Genome Wide ◽  
Coding Variants

Genome-wide association studies have shown that the far majority of disease-associated variants reside in the non-coding regions of the genome, suggesting that gene regulatory changes contribute to disease risk. To identify truly causal non-coding variants and their affected target genes remains challenging but is a critical step to translate the genetic associations to molecular mechanisms and ultimately clinical applications. Here we review genomic/epigenomic resources and in silico tools that can be used to identify causal non-coding variants and experimental strategies to validate their functionalities. Impact statement Most signals from genome-wide association studies (GWASs) map to the non-coding genome, and functional interpretation of these associations remained challenging. We reviewed recent progress in methodologies of studying the non-coding genome and argued that no single approach allows one to effectively identify the causal regulatory variants from GWAS results. By illustrating the advantages and limitations of each method, our review potentially provided a guideline for taking a combinatorial approach to accurately predict, prioritize, and eventually experimentally validate the causal variants.

scholarly journals Discovering patterns of pleiotropy in genome-wide association studies

2018 ◽  
Author(s):  
Jianan Zhana ◽  
Jessica van Setten ◽  
Jennifer Brody ◽  
Brenton Swenson ◽  
Anne M. Butler ◽  
...  
Keyword(s):  
Gold Standard ◽  
Disease Risk ◽  
Association Studies ◽  
Meta Analysis ◽  
Linkage Disequilibrium Block ◽  
Genome Wide Association ◽  
Superior Performance ◽  
Great Success ◽  
Genome Wide Association Studies ◽  
Genome Wide

AbstractMotivationGenome-wide association studies have had great success in identifying human genetic variants associated with disease, disease risk factors, and other biomedical phenotypes. Many variants are associated with multiple traits, even after correction for trait-trait correlation. Discovering subsets of variants associated with a shared subset of phenotypes could help reveal disease mechanisms, suggest new therapeutic options, and increase the power to detect additional variants with similar pattern of associations. Here we introduce two methods based on a Bayesian framework, SNP And Pleiotropic PHenotype Organization (SAPPHO), one modeling independent phenotypes (SAPPHO-I) and the other incorporating a full phenotype covariance structure (SAPPHO-C). These two methods learn patterns of pleiotropy from genotype and phenotype data, using identified associations to discover additional associations with shared patterns.ResultsThe SAPPHO methods, along with other recent approaches for pleiotropic association tests, were assessed using data from the Atherosclerotic Risk in Communities (ARIC) study of 8,000 individuals, whose gold-standard associations were provided by meta-analysis of 40,000 to 100,000 individuals from the CHARGE consortium. Using power to detect gold-standard associations at genome-wide significance (0.05 family-wise error rate) as a metric, SAPPHO performed best. The SAPPHO methods were also uniquely able to select the most significant variants in a parsimonious model, excluding other less likely variants within a linkage disequilibrium block. For meta-analysis, the SAPPHO methods implement summary modes that use sufficient statistics rather than full phenotype and genotype data. Meta-analysis applied to CHARGE detected 16 additional associations to the gold-standard loci, as well as 124 novel loci, at 0.05 false discovery rate. Reasons for the superior performance were explored by performing simulations over a range of scenarios describing different genetic architectures. With SAPPHO we were able to learn genetic structures that were hidden using the traditional univariate tests.Availabilityhttps://bitbucket.org/baderlab/fast/wiki/Home. SAPPHO software is available under the GNU General Public License, v2.

Primary Sjögren Syndrome: Etiology and Pathogenesis

2017 ◽  
Author(s):  
E. William St. Clair ◽  
Stephanie L Giattino
Keyword(s):  
Animal Models ◽  
Disease Risk ◽  
Association Studies ◽  
Sjögren Syndrome ◽  
Genome Wide Association ◽  
Inflammatory Disorder ◽  
Sjogren Syndrome ◽  
Genome Wide Association Studies ◽  
Primary Sjögren Syndrome ◽  
Genome Wide

Primary Sjögren syndrome is a chronic inflammatory disorder of the lacrimal and salivary glands resulting in oral and ocular dryness. It also has extraglandular manifestations that may affect the lung, kidneys, nervous system, and other organs. The etiology and pathogenesis of primary Sjögren syndrome are incompletely understood. A working hypothesis considers the disease to be driven by a complex interplay of environmental, genetic, and epigenetic factors. Recent genome-wide association studies confirm the previously shown contribution of major histocompatibility (MHC) locus to disease susceptibility and illuminate several non-MHC loci, which add to disease risk. New gene expression studies of peripheral blood and salivary gland tissue provide further molecular detail about the role of innate and adaptive immune pathways involved in disease mechanisms. In particular, upregulated expression of interferon and B cell–activating factor appear to play key roles in this process. Despite their drawbacks, experimental animal models continue to stimulate new lines of research that are advancing our understanding of human disease. This knowledge has been translated into new therapeutic approaches currently under evaluation in clinical trials. This review contains 5 figures, 2 tables, and 67 references. Key words: adaptive immunity, animal models, epigenetics, genome-wide association studies, innate immunity, interferon signature, lymphoma pathogenesis, nucleic acid sensing, primary Sjögren syndrome 

scholarly journals Use of support vector machines for disease risk prediction in genome-wide association studies: Concerns and opportunities

2012 ◽  
Vol 33 (12) ◽  
pp. 1708-1718 ◽  
Author(s):  
Florian Mittag ◽  
Finja Büchel ◽  
Mohamad Saad ◽  
Andreas Jahn ◽  
Claudia Schulte ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Risk Prediction ◽  
Disease Risk ◽  
Association Studies ◽  
Genome Wide Association ◽  
Support Vector ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Vector Machines

scholarly journals 2018 George Lyman Duff Memorial Lecture

2019 ◽  
Vol 39 (10) ◽  
pp. 1925-1937 ◽  
Author(s):  
Ruth McPherson
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Disease Risk ◽  
Association Studies ◽  
Genome Wide Association ◽  
Data Sets ◽  
Genome Wide Association Studies ◽  
Coronary Artery Disease Risk ◽  
Genome Wide ◽  
Artery Disease

Recent studies have led to a broader understanding of the genetic architecture of coronary artery disease and demonstrate that it largely derives from the cumulative effect of multiple common risk alleles individually of small effect size rather than rare variants with large effects on coronary artery disease risk. The tools applied include genome-wide association studies encompassing over 200 000 individuals complemented by bioinformatic approaches including imputation from whole-genome data sets, expression quantitative trait loci analyses, and interrogation of ENCODE (Encyclopedia of DNA Elements), Roadmap Epigenetic Project, and other data sets. Over 160 genome-wide significant loci associated with coronary artery disease risk have been identified using the genome-wide association studies approach, 90% of which are situated in intergenic regions. Here, I will describe, in part, our research over the last decade performed in collaboration with a series of bright trainees and an extensive number of groups and individuals around the world as it applies to our understanding of the genetic basis of this complex disease. These studies include computational approaches to better understand missing heritability and identify causal pathways, experimental approaches, and progress in understanding at the molecular level the function of the multiple risk loci identified and potential applications of these genomic data in clinical medicine and drug discovery.

scholarly journals Efficient identification of context dependent subgroups of risk from genome-wide association studies

2014 ◽  
Vol 13 (2) ◽  
Author(s):  
Greg Dyson ◽  
Charles F. Sing
Keyword(s):  
Prediction Models ◽  
Disease Risk ◽  
Hypothesis Test ◽  
Association Studies ◽  
Genome Wide Association ◽  
Computational Time ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Genomic Variations ◽  
Genome Wide

AbstractWe have developed a modified Patient Rule-Induction Method (PRIM) as an alternative strategy for analyzing representative samples of non-experimental human data to estimate and test the role of genomic variations as predictors of disease risk in etiologically heterogeneous sub-samples. A computational limit of the proposed strategy is encountered when the number of genomic variations (predictor variables) under study is large (>500) because permutations are used to generate a null distribution to test the significance of a term (defined by values of particular variables) that characterizes a sub-sample of individuals through the peeling and pasting processes. As an alternative, in this paper we introduce a theoretical strategy that facilitates the quick calculation of Type I and Type II errors in the evaluation of terms in the peeling and pasting processes carried out in the execution of a PRIM analysis that are under-estimated and non-existent, respectively, when a permutation-based hypothesis test is employed. The resultant savings in computational time makes possible the consideration of larger numbers of genomic variations (an example genome-wide association study is given) in the selection of statistically significant terms in the formulation of PRIM prediction models.

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/.

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