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 Risk prediction using genome-wide association studies

2010 ◽  
Vol 34 (7) ◽  
pp. 643-652 ◽  
Author(s):  
Charles Kooperberg ◽  
Michael LeBlanc ◽  
Valerie Obenchain
Keyword(s):  
Risk Prediction ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

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 

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.

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