Dataset Information

PGMRA: a web server for (phenotype x genotype) many-to-many relation analysis in GWAS.

ABSTRACT: It has been proposed that single nucleotide polymorphisms (SNPs) discovered by genome-wide association studies (GWAS) account for only a small fraction of the genetic variation of complex traits in human population. The remaining unexplained variance or missing heritability is thought to be due to marginal effects of many loci with small effects and has eluded attempts to identify its sources. Combination of different studies appears to resolve in part this problem. However, neither individual GWAS nor meta-analytic combinations thereof are helpful for disclosing which genetic variants contribute to explain a particular phenotype. Here, we propose that most of the missing heritability is latent in the GWAS data, which conceals intermediate phenotypes. To uncover such latent information, we propose the PGMRA server that introduces phenomics--the full set of phenotype features of an individual--to identify SNP-set structures in a broader sense, i.e. causally cohesive genotype-phenotype relations. These relations are agnostically identified (without considering disease status of the subjects) and organized in an interpretable fashion. Then, by incorporating a posteriori the subject status within each relation, we can establish the risk surface of a disease in an unbiased mode. This approach complements-instead of replaces-current analysis methods. The server is publically available at http://phop.ugr.es/fenogeno.

SUBMITTER: Arnedo J

PROVIDER: S-EPMC3692099 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

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PGMRA: a web server for (phenotype x genotype) many-to-many relation analysis in GWAS.

Arnedo Javier J del Val Coral C de Erausquin Gabriel Alejandro GA Romero-Zaliz Rocío R Svrakic Dragan D Cloninger Claude Robert CR Zwir Igor I

Nucleic acids research 20130612 Web Server issue

It has been proposed that single nucleotide polymorphisms (SNPs) discovered by genome-wide association studies (GWAS) account for only a small fraction of the genetic variation of complex traits in human population. The remaining unexplained variance or missing heritability is thought to be due to marginal effects of many loci with small effects and has eluded attempts to identify its sources. Combination of different studies appears to resolve in part this problem. However, neither individual G ...[more]

PMID: 23761451

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Project description:Demetra Application is a holistic integrated and scalable bioinformatics web‑based tool designed to assist medical experts and researchers in the process of diagnosing endometriosis. The application identifies the most prominent gene variants and single nucleotide polymorphisms (SNPs) causing endometriosis using the genomic data provided for the patient by a medical expert. The present study analyzed >28.000 endometriosis‑related publications using data mining and semantic techniques aimed towards extracting the endometriosis‑related genes and SNPs. The extracted knowledge was filtered, evaluated, annotated, classified, and stored in the Demetra Application Database (DAD). Moreover, an updated gene regulatory network with the genes implements in endometriosis was established. This was followed by the design and development of the Demetra Application, in which the generated datasets and results were included. The application was tested and presented herein with whole‑exome sequencing data from seven related patients with endometriosis. Endometriosis‑related SNPs and variants identified in genome‑wide association studies (GWAS), whole‑genome (WGS), whole‑exome (WES), or targeted sequencing information were classified, annotated and analyzed in a consolidated patient profile with clinical significance information. Probable genes associated with the patient's genomic profile were visualized using several graphs, including chromosome ideograms, statistic bars and regulatory networks through data mining studies with relative publications, in an effort to obtain a representative number of the most credible candidate genes and biological pathways associated with endometriosis. An evaluation analysis was performed on seven patients from a three‑generation family with endometriosis. All the recognized gene variants that were previously considered to be associated with endometriosis were properly identified in the output profile per patient, and by comparing the results, novel findings emerged. This novel and accessible webserver tool of endometriosis to assist medical experts in the clinical genomics and precision medicine procedure is available at http://geneticslab.aua.gr/.

Dataset Information

PGMRA: a web server for (phenotype x genotype) many-to-many relation analysis in GWAS.

Publications

PGMRA: a web server for (phenotype x genotype) many-to-many relation analysis in GWAS.

Similar Datasets

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