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Synthesis-View: visualization and interpretation of SNP association results for multi-cohort, multi-phenotype data and meta-analysis

BACKGROUND: Initial genome-wide association study (GWAS) discoveries are being further explored through the use of large cohorts across multiple and diverse populations involving meta-analyses within large consortia and networks. Many of the additional studies characterize less than 100 single nucle...

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Autores principales: Pendergrass, Sarah A, Dudek, Scott M, Crawford, Dana C, Ritchie, Marylyn D
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3012023/
https://www.ncbi.nlm.nih.gov/pubmed/21162740
http://dx.doi.org/10.1186/1756-0381-3-10
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author Pendergrass, Sarah A
Dudek, Scott M
Crawford, Dana C
Ritchie, Marylyn D
author_facet Pendergrass, Sarah A
Dudek, Scott M
Crawford, Dana C
Ritchie, Marylyn D
author_sort Pendergrass, Sarah A
collection PubMed
description BACKGROUND: Initial genome-wide association study (GWAS) discoveries are being further explored through the use of large cohorts across multiple and diverse populations involving meta-analyses within large consortia and networks. Many of the additional studies characterize less than 100 single nucleotide polymorphisms (SNPs), often include multiple and correlated phenotypic measurements, and can include data from multiple-sites, multiple-studies, as well as multiple race/ethnicities. New approaches for visualizing resultant data are necessary in order to fully interpret results and obtain a broad view of the trends between DNA variation and phenotypes, as well as provide information on specific SNP and phenotype relationships. RESULTS: The Synthesis-View software tool was designed to visually synthesize the results of the aforementioned types of studies. Presented herein are multiple examples of the ways Synthesis-View can be used to report results from association studies of DNA variation and phenotypes, including the visual integration of p-values or other metrics of significance, allele frequencies, sample sizes, effect size, and direction of effect. CONCLUSIONS: To truly allow a user to visually integrate multiple pieces of information typical of a genetic association study, innovative views are needed to integrate multiple pieces of information. As a result, we have created "Synthesis-View" software for the visualization of genotype-phenotype association data in multiple cohorts. Synthesis-View is freely available for non-commercial research institutions, for full details see https://chgr.mc.vanderbilt.edu/synthesisview.
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spelling pubmed-30120232010-12-30 Synthesis-View: visualization and interpretation of SNP association results for multi-cohort, multi-phenotype data and meta-analysis Pendergrass, Sarah A Dudek, Scott M Crawford, Dana C Ritchie, Marylyn D BioData Min Software Article BACKGROUND: Initial genome-wide association study (GWAS) discoveries are being further explored through the use of large cohorts across multiple and diverse populations involving meta-analyses within large consortia and networks. Many of the additional studies characterize less than 100 single nucleotide polymorphisms (SNPs), often include multiple and correlated phenotypic measurements, and can include data from multiple-sites, multiple-studies, as well as multiple race/ethnicities. New approaches for visualizing resultant data are necessary in order to fully interpret results and obtain a broad view of the trends between DNA variation and phenotypes, as well as provide information on specific SNP and phenotype relationships. RESULTS: The Synthesis-View software tool was designed to visually synthesize the results of the aforementioned types of studies. Presented herein are multiple examples of the ways Synthesis-View can be used to report results from association studies of DNA variation and phenotypes, including the visual integration of p-values or other metrics of significance, allele frequencies, sample sizes, effect size, and direction of effect. CONCLUSIONS: To truly allow a user to visually integrate multiple pieces of information typical of a genetic association study, innovative views are needed to integrate multiple pieces of information. As a result, we have created "Synthesis-View" software for the visualization of genotype-phenotype association data in multiple cohorts. Synthesis-View is freely available for non-commercial research institutions, for full details see https://chgr.mc.vanderbilt.edu/synthesisview. BioMed Central 2010-12-16 /pmc/articles/PMC3012023/ /pubmed/21162740 http://dx.doi.org/10.1186/1756-0381-3-10 Text en Copyright ©2010 Pendergrass et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<url>http://creativecommons.org/licenses/by/2.0</url>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Software Article
Pendergrass, Sarah A
Dudek, Scott M
Crawford, Dana C
Ritchie, Marylyn D
Synthesis-View: visualization and interpretation of SNP association results for multi-cohort, multi-phenotype data and meta-analysis
title Synthesis-View: visualization and interpretation of SNP association results for multi-cohort, multi-phenotype data and meta-analysis
title_full Synthesis-View: visualization and interpretation of SNP association results for multi-cohort, multi-phenotype data and meta-analysis
title_fullStr Synthesis-View: visualization and interpretation of SNP association results for multi-cohort, multi-phenotype data and meta-analysis
title_full_unstemmed Synthesis-View: visualization and interpretation of SNP association results for multi-cohort, multi-phenotype data and meta-analysis
title_short Synthesis-View: visualization and interpretation of SNP association results for multi-cohort, multi-phenotype data and meta-analysis
title_sort synthesis-view: visualization and interpretation of snp association results for multi-cohort, multi-phenotype data and meta-analysis
topic Software Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3012023/
https://www.ncbi.nlm.nih.gov/pubmed/21162740
http://dx.doi.org/10.1186/1756-0381-3-10
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