GPA-MDS: A Visualization Approach to Investigate Genetic Architecture among Phenotypes Using GWAS Results
Genome-wide association studies (GWAS) have identified tens of thousands of genetic variants associated with hundreds of phenotypes and diseases, which have provided clinical and medical benefits to patients with novel biomarkers and therapeutic targets. Recently, there has been accumulating evidenc...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | International Journal of Genomics |
| Online Access: | http://dx.doi.org/10.1155/2016/6589843 |
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| _version_ | 1832560903642415104 |
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| author | Wei Wei Paula S. Ramos Kelly J. Hunt Bethany J. Wolf Gary Hardiman Dongjun Chung |
| author_facet | Wei Wei Paula S. Ramos Kelly J. Hunt Bethany J. Wolf Gary Hardiman Dongjun Chung |
| author_sort | Wei Wei |
| collection | DOAJ |
| description | Genome-wide association studies (GWAS) have identified tens of thousands of genetic variants associated with hundreds of phenotypes and diseases, which have provided clinical and medical benefits to patients with novel biomarkers and therapeutic targets. Recently, there has been accumulating evidence suggesting that different complex traits share a common risk basis, namely, pleiotropy. Previously, a statistical method, namely, GPA (Genetic analysis incorporating Pleiotropy and Annotation), was developed to improve identification of risk variants and to investigate pleiotropic structure through a joint analysis of multiple GWAS datasets. While GPA provides a statistically rigorous framework to evaluate pleiotropy between phenotypes, it is still not trivial to investigate genetic relationships among a large number of phenotypes using the GPA framework. In order to address this challenge, in this paper, we propose a novel approach, GPA-MDS, to visualize genetic relationships among phenotypes using the GPA algorithm and multidimensional scaling (MDS). This tool will help researchers to investigate common etiology among diseases, which can potentially lead to development of common treatments across diseases. We evaluate the proposed GPA-MDS framework using a simulation study and apply it to jointly analyze GWAS datasets examining 18 unique phenotypes, which helps reveal the shared genetic architecture of these phenotypes. |
| format | Article |
| id | doaj-art-d6b4dfb0f2bd4298990d4cbbb48bd61f |
| institution | Kabale University |
| issn | 2314-436X 2314-4378 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Genomics |
| spelling | doaj-art-d6b4dfb0f2bd4298990d4cbbb48bd61f2025-02-03T01:26:27ZengWileyInternational Journal of Genomics2314-436X2314-43782016-01-01201610.1155/2016/65898436589843GPA-MDS: A Visualization Approach to Investigate Genetic Architecture among Phenotypes Using GWAS ResultsWei Wei0Paula S. Ramos1Kelly J. Hunt2Bethany J. Wolf3Gary Hardiman4Dongjun Chung5Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USADepartment of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USADepartment of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USADepartment of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USADepartment of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USADepartment of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USAGenome-wide association studies (GWAS) have identified tens of thousands of genetic variants associated with hundreds of phenotypes and diseases, which have provided clinical and medical benefits to patients with novel biomarkers and therapeutic targets. Recently, there has been accumulating evidence suggesting that different complex traits share a common risk basis, namely, pleiotropy. Previously, a statistical method, namely, GPA (Genetic analysis incorporating Pleiotropy and Annotation), was developed to improve identification of risk variants and to investigate pleiotropic structure through a joint analysis of multiple GWAS datasets. While GPA provides a statistically rigorous framework to evaluate pleiotropy between phenotypes, it is still not trivial to investigate genetic relationships among a large number of phenotypes using the GPA framework. In order to address this challenge, in this paper, we propose a novel approach, GPA-MDS, to visualize genetic relationships among phenotypes using the GPA algorithm and multidimensional scaling (MDS). This tool will help researchers to investigate common etiology among diseases, which can potentially lead to development of common treatments across diseases. We evaluate the proposed GPA-MDS framework using a simulation study and apply it to jointly analyze GWAS datasets examining 18 unique phenotypes, which helps reveal the shared genetic architecture of these phenotypes.http://dx.doi.org/10.1155/2016/6589843 |
| spellingShingle | Wei Wei Paula S. Ramos Kelly J. Hunt Bethany J. Wolf Gary Hardiman Dongjun Chung GPA-MDS: A Visualization Approach to Investigate Genetic Architecture among Phenotypes Using GWAS Results International Journal of Genomics |
| title | GPA-MDS: A Visualization Approach to Investigate Genetic Architecture among Phenotypes Using GWAS Results |
| title_full | GPA-MDS: A Visualization Approach to Investigate Genetic Architecture among Phenotypes Using GWAS Results |
| title_fullStr | GPA-MDS: A Visualization Approach to Investigate Genetic Architecture among Phenotypes Using GWAS Results |
| title_full_unstemmed | GPA-MDS: A Visualization Approach to Investigate Genetic Architecture among Phenotypes Using GWAS Results |
| title_short | GPA-MDS: A Visualization Approach to Investigate Genetic Architecture among Phenotypes Using GWAS Results |
| title_sort | gpa mds a visualization approach to investigate genetic architecture among phenotypes using gwas results |
| url | http://dx.doi.org/10.1155/2016/6589843 |
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