graph-GPA 2.0: improving multi-disease genetic analysis with integration of functional annotation data
Genome-wide association studies (GWAS) have successfully identified a large number of genetic variants associated with traits and diseases. However, it still remains challenging to fully understand the functional mechanisms underlying many associated variants. This is especially the case when we are...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-07-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2023.1079198/full |
| _version_ | 1797782096509403136 |
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| author | Qiaolan Deng Arkobrato Gupta Hyeongseon Jeon Hyeongseon Jeon Jin Hyun Nam Ayse Selen Yilmaz Won Chang Maciej Pietrzak Lang Li Hang J. Kim Dongjun Chung Dongjun Chung |
| author_facet | Qiaolan Deng Arkobrato Gupta Hyeongseon Jeon Hyeongseon Jeon Jin Hyun Nam Ayse Selen Yilmaz Won Chang Maciej Pietrzak Lang Li Hang J. Kim Dongjun Chung Dongjun Chung |
| author_sort | Qiaolan Deng |
| collection | DOAJ |
| description | Genome-wide association studies (GWAS) have successfully identified a large number of genetic variants associated with traits and diseases. However, it still remains challenging to fully understand the functional mechanisms underlying many associated variants. This is especially the case when we are interested in variants shared across multiple phenotypes. To address this challenge, we propose graph-GPA 2.0 (GGPA 2.0), a statistical framework to integrate GWAS datasets for multiple phenotypes and incorporate functional annotations within a unified framework. Our simulation studies showed that incorporating functional annotation data using GGPA 2.0 not only improves the detection of disease-associated variants, but also provides a more accurate estimation of relationships among diseases. Next, we analyzed five autoimmune diseases and five psychiatric disorders with the functional annotations derived from GenoSkyline and GenoSkyline-Plus, along with the prior disease graph generated by biomedical literature mining. For autoimmune diseases, GGPA 2.0 identified enrichment for blood-related epigenetic marks, especially B cells and regulatory T cells, across multiple diseases. Psychiatric disorders were enriched for brain-related epigenetic marks, especially the prefrontal cortex and the inferior temporal lobe for bipolar disorder and schizophrenia, respectively. In addition, the pleiotropy between bipolar disorder and schizophrenia was also detected. Finally, we found that GGPA 2.0 is robust to the use of irrelevant and/or incorrect functional annotations. These results demonstrate that GGPA 2.0 can be a powerful tool to identify genetic variants associated with each phenotype or those shared across multiple phenotypes, while also promoting an understanding of functional mechanisms underlying the associated variants. |
| first_indexed | 2024-03-13T00:06:13Z |
| format | Article |
| id | doaj.art-d3df1ef2f8df4e77bedbd48f98b49c79 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-03-13T00:06:13Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-d3df1ef2f8df4e77bedbd48f98b49c792023-07-13T03:44:00ZengFrontiers Media S.A.Frontiers in Genetics1664-80212023-07-011410.3389/fgene.2023.10791981079198graph-GPA 2.0: improving multi-disease genetic analysis with integration of functional annotation dataQiaolan Deng0Arkobrato Gupta1Hyeongseon Jeon2Hyeongseon Jeon3Jin Hyun Nam4Ayse Selen Yilmaz5Won Chang6Maciej Pietrzak7Lang Li8Hang J. Kim9Dongjun Chung10Dongjun Chung11The Interdisciplinary PhD Program in Biostatistics, The Ohio State University, Columbus, OH, United StatesThe Interdisciplinary PhD Program in Biostatistics, The Ohio State University, Columbus, OH, United StatesDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH, United StatesPelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, United StatesDivision of Big Data Science, Korea University Sejong Campus, Sejong, Republic of KoreaDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH, United StatesDivision of Statistics and Data Science, University of Cincinnati, Cincinnati, OH, United StatesDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH, United StatesDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH, United StatesDivision of Statistics and Data Science, University of Cincinnati, Cincinnati, OH, United StatesDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH, United StatesPelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, United StatesGenome-wide association studies (GWAS) have successfully identified a large number of genetic variants associated with traits and diseases. However, it still remains challenging to fully understand the functional mechanisms underlying many associated variants. This is especially the case when we are interested in variants shared across multiple phenotypes. To address this challenge, we propose graph-GPA 2.0 (GGPA 2.0), a statistical framework to integrate GWAS datasets for multiple phenotypes and incorporate functional annotations within a unified framework. Our simulation studies showed that incorporating functional annotation data using GGPA 2.0 not only improves the detection of disease-associated variants, but also provides a more accurate estimation of relationships among diseases. Next, we analyzed five autoimmune diseases and five psychiatric disorders with the functional annotations derived from GenoSkyline and GenoSkyline-Plus, along with the prior disease graph generated by biomedical literature mining. For autoimmune diseases, GGPA 2.0 identified enrichment for blood-related epigenetic marks, especially B cells and regulatory T cells, across multiple diseases. Psychiatric disorders were enriched for brain-related epigenetic marks, especially the prefrontal cortex and the inferior temporal lobe for bipolar disorder and schizophrenia, respectively. In addition, the pleiotropy between bipolar disorder and schizophrenia was also detected. Finally, we found that GGPA 2.0 is robust to the use of irrelevant and/or incorrect functional annotations. These results demonstrate that GGPA 2.0 can be a powerful tool to identify genetic variants associated with each phenotype or those shared across multiple phenotypes, while also promoting an understanding of functional mechanisms underlying the associated variants.https://www.frontiersin.org/articles/10.3389/fgene.2023.1079198/fullgenome-wide association studiesGWAS summary statisticscomplex traitsgenetic correlationfunctional annotation |
| spellingShingle | Qiaolan Deng Arkobrato Gupta Hyeongseon Jeon Hyeongseon Jeon Jin Hyun Nam Ayse Selen Yilmaz Won Chang Maciej Pietrzak Lang Li Hang J. Kim Dongjun Chung Dongjun Chung graph-GPA 2.0: improving multi-disease genetic analysis with integration of functional annotation data Frontiers in Genetics genome-wide association studies GWAS summary statistics complex traits genetic correlation functional annotation |
| title | graph-GPA 2.0: improving multi-disease genetic analysis with integration of functional annotation data |
| title_full | graph-GPA 2.0: improving multi-disease genetic analysis with integration of functional annotation data |
| title_fullStr | graph-GPA 2.0: improving multi-disease genetic analysis with integration of functional annotation data |
| title_full_unstemmed | graph-GPA 2.0: improving multi-disease genetic analysis with integration of functional annotation data |
| title_short | graph-GPA 2.0: improving multi-disease genetic analysis with integration of functional annotation data |
| title_sort | graph gpa 2 0 improving multi disease genetic analysis with integration of functional annotation data |
| topic | genome-wide association studies GWAS summary statistics complex traits genetic correlation functional annotation |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2023.1079198/full |
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