Incorporating biological pathways via a Markov random field model in genome-wide association studies.
Genome-wide association studies (GWAS) examine a large number of markers across the genome to identify associations between genetic variants and disease. Most published studies examine only single markers, which may be less informative than considering multiple markers and multiple genes jointly bec...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2011-04-01
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| Series: | PLoS Genetics |
| Online Access: | http://europepmc.org/articles/PMC3072362?pdf=render |
| _version_ | 1828176586493394944 |
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| author | Min Chen Judy Cho Hongyu Zhao |
| author_facet | Min Chen Judy Cho Hongyu Zhao |
| author_sort | Min Chen |
| collection | DOAJ |
| description | Genome-wide association studies (GWAS) examine a large number of markers across the genome to identify associations between genetic variants and disease. Most published studies examine only single markers, which may be less informative than considering multiple markers and multiple genes jointly because genes may interact with each other to affect disease risk. Much knowledge has been accumulated in the literature on biological pathways and interactions. It is conceivable that appropriate incorporation of such prior knowledge may improve the likelihood of making genuine discoveries. Although a number of methods have been developed recently to prioritize genes using prior biological knowledge, such as pathways, most methods treat genes in a specific pathway as an exchangeable set without considering the topological structure of a pathway. However, how genes are related with each other in a pathway may be very informative to identify association signals. To make use of the connectivity information among genes in a pathway in GWAS analysis, we propose a Markov Random Field (MRF) model to incorporate pathway topology for association analysis. We show that the conditional distribution of our MRF model takes on a simple logistic regression form, and we propose an iterated conditional modes algorithm as well as a decision theoretic approach for statistical inference of each gene's association with disease. Simulation studies show that our proposed framework is more effective to identify genes associated with disease than a single gene-based method. We also illustrate the usefulness of our approach through its applications to a real data example. |
| first_indexed | 2024-04-12T04:46:45Z |
| format | Article |
| id | doaj.art-592083fbb7ac45fb84d0d6cb5f4a1ec5 |
| institution | Directory Open Access Journal |
| issn | 1553-7390 1553-7404 |
| language | English |
| last_indexed | 2024-04-12T04:46:45Z |
| publishDate | 2011-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Genetics |
| spelling | doaj.art-592083fbb7ac45fb84d0d6cb5f4a1ec52022-12-22T03:47:28ZengPublic Library of Science (PLoS)PLoS Genetics1553-73901553-74042011-04-0174e100135310.1371/journal.pgen.1001353Incorporating biological pathways via a Markov random field model in genome-wide association studies.Min ChenJudy ChoHongyu ZhaoGenome-wide association studies (GWAS) examine a large number of markers across the genome to identify associations between genetic variants and disease. Most published studies examine only single markers, which may be less informative than considering multiple markers and multiple genes jointly because genes may interact with each other to affect disease risk. Much knowledge has been accumulated in the literature on biological pathways and interactions. It is conceivable that appropriate incorporation of such prior knowledge may improve the likelihood of making genuine discoveries. Although a number of methods have been developed recently to prioritize genes using prior biological knowledge, such as pathways, most methods treat genes in a specific pathway as an exchangeable set without considering the topological structure of a pathway. However, how genes are related with each other in a pathway may be very informative to identify association signals. To make use of the connectivity information among genes in a pathway in GWAS analysis, we propose a Markov Random Field (MRF) model to incorporate pathway topology for association analysis. We show that the conditional distribution of our MRF model takes on a simple logistic regression form, and we propose an iterated conditional modes algorithm as well as a decision theoretic approach for statistical inference of each gene's association with disease. Simulation studies show that our proposed framework is more effective to identify genes associated with disease than a single gene-based method. We also illustrate the usefulness of our approach through its applications to a real data example.http://europepmc.org/articles/PMC3072362?pdf=render |
| spellingShingle | Min Chen Judy Cho Hongyu Zhao Incorporating biological pathways via a Markov random field model in genome-wide association studies. PLoS Genetics |
| title | Incorporating biological pathways via a Markov random field model in genome-wide association studies. |
| title_full | Incorporating biological pathways via a Markov random field model in genome-wide association studies. |
| title_fullStr | Incorporating biological pathways via a Markov random field model in genome-wide association studies. |
| title_full_unstemmed | Incorporating biological pathways via a Markov random field model in genome-wide association studies. |
| title_short | Incorporating biological pathways via a Markov random field model in genome-wide association studies. |
| title_sort | incorporating biological pathways via a markov random field model in genome wide association studies |
| url | http://europepmc.org/articles/PMC3072362?pdf=render |
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