Network Graph Analysis of Gene-Gene Interactions in Genome-Wide Association Study Data
Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BioMed Central
2012-12-01
|
| Series: | Genomics & Informatics |
| Subjects: | |
| Online Access: | http://genominfo.org/upload/pdf/gni-10-256.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832572549191434240 |
|---|---|
| author | Sungyoung Lee Min-Seok Kwon Taesung Park |
| author_facet | Sungyoung Lee Min-Seok Kwon Taesung Park |
| author_sort | Sungyoung Lee |
| collection | DOAJ |
| description | Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs). For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR) is one of the powerful and efficient methods for detecting high-order gene-gene (GxG) interactions. However, the biological interpretation of GxG interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified GxG interactions. The proposed network graph analysis consists of three steps. The first step is for performing GxG interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified GxG interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE) data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform GxG interaction analysis of body mass index (BMI). Our network graph analysis successfully showed that many identified GxG interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of GxG interactions. |
| format | Article |
| id | doaj-art-64c728e39308450598721a635ef2ef82 |
| institution | Kabale University |
| issn | 1598-866X 2234-0742 |
| language | English |
| publishDate | 2012-12-01 |
| publisher | BioMed Central |
| record_format | Article |
| series | Genomics & Informatics |
| spelling | doaj-art-64c728e39308450598721a635ef2ef822025-02-02T09:14:00ZengBioMed CentralGenomics & Informatics1598-866X2234-07422012-12-0110425626210.5808/GI.2012.10.4.25626Network Graph Analysis of Gene-Gene Interactions in Genome-Wide Association Study DataSungyoung Lee0Min-Seok Kwon1Taesung Park2Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-747, Korea.Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-747, Korea.Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-747, Korea.Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs). For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR) is one of the powerful and efficient methods for detecting high-order gene-gene (GxG) interactions. However, the biological interpretation of GxG interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified GxG interactions. The proposed network graph analysis consists of three steps. The first step is for performing GxG interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified GxG interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE) data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform GxG interaction analysis of body mass index (BMI). Our network graph analysis successfully showed that many identified GxG interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of GxG interactions.http://genominfo.org/upload/pdf/gni-10-256.pdfgene-gene interactiongeneralized multifactor dimensionality reductiongenome-wide association studygraph analysisgraphic processing unitsnetwork graph |
| spellingShingle | Sungyoung Lee Min-Seok Kwon Taesung Park Network Graph Analysis of Gene-Gene Interactions in Genome-Wide Association Study Data Genomics & Informatics gene-gene interaction generalized multifactor dimensionality reduction genome-wide association study graph analysis graphic processing units network graph |
| title | Network Graph Analysis of Gene-Gene Interactions in Genome-Wide Association Study Data |
| title_full | Network Graph Analysis of Gene-Gene Interactions in Genome-Wide Association Study Data |
| title_fullStr | Network Graph Analysis of Gene-Gene Interactions in Genome-Wide Association Study Data |
| title_full_unstemmed | Network Graph Analysis of Gene-Gene Interactions in Genome-Wide Association Study Data |
| title_short | Network Graph Analysis of Gene-Gene Interactions in Genome-Wide Association Study Data |
| title_sort | network graph analysis of gene gene interactions in genome wide association study data |
| topic | gene-gene interaction generalized multifactor dimensionality reduction genome-wide association study graph analysis graphic processing units network graph |
| url | http://genominfo.org/upload/pdf/gni-10-256.pdf |
| work_keys_str_mv | AT sungyounglee networkgraphanalysisofgenegeneinteractionsingenomewideassociationstudydata AT minseokkwon networkgraphanalysisofgenegeneinteractionsingenomewideassociationstudydata AT taesungpark networkgraphanalysisofgenegeneinteractionsingenomewideassociationstudydata |