Efficient Strategy to Identify Gene-Gene Interactions and Its Application to Type 2 Diabetes
Over the past decade, the detection of gene-gene interactions has become more and more popular in the field of genome-wide association studies (GWASs). The goal of the GWAS is to identify genetic susceptibility to complex diseases by assaying and analyzing hundreds of thousands of single-nucleotide...
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BioMed Central
2016-12-01
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| Series: | Genomics & Informatics |
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| Online Access: | http://genominfo.org/upload/pdf/gni-14-160.pdf |
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| author | Donghe Li Sungho Won |
| author_facet | Donghe Li Sungho Won |
| author_sort | Donghe Li |
| collection | DOAJ |
| description | Over the past decade, the detection of gene-gene interactions has become more and more popular in the field of genome-wide association studies (GWASs). The goal of the GWAS is to identify genetic susceptibility to complex diseases by assaying and analyzing hundreds of thousands of single-nucleotide polymorphisms. However, such tests are computationally demanding and methodologically challenging. Recently, a simple but powerful method, named “BOolean Operation-based Screening and Testing” (BOOST), was proposed for genome-wide gene-gene interaction analyses. BOOST was designed with a Boolean representation of genotype data and is approximately equivalent to the log-linear model. It is extremely fast, and genome-wide gene-gene interaction analyses can be completed within a few hours. However, BOOST can not adjust for covariate effects, and its type-1 error control is not correct. Thus, we considered two-step approaches for gene-gene interaction analyses. First, we selected gene-gene interactions with BOOST and applied logistic regression with covariate adjustments to select gene-gene interactions. We applied the two-step approach to type 2 diabetes (T2D) in the Korea Association Resource (KARE) cohort and identified some promising pairs of single-nucleotide polymorphisms associated with T2D. |
| format | Article |
| id | doaj-art-776cb3fbdf0e42f6931002bc668b51f2 |
| institution | Kabale University |
| issn | 1598-866X 2234-0742 |
| language | English |
| publishDate | 2016-12-01 |
| publisher | BioMed Central |
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| series | Genomics & Informatics |
| spelling | doaj-art-776cb3fbdf0e42f6931002bc668b51f22025-02-02T04:35:58ZengBioMed CentralGenomics & Informatics1598-866X2234-07422016-12-0114416016510.5808/GI.2016.14.4.160172Efficient Strategy to Identify Gene-Gene Interactions and Its Application to Type 2 DiabetesDonghe Li0Sungho Won1Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul 08826, Korea.Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul 08826, Korea.Over the past decade, the detection of gene-gene interactions has become more and more popular in the field of genome-wide association studies (GWASs). The goal of the GWAS is to identify genetic susceptibility to complex diseases by assaying and analyzing hundreds of thousands of single-nucleotide polymorphisms. However, such tests are computationally demanding and methodologically challenging. Recently, a simple but powerful method, named “BOolean Operation-based Screening and Testing” (BOOST), was proposed for genome-wide gene-gene interaction analyses. BOOST was designed with a Boolean representation of genotype data and is approximately equivalent to the log-linear model. It is extremely fast, and genome-wide gene-gene interaction analyses can be completed within a few hours. However, BOOST can not adjust for covariate effects, and its type-1 error control is not correct. Thus, we considered two-step approaches for gene-gene interaction analyses. First, we selected gene-gene interactions with BOOST and applied logistic regression with covariate adjustments to select gene-gene interactions. We applied the two-step approach to type 2 diabetes (T2D) in the Korea Association Resource (KARE) cohort and identified some promising pairs of single-nucleotide polymorphisms associated with T2D.http://genominfo.org/upload/pdf/gni-14-160.pdfepistasisgene-gene interactiongenome-wide association studytype 2 diabetes mellitus |
| spellingShingle | Donghe Li Sungho Won Efficient Strategy to Identify Gene-Gene Interactions and Its Application to Type 2 Diabetes Genomics & Informatics epistasis gene-gene interaction genome-wide association study type 2 diabetes mellitus |
| title | Efficient Strategy to Identify Gene-Gene Interactions and Its Application to Type 2 Diabetes |
| title_full | Efficient Strategy to Identify Gene-Gene Interactions and Its Application to Type 2 Diabetes |
| title_fullStr | Efficient Strategy to Identify Gene-Gene Interactions and Its Application to Type 2 Diabetes |
| title_full_unstemmed | Efficient Strategy to Identify Gene-Gene Interactions and Its Application to Type 2 Diabetes |
| title_short | Efficient Strategy to Identify Gene-Gene Interactions and Its Application to Type 2 Diabetes |
| title_sort | efficient strategy to identify gene gene interactions and its application to type 2 diabetes |
| topic | epistasis gene-gene interaction genome-wide association study type 2 diabetes mellitus |
| url | http://genominfo.org/upload/pdf/gni-14-160.pdf |
| work_keys_str_mv | AT dongheli efficientstrategytoidentifygenegeneinteractionsanditsapplicationtotype2diabetes AT sunghowon efficientstrategytoidentifygenegeneinteractionsanditsapplicationtotype2diabetes |