NDLI: IGENT: efficient entropy based algorithm for genome-wide gene-gene interaction analysis

Content Provider	Springer Nature : BioMed Central
Author	Kwon, Min-Seok Park, Mira Park, Taesung
Abstract	Background With the development of high-throughput genotyping and sequencing technology, there are growing evidences of association with genetic variants and complex traits. In spite of thousands of genetic variants discovered, such genetic markers have been shown to explain only a very small proportion of the underlying genetic variance of complex traits. Gene-gene interaction (GGI) analysis is expected to unveil a large portion of unexplained heritability of complex traits. Methods In this work, we propose IGENT, Information theory-based GEnome-wide gene-gene iNTeraction method. IGENT is an efficient algorithm for identifying genome-wide gene-gene interactions (GGI) and gene-environment interaction (GEI). For detecting significant GGIs in genome-wide scale, it is important to reduce computational burden significantly. Our method uses information gain (IG) and evaluates its significance without resampling. Results Through our simulation studies, the power of the IGENT is shown to be better than or equivalent to that of that of BOOST. The proposed method successfully detected GGI for bipolar disorder in the Wellcome Trust Case Control Consortium (WTCCC) and age-related macular degeneration (AMD). Conclusions The proposed method is implemented by C++ and available on Windows, Linux and MacOSX.
Related Links	https://bmcmedgenomics.biomedcentral.com/counter/pdf/10.1186/1755-8794-7-S1-S6.pdf
Ending Page	11
Page Count	11
Starting Page	1
File Format	HTM / HTML
ISSN	17558794
DOI	10.1186/1755-8794-7-S1-S6
Journal	BMC Medical Genomics
Issue Number	1
Volume Number	7
Language	English
Publisher	BioMed Central
Publisher Date	2014-05-08
Access Restriction	Open
Subject Keyword	Human Genetics Microarrays Gene Expression Support Vector Machine Random Forest Information Gain Multifactor Dimensionality Reduction Wellcome Trust Case Control Consortium
Content Type	Text
Resource Type	Article
Subject	Genetics (clinical) Genetics
Journal Impact Factor	2.1/2023
5-Year Journal Impact Factor	2.5/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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