期刊
EXPERT REVIEW OF MOLECULAR DIAGNOSTICS
卷 4, 期 6, 页码 795-803出版社
TAYLOR & FRANCIS AS
DOI: 10.1586/14737159.4.6.795
关键词
data mining; epistasis; genetic architecture; machine learning; software
类别
资金
- NHLBI NIH HHS [HL65234] Funding Source: Medline
- NIAID NIH HHS [AI059694] Funding Source: Medline
- NICHD NIH HHS [HD047447] Funding Source: Medline
Understanding the relationship between DNA sequence variations and biologic traits Is expected to Improve the diagnosis, prevention and treatment of common human diseases. Success in characterizing genetic architecture will depend on our ability to address nonlinearttles In the genotype-to-phenotype mapping relationship as a result of gone-gene Interactions, or epistasis. This review addresses the challenges associated with the detection and characterization of epistasis. A novel strategy known as muttifactor dimensionality reduction that was specifically designed for the Identification of multilocus genetic effects Is presented. Several case studies that demonstrate the detection of gene-gene Interactions In common diseases such as atrial fibrillation, Type 11 diabetes and essential hypertension are also discussed.
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