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Lookup NU author(s): Dr Joanna Biernacka, Professor Heather Cordell
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Recently, genome-wide association studies have substantially expanded our knowledge about genetic variants that influence the susceptibility to complex diseases. Although standard statistical tests for each single-nucleotide polymorphism (SNP) separately are able to capture main genetic effects, different approaches are necessary to identify SNPs that influence disease risk jointly or in complex interactions. Experimental and simulated genome-wide SNP data provided by the Genetic Analysis Workshop 16 afforded an opportunity to analyze the applicability and benefit of several machine learning methods. Penalized regression, ensemble methods, and network analyses resulted in several new findings while known and simulated genetic risk variants were also identified. In conclusion, machine learning approaches are promising complements to standard single-and multi-SNP analysis methods for understanding the overall genetic architecture of complex human diseases. However, because they are not optimized for genome-wide SNP data, improved implementations and new variable selection procedures are required. Genet. Epidemiol. 33 (Suppl. 1):S51-S57, 2009. (C) 2009 Wiley-Liss, Inc.
Author(s): Szymczak S, Biernacka JM, Cordell HJ, Gonzalez-Recio O, Konig IR, Zhang HP, Sun YV
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: Genetic Epidemiology: 16th Genetic Analysis Workshop/17th Annual Meeting of the International Genetic Epidemiology Society
Year of Conference: 2009
Pages: S51-S57
ISSN: 0741-0395
Publisher: John Wiley & Sons, Inc.
URL: http://dx.doi.org/10.1002/gepi.20473
DOI: 10.1002/gepi.20473
Library holdings: Search Newcastle University Library for this item
ISBN: 10982272
