Machine learning in genome-wide association studies
dc.contributor.author | Szymczak, Silke | en_US |
dc.contributor.author | Biernacka, Joanna M. | en_US |
dc.contributor.author | Cordell, Heather J. | en_US |
dc.contributor.author | González-Recio, Oscar | en_US |
dc.contributor.author | König, Inke R. | en_US |
dc.contributor.author | Zhang, Heping | en_US |
dc.contributor.author | Sun, Yan V. | en_US |
dc.date.accessioned | 2010-01-05T15:09:46Z | |
dc.date.available | 2010-03-01T21:10:28Z | en_US |
dc.date.issued | 2009 | en_US |
dc.identifier.citation | Szymczak, Silke; Biernacka, Joanna M.; Cordell, Heather J.; GonzÁlez-Recio, Oscar; KÖnig, Inke R.; Zhang, Heping; Sun, Yan V. (2009). "Machine learning in genome-wide association studies." Genetic Epidemiology 33(S1): S51-S57. <http://hdl.handle.net/2027.42/64533> | en_US |
dc.identifier.issn | 0741-0395 | en_US |
dc.identifier.issn | 1098-2272 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/64533 | |
dc.identifier.uri | http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=19924717&dopt=citation | en_US |
dc.description.abstract | 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. © 2009 Wiley-Liss, Inc. | en_US |
dc.format.extent | 110450 bytes | |
dc.format.extent | 3118 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.publisher | Wiley Subscription Services, Inc., A Wiley Company | en_US |
dc.subject.other | Life and Medical Sciences | en_US |
dc.subject.other | Genetics | en_US |
dc.title | Machine learning in genome-wide association studies | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Biological Chemistry | en_US |
dc.subject.hlbsecondlevel | Genetics | en_US |
dc.subject.hlbsecondlevel | Molecular, Cellular and Developmental Biology | en_US |
dc.subject.hlbtoplevel | Health Sciences | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan | en_US |
dc.contributor.affiliationother | Institut fÜr Medizinische Biometrie und Statistik, UniversitÄt zu LÜbeck, LÜbeck, Germany ; Institut fÜr Medizinische Biometrie und Statistik, UniversitÄt zu LÜbeck, Maria-Goeppert-Str. 1, 23562 LÜbeck, Germany | en_US |
dc.contributor.affiliationother | Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota | en_US |
dc.contributor.affiliationother | Institute of Human Genetics, International Centre for Life, Newcastle University, Central Parkway, Newcastle upon Tyne, United Kingdom | en_US |
dc.contributor.affiliationother | Department of Dairy Science, University of Wisconsin-Madison, Madison, Wisconsin | en_US |
dc.contributor.affiliationother | Institut fÜr Medizinische Biometrie und Statistik, UniversitÄt zu LÜbeck, LÜbeck, Germany | en_US |
dc.contributor.affiliationother | Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, Connecticut | en_US |
dc.identifier.pmid | 19924717 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/64533/1/20473_ftp.pdf | |
dc.identifier.doi | 10.1002/gepi.20473 | en_US |
dc.identifier.source | Genetic Epidemiology | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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