title: Machine learning in genome-wide association studies.
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.
authors.affiliation: Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Germany. silke.szymczak@imbs.uni-luebeck.de
authors.forename: Silke
authors.initials: S
authors.lastname: Szymczak
authors.forename: Joanna M
authors.initials: JM
authors.lastname: Biernacka
authors.forename: Heather J
authors.initials: HJ
authors.lastname: Cordell
authors.forename: Oscar
authors.initials: O
authors.lastname: González-Recio
authors.forename: Inke R
authors.initials: IR
authors.lastname: König
authors.forename: Heping
authors.initials: H
authors.lastname: Zhang
authors.forename: Yan V
authors.initials: YV
authors.lastname: Sun
mesh_terms: D001185:Artificial Intelligence; D001499:Bayes Theorem; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D006801:Humans; D017720:Molecular Epidemiology; D016571:Neural Networks, Computer; D020641:Polymorphism, Single Nucleotide; D012044:Regression Analysis