title: The statistical properties of gene-set analysis.
abstract: The rapid increase in loci discovered in genome-wide association studies has created a need to understand the biological implications of these results. Gene-set analysis provides a means of gaining such understanding, but the statistical properties of gene-set analysis are not well understood, which compromises our ability to interpret its results. In this Analysis article, we provide an extensive statistical evaluation of the core structure that is inherent to all gene- set analyses and we examine current implementations in available tools. We show which factors affect valid and successful detection of gene sets and which provide a solid foundation for performing and interpreting gene-set analysis.
authors.affiliation: Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research/VU University Amsterdam, Amsterdam 1081 HV, Netherlands.|Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen 6525 EC, Netherlands.
authors.forename: Christiaan A
authors.initials: CA
authors.lastname: de Leeuw
authors.affiliation: Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA.|Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.
authors.forename: Benjamin M
authors.initials: BM
authors.lastname: Neale
authors.affiliation: Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen 6525 EC, Netherlands.
authors.forename: Tom
authors.initials: T
authors.lastname: Heskes
authors.affiliation: Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research/VU University Amsterdam, Amsterdam 1081 HV, Netherlands.|Department of Clinical Genetics, VU University Medical Centre Amsterdam, Neuroscience Campus Amsterdam, Amsterdam 1007 MB, Netherlands.
authors.forename: Danielle
authors.initials: D
authors.lastname: Posthuma
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D055106:Genome-Wide Association Study / Q000379:methods*; D023281:Genomics / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D012984:Software