title: Risk in complex genetics: "all models are wrong but some are useful".
abstract: Although genome-wide association studies (GWAS) have proven remarkably effective at identifying reliably associated genetic variants, the biology underlying these discoveries is rarely immediately apparent and in most cases seems bound to require extensive fine mapping and functional analysis before it is revealed. In this context, it is logical and appropriate to try to interrogate available genetic data for biological insights. However, because such efforts invariably depend upon mathematical modeling, misperceptions can easily arise if the relevant mathematical properties are overlooked or forgotten. In this report, we will examine these mathematical issues, highlight some of the more common misconceptions, and hopefully help to clarify the somewhat blurry distinction between biology and mathematics that can so easily undermine and obscure the value of GWAS discoveries.
authors.affiliation: Department of Clinical Neuroscience, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK. sjs1016@mole.bio.cam.ac.uk
authors.forename: Stephen
authors.initials: S
authors.lastname: Sawcer
authors.forename: James
authors.initials: J
authors.lastname: Wason
mesh_terms: D000818:Animals; D004777:Environment; D005787:Gene Frequency; D008040:Genetic Linkage; D020022:Genetic Predisposition to Disease; D005823:Genetics / Q000639:trends*; D055106:Genome-Wide Association Study; D006801:Humans; D008959:Models, Neurological; D009422:Nervous System Diseases / Q000235:genetics; D009446:Neurobiology / Q000639:trends*; D016017:Odds Ratio; D012307:Risk Factors