title: Genetics, statistics and human disease: analytical retooling for complexity.
abstract: Molecular biologists and geneticists alike now acknowledge that most common human diseases with a genetic component are likely to have complex etiologies. Yet despite this belief, many statistical geneticists continue applying, in slightly new and different ways, methodologies that were developed to dissect much simpler etiologies. In this article, we characterize, with examples, the various factors that can complicate genetic analysis and demonstrate their shared features and how they affect genetic analysis. We describe a variety of approaches that are currently available, revealing methodological gaps and suggesting new directions for method development. Finally, we propose a comprehensive two-step approach to analysis that systemically addresses the different genetic factors that are likely to underlie complex diseases.
authors.affiliation: Neuroscience Graduate Program, Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, TN 37240, USA.
authors.forename: Tricia A
authors.initials: TA
authors.lastname: Thornton-Wells
authors.forename: Jason H
authors.initials: JH
authors.lastname: Moore
authors.forename: Jonathan L
authors.initials: JL
authors.lastname: Haines
mesh_terms: D030342:Genetic Diseases, Inborn / Q000235:genetics; D018740:Genetic Heterogeneity; D005826:Genetics, Medical / Q000379:methods / Q000639:trends; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci