title: Endophenotype best practices.
abstract: This review examines the current state of electrophysiological endophenotype research and recommends best practices that are based on knowledge gleaned from the last decade of molecular genetic research with complex traits. Endophenotype research is being oversold for its potential to help discover psychopathology relevant genes using the types of small samples feasible for electrophysiological research. This is largely because the genetic architecture of endophenotypes appears to be very much like that of behavioral traits and disorders: they are complex, influenced by many variants (e.g., tens of thousands) within many genes, each contributing a very small effect. Out of over 40 electrophysiological endophenotypes covered by our review, only resting heart, a measure that has received scant advocacy as an endophenotype, emerges as an electrophysiological variable with verified associations with molecular genetic variants. To move the field forward, investigations designed to discover novel variants associated with endophenotypes will need extremely large samples best obtained by forming consortia and sharing data obtained from genome wide arrays. In addition, endophenotype research can benefit from successful molecular genetic studies of psychopathology by examining the degree to which these verified psychopathology-relevant variants are also associated with an endophenotype, and by using knowledge about the functional significance of these variants to generate new endophenotypes. Even without molecular genetic associations, endophenotypes still have value in studying the development of disorders in unaffected individuals at high genetic risk, constructing animal models, and gaining insight into neural mechanisms that are relevant to clinical disorder.
authors.affiliation: University of Minnesota, United States. Electronic address: wiacono@umn.edu.
authors.forename: William G
authors.initials: WG
authors.lastname: Iacono
authors.affiliation: University of Minnesota, United States.
authors.forename: Stephen M
authors.initials: SM
authors.lastname: Malone
authors.affiliation: University of Colorado Boulder, United States.
authors.forename: Scott I
authors.initials: SI
authors.lastname: Vrieze
mesh_terms: D035843:Biomedical Research / Q000592:standards; D055724:Electrophysiological Phenomena; D058068:Endophenotypes; D055106:Genome-Wide Association Study / Q000592:standards; D006801:Humans
keywords: Biomarker; Candidate gene; Data sharing; Endophenotype; GREML; GWAS; Genes; Heritability