5.2 Endophenotype-Based Genetic Inference: Characterizing Variants and Modeling Liability
Year: 2017
Type: conference-abstract
Abstract: Background: Although case–control studies based upon diagnostic status are beginning to disentangle the complex polygenic burden of psychotic and affective disorders, other methods will likely be necessary to fully identify and characterize risk genes. Endophenotypes, traits genetically correlated with an illness, can help characterize the impact of risk genes by providing genetically relevant traits that are more tractable than the behavioral symptoms that classify mental illness. Furthermore, endophenotypes may improve our power to detect genes influencing risk of illness by being closer to the level of gene action and with larger genetic effect sizes or by providing added statistical power through their ability to quantitatively rank people within diagnostic categories. To date, the promise of endophenotypes has yet to be fully realized, as these traits are rarely collected in large enough samples to provide meaningful biological insights and because analytic approaches that include endophenotypes are often suboptimal. Methods: Here, we discuss recent analytic advances that help to empirically rank endophenotypes, methods using gene-centric techniques to test for endophenotypic variation, a novel approach for empirically and explicitly defining an individual upon a liability distribution and methods for bivariate genome search. Results: Empirical examples for each of these analytic methods will be provided from ongoing case–control and family-based studies. Conclusion: Together, these scientific strategies and associated findings provide novel utility to the endophenotype concept, extending Irv Gottesman’s work and legacy.
Source: Schizophrenia Bulletin
Author: David Glahn
Institution: Yale University
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2600786392