Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 7-Day Trial for You or Your Team.

Learn More →

An information-theoretic approach to estimating the composite genetic effects contributing to variation among generation means: Moving beyond the joint-scaling test for line cross analysis

An information-theoretic approach to estimating the composite genetic effects contributing to... The pace and direction of evolution in response to selection, drift, and mutation are governed by the genetic architecture that underlies trait variation. Consequently, much of evolutionary theory is predicated on assumptions about whether genes can be considered to act in isolation, or in the context of their genetic background. Evolutionary biologists have disagreed, sometimes heatedly, over which assumptions best describe evolution in nature. Methods for estimating genetic architectures that favor simpler (i.e., additive) models contribute to this debate. Here we address one important source of bias, model selection in line cross analysis (LCA). LCA estimates genetic parameters conditional on the best model chosen from a vast model space using relatively few line means. Current LCA approaches often favor simple models and ignore uncertainty in model choice. To address these issues we introduce Software for Analysis of Genetic Architecture (SAGA), which comprehensively assesses the potential model space, quantifies model selection uncertainty, and uses model weighted averaging to accurately estimate composite genetic effects. Using simulated data and previously published LCA studies, we demonstrate the utility of SAGA to more accurately define the components of complex genetic architectures, and show that traditional approaches have underestimated the importance of epistasis. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Evolution Oxford University Press

An information-theoretic approach to estimating the composite genetic effects contributing to variation among generation means: Moving beyond the joint-scaling test for line cross analysis

Blackmon, Heath; Demuth, Jeffery P.
Evolution , Volume 70 (2): 13 – Feb 1, 2016
Download PDF
13 pages

Loading next page...
 
/lp/oxford-university-press/an-information-theoretic-approach-to-estimating-the-composite-genetic-VC3DWrjxuJ

References (89)

Copyright
© 2016, Society for the Study of Evolution
ISSN
0014-3820
eISSN
1558-5646
DOI
10.1111/evo.12844
Publisher site
See Article on Publisher Site

Abstract

The pace and direction of evolution in response to selection, drift, and mutation are governed by the genetic architecture that underlies trait variation. Consequently, much of evolutionary theory is predicated on assumptions about whether genes can be considered to act in isolation, or in the context of their genetic background. Evolutionary biologists have disagreed, sometimes heatedly, over which assumptions best describe evolution in nature. Methods for estimating genetic architectures that favor simpler (i.e., additive) models contribute to this debate. Here we address one important source of bias, model selection in line cross analysis (LCA). LCA estimates genetic parameters conditional on the best model chosen from a vast model space using relatively few line means. Current LCA approaches often favor simple models and ignore uncertainty in model choice. To address these issues we introduce Software for Analysis of Genetic Architecture (SAGA), which comprehensively assesses the potential model space, quantifies model selection uncertainty, and uses model weighted averaging to accurately estimate composite genetic effects. Using simulated data and previously published LCA studies, we demonstrate the utility of SAGA to more accurately define the components of complex genetic architectures, and show that traditional approaches have underestimated the importance of epistasis.

Journal

Evolution – Oxford University Press

Published: Feb 1, 2016

Keywords: Composite genetic effects; epistasis; genetic architecture; joint-scaling test; line cross analysis

There are no references for this article.