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The quantitative genetics of a complex trait under continuous directional selection
Author(s) -
Careau Vincent,
Wolak Matthew,
Carter Patrick A,
GARLAND THEODORE
Publication year - 2012
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.26.1_supplement.1073.1
Subject(s) - selection (genetic algorithm) , trait , biology , statistics , covariance , quantitative genetics , directional selection , genetic correlation , genetic model , zoology , genetic variation , genetics , mathematics , computer science , gene , machine learning , programming language
We analyzed data from a long‐term artificial selection experiment that includes 4 lines of mice bred for high voluntary wheel running (HR) and 4 non‐selected control (C) lines. The HR lines reached a selection limit at generation ~16, running ~3‐fold more revolutions/day than C lines. In addition, wheel running varied across generations in an apparently cyclical fashion in both HR and C. We used the first 25 generations to estimate quantitative genetic parameters before, during, and after the selection limit was reached. We used ASReml‐R to apply the “animal model”, a linear mixed‐model that uses all the information on the coefficients of co‐ancestry among individuals in a pedigree. Our preliminary results indicate additive genetic variance ( V A ) was not eliminated in HR lines after the limit was reached. However, the selection regime led to a negative covariance between V A and maternal genetic variance ( V M ), which could maintain V A in the selected trait and potentially explain the presence of a cycle. We also found that the genetic correlation between mean running speed and duration of wheel running tended to be lower in females than in males, which may explain why the response to selection was achieved differently in females (mainly speed) and males (both speed and duration). Supported by NSF grants IOS‐1121273 to TG and EF0328594 to PAC, and a NSERC postdoctoral fellowship to VC.