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Evolution and Selection of Quantitative Traits$
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Bruce Walsh and Michael Lynch

Print publication date: 2018

Print ISBN-13: 9780198830870

Published to Oxford Scholarship Online: September 2018

DOI: 10.1093/oso/9780198830870.001.0001

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Analysis of Short-term Selection Experiments: 1. Least-squares Approaches

Analysis of Short-term Selection Experiments: 1. Least-squares Approaches

(p.591) 18 Analysis of Short-term Selection Experiments: 1. Least-squares Approaches
Evolution and Selection of Quantitative Traits

Bruce Walsh

Michael Lynch

Oxford University Press

This chapter examines short-term (a few generations) selection response in the mean of a trait. Traditionally, such experiments are analyzed using least-squares (LS) approaches. While ordinary LS (OLS) is often used, genetic drift causes the residual to be both correlated and heteroscedastic, resulting in the sampling variances given by OLS being too small. This chapter details the appropriate general LS (GLS) approaches to properly account for this residual error structure. It also reviews some of the common features observed in short-term selection experiments and examines experimental designs, such as the use of a control population versus a divergence-selection approach. It concludes by discussing another linear model used mainly by plant breeders, generation-means analysis (GMA), wherein remnant seed for several generations of response are crossed and then grown in a common garden. Such an analysis can provide insight into the genetic nature of any response.

Keywords:   drift variance, directional asymmetries, evolve & resequence experiments, evolutionary variance, generation means analysis, genetic asymmetry, GLS regression, GMA, Nicholas’ criterion, OLS regression, rank data, realized heritabilities, reversed response, variation in response

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