A Multivariate Analysis of Genetic Constraints to Life History Evolution in a Wild Population of Red Deer. Issue 4 (2nd October 2014)

• Record Type: Journal Article
• Authors: Walling, Craig A; Morrissey, Michael B; Foerster, Katharina; Clutton-Brock, Tim H; Pemberton, Josephine M; Kruuk, Loeske E B
• Is Part Of: Genetics. Volume 198:Issue 4(2014)
• Page Start: 1735
• Page End: 1749
• Publication Date: 2014-10-02
• Subjects: genetic correlations -- life history trade-off -- heritability -- sexual antagonism -- selection; Genetics -- Periodicals; 576.5
• DOI: 10.1534/genetics.114.164319
• Languages: English
• ISSNs: 0016-6731

Abstract

Abstract: Evolutionary theory predicts that genetic constraints should be widespread, but empirical support for their existence is surprisingly rare. Commonly applied univariate and bivariate approaches to detecting genetic constraints can underestimate their prevalence, with important aspects potentially tractable only within a multivariate framework. However, multivariate genetic analyses of data from natural populations are challenging because of modest sample sizes, incomplete pedigrees, and missing data. Here we present results from a study of a comprehensive set of life history traits (juvenile survival, age at first breeding, annual fecundity, and longevity) for both males and females in a wild, pedigreed, population of red deer ( Cervus elaphus ). We use factor analytic modeling of the genetic variance–covariance matrix (G ) to reduce the dimensionality of the problem and take a multivariate approach to estimating genetic constraints. We consider a range of metrics designed to assess the effect of G on the deflection of a predicted response to selection away from the direction of fastest adaptation and on the evolvability of the traits. We found limited support for genetic constraint through genetic covariances between traits, both within sex and between sexes. We discuss these results with respect to other recent findings and to the problems of estimating these parameters for natural populations.