Comparative quantitative genetics: evolution of the G matrix

Steppan, Scott J.; Phillips, Patrick C.; Houle, David

doi:10.1016/s0169-5347(02)02505-3

Cited by 512 publications

(714 citation statements)

References 54 publications

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“…A number of techniques for comparison of G matrices have been proposed (Roff, 2000;Steppan et al, 2002). The most widely used technique in the last few years is that known as the Flury model .…”

Section: Methodsmentioning

confidence: 99%

Geographic variation in the G matrices of wild populations of the barn swallow

et al. 2004

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In this paper, we present an analysis of genetic variation in three wild populations of the barn swallow, Hirundo rustica. We estimated the P, E, and G matrices for six linear morphological measurements and tested for variation among populations using the Flury hierarchical method and the jackknife followed by MANOVA method. Because of nonpositive-definite matrices, we had to employ 'bending' to analyse the G and E matrices with the Flury method. Both statistical methods agree in finding that the P and G matrices are significantly different but comparison between the analysis of the P matrices and pairwise analyses of the P, E, and G matrices suggests caution in interpreting the Flury results concerning differences in matrix structure. The significant variation among the populations in the G matrices appears to be due in large measure to the most geographically distant population.

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Section: Methodsmentioning

confidence: 99%

Geographic variation in the G matrices of wild populations of the barn swallow

et al. 2004

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“…This suggests that within beef cattle, breeding for muscularity and heavy carcasses may have changed the genetic characteristics of the slaughter traits. Genetic correlations can indeed evolve because of selection (Falconer and Mackay, 1996;Roff, 1996;Kause et al, 2001;Steppan et al, 2002). The third potential reason is that the breeds are both managed differently and held mostly at different farms.…”

Section: Trait Variationmentioning

confidence: 99%

Genetic parameters for carcass weight, conformation and fat in five beef cattle breeds

Kause

Mikkola

Strandén

et al. 2015

Animal

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Profitability of beef production can be increased by genetically improving carcass traits. To construct breeding value evaluations for carcass traits, breed-specific genetic parameters were estimated for carcass weight, carcass conformation and carcass fat in five beef cattle breeds in Finland (Hereford, Aberdeen Angus, Simmental, Charolais and Limousin). Conformation and fat were visually scored using the EUROP carcass classification. Each breed was separately analyzed using a multitrait animal model. A total of 6879-19 539 animals per breed had phenotypes. For the five breeds, heritabilities were moderate for carcass weight ( h 2 = 0.39 to 0.48, s.e. = 0.02 to 0.04) and slightly lower for conformation ( h 2 = 0.30 to 0.44, s.e. = 0.02 to 0.04) and carcass fat ( h 2 = 0.29 to 0.44, s.e. = 0.02 to 0.04). The genetic correlation between carcass weight and conformation was favorable in all breeds (r G = 0.37 to 0.53, s.e. = 0.04 to 0.05), heavy carcasses being genetically more conformed. The phenotypic correlation between carcass weight and carcass fat was moderately positive in all breeds (r P = 0.21 to 0.32), implying that increasing carcass weight was related to increasing fat levels. The respective genetic correlation was the strongest in Hereford (r G = 0.28, s.e. = 0.05) and Angus (r G = 0.15, s.e. = 0.05), the two small body-sized British breeds with the lowest conformation and the highest fat level. The correlation was weaker in the other breeds (r G = 0.08 to 0.14). For Hereford, Angus and Simmental, more conformed carcasses were phenotypically fatter (r P = 0.11 to 0.15), but the respective genetic correlations were close to zero (r G = − 0.05 to 0.04). In contrast, in the two large body-sized and muscular French breeds, the genetic correlation between conformation and fat was negative and the phenotypic correlation was close to zero or negative (Charolais: r G = − 0.18, s.e. = 0.06, r P = 0.02; Limousin: r G = − 0.56, s.e. = 0.04, r P = − 0.13). The results indicate genetic variation for the genetic improvement of the carcass traits, favorable correlations for the simultaneous improvement of carcass weight and conformation in all breeds, and breed differences in the correlations of carcass fat.

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“…Clearly, quite different sets of heritability, the coefficient of additive variation, or the parameters could be important under the two scenarios G matrix, are derived from the additive genetic variance if we want to estimate past and future rates of evolution. of a trait; cf., e.g., Lande and Arnold (1983), Houle…”

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confidence: 99%

“…To assess which alternative is more prevalent in nature, (1992), Lynch and Walsh (1998), and Hansen et al population genetic theory can be informative in two (2003); see Steppan et al (2002) for review. On the ways.…”

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confidence: 99%

Soft Sweeps

2005

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A population can adapt to a rapid environmental change or habitat expansion in two ways. It may adapt either through new beneficial mutations that subsequently sweep through the population or by using alleles from the standing genetic variation. We use diffusion theory to calculate the probabilities for selective adaptations and find a large increase in the fixation probability for weak substitutions, if alleles originate from the standing genetic variation. We then determine the parameter regions where each scenario-standing variation vs. new mutations-is more likely. Adaptations from the standing genetic variation are favored if either the selective advantage is weak or the selection coefficient and the mutation rate are both high. Finally, we analyze the probability of "soft sweeps," where multiple copies of the selected allele contribute to a substitution, and discuss the consequences for the footprint of selection on linked neutral variation. We find that soft sweeps with weaker selective footprints are likely under both scenarios if the mutation rate and/or the selection coefficient is high.

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Comparative quantitative genetics: evolution of the G matrix

Cited by 512 publications

References 54 publications

Geographic variation in the G matrices of wild populations of the barn swallow

Geographic variation in the G matrices of wild populations of the barn swallow

Genetic parameters for carcass weight, conformation and fat in five beef cattle breeds

Soft Sweeps

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