A Bootstrap Comparison of Genetic Covariance Matrices

Goodnight, Charles J.; Schwartz, James M.

doi:10.2307/2533561

Cited by 32 publications

(36 citation statements)

References 28 publications

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“…The correct bootstrapping procedure in this case would have been to resample between the lines within each of the reciprocal crosses for G5 and between the ¢ve families within each line for G37. This is because the line was the level containing the genetic information at G5, but the family was the level containing the genetic information at G37 (Goodnight & Schwartz 1997). However, it is unlikely that resampling between such a small number of data points would give a reliable approximation of the distribution in each case, a fundamental assumption behind the bootstrapping procedure (Manly 1997).…”

Section: (B) the Change In Genetic Correlation Between Generationsmentioning

confidence: 99%

Evolution of the genetic covariance between male and female components of mate recognition: an experimental test

Blows

1999

Proc. R. Soc. Lond. B

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The evolution of a positive genetic correlation between male and female components of mate recognition systems will result as a consequence of assortative mating and, in particular, is central to a number of theories of sexual selection. Although the existence of such genetic correlations has been investigated in a number of taxa, it has yet to be shown that such correlations evolve and whether they may evolve as rapidly as suggested by sexual selection models. In this study, I used a hybridization experiment to disrupt natural mate recognition systems and then observed the subsequent evolutionary dynamics of the genetic correlation between male and female components for 56 generations in hybrids between Drosophila serrata and Drosophila birchii. The genetic correlation between male and female components evolved from 0.388 at generation 5 to 1.017 at generation 37 and then declined to 70.040 after a further 19 generations. These results indicated that the genetic basis of the mate recognition system in the hybrid populations evolved rapidly. The initial rapid increase in the genetic correlation was consistent with the classic assumption that male and female components will coevolve under sexual selection. The subsequent decline in genetic correlation may be attributable to the ¢xation of major genes or, alternatively, may be a result of a cyclic evolutionary change in mate recognition.

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Section: (B) the Change In Genetic Correlation Between Generationsmentioning

confidence: 99%

Evolution of the genetic covariance between male and female components of mate recognition: an experimental test

Blows

1999

Proc. R. Soc. Lond. B

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“…There have been attempts to compare the G matrices of different populations, species, or higher taxa (Roff 2000), and a variety of comparative methods have been applied (Lofsvold 1986;Kohn and Atchley 1988;Shaw et al 1995;Cheverud 1996;Paulsen 1996;Goodnight and Schwartz 1997;Arnold and Phillips 1999;. One of the first methods used was the Mantel test (Lofsvold 1986;Kohn and Atchley 1988), but this method has been strongly criticized on theoretical grounds (Shaw 1992).…”

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confidence: 99%

Comparing Gmatrices: A Manova Approach

Roff¹

2002

Evolution

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Abstract. There is considerable interest in comparing genetic variance-covariances matrices (G matrix). However, present methods are difficult to implement and cannot readily be extended to incorporate effects of other variables such as habitat, sex, or location. In this paper I present a method based on MANOVA that can be done using only standard statistical packages (coding for the method using SPLUS is available from the author). The crux of the approach is to use the jackknife method to estimate the pseudovalues of the estimates; these estimates can then be used as datapoints in a MANOVA. I illustrate the method using two published datasets: (1) variation in G matrices resulting from differences in rearing condition, species, and sex in the crickets Gryllus firmus and G. pennsylvanicus; and (2) variation in G matrices associated with habitat and history in the amphipod Gammarus minus.

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“…Those κ i 's can be consistently estimated; based on these estimations, weighted sums of chi-square variables can be simulated in order to obtain approximate critical values. Bootstrap estimates of the same critical values can also be produced (Zhang and Boos 1992;Goodnight and Schwartz 1997;Zhu, Ng, and Jing 2002). Other authors (Yuan and Bentler 1997;Boik 2002) recommend approximating the distribution of the weighted sum of chi-squares by modifying the number of degrees of freedom of the Gaussian chi-square limit distribution.…”

Section: Adjusted Lrt Statistics Versus Adjusted Null Asymptotic Distmentioning

confidence: 99%