Literature DB >> 19352739

Sampling distributions, biases, variances, and confidence intervals for genetic correlations.

B H Liu1, S J Knapp, D Birkes.   

Abstract

Genetic correlations (rho ( g )) are frequently estimated from natural and experimental populations, yet many of the statistical properties of estimators of rho ( g ) are not known, and accurate methods have not been described for estimating the precision of estimates of rho ( g ). Our objective was to assess the statistical properties of multivariate analysis of variance (MANOVA), restricted maximum likelihood (REML), and maximum likelihood (ML) estimators of rho ( g ) by simulating bivariate normal samples for the one-way balanced linear model. We estimated probabilities of non-positive definite MANOVA estimates of genetic variance-covariance matrices and biases and variances of MANOVA, REML, and ML estimators of rho ( g ), and assessed the accuracy of parametric, jackknife, and bootstrap variance and confidence interval estimators for rho ( g ). MANOVA estimates of rho ( g ) were normally distributed. REML and ML estimates were normally distributed for rho ( g ) = 0.1, but skewed for rho ( g ) = 0.5 and 0.9. All of the estimators were biased. The MANOVA estimator was less biased than REML and ML estimators when heritability (H), the number of genotypes (n), and the number of replications (r) were low. The biases were otherwise nearly equal for different estimators and could not be reduced by jackknifing or bootstrapping. The variance of the MANOVA estimator was greater than the variance of the REML or ML estimator for most H, n, and r. Bootstrapping produced estimates of the variance of rho ( g ) close to the known variance, especially for REML and ML. The observed coverages of the REML and ML bootstrap interval estimators were consistently close to stated coverages, whereas the observed coverage of the MANOVA bootstrap interval estimator was unsatisfactory for some H, rho ( g ), n, and r. The other interval estimators produced unsatisfactory coverages. REML and ML bootstrap interval estimates were narrower than MANOVA bootstrap interval estimates for most H, rho ( g ), n, and r.

Entities:  

Year:  1997        PMID: 19352739     DOI: 10.1007/s001220050375

Source DB:  PubMed          Journal:  Theor Appl Genet        ISSN: 0040-5752            Impact factor:   5.699


  10 in total

1.  Nonparametric confidence interval estimators for heritability and expected selection response.

Authors:  S J Knapp; W C Bridges; M H Yang
Journal:  Genetics       Date:  1989-04       Impact factor: 4.562

2.  Monte carlo studies of plant mating system estimation models: the one-pollen parent and mixed mating models.

Authors:  D J Schoen; M T Clegg
Journal:  Genetics       Date:  1986-04       Impact factor: 4.562

3.  A comparison of planned unbalanced designs for estimating heritability in perennial tree crops.

Authors:  B G McCutchan; J X Ou; G Namkoong
Journal:  Theor Appl Genet       Date:  1985-12       Impact factor: 5.699

4.  Probabilities of negative estimates of genetic variances.

Authors:  W C Bridges; S J Knapp
Journal:  Theor Appl Genet       Date:  1987-06       Impact factor: 5.699

5.  Design of multivariate selection experiments to estimate genetic parameters.

Authors:  N D Cameron; R Thompson
Journal:  Theor Appl Genet       Date:  1986-07       Impact factor: 5.699

6.  Parametric and jackknife confidence interval estimators for two-factor mating design genetic variance ratios.

Authors:  S J Knapp; W C Bridges
Journal:  Theor Appl Genet       Date:  1988-09       Impact factor: 5.699

7.  Statistical genetics of an annual plant, Impatiens capensis. I. Genetic basis of quantitative variation.

Authors:  T Mitchell-Olds; J Bergelson
Journal:  Genetics       Date:  1990-02       Impact factor: 4.562

8.  Laboratory estimates of heritabilities and genetic correlations in nature.

Authors:  B Riska; T Prout; M Turelli
Journal:  Genetics       Date:  1989-12       Impact factor: 4.562

9.  The genetic correlation between characters maintained by selection, linkage and inbreeding.

Authors:  R Lande
Journal:  Genet Res       Date:  1984-12       Impact factor: 1.588

10.  Estimating the precision of estimates of genetic parameters realized from multiple-trait selection experiments.

Authors:  F C Gunsett; K N Andriano; J J Rutledge
Journal:  Biometrics       Date:  1982-12       Impact factor: 2.571

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.