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Mixed model approaches for diallel analysis based on a bio-model

Published online by Cambridge University Press:  14 April 2009

Jun Zhu  and
Bruce S. Weir
Jun Zhu
Affiliation:
Department of Agronomy, Zhejiang Agricultural University, Hangzhou, Zhejiang, China
Bruce S. Weir*
Affiliation:
Program in Statistical Genetics, Department of Statistics, North Carolina State University, Raleigh, NC 27695-8203, USA
*
* Corresponding author.
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A MINQUE(l) procedure, which is minimum norm quadratic unbiased estimation (MINQUE) method with 1 for all the prior values, is suggested for estimating variance and covariance components in a bio-model for diallel crosses. Unbiasedness and efficiency of estimation were compared for MINQUE(l), restricted maximum likelihood (REML) and MINQUE(θ) which has parameter values for the prior values. MINQUE(l) is almost as efficient as MINQUE(θ) for unbiased estimation of genetic variance and covariance components. The bio-model is efficient and robust for estimating variance and covariance components for maternal and paternal effects as well as for nuclear effects. A procedure of adjusted unbiased prediction (AUP) is proposed for predicting random genetic effects in the bio-model. The jack-knife procedure is suggested for estimation of sampling variances of estimated variance and covariance components and of predicted genetic effects. Worked examples are given for estimation of variance and covariance components and for prediction of genetic merits.

Type
Research Article
Information
Genetics Research , Volume 68 , Issue 3 , December 1996 , pp. 233 - 240
Copyright
Copyright © Cambridge University Press 1996

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