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On-line Access: 2024-08-27
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ZHU Jun. MIXED LINEAR MODEL APPROACHES FOR ANALYZING GENETIC MODELS OF COMPLEX QUANTITATIVE TRAITS[J]. Journal of Zhejiang University Science A, 2000, 1(1): 78-90.
@article{title="MIXED LINEAR MODEL APPROACHES FOR ANALYZING GENETIC MODELS OF COMPLEX QUANTITATIVE TRAITS",
author="ZHU Jun",
journal="Journal of Zhejiang University Science A",
volume="1",
number="1",
pages="78-90",
year="2000",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2000.0078"
}
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%T MIXED LINEAR MODEL APPROACHES FOR ANALYZING GENETIC MODELS OF COMPLEX QUANTITATIVE TRAITS
%A ZHU Jun
%J Journal of Zhejiang University SCIENCE A
%V 1
%N 1
%P 78-90
%@ 1869-1951
%D 2000
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2000.0078
TY - JOUR
T1 - MIXED LINEAR MODEL APPROACHES FOR ANALYZING GENETIC MODELS OF COMPLEX QUANTITATIVE TRAITS
A1 - ZHU Jun
J0 - Journal of Zhejiang University Science A
VL - 1
IS - 1
SP - 78
EP - 90
%@ 1869-1951
Y1 - 2000
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2000.0078
Abstract: New approaches based on general mixed linear models were presented for analyzing complex quantitative traits in animal models, seed models and QTL (quantitative trait locus) mapping models. Variances and covariances can be appropriately estimated by MINQUE (minimum norm quadratic unbiased estimation) approaches. Random genetic effects can be predicted without bias by LUP (linear unbiased prediction) or AUP (adjusted unbiased prediction) methods. Mixed-model based composite interval mapping (MCIM) methods are suitable for efficiently searching QTLs along the whole genome. bayesian methods and Markov Chain Monte Carlo (MCMC) methods can be applied in analyzing parameters of random effects as well as their variances.
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