adm.mq: An ADM model with MINQUE analysis

In qgtools: Generalized Quantitative Genetics Data Analyses

Description

An ADM model can be analyzed by MINQUE approach, requiring no specific genetic mating design or balance data. For reliable results, parents and F1s, parents and F2s, are preferred.

Usage

adm.mq(Y, Ped)

Arguments

Y	A trait matrix including one or more traits.
Ped	A pedigree matrix including Env, Female, Male, Generation, with or without block is required. So the matrix should include either 4 columns or 5 columns.

Details

A pedigree matrix used for analysis is required in the order of Environment (column 1), Female(column 2), Male(column 3), Generation (column 4). Column 5 for block can be default. Even though there is only one environment, this column is needed.

Value

Return a list of results: estimated variance components, estimated fixed effects, and predicted random effects

Author(s)

Jixiang Wu <[e-mail]>

References

Rao, C.R. 1971. Estimation of variance and covariance components-MINQUE theory. J Multiva Ana 1:19

Wu, J., J. N. Jenkins, J. C. McCarty, K. Glover, and W. Berzonsky. 2010. Presentation titled by "Unbalanced Genetic Data Analysis: model evaluation and application" was offered at ASA, CSSA, & SSSA 2010 International Annual Meetings, Long Beach, CA.

Wu, J., J. N. Jenkins, and J.C., McCarty. 2011. A generalized approach and computer tool for quantitative genetics study. Proceedings Applied Statistics in Agriculture, April 25-27, 2010, Manhattan, KS. p.85-106.

Wu, J. 2012. GenMod: An R package for various agricultural data analyses. ASA, CSSA, and SSSA 2012 International Annual Meetings, Cincinnati, OH, p 127

Wu J., Bondalapati K., Glover K., Berzonsky W., Jenkins J.N., McCarty J.C. 2013. Genetic analysis without replications: model evaluation and application in spring wheat. Euphytica. 190:447-458

Zhu J. 1989. Estimation of Genetic Variance Components in the General Mixed Model. Ph.D. Dissertation, NC State University, Raleigh, U.S.A

Examples

 library(qgtools)
 data(cotf2)
 
 dat=cotf2[which(cotf2$Env==1),]
 Ped=dat[,c(1:5)]
 Y=dat[,-c(1:5)]
 
 res=adm.mq(Y,Ped)
 res$Var
 res$FixedEffect
 res$RandomEffect
 ##End

qgtools documentation built on Dec. 19, 2019, 1:09 a.m.