Author Information

L. D. Van Vleck
R. K. Splan

Abstract

Portable Fortran based programs (MTDFREML) were developed using a derivative-free algorithm to obtain REML estimates of (co)variance components. Computations are based on Henderson's mixed model equations for multiple-trait models with missing observations on some traits and incorporation of relationships among relatives. Many fixed and random factors are allowed with number of levels dependent on computer memory. Data sets with more than 40,000 genetic effects have been analyzed. Options allow solving MME at convergence. Constraints are automatically imposed. Expectations, standard errors of contrasts of solutions for fixed effects and prediction error variances of solutions for random effects can be obtained. Dimensions can be changed to match data with computer capability. A Fortran compiler is necessary. No fee is charged but the University of Waterloo must certify a license has been obtained for sparse matrix subroutines (SPARSPAK) used in the program. As an example, birth weights of 4891 progeny of 389 sires nested within 12 breeds and of 2893 dams nested within 3 breeds of dam were analyzed to estimate components of variance due to sires and dams and to estimate differences among breeds of sires. For MTDFREML the analysis was trivial but for PROC MIXED the analysis was impossible unless dams were dropped from the model.

Keywords

Variance component estimation, Mixed model equations, Sparse matrix methods

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Apr 26th, 10:30 AM

AN ALTERNATIVE FOR MIXED MODEL ANALYSES OF LARGE, MESSY DATA SETS (MTDFREML)

Portable Fortran based programs (MTDFREML) were developed using a derivative-free algorithm to obtain REML estimates of (co)variance components. Computations are based on Henderson's mixed model equations for multiple-trait models with missing observations on some traits and incorporation of relationships among relatives. Many fixed and random factors are allowed with number of levels dependent on computer memory. Data sets with more than 40,000 genetic effects have been analyzed. Options allow solving MME at convergence. Constraints are automatically imposed. Expectations, standard errors of contrasts of solutions for fixed effects and prediction error variances of solutions for random effects can be obtained. Dimensions can be changed to match data with computer capability. A Fortran compiler is necessary. No fee is charged but the University of Waterloo must certify a license has been obtained for sparse matrix subroutines (SPARSPAK) used in the program. As an example, birth weights of 4891 progeny of 389 sires nested within 12 breeds and of 2893 dams nested within 3 breeds of dam were analyzed to estimate components of variance due to sires and dams and to estimate differences among breeds of sires. For MTDFREML the analysis was trivial but for PROC MIXED the analysis was impossible unless dams were dropped from the model.