ASReml软件讲义 ASReml workshop.ppt

ARMS Fusion 2007 ASReml Workshop Harry Wu UPSC, Swedish University of Agriculture Science, Sweden CSIRO Plant Industry, Canberra, Australia Workshop Outline Linear model Mixed linear model Breeding values ASReml and ConTEXT Primer Example of full-sib mating Example of diallel mating Row-Column design Longitudinal data Spatial analysis 1. What Is a Linear Model? Put Experiment into a Linear Model Put the Linear Model into Matrix Useful Matrix Operations Transpose Multiplication Trace Determinant Inverse Direct sum ( ) Direct product ( ) 2. What Is Mixed Linear Model A combination of fixed effects and random effects. – Fixed: where there are different populations (levels), each with its own mean. We are mostly interested in estimating the means. – Random: the levels are random samples from one population. We are interested in the variances (although we might want prediction for the levels). Very powerful at dealing with unbalanced data What are some fixed and random effects? An Example of Mixed Linear Model Mixed Linear Model Solution of Mixed Linear Model Traditional Mixed Linear Model in Tree Breeding Complex Mixed Linear Model Solution of Mixed Linear Model REML ASReml Likelihood Ratio Test Fixed effects must be the same in both models Hierarchical models only For single variances 2 * D Log Likelihood ~ where D Log Likelihood is the LL difference with and without the effect (Section 2.5) For multiple variances For correlations against 0 against 1 Other Model Comparators Non-hierarchical models Akaike Information Criterion – Minimise AIC = -2*LogL+2p (p=no. vc’s) Bayes Information Criterion – Minimise BIC = -2*LogL+p*log(dfe) 3. Basic Concept of Breeding Value Basic Concept of Breeding Value Basic Concept of Breeding Value Basic Concept of Breeding Value Basic Concept of Breeding Value 4. ASReml primer ConTEXT Primer 1. Install ConText using ContextSetup.exe 2. Copy ASREML.chl to the c:/program files/context/highlight