2 Models Provided in Animal Method

1. Sex Model (A+D+L+M+AE+DE+LE+ME)

Sex model partitions genetic variance into additive, dominance, sex-linkage and maternal variance components, as well as their interactions with environments, additive × environment, dominance × environment, sex-linkage × environment and maternal × environment variance components.

2. SexM model (A+D+L+Am+Dm+AE+DE+LE+AmE+DmE)

Sex model partitions genetic variance into additive, dominance, sex-linkage, additive-maternal and dominance-maternal variance components, as well as their interactions with environments, additive × environment, dominance × environment, sex-linkage × environment, additive-maternal × environment and dominance-maternal × environment variance components.

　

Format of input data

The format of data is shown in files MiceData.txt in the Sample folder.

The first six fixed columns in the above data files represent environment (e.g. year or location), female, male, cross and block and sex. From the seventh column on, trait(s) values could be listed, at most, 100 traits could be accepted in a file.

The generation code is 0 for parent, 1 for F₁, and 2 for F₂. The Sex code is 1 for gene type XY (or ZW), and 2 for XX (or ZZ). The minimum value is 1 for all other four fixed column. Correspondingly, the maximum value for each fixed column is the total number of environments for the first column, of parents for the second and third columns, and number of blocks within each environment for the fifth column. The code should be continuous integers and arranged in order. Each missing trait value is denoted by a dot(".").

All the models within Animal method share the same Coefficient-setting Box, which will pop out automatically when you select an agronomy model from Animal menu.

　

1. Have Block effect
For an input file, if the block column contains more than 1 block, both No and Yes options enable. Check No for no or ignoring block effect for raw data, then the "block" will be considered as replication if more than one block included. Check Yes for involving block effect in raw data. If there is only 1 block in the raw data, the option should be No.
2. Jackknife Kind
If there are no block effect in data or block considered as replication, Cell will be the default value of Jackknife kind. When there is block effect in data, either Block or Cell is acceptable.
3. Jackknife Number
If the Jackknife Kind is Block, Jackknife Number resampling unit is default 1. If Jackknife Kind is Cell, Jackknife Number of resampling unit can range form 1 to 9 (1 is recommended, but for big data it will be time exhausting).
4. Random effects predict method
AUP(adjusted unbiased prediction) or LUP(linear unbiased prediction) can be used in predicting random effects, such as additive effect, dominant effect, et al.
5. Language
Select the Language the output file in. Both Chinese and English are available.
6. Condition or Not
If you do not want to invoke this function, please choose No, which is default. If you have chosen the Yes option, you should better select the Condition Type value and Step number. If you choose contribution, condition type will automatically be Final, step number be 1, and only variance module will be run. No matter Yes or Contribution option checked, there should be more than 1 traits listed in an input file if you really want to employ Condition analysis.
7. Condition Type
Step and Final are two kinds of strategies for conditional analysis. e.g., if you choose Step with step number 2 and there are four traits listed, the program will take the conditional analysis twice, calculating the trait3|trait1(trait3 condition on trait1), trait4|trait2. If you choose Final type, the step number will equal the default 1, and if there are N traits listed in the input file, the program will sequentially calculate traitN|trait1, traitN|trait2, traitN|trait3....traitN|traitN-1.
8. Step
Step number ranges from 1 to 9, which should be smaller than the total trait number accomodated in an input file. The certain value selected according to your input data and research interests.
9. Var
If you check this option, the program will estimate variance components and predict random effects. The results are automatically saved in file filename.var (filename is the name of input file).
10. Cov
If you tick this option, the program will estimate covariance components and correlation coefficients, but you should choose Var and/or Het option first. The results are automatically saved in file filename.cov.
11. Run
Run the data with selected model and model.
12. Cancel
Leave the Coefficient-setting Box, and do nothing.
	Home Next