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6 Models Provided in Seed Method |
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1.
ADM(3n) Model (A+D+M+AE+DE+ME) |
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Additive-dominance-maternal model for
triploid seed partitions genetic variance into additive, dominance and maternal
variance components, as well as their interactions with environments,
additive × environment, dominance × environment and maternal × environment
variance components. |
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2.
GoGe Model
(Ao+Do+Ae+De+AoE+DoE+AeE+DeE) |
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Embryo-endosperm model, partitions
genetic variance into additive-embryo, dominance-embryo, additive-endosperm
and dominance-endosperm variance components, as well as their interactions
with environments, additive-embryo × environment, dominant-embryo ×
environment, additive-endosperm × environment and dominance-endosperm ×
environment variance components. |
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3.
GoCGm(2n) Model
(A+D+C+Am+Dm+AE+DE+CE+AmE+DmE) |
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Embryo-cytoplasm-maternal model for
diploid seed partitions genetic variance into additive, dominance, cytoplasm,
additive-maternal, dominance-maternal variance components, as well as their
interactions with environments, additive × environment, dominant ×
environment, cytoplasm × environment, additive-maternal × environment,
dominance-maternal × environment variance components. |
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4.
GoCGm(3n) Model
(A+D+C+Am+Dm+AE+DE+CE+AmE+DmE) |
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Embryo-cytoplasm-maternal model for
triploid seed partitions genetic variance into additive, dominance,
cytoplasm, additive-maternal, dominance-maternal variance components, as well
as their interactions with environments, additive × environment, dominant ×
environment, cytoplasm × environment, additive-maternal × environment,
dominance-maternal × environment variance components. |
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5.
GoGcGm Model
(Ao+Do+Ae+De+Am+Dm+AoE+DoE+AeE+DeE+AmE+DmE) |
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Embryo-endosperm-maternal
model, partitions genetic variance into additive-embryo, dominance-embryo,
additive-endosperm, dominance-endosperm, additive-maternal and
dominance-maternal variance components, as well as their interactions with
environments, additive-embryo × environment, dominance-embryo × environment,
additive-maternal × environment and dominance-maternal × environment variance
components. |
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6.
GoGeCGm
Model(Ao+Do+Ae+De+C+Am+Dm+AoE+DoE+AeE+DeE+CE+AmE+DmE) |
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Embryo-endosperm-cytoplasm-maternal
model partitions genetic variance into additive-embryo, dominance-embryo,
additive-endosperm, dominance-endosperm, cytoplasm, additive-maternal and
dominance-maternal variance components, as well as their interactions with
environments, additive-embryo × environment, dominance-embryo × environment,
additive-endosperm × environment, dominance-endosperm × environment,
additive-maternal × environment and dominance-maternal × environment variance
components. |
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Format of input data |
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The format of data is shown in files CotSeed.txt, CotSeedBoll.txt in the Sample folder . The first five fixed columns in the above data
files represent environment (e.g. year or location), female, male, cross and
block. From the sixth 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,
2 for F2, 3 for BC1 (F1×P1), 4 for BC2 (F1×P2),
5 for RBC1 (P1×F1),6 for RBC2 (P2×F1),
7 for F3, 8 for F4, 9 for BC1×F2, 10 for
BC2×F2, 11 for RBC1×F2, and 12 for RBC2×F2.
The minimum value is 1 for all other four fixed column. Correspondingly, the
maximum value for each fixed column is the number of environments for the
first column, number 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("."). |
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All the models within Seed method share the same Coefficient-setting
Box, which will pop out automatically when you selecte an agronomy model
from Seed menu. |
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1. Have Block effect |
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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. |
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2.
Jackknife Kind |
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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. |
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3.
Jackknife Number |
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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). |
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4. Random effects predict method |
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AUP(adjusted unbiased prediction) or
LUP(linear unbiased prediction) can be used in predicting random effects,
such as additive effect, dominant effect, et al. |
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5. Language |
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Select the Language the output file in.
Both Chinese and English are available. |
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6. Condition or Not |
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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. |
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7. Condition Type |
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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. |
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8. Step |
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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. |
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9. Estimate Covariance between Seed and Maternal Effect |
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For models of 2nGoCGm and 3nGoCGm,
this option is available. If choose Yes covariance between Seed and Maternal
Effect will be estimated. |
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10. Correlation with Agronomy Traits |
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For models of 2nGoCGm and 3nGoCGm, this option is
available. If check the option, it will estimate the correlation between seed
traits and maternal traits. |
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11. Number of Seed Traits |
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For models of 2nGoCGm and 3nGoCGm,
this option is available. If want to estimate the correlation between seed
traits and maternal traits, how many seed traits listed in the raw data
should be declared. If there are K seed traits listed among N traits
(K<N), the K seed traits should be lacated at the beginning of the N
traits. |
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12. Var |
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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). |
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13. Het |
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If you tick this option, the program will predict
heterosis. The results are automatically saved in file filename.pre. For
models of ADM(3n) model, GoGe,GoGeGm, and GoGeCGm,
this option is unavailable. |
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14. Cov |
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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. |
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15. Run |
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Run the data with selected model and
model. |
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16. Cancel |
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Leave the Coefficient-setting Box, and do nothing. |
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