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5 Models Provided in Agronomy Method |
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1.
A Model (G+GE) |
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Additive model partitions genetic variance into additive and additive ��
environment variance components. |
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2.
AD Model (A+D+AE+DE) |
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Additive��Dominance model, partitions genetic variance into
additive, dominance variance components, as well as their interactions with
environments, additive �� environment, dominant �� environment variance
components. |
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3.
AD+AA Model (A+D+AA+AE+DE��AAE) |
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Additive��Dominance��Epistasis model, partitions genetic
variance into additive, dominance, and additive-additive epistasis variance components,
as well as their interactions with environments, additive �� environment,
dominant �� environment, epistasis �� environment variance components. |
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4.
ADM Model (A+D+M+AE+DE+ME) |
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Additive + Dominance + Maternal model,
partitions genetic variance into additive, dominance, and maternal variance
components, as well as their interactions with environments, additive ��
environment, dominance �� environment, maternal �� environment variance
components. |
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5.
ADMP Model (A+D+M+P+AE+DE+ME+PE) |
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Additive + Dominance + Maternal +
Paternal model, partitions genetic variance into additive, dominance,
maternal and paternal variance components, as well as their interactions with
environments, additive �� environment, dominance �� environment, maternal �� environment
and paternal �� environment variance components. |
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6.
AMC Model (A+M+C) |
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Anther Additive + Maternal + Cytoplasmic model, partitions genetic variance into anther additive (gematic additive),
maternal and cytoplasmic variance components |
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Format of input data |
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The format of data is shown in files CotData.txt, CotF1F2.txt, CotF2.txt in the Sample folder, and CotBoll.txt is a good example data with time-specific records for conditional analysis. 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, no more than 100 traits could be accepted in
a file. The cross
code is 0 for parent, 1 for F1, 2 for F2, 3 for F1
�� Pi, 4 for F1 �� Pj, 5 for Pi �� F1,
and 6 for Pj �� F1. 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("."). |
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All the models within Agronomy method share the same Coefficient-setting
Box, which will pop out automatically when you select an agronomy model
from Agronomy 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 of the output file. 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. 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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10.
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 A, ADM, ADMP, and ADGE models, this
option is unavailable.
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11.
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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12.
Run |
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Run the data with selected model and model. |
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13.
Cancel |
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Leave
the Coefficient-setting Box, and do nothing. |
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