4 - Central Institute of Brackishwater Aquaculture
4 - Central Institute of Brackishwater Aquaculture
4 - Central Institute of Brackishwater Aquaculture
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Natlonal Workshop-cum-Training on Biolnformatics and Information Management In <strong>Aquaculture</strong><br />
has not been to maximize memory requirements. Together with the large<br />
amount <strong>of</strong> program code PEST does require a certain minimum amount <strong>of</strong><br />
processing capability.<br />
ASREML<br />
ASREML estimates variance component under a general mixed model by<br />
restricted maximum likelihood (REML). Its scope covers genetic, multivariate,<br />
repeated measures, spatial and multi-environment analyses. It uses the average<br />
information algorithm and sparse matrix technique to efficiently solve large<br />
mixed models. The user interface is basic and assumes a good understanding <strong>of</strong><br />
the models that can be fitted; the results may need to be imported into another<br />
statistical / reporting program for further processing. ASREML enables limited<br />
testing <strong>of</strong> some fixed effect in the model. ASREML is available in complied form<br />
<strong>of</strong> MSDOS, Windows 951 NT. We need an ASCII editor to prepare the data and<br />
parameter file before running ASREML. Base name is in the name <strong>of</strong> .as<br />
command file. Output file names are generated from the input name by changing<br />
file extension from .as to .ars which is primary output file summaries the data,<br />
iteration sequence, the final variance parameters and solutions for fixed effects.<br />
The pin is an input file required for predicting means and functions <strong>of</strong> the<br />
variance components when the P option is specific. . pvs is the report produced<br />
with P option. In ASREML blank space in the data set can be taken care by "*"<br />
mark.<br />
12. Conclusion<br />
Determination <strong>of</strong> breeding value i.e, genetic merit <strong>of</strong> the individual is very<br />
important in selective breeding studies. Success in selective breeding program<br />
depends on the correct ranking <strong>of</strong> individuals according to its genetic merits.<br />
Different statistical packages can be utilized for this. However, SAS proved to be<br />
most effective program for that. However, other programs like ASREML is also<br />
equally effective and can also be utilized for determining different parameters for<br />
selective breeding studies.
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