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Partial Diallel Analysis

Partial diallel online for free with Kempthorne and Curnow approach. GCA, SCA and SE.

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When to use it

Use when only a subset of all possible single-cross combinations among parents were actually made (the labour of a full diallel grows with n squared). Kempthorne and Curnow (1961) developed the partial-diallel framework so general combining ability (GCA) can still be estimated efficiently from the reduced cross set, with SCA estimable for the realised crosses only.

When NOT to use it

Do not use when every possible cross was made; the full diallel (Griffing methods 1 to 4) gives more powerful tests. Do not use when crosses were chosen non-randomly to favour expected good combiners; the resulting GCA estimates are biased. The cross set should be a balanced sample of the n by n matrix.

What you get

ANOVA partition into GCA, SCA, and error using the partial-diallel design matrix. GCA estimate per parent with SE and t-test against zero. SCA estimate for each realised cross with SE. Variance components sigma-squared GCA and sigma-squared SCA, with their ratio.

How to interpret the output

GCA effects identify good general combiners as in a full diallel; the loss in precision relative to a full diallel depends on the fraction of crosses made. SCA effects are estimable only for crosses that were realised. The GCA-to-SCA variance ratio indicates whether selection on parents (additive) or selection on specific combinations (non-additive) is the more efficient strategy.

Common pitfalls

Try it in StatVeda

The Partial Diallel Analysis engine runs entirely in the browser. No signup, no install, no data sent to a server. Paste your data, hit Run, copy the output.

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References

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