traitstats.genpar performs genetic variability analysis for all the traits in the DataFile.
traitstats.genpar(Treatment, Replication, DataFile)
Treatment column in the DataFile
Replication column in the DataFile
Input Data file name
traitstats.genpar performs genetic parameter analysis from the input values extracted from the ANOVA analysis for individual traits and computes several variability estimates. .
The phenotypic, genotypic and environmental variance (σ2p, σ2g and σ2e ) are obtained from the ANOVA tables according to the expected value of mean square described by Federer and Searle (1976) as follows:
σ2p = Mean sum of squares of test treatments
σ2e = Mean sum of squares of residuals
σ2g = σ2p − σ2e
Phenotypic and genotypic coefficients of variation (PCV and GCV) are estimated according to Burton (1951, 1952) as follows:
PCV = [σ2p ⁄ √ (x)] × 100
GCV = [σ2g ⁄ √ (x)] × 100
Where x is the mean.
The estimates of PCV and GCV are categorised according to Sivasubramanian and Madhavamenon (1978) as follows:
|x < 10||Low|
|10 ≤ x < 20||Medium|
The broad-sense heritability (H2) is calculated according to method of Lush (1940) as follows:
H2 = σ2g ⁄ σ2p
The estimates of broad-sense heritability (H2) are categorised according to Robinson (1966) as follows:
|x < 30||Low|
|30 ≤ x < 60||Medium|
Genetic advance (GA) is estimated and categorised according to Johnson et al., (1955) as follows:
GA = k × σg × [H2 ⁄ 100]
Where the constant k is the standardized selection differential or selection intensity. The value of k at 5% proportion selected is 2.063. Values of k at other selected proportions are available in Appendix Table A of Falconer and Mackay (1996).
Genotypic Coefficient of Variation and GCV Category
Phenotypic Coefficient of Variation and PCV Category
Heritability (broad sense)and h2 Category
Genetic Advance and GA Category
Genetic Advance percent Mean and GAM Category
Genetic parameter analysis need to be performed only if the sum of squares of treatment: Test is significant Negative estimates of variance components is computed are not abnormal. Ref. Dudley and Moll (1969).
Nitesh, S.D., Parashuram Patroti and Shilpa Parashuram
Lush JH. (1940). Intra-sire correlations or regressions of offspring on dam as a method of estimating heritability of characteristics. Proceedings of the American Society of Animal Nutrition, 1940(1):293-301.
Burtone GW and De Vane GM. (1953). Estimating heritability in tall Fescus (Festuca arundinaceae) from replicated clonal material. Agronomy Journal,45:478-481.
Johnson HW, Robinson HF and Comstock RE. (1955). Estimates of genetic and environmental variability in soybeans. Agronomy Journal, 47:314-318.
Robinson HF. (1966). Quantitative genetics in relation to breeding on centennial of Mendelism. Indian Journal of Genetics and Plant Breeding, 171.
Dudley JW and Moll RH. (1969). Interpretation and Use of estimates of heritability of genetics variance in Plant Breeding. Crop Science, 9:257-262.
Sivasubramaniam S and Madhavamenon P. (1973). Genotypic and Phenotypic variability in rice. The Madras Agricultural Journal, 60:1093-1096.
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