Biometrical Methods in Quantitative Genetic Analysis.

Hill, W. G.; Singh, Rekha; Chaudhary, B.D.

doi:10.2307/2530404

Cited by 259 publications

(251 citation statements)

References 0 publications

Supporting

Mentioning

244

Contrasting

Unclassified

Order By: Relevance

“…Phenotypic (PCV) and genotypic (GCV) coefficients of variation were evaluated according to the methods as follows [10–13]:

\begin{matrix} T3 \\ G e n o t y p i c c o e f f i c i e n t o f v a r i a t i o n (G C V) \\ = \frac{σ_{g}^{2}}{\bar{X}} \times 100, \end{matrix}

where σ g 2 is genotypic variance and

\bar{X}

is population mean.

\begin{matrix} T3 \\ P h e n o t y p i c c o e f f i c i e n t o f v a r i a t i o n (P C V) \\ = \frac{σ_{p}^{2}}{\bar{X}} \times 100, \end{matrix}

where σ p 2 is phenotypic variance and

\bar{X}

is population mean.…”

Section: Methodsmentioning

confidence: 99%

“…The genotypic and phenotypic correlation coefficients between growth and yield contributing character were calculated as follows [13]:

\begin{matrix} T1 \\ G e n o t y p i c c o r r e l a t i o n, r_{g (x y)} = \frac{{C o v}_{(g) 1.2}}{\sqrt{σ^{{normal2}_{(|, g) normal1}} σ^{{normal2}_{(|, g) normal2}}}} . \end{matrix}

Cov ( g )1.2 is genotypic covariance between the variables X and Y , σ 2 ( g )1 is genotypic variance of the variable X 1 , and σ 2 ( g )2 is genotypic variance of the variable X 2 .

\begin{matrix} T1 \\ P h e n o t y p i c c o r r e l a t i o n, r_{p (x y)} = \frac{{C o v}_{(p) 1.2}}{\sqrt{σ^{{normal2}_{(|, p) normal1}} σ^{{normal2}_{(|, p) normal2}}}} . \end{matrix}

Cov ( p )1.2 is phenotypic covariance between the variables X and Y , σ 2 ( p )1 is phenotypic variance of the variable X 1 , and σ 2 ( p )2 is Phenotypic variance of the variable X 2 .…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Genetic Variability of Exotic Sugarcane Genotypes

et al. 2017

View full text Add to dashboard Cite

Sugarcane is the main sugar producing crop in Bangladesh. However, improvement of this crop through breeding is limited due to lack of genetic diversity. Therefore, genetic variability and diversity assessment are necessarily important for the foreign introduced materials. Experiment was conducted with 9 exotic sugarcane genotypes at Regional Station, Bangladesh Sugar Crop Research Institute, Gazipur, during 2012-13, following RCBD. Data were collected on different growth and yield contributing characters. Individual cane weight exhibited high genotypic coefficient of variation and phenotypic coefficient of variation. Leaf blade length, leaf blade width, fresh leaf weight, dried leaf weight, number of tillers, millable cane, bud size, cane diameter, internodes number, internode length, plant height, stalk length, brix%, and individual cane weight showed high heritability. Individual cane weight showed highly significant and positive correlation with cane diameter, internode length, and stalk length, whereas path coefficient analysis revealed that cane diameter had maximum positive direct effect on individual cane weight followed by internode length, number of tillers, and chlorophyll content. Results indicate that the genotypes should be selected on the basis of individual cane weight, cane diameter, and millable canes number for future breeding to get higher sugarcane yield in respect to juice and brix content.

show abstract

“…Phenotypic (PCV) and genotypic (GCV) coefficients of variation were evaluated according to the methods as follows [10–13]:

\begin{matrix} T3 \\ G e n o t y p i c c o e f f i c i e n t o f v a r i a t i o n (G C V) \\ = \frac{σ_{g}^{2}}{\bar{X}} \times 100, \end{matrix}

where σ g 2 is genotypic variance and

\bar{X}

is population mean.

\begin{matrix} T3 \\ P h e n o t y p i c c o e f f i c i e n t o f v a r i a t i o n (P C V) \\ = \frac{σ_{p}^{2}}{\bar{X}} \times 100, \end{matrix}

where σ p 2 is phenotypic variance and

\bar{X}

is population mean.…”

Section: Methodsmentioning

confidence: 99%

“…The genotypic and phenotypic correlation coefficients between growth and yield contributing character were calculated as follows [13]:

\begin{matrix} T1 \\ G e n o t y p i c c o r r e l a t i o n, r_{g (x y)} = \frac{{C o v}_{(g) 1.2}}{\sqrt{σ^{{normal2}_{(|, g) normal1}} σ^{{normal2}_{(|, g) normal2}}}} . \end{matrix}

Cov ( g )1.2 is genotypic covariance between the variables X and Y , σ 2 ( g )1 is genotypic variance of the variable X 1 , and σ 2 ( g )2 is genotypic variance of the variable X 2 .

\begin{matrix} T1 \\ P h e n o t y p i c c o r r e l a t i o n, r_{p (x y)} = \frac{{C o v}_{(p) 1.2}}{\sqrt{σ^{{normal2}_{(|, p) normal1}} σ^{{normal2}_{(|, p) normal2}}}} . \end{matrix}

Cov ( p )1.2 is phenotypic covariance between the variables X and Y , σ 2 ( p )1 is phenotypic variance of the variable X 1 , and σ 2 ( p )2 is Phenotypic variance of the variable X 2 .…”

Section: Methodsmentioning

confidence: 99%

Genetic Variability of Exotic Sugarcane Genotypes

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Genotypic and phenotypic variances of each trait estimation used the formula suggested by Singh and Chaudhary (1985). The GCV and PCV values classified as low (0%-10%), moderate (10%-20%), and high (≥20%) was, according to Sivasubramanian and Madhavamenon (1973).…”

Section: Estimation Of Genotypic and Phenotypic Variancesmentioning

confidence: 99%

Characterization and Genetic Analysis of the Selected Rice Mutant Populations

CHOWDHURY¹,

ISLAM²,

MIM³

et al. 2023

SABRAOJBG

View full text Add to dashboard Cite

The development of mutant populations, followed by their characterization, offers a significant opportunity to isolate genotypes and genes with desired traits of interest. This paper assessed the agronomic performance, genetic variability, and yield-related characteristics of 22 M3 generation mutants (gamma ray-irradiated) of rice derived from a promising local rice genotype (Fatema dhan). The seeds of the selected mutants, the original parent, and three cultivars were grown in a randomized complete block design at the research farm of the Department of Genetics and Plant Breeding, Bangladesh Agricultural University, Mymensingh-2202, Bangladesh. Few mutants exhibited superior quantitative phenotypic traits compared with parental genotypes and check varieties. Mutant lines 1 and 83 required minimum days to reach maturity, and mutant lines 9, 17, and 80 exhibited significantly higher yield per plant than the parent and check varieties. Heritability analysis and genetic parameters revealed that genetic components mostly controlled all observed traits, with a minor influence on the environment. The higher phenotypic and genotypic coefficient of variation, heritability, and genetic gain confirmed possible rice yield improvement through phenotypic selection. The traits, including days to first flowering and maturity, plant height, and panicle length, showed a significant positive correlation with yield. The principal component analysis revealed that the first two components explained 69% of the total variation between genotypes. Thus, the promising mutant lines (1, 9, 17, 80, and 83) isolated in this study can serve for the development of high-yielding and early-maturing rice varieties.

show abstract

“…where, δ 2 g = genotypic variance, δ 2 p = phenotypic variance, r = number of replications, s = number of seasons, δ 2 sg = season × genotype interaction variance and δ 2 e = error variance. Phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) were calculated as described by Singh and Chaudhary [34].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Kenya Stem Rust Observation Nursery Wheat Genotypes for Yield and Yield Components under Artificial Rust Conditions

et al. 2021

View full text Add to dashboard Cite

Common wheat (Triticum aestivum L.) is a major cereal crop contributing to global food and nutrition which necessitates the deployment of wheat genotypes that are high yielding and resistant to prevailing diseases. The objective of this study was to evaluate Kenya stem rust observation nursery (KSRON) wheat genotypes for grain yield, yield components and stem rust resistance. A 3-season field experiment was set up in a 25 × 7 alpha lattice design at Kenya Agricultural and Livestock Research Organization (KALRO), Njoro (35°56′60′′ E; 0°20′60′′ S). Significant (p ≤ 0.001) differences due to seasons, genotype and genotype × season interaction was observed for all the traits except days to heading (DH) and number of seeds per spike (SS). Seasons, genotype and genotype × season interaction explained 48.2%, 25.23% and 26.67% of the total variability in grain yield (GY), respectively. Significant (p ≤ 0.05) variation of means between seasons for all the traits were higher in the 2019 main season than the 2019 and 2020 off-seasons except for 1000-kernel weight (TKW). Heritability (H2) estimates ranged from 49.58% for GY to 94.11% for DH. GY had a positive genetic and phenotypic correlation with all the traits except for DH (rg = −0.13, rp = −0.09), stem rust (SR) (rg = −0.53 ***, rp = −0.38 ***) and yellow rust (YR) (rg = −0.28 ***, rp = −0.19 *) severity. SR caused 22%, 14% and 13% reduction in TKW, GY and biomass (BM), respectively. YR caused 2% and 4% reduction in GY and BM. This study identified KSRON 13, KSRON 31, KSRON 40, KSRON 52, KSRON 53, KSRON 78, KSRON 80 and KSRON 144 as the best high GY (8.43–7.79 t ha−1) as well as resistant to SR (AUDPC; 99.0–170.67) and YR (AUDPC; 18.67–128.33).

show abstract

Biometrical Methods in Quantitative Genetic Analysis.

Cited by 259 publications

References 0 publications

Genetic Variability of Exotic Sugarcane Genotypes

Genetic Variability of Exotic Sugarcane Genotypes

Characterization and Genetic Analysis of the Selected Rice Mutant Populations

Evaluation of Kenya Stem Rust Observation Nursery Wheat Genotypes for Yield and Yield Components under Artificial Rust Conditions

Contact Info

Product

Resources

About