Genetic Variability Analysis in R Software

[@martahailemariam4887]

Good presentation!!

[@amourlafleur1762]

you are the best teacher

[@ambexzerfu8095]

well done

[@truongphu7407]

Thanks for for your video, could you please tell me how to calculate narrow sense heritability by R?

[@bhumikasinghlodhi6632]

Very well explained.
Please help in getting genotypic and phenotypic path diagram also.

[@wakjiratesfahun3682]

Okay ,I will try it.

[@chemedabirhanu4189]

Thank you Wakjira, please can you do genetic variability analysis using ASRmel-R

[@wakjiratesfahun3682]

The package is not available for free. If you can share with me I will be happy

[@chemedabirhanu4189]

@@wakjiratesfahun3682 i will try

[@gizawwegayehu6103]

And also for this Genetic parameters for over location experimets

[@ashutosh_sawarkar]

nicely explained. can you tel me about how can we analysis of two season data i.e. Pooled analysis in R?

[@wakjiratesfahun3682]

I already published a video on that. Please check my channel .

[@rakeshrout6688]

Can you plot path diagram in R

[@muhammadfarouqfaizalhaque8522]

Thanks for the video
I have a problem tho, when u did gen and pheno correlation analysis using this package it somehow ended with correlation value always bigger than 1
What's possibly wrong with this?

[@wakjiratesfahun3682]

Genotypic correlation values cannot be larger than 1. Genotypic correlation is a measure that ranges between -1 and 1, indicating the strength and direction of the relationship between genotypes for a given trait or parameter. A genotypic correlation of 1 indicates a perfect positive correlation, where the genotypes consistently vary in the same direction. Conversely, a genotypic correlation of -1 indicates a perfect negative correlation, where the genotypes consistently vary in opposite directions.
If you encounter a reported genotypic correlation value larger than 1, it is likely an error or a misunderstanding. Genotypic correlations are typically calculated using appropriate statistical methods, such as Pearson's correlation coefficient or Spearman's rank correlation coefficient, which are bounded between -1 and 1.
It's important to double-check the source or context of the information if you come across a genotypic correlation value larger than 1, as it is not consistent with the expected range of correlation values.

[@agrianalyze]

The genotypic correlation is calculated by Genotypic covariance between two variable divided by square root of genotypic variance of both the variables and some times it can be out of range. Refer the anova of both the variables and ancova table for both the variables.

[@tesfayesisay7822]

Hi thank you for your good explanation about this software but i have a question can we find correlation for the same paramater like Ph with Ph and so on

[@wakjiratesfahun3682]

Yeah, but correlation with same parameter always is one and won't display or include the result in our presentation.

[@tesfayesisay7822]

But your genotypic correlation shows example DTE with DTE 0.98, DTF with DTF 0.8892 and like

[@wakjiratesfahun3682]

@@tesfayesisay7822 Yeah, you are right. But the magnitude of genotypic correlation for the same parameter can vary for several reasons. Here are a few factors that can contribute to the differences in magnitude:
1. Genetic Variation: Genotypic correlation is based on the genetic variation present in a population. If the population being studied has a higher level of genetic diversity, it can result in a wider range of genotypic values for a given parameter. This increased variability can lead to differences in the magnitude of genotypic correlation.
2.Environmental Variation: Environmental factors play a significant role in shaping the expression of genetic traits. If the environment in which the genotypes are evaluated varies across different studies or populations, it can influence the magnitude of the genotypic correlation. Different environmental conditions can interact with genetic factors in complex ways, resulting in variations in the correlation magnitude.
3. Sample Size and Composition: The size and composition of the study population can also impact the genotypic correlation. If the sample size is small, it may not adequately represent the overall genetic diversity, leading to less reliable estimates of correlation. Additionally, if the population studied is not representative of the broader population, it can introduce biases and affect the correlation magnitude.
4. Allelic Effects: Genotypic correlation is influenced by the effects of individual alleles on the parameter of interest. The specific combination of alleles present in different populations or studies can vary, resulting in differences in the magnitude of genotypic correlation. The presence of different alleles with varying effect sizes can contribute to variations in the correlation magnitude.
5. Genetic Interactions: Genes often interact with each other, and these interactions can affect the correlation between genotypes and parameters. If there are different patterns of genetic interactions in different populations or studies, it can lead to differences in the magnitude of genotypic correlation.
so,it's important to consider these factors when interpreting genotypic correlation results. The context in which the studies were conducted, including the population, environment, and genetic factors involved, can all contribute to the observed differences in correlation magnitudes.
Thank you

[@umagenetics3975]

@@wakjiratesfahun3682 Nice information sir. Sharing the originalmarticle could also be useful because we can cite the article in the publication.

[@gizawwegayehu6103]

Excellent vidieo , Is it possible to use it for any design example simple lattice!

[@wakjiratesfahun3682]

Just only for RCBD.

[@hananshiferaw8543]

@@wakjiratesfahun3682 would you try to do another video for alpha lattice design too.

[@hanikayalorath]

How can I make the path diagram

[@gizawwegayehu6103]

Have you any solution if Gv and GCV becames negative and heritability above 100 and Genetic advance and genetic advance as percentage of means becames above 100 as well!

[@wakjiratesfahun3682]

Okay I will back to you with answers.

[@agrianalyze]

Genotypic correlation becomes negative when genotypic Mean Square is less than error Mean Square in ANOVA that means for your genotypes that specific trait has no significant variation. You should drop that variable from further analysis

[@yididyatube-8646]

Please Dr I want your help. how can I contact you?

[@afeezoluniyishittuirri7645]

Can we use same for any experimental design?

[@wakjiratesfahun3682]

I was trying for different types of experimental designs but it's not working. I am still finding a solution and I will let you know as soon as possible.

[@agrianalyze]

It will work only for RCBD design

[@AW12]

is this analysis used for any design?

[@wakjiratesfahun3682]

No. It designed for RCB design.