Variability analysis in R - I: Genetic parameters

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The blog is first part of a series of three blogs related to variability analysis using R software. The blog demonstrates utilization of variability package available in R for getting genetic parameters like genotypic and phenotypic variance, coefficient of variation, heritability (broad sense), genetic advance & genetic advance as a percentage of mean for more than one traits in one line of code. (Reading time 14 min.)

 

Step-I Arrangement of data in Excel file

 The data consist of 28 genotypes, 2 replication and 7 traits. First arrange genotypes followed by replication and than seven traits. The file is named vardata. You can download the excel file. Have a look at snip of data.


Step-II Import the excel data file in RStudio

On the upper right quadrant in RStudio there is tab of import dataset. Click on it and choose From Excel option. Browse for the excel file and click on import.

After successful import one can see the dataset in Global Environment by the name vardata


 

Step-III Import the variability package from CRAN

For our analysis we need a package named variability. Follow the below mentioned steps to install the package:

Click on Package

Click on install

Ensure "Repository (CRAN)" is selected in Install from option 

Type name of package i.e. variability

Click on install

Make sure the system is connected to internet in order to download the package.

Step-IV R script

Lines in blue are script. Type these blue lines and press Ctrl+Enter run the lines.

Open a new script by clicking on New and than selecting R script or by pressing Ctrl+Shift+N. 

Note: R is case sensitive 

library(variability)
 
The library function will load the package named variability. Loading a package means activating all functions residing inside the package. Another way to load the package is to click on the check box located prior to package name.

 
gen.var(vardata[3:9],vardata$Genotypes,vardata$Replication)
 
The variability package has a function named gen.var() which will allow us to estimate genetic parameters. Let me breakdown this line
gen.var is our function belonging to variability package
vardata[3:9] here square bracket are used for indexing. 3:9 means our data is located from column 3 to column 9 in "vardata" dataset
vardata$Genotypes is used to use genotypes as an input. R will look for a column named Genotypes in vardata dataset
vardata$Replication is used to use replication as an input. R will look for a column named Replication in vardata dataset

You can see the output in console. Select the output and copy paste it in word or text format as per your requirement.

Output


$PH
$PH[[1]]
Analysis of Variance Table

Response: data2
            Df Sum Sq Mean Sq F value    Pr(>F)    
replication  1   25.1  25.112  0.4693 0.4991606    
genotype    27 6285.1 232.782  4.3501 0.0001398 ***
Residuals   27 1444.8  53.512                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

$PH[[2]]
                                             A
Maximum                               139.0000
Minimum                                86.0000
Grand Mean                            116.3518
Standard Error of Mean (SEm)            5.1726
Critical Difference (CD) 5%            15.0095
Critical Difference (CD) 1%            20.2680
Environmental Variance                 53.5116
Genotypic Variance                     89.6352
Phenotypic Variance                   143.1468
Environmental Coefficient of Variance   6.2871
Genotypic Coefficient of Variance       8.1370
Phenotypic Coefficient of Variance     10.2830
Heritability (Broad Sense)              0.6262
Genetic Advance                        15.4332
Genetic Advance as percentage of mean  13.2643
 
$LL
$LL[[1]]
Analysis of Variance Table

Response: data2
            Df  Sum Sq Mean Sq F value    Pr(>F)    
replication  1   32.47  32.467  2.2597 0.1443879    
genotype    27 1680.93  62.257  4.3330 0.0001447 ***
Residuals   27  387.94  14.368                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

$LL[[2]]
                                            A
Maximum                               72.0000
Minimum                               40.5000
Grand Mean                            53.7971
Standard Error of Mean (SEm)           2.6803
Critical Difference (CD) 5%            7.7775
Critical Difference (CD) 1%           10.5023
Environmental Variance                14.3680
Genotypic Variance                    23.9443
Phenotypic Variance                   38.3123
Environmental Coefficient of Variance  7.0459
Genotypic Coefficient of Variance      9.0958
Phenotypic Coefficient of Variance    11.5056
Heritability (Broad Sense)             0.6250
Genetic Advance                        7.9689
Genetic Advance as percentage of mean 14.8129 
 
.... analysis of another 5 traits (trimmed to save space)

 

How analysis by this package has edge over other tools? 

Have a close look at a analysis of SFR trait of our data.
$SFR
$SFR[[1]]
Analysis of Variance Table

Response: data2
            Df Sum Sq Mean Sq F value Pr(>F)
replication  1   1.26  1.2600  0.1664 0.6866
genotype    27 158.11  5.8561  0.7732 0.7458
Residuals   27 204.50  7.5741               

$SFR[[2]]
                                      A                                                              
Maximum                               17.8                                                           
Minimum                               8.2                                                            
Grand Mean                            12.8714                                                        
Standard Error of Mean (SEm)          1.946                                                          
Critical Difference (CD) 5%           5.6469 NS                                                      
Critical Difference (CD) 1%           7.6252 NS                                                      
Environmental Variance                7.5741                                                         
Genotypic Variance                    Note: GV is negative -0.859                                    
Phenotypic Variance                   6.7151                                                         
Environmental Coefficient of Variance 21.3815                                                        
Genotypic Coefficient of Variance     Note: GV is negative,GCV calculated by using 
                                      absolute GV 7.2006
Phenotypic Coefficient of Variance    20.1326                                                        
Heritability (Broad Sense)            -0.1279                                                        
Genetic Advance                       -0.6829                                                        
Genetic Advance as percentage of mean -5.3055

For the SFR trait, genotype component in RBD analysis is non-significant which leads to genotype mean square less then residual mean square making genotypic variance negative. We should remove this traits from variability study.

The currently available packages and online analysis website misleads researchers by taking absolute value of genetypic variance. By carrying out analysis with this package, the researchers will get note when the genotypic variance is negative.

Suggestions, Comments and Quires are welcomed

Happy Learning!

If you prefer watching content over reading it, tune to YouTube video 


If you are using the variability R package in research work you can cite it as:

Raj Popat, Rumit Patel and Dinesh Parmar (2020). variability: Genetic Variability Analysis for Plant Breeding Research. R package version 0.1.0.https://CRAN.R-project.org/package=variability

Topics you might be interested in:

Varibility analysis in R - II: Genotypic & Phenotypic correlation
Varibility analysis in R - III: Genotypic Path Analysis
R packages for Analysis of Design of Experiments 
 
 
Feel Free to Connect with me on other platforms

Twitter Handle Research Gate Profile  My Blog on Statistics  My Youtube Channel  https://www.facebook.com/raj.popat.10/

 

Comments

  1. UnknownDecember 3, 2020 at 10:07 AM

    wonderful information.Keep it up and do for other designs also. specially for agronomical designs

    ReplyDelete
    Replies
    1. Dr. RAJ POPATDecember 3, 2020 at 10:43 AM

      Thanks for the appreciation and suggestion!
      Regards
      RAAJ

      Delete
  2. Na kehvayDecember 3, 2020 at 10:43 AM

    Good information provided by you. Easy access to steps of procedure is always essential for any type of analysis.

    ReplyDelete
  3. UnknownDecember 18, 2020 at 9:54 PM

    Sir, Please upload R script for Alpha lattice design for missing data

    ReplyDelete
  4. ProfndubuisimachebeFebruary 28, 2021 at 9:00 PM

    I am indeed very happy and grateful to you for making this video. Running genetic and phenotypic analysis, heritability and path analysis has been a challenge to many students of genetics. Well done! Can you assist me with video handling linear mixed model in r. Thanks

    ReplyDelete
  5. Purna DhanukJuly 3, 2021 at 1:43 PM

    sir will this R script work for alpha lattice design?

    ReplyDelete
  6. NasreldinMarch 13, 2022 at 1:27 AM

    Thank you for useful analysis, also let colleagues know the package

    ReplyDelete
  7. NasreldinMarch 13, 2022 at 1:37 AM

    question how can I save output file?

    ReplyDelete
  8. ALEXANDER SILVAMarch 30, 2022 at 7:27 PM

    Can I run this funtion with the follow programtion: gen.var(Data, Genotipe, Enviroment, Replication)?

    ReplyDelete
    Replies
    1. Dr. RAJ POPATApril 1, 2022 at 12:05 AM

      Nope! This is suitable for only single environment

      Delete

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