Hello.
In this video I would like to explain you how to calculate genetic diversity parameters, which is practical example for presentation within the module 2.
For this purpose we will use database of genotype information.
The name of the file which includes database is Testset.
This database include information about five microsatellite markers which were genotyped for 15 animals originating from three subpopulations.
For the calculation we will need the program Genelex.
Our first task is to calculate average observed and expected heterozygosity, effective allele number and Wright's
fixation index Fis across markers and subpopulations in the dataset and also to describe the level of diversity which result from all analyzed parameters.
The second task is to calculate F statistics, that means Fis, Fit and FST indexes and to describe the level of diversity in the dataset.
Third task is related to the evaluation of genetic distances across subpopulations, which are derived from Wright's fixation index and Nei's genetic distances and fourth task is related to the state of diversity,
explanation of level of diversity within and across breeds and also to analysis of genetic differentiation in the dataset based on the principal component analysis.
First, I would like to explain you organization of data in the Testset file.
The type of this file is Genepop.
If you open this file you can see several information.
In the first row you have the name of the dataset.
You can use any other name. If you want you can use for example name of breed or name of the localization and so on.
In the next few rows you can see the ID of genetic markers.
In this case, the IDs are general.
That means the name of the first genetic marker is loci 1, name of the second one is loci 2, and so on.
In the next row you have abbreviation POP.
This row is used if you would like to differentiate between the name of markers and then genotype information for animals in population number one.
But you also need to use this row if you would like to differentiate between two different populations.
These animals are belonging to population number one, these animals are belonging to the population number two, and so on.
In this row you have information about the animal ID, in this case A1, and then information about the genotypes for microsatellite genetic markers.
In first column you have information about the genotype for first genetic marker, in the second column for the second genetic marker, in the third column for the third genetic marker, and so on.
Also in this case, each animal has two alleles in the genotype, but the alleles are encoded by three numbers.
You can see for example in this row that this is the genotype for first genetic marker for animal 1 in population 1.
The first allele is encoded as 262 and the second one is encoded by 264.
For this marker, first animal from first population is heterozygous.
If we go to the second one, we can see that for second genetic markers the first animal from the first population is homozygous.
If you would like to analyze biallelic genetic markers such as single nucleotide polymorphism, we can basically use only one number for one allele (for encoding one allele)
that means you will have two different numbers in the genotype, not six as in this case. But depending on you.
Please use only coding by using numbers not letters.
If you would like, you can use program Genalex directly from the folder because you don't need to install it, but you can very easy to upload this program from the Internet.
Just open Google, write the name of the program, open first link and in the section download you can see that
here you can find the program including also short manual for the program and also explanation for formulas which are used for the calculation of genetic diversity parameters.
Program as I said, you don't need to install. If you want to open this program, double click on the icon of the program and then this is important you need to allow macros.
Now the program is starting. Where you find the program?
You find the program directly here or just to open add-ins and inside add-ins you can see here is the name of the program and all of the options which are included in this program.
You just open it by clicking on the on the name of the program.
First step is import of the data to the program.
As I said, the type of the database which we will use for the calculation is Genepop.
Thus, please click on the Genepop.
In this window you can select the type of the data.
We know that we have codominant data.
We can also change the source of data.
But right now we will use single genepop file.
Just click on OK and now you need to select the localization of your database in your computer.
In my case is database stored in the folder on the desktop.
Double click on the name of the database or the name of the file
and now the program is asking you if you would like to save this Excel sheet.
If not, just click on Cancel.
It is very easy to repeat the analysis because procedure is not time consuming.
After importing of data you can see that you have several information in the excel.
In the first row you have information about the number of markers in the database, then number of animals in the database
and the number of subpopulations in the database.
In next column, in this row you have information about the number of animals within each subpopulation.
That means in first subpopulation we have five animals.
In the second one, we have also five animals
and also in the third one.
In the second row, you have information about the name of the dataset, type of import and also name of the population.
If you would like, you can change the name of the population to the name of breeds which you analyze.
But right now is this one not so important.
In the third row you have information about the data which are below this row.
That means in this column we have information about the animal ID.
In this one, we have information about the population from which animals are coming,
and in the next columns we have information about the genotypes of animals.
This is first genotype for loci 1, second genotype for first animals for loci 2, third genotype for first animal for loci 3 and so on.
Now we would like to make analysis. How to do it?
Please click on add-ins and select the name of the program because observed and expected heterozygosity,
effective number of alleles and also Wright´s fixation index are parameters which are calculated from the frequency of alleles, i.e.
in the first step we need to select option Frequency.
You can see that program is asking you about the type of data.
We know that the type of data is in our case codominant.
Then is also informing you how many markers you have in the database, how many samples, how many subpopulations.
Click on OK and now you can select the type of analysis which you would like to perform.
We would like to calculate heterozygosity (observed and expected), Wright's statistics and also effective number of alleles.
For this task, please select the option Het, Fstat, Poly by pop. Because we know that we need to also calculate
Nei's genetic distances and Wright's Fst index to quantify the level of genetic differentiation in the population/ metapopulation
we need to select in the third part of this window the option Nei's distance and Fst. Please click on it and then Ok.
Now you can see that the program was calculating the results.
The results are stored in the sheet HFP, NeiP and FstP.
Please select the HFP sheet.
Here you can see in the first row the type of analysis or the name of the analysis.
In this sheet you have information about heterozygosity, F statistics and polymorphism by population for codominant data.
In this part you have only description about the name of the dataset, number of loci (or genetic markers), number of samples, number of subpopulations.
In this row you have information
about abbreviations which are used in table below.
For example, N means sample size, Na means number of alleles.
Ne means effective number of alleles, I is information index, Ho is observed heterozygosity, He is expected heterozygosity and F is Wright's fixation index Fis.
In this table you can see values, which were calculated separately for each marker and population.
But we need basically average values for each population across all genetic markers evaluated. Hence, this table is more important for us.
In this table you can see number of animals in each population, average number of alleles, average effective
number of alleles, average observed heterozygosity, average expected heterozygosity and average Wright's fixation index Fis.
For the practical example, we need to speak about the observed heterozygosity, expected heterozygosity, effective number of alleles and Wright's fixation index Fis.
If we compare average values for our three populations, we can see that population 1 reached the highest average effective number of alleles
that means in this case, if we are speaking only about the effective number of alleles, the level of diversity is in this population the highest.
But when we check observed heterozygosity, we can see that based on average values, the value is the highest in case of population number 2.
In the case of expected heterozygosity,
,on the other side, the value is highest in case of population number 1.
But if we check all of values for heterozygosities, we can see that the value is higher than 0.5
that means in each population is higher proportion of heterozygous genotypes compared to the homozygous ones.
Wright's fixation index can reach value from -1 to 1.
Negative value means that in your population you have higher proportion of heterozygous genotypes and opposite
if you have values greater than 0, that means positive values, in your population, you have prevalence of homozygous genotypes.
In our case, the values of Wright's Fis index confirm that the level of diversity is really highest in the case of population number 2 compared to population number 1 and population number 3.
But we can also see that also in the case of population number 3 is the value negative.
This means that the proportion of heterozygous animals is higher. In the case of population number 1, the average value of Wright's Fis index is close to zero
that means the proportion of homozygous and heterozygous genotypes is relatively balanced.
This program is very good in summary statistics because provide you also total average for all populations and all genetic markers evaluated.
The second task was calculation of Wright's F indexes.
You can find information about the average value of Wright's fixation indexes in this part of the results. You can
see that even if in metapopulation is proportion of homozygous genotypes higher, within the populations we really observed higher proportion of heterozygous genotypes.
According to Fst value, we can say that the populations are really good differentiated from each other.
When we check information which are below this table, we can see also percentage of polymorphic loci and other
information related to the methodology for calculation of parameters which are included in this sheet.
Now we can go to the third excel sheet with the name NeiP.
In this excel sheet you can find two matrices.
The first one is genetic distance matrix and the second one is genetic identity matrix.
For our practical example, we need mainly information from the first matrix. Based on the matrix, we can see which population is more differentiated or the most differentiated from others in our dataset.
This value is basically value which tell us genetic distances between population 1 and population 1. Value is zero
that means logically that these two populations are the same.
If the value is zero in case of Nei's genetic distances, that means that really population are genetically same.
If the value is higher or close to one that means that the populations are genetically different.
You can see that here we can find also values which are higher than 1 and this is mainly due to the formula for the calculation of Nei's genetic distances.
We have several methods which are modification of standard Nei's genetic distances.
This is also the reason why the maximum value is in this case not 1.
From our three populations, we can say that population 1 and population 3 are really genetically connected and the highest genetic distance is between population 1 and population 2.
We can compare the Nei's genetic distances with Wright's FST matrix if the results are the same or if there is some difference.
In the case of Wright's Fst index is valid
the same approach for the explanation of the results
that means if the value is 0 populations are genetically similar and if the values is 1 populations are totally genetically different.
We can say that Wright's Fst matrix shows us the same results.
That means population 1 and population 3 are genetically connected and the highest genetic distance is between population 1 and population 2.
The last task in practical example is calculation or estimation of genetic differentiation based on the principal component analysis.
If we would like to make principal component analysis in Genalex, the first step is calculation of genetic distances not on population level but on the individual level.
We need to go back to the original dataset and now we need to calculate genetic distances on individual level.
For this purpose, we use option Distance and then please click on the Genetic. You don't need to change parameters which are set,
just click on OK and now we can make principal component analysis by selecting option PCoA and then analysis.
We can change the type of methodological approach, but right now is this not so important.
We can let it as it is. What is good to change is graph options because it will be good to have different colors for animals from different populations.
We can just select color code pops and then click on OK. Here are the results from the principal component analysis.
In the first part of the Excel sheet you can see general information about the dataset and procedure which was made.
Then in the table below, you have information about the proportion of variance which is explained by axes.
Usually first and second axes are used for visualization because these two axes generally describe really high percentage of variation in the dataset.
You can see also here that first and second axis describe 42.04% of variance in the dataset.
What we see on the figure, we can see that our populations are genetically separated, population number 2 is here and population number one and three are on the left.
This confirms the results from the Nei's genetic distances and Wright's Fst index because based on these two matrices we know that there is closer connection between population 1 and population 3.
This is all from this practical part.
If you have more questions about the program Genelex or coding of data, please write me email. My email address you can find on the presentation from the theoretical part.
Thank you for your attention.