Professor, Renewable Resources
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You can Download POPGENE from this website. Remember to register because it will allow us to contact you electronically concerning future updates etc. You should also Download the "Quick User Guide". However, if your have previously registered, you do not have to register again each time you download an upgrade version of POPGENE. To download without registering, do the following:
1. On POPGENE Home page, click on "Download latest version"
2. You are in a new page where you will see "Please Register First!". Don't type your name and e-mail address. Just click on the "Submit" button.
3. You are now in a new page. Click on the "No - return to >>> Download Area <<<" button.
4. You are now in the "Download" page. Follow the on-screen instructions.
How do I install POPGENE ?
Start Microsoft® Window (Windows 3.11, Windows 95 and Windows NT).
Go to the directory where you downloaded POPGENE.
Type or double click on the file you saved.
Follow the instructions on your computer screen. Go to the directory where you have installed POPGENE and double click the POPGENE icon to start the program.
How do I uninstall POPGENE ?
32- bit version
Start Microsoft® Windows.
Select "Start" menu, "Settings", then "Control Panel"
Select "Add/Remove Programs" and double click on "Population Genetics Analysis (32-bit Version)".
Follow the instructions on your computer screen.
16- bit version
n Start Microsoft® Windows.
Select PopGen16 folder.
Double click on "Uninstall PopGen16".
Follow the instructions on your computer screen.
How do I analyze RAPD data using POPGENE ?
RAPDs are predominantly dominant markers so that we cannot distinguish between the 1/1 and the 1/0 RAPD phenotypes in diploid tissues. Procedure to estimate RAPD frequencies is straightforward if the study populations are in Hardy-Weinberg Equilibrium (see Clark and Lanigan in Mol Biol Evol 10:1096-1111, 1993). However, most populations under investigation are not in HWE.
POPGENE enables you to estimate population genetic parameters under Hardy-Weinberg Equilibrium (HWE) and Hardy-Weinberg Disequilibrium (HWD). When you have estimates of departure from HWE (i.e., Fissub> values) for your populations, for example from previous study of co-dominant markers such as isozymes, you can input the Fis values into your data input file. Then, specify "Hardy-Weinberg Disequilibrium" on the POPGENE menu when you analyze the data. POPGENE will now estimate RAPD frequencies using the algorithm given by Chong, Yang and Yeh (see Current Genetics 26:374-376, 1994). If you only have Fis values for some populations, those populations without Fis values are automatically assigned a Fis value of zero.
Notice that if you have Fis values in your data input file but you specify "Hardy-Weinberg Equilibrium" on the POPGENE menu when you analyze the data, POPGENE will disregard your Fis values and all populations are automatically assigned a Fis value of zero.
Notice that you may examine the effect of departure from HWE on population genetic estimates in your populations by comparing the results based on HWE with the results based on HWD. Even if you do not have Fis values for your populations, you probably can use information pertaining to their population size, age, degree of isolation etc.
How do I interpret the POPGENE output for "Neutrality" test?You compare the "Obs. F" to "L*95" and "U*95", which are respectively, the lower and upper 95% confidence interval. If "Obs. F" is within this confidence interval, the locus is neutral; otherwise, it is not.
How do I cite POPGENE ?
We will submit POPGENE to the Journal of Heredity in 1998. At this time, you can cite POPGENE as:
YEH, FRANCIS C., YANG, R-C., BOYLE, TIMOTHY, B.J., YE, Z-H., and MAO, JUDY X. 1997. POPGENE, the user-friendly shareware for population genetic analysis. Molecular Biology and Biotechnology Centre, University of Alberta, Canada.
Some users prefer citation out of a journal, and for that, you can cite POPGENE as:
YEH, F.C. and BOYLE, T.J.B. 1997. Population genetic analysis of co-dominant and dominant markers and quantitative traits. Belgian Journal of Botany 129: 157.
You have at least four options:
Read the POPGENE "Quick User Guide".
There are basically three reasons:
(1) You do not have the correct hardware and software configuration. Please check the hardware and software requirements for POPGENE in the "Quick User Guide".
(3) There is a problem in what you specified in the header section and/or your data in your data input file. For example, you specified "7" loci but gave names for six or eight loci, or that your data structure did not correspond to what you specified. You will see a specific error message such as " The population 1 individual 1 has incorrect number of loci ".
Why the "Help" function only has Window related topics?
We have yet to completed the "Help" function for POPGENE topics. We released POPGENE without it because it has been our experience that users can run POPGENE very effectively with assistance from the accompanying "Quick User Guide".
We wish to state clearly that our "Help" function will focus only on helping the users to run the program. We do not have the financial means to develop a very detailed "Help" function that will also guide you on population genetics and statistics. We have listed all the relevant references in our "Quick User Guide" and in the output. It is our hope that POPGENE users are familiar with the genetic statistics that are available in POPGENE.
POPGENE users are dedicated plant and animal population geneticists worldwide, working in governments, research organizations, and academic institutions.
All registered POPGENE users will be informed by email of recent upgrades so that they can download the latest version. This is the reason why you should register.
If you do not have email, use the email address of a friend. We will only contact POPGENE users by email due to cost consideration.
What are those files with the "dat" extension in my POPGENE directory?They are sample data files for your convenience. Files "Diptest.dat" and "Haptest.dat" are respectively, diploid and haploid test data for co-dominant markers. Files "Rapddip.dat" and "Rapdhap.dat" are respectively, diploid and haploid test data for dominant markers.
What is Shanon Index ?
Q. I am working with RAPDs and I would like to use Shanon index because I have no information concerning HWE. I would like to know precisely the formula used. Do you use shanon index per locus or do you use it per primer ?
Answer. Use of Shannon index should be considered as the last resort because it has little genetic interpretation except for measuring the level of diversity. If the organism you work with is outcrossing species, then the assumption of WHE may not be terribly wrong. It seems common in the literature to make such assumption to estimate marker and null frequencies and subsequently estimate population genetic structure (e.g., Lynch and Milligan, 1994. Mol. Ecol. 3:91-99; Chong et al. 1994. Curr. Genet. 26: 374-376). If the organism is a selfing or inbred species, it is probably reasonably assumed that the two RAPD phenotypes observed are likely to be marker and null homozygotes because there is little heterozygosity particularly under selfing. In this you can analyze the data as if it is from haploid individuals. It is more appropriate to use Shanon Index for each locus than for each primer because each RAPD band (locus) represents a different location over the genome where a DNA sequence is complemnetary to the same primer. In this case, the formula for Shannon's index is: -sum pi log2 pi, where pi is the frequency of the presence or absence of the band (i=1, 2). The mean diversity may be estimated by an average of index values over individual loci.
What are "Variable as column" and "Record as column" under "Data Format" in POPGENE command?
When the genotypes or haploidtypes of each individual are recorded in columns on a line, as in the sample data files, users should click on "Variable as column". However, when the columns on a line are the genotypes/haploidtype at a single locus for all individuals, users should click on "Record as column".
What are "Single populations", "Groups" and "Multiple populations" under "Hierarchical analysis" in POPGENE command?
When you analyse your data for the first time, you should specify both "Single populations" and "Multiple populations" so that you get the complete analysis for your data.
POPGENE is interactive, in the context that you can tell which populations and loci to include in the analysis. Later on, you might just want to re-examine genetic parameters of specific populations, individually, using just the "Single populations" command. This will cut down on the amount of output for the analysis. Notice that if you only specify "Single populations", you have no output for those test statistics involving more than one populations (i.e., homogenetity test, genetic distance, dendrogram, gene flow, F-test) even if you ask for them in the command boxes. If you want these test statistics, you must also specify the "Multiple populations" command.
When you specify only the "Multiple populations" command, you will have all those test statistics involving more than one populations (i.e., homogenetity test, genetic distance, dendrogram, gene flow, F-test), plus the remaining test statistic (i.e., allele frequency and number, Shannon index, HW-tests etc.) computed from the overall data.
The "Groups" command allows users to specify nesting of populations. Current version (1.21) gives only 2 levels, for example, among species, among populations within species, and within populations. We can program POPGENE for n-levels of nesting. However, the problem is that the interactive part (i.e., when user has to specify the nesting of populations during the analysis stage) becomes quite messy when users have many populations. We are still working on the design logic.
We strongly recommend that users try to run POPGENE using our sample data files. Specify the different commands and examine the outputs. You will quickly master POPGENE.
What is the on-screen error message in 16-bit version of POPGENE when I analyzed a large number of populations, loci and alleles or when I have opened many files? Is there a problem with the programming?
The answer is "NO". There is no problem with the programming. Let us explain how POPGENE handles the output of your data analyses.
POPGENE displays the results of your data analyses on the computer screen. When you have many populations, loci and alleles, and when you wanted to compute all available genetic parameters, you would have hundreds/thousands of lines of output. When the number of lines of output exceeded the limit of the POPGENE editor, POPGENE could not display all your results on screen and you would receive an error message. But don't worry, your results have already been saved on your computer! This is because POPGENE automatically saved the complete results of your data analyses into a file with the "rst" extension. For example, if you specified that your data file was "My.dat", then the complete results of your data analysis would be in file "My.rst". So, open this file with your favorite word-processor and view the complete results of your data analyses.
How much free RAM in your computer would determine how many lines POPGENE could display on your screen. POPGENE editor is RAM based to allow fast on-screen scrolling. So, try to free up your computer RAM when running POPGENE by closing other programs and also screens that are not in use within POPGENE. For example, you might be running several data files with POPGENE. Try to close those on-screen data and output files before you call up a new data file for analyses.
The editor for the 32-bit version of POPGENE does not have such problem unless the computer only has 8 MB RAM and limited free disk space due to advanced memory management under Windows 95 & NT.
We want to beta test it later this year and distribute it in 1998. Right now, the module under development can compute a number of parameters, including Fst for individual quantitative traits. We still have to incorporate the estimation of confidence intervals based on bootstrapping, the comparison between Fis from marker genes and quantitative traits, and the among-population comparison of multitrait covariance matrices (phenotypic/genetic/environmental). You can read some of these in a paper by Yang, Yeh and Yanchuck in Genetics 142:1045-1052 (1996). Notice that the analysis of quantitative traits will be computationally demanding. You will need a fast CPU (Pentium-based PC) and much more random access memory (RAM) than for analyzing co-dominant and dominant markers.
When will you release a POPGENE version for other platforms, such as Mac OS and Unix ?
We do have a large number of inquiries concerning POPGENE for the Mac OS, especially from Europe. At this time, we have no plan to develop POPGENE for other computer platforms. This is strictly an economic decision because we must secure additional fund to finance this work. POPGENE has over 60,000 lines of codes, and many built-in library functions which are specific to the Borland C++ compiler that we used. The compiled codes exceeded 3,490,000 lines and it will be naive to think that Mac conversion will be an easy task.
However, Mac users can run POPGENE on PowerPc, but must first install a software such as "Virtual PC" from Connectix on the PowerPc. "Virtual PC" uses between 150-260MB of your disk as if it is a "C" drive (in PC terminology) and installs Windows 95 on it. The 32-bit version of POPGENE rans effortlessly. To be able to use "Virtual PC", your PowerPc processor must be the 604 or 604e chip running at speed of 100Mhz and higher; or any 603e chip running at 180Mhz or faster. The PowerPc must also have Mac OS 7.5.5 or later and with a minimum of 24 MB RAM.
POPGENE should also run under "Soft Windows" on PowerPc, but we have not tested POPGENE under "Soft Windows". We would appreciate those with "Soft Windows" to test run POPGENE and tell us the outcome.
Send mail to francis yeh with questions or comments about this web site. Last modified: 十月 04, 2001