POPGENE VERSION 1.31 Quick User Guide
Contents
1. OUO y OOOO O O O A A p cdedcocodudoodudodudou dudoocordudududco AHH FOO H C O O O O O O O O O O CC O 00006 Qoooooooooooormududooo H eMudouddudooooududdududooocooconm 1 fi c n uuu I C 2 2 hot I 0 0 sh Ps IHoOn O O nd O d mn O mn d d l mn n n mn nd n mn md m d m m O C C C CCCCC CC0C oO O lt lt lt lt lt 0 c Hn Hn n n O n n O mH d dH dH k D D O G A c c mn dn mn n n n n dn d d 0 3 c c c c C d n n dn dn dn dn d d o d G iH 222 d POPGENE VERSION 1 31 28 How to Contact Us While we have carried out a fairly extensive testing of the package using various data we would appreciate that you let us know any bug or abnormal behavior of the program during your use with POPGENE We would also like to hear from you on improvements required in future releases POPGENE is on website at http www ualberta ca fyeh or contact Dr Fra2. POPGENE VERSION 1 31 10 What Can POPGENE Do for Your Data POPGENE has the following main features and capacities that may be of interest to you during its use for your data analysis Windows In view of potentially large raw data sets and outputs encountered in population genetics analysis we have spent a considerable amount of time and effort to develop a Window text file editor that enables a loading display of very large documents Much of baseline information needed to develop our Window text editor was found in BIGED the FREE source code distributed by Terry Richards Software This editor is not limited to 64K of text a drawback of many Windows applications written in Borland C Now with POPGENE the only limitation to the size of your data Is how much memory RAM your computer has Other convenient and useful features that we have developed are assembled the Tool Bar You may change fonts and font sizes of your document so that you can for example view very long lines in the document You may also cut and paste your output for inclusion into your word processing software Computing The modules for co dominant and dominant markers are currently limited to a maximum 01 1400 populations 150 groups 1000 loci 10 characters Alpha numeric for a locus name automatically truncates to 10 if more than 10 characters are given The number of alleles per locus is limited to 9 1 9 if you use the numerals to c
3. EREEREEEEEEEE BREESE L 5 5 E E E E P E E E F E P P E E P P P P P P P ERE gt gt gt gt gt AA AA AA AA AA AA AA AA AA E POPGENE VERSION 1 31 16 If your answer is then Delete Locus dialog box up for your selection of 1001 to be deleted Ezs Population Genetic Edt Search CoDominant Dominant Quanttetive Window Help Tea 515 Tv 569 Delete Locus Retained Locus Removed Locus AAT 3 DIA 1 MDH 4 EREEEESEEEEE EEEEEEEEEEEE A A A A A A A A A A A A I POPGENE VERSION 1 31 17 5 Next answer the question Do you want to retain all populations for further analysis Test Data Set II Diploid Data Number of populations 4 Number of loci 21 Locus name 1 2 AAT 3 ACO ADH DIA 1 DIA 3 EST 2 GDH G6P HA IDH 1 MDH 2 MDH 3 MDH 4 1 PEP 2 PGI 2 PGM SPG 2 AA AA AA BB AB AA AA AA AA AB AA AA AA AA AA AA AA BB AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AB BB AC AA AB BB AA AA AA AA AA AA AA AA AA AB AA AA AA AA AA BC AC AB AB AB AA AA AA AA AA AA AA AA AA AA CC AA BB AB AA AA AA AA AA AA AA AA AA AA AA AA AA AB AC AA BB AA AA AA AA AA AA AA AC AA AA AA AB AA AA AA AA AB AB AC AA AB AB AA A
4. dominant markers and quantitative traits This package provides the Windows graphical user interface that makes population genetics analysis more accessible for the casual computer user and more convenient for the experienced computer user Simple menus and dialog box selections enable you to perform complex analysis and produce scientifically sound statistics thereby assisting you to adequately analyze population genetic structure using the target markers traits The current version version 1 30 is designed specifically for the analysis of co dominant and dominant markers using haploid and diploid data It performs most types of data analysis encountered in population genetics and related fields It can be used to compute summary statistics e g allele frequency gene diversity genetic distance F statistics multilocus structure etc for 1 single locus single populations 2 single locus multiple populations 3 multilocus single populations and 4 multilocus multiple populations Version 1 30 also includes the module for quantitative traits However we are not supporting it at this time because of the large RAM requirement when analyzing quantitative genetic data Hardware and Software Requirements To be able to run POPGENE your hardware and software must meet the following minimum requirements Hardware An IBM PC or compatible with an Intel 386 processor or higher CPU and 8 or more MB of random access memory RAM A ma
5. 2 Insert the POPGENE Disk into your floppy drive CD Drive 3 Run A POPGEN16 EXE for 16 bit operating environment under Windows 3 11 or POPGEN32 for 16 bit operating environment under Windows 95 98 and NT where is the floppy drive number or CD drive number on your computer 4 Follow the instruction on your screen 5 Go to the directory where you installed POPGENE and double click the POPGENE icon to start the program Installing POPGENE that you downloaded from website 1 Start Microsoft Window Windows 3 11 Windows 95 98 and NT 2 Go to the directory where you stored your downloaded POPGENE 3 Type or double click on the file you saved 4 Follow the instructions on your computer screen 5 Go to the directory where you have installed POPGENE and double click the POPGENE icon to start the program Uuinstall POPGENE 32 bit version Start Microsoft Windows and select Start menu Settings then Control Panel Select Add Remove Programs and double click on Population Genetics Analysis Follow the instructions on your computer screen 16 bit version Start Microsoft Windows and select PopGen16 folder Double click on Uninstall PopGen16 Follow the instructions on your computer screen POPGENE VERSION 1 31 3 Introduction POPGENE is a user friendly Microsoft Window based computer package for the analysis of genetic variation among and within natural populations using co dominant and
6. AAT 1 AAT 2 ACO ADH APH DIA 2 DIA 3 GDH G6H IDH MDH 1 MDH 2 MDH 3 MDH 4 ME PGI PGM 6PG 1 6PG 2 ID 1 ee A 22 gt 52 Se li 215722 3E eS cds al le GE S Qu 2 icu 3e lt ly 120 3 up SI 27 dt 920 Et al ID 2 1 Togs OX 1 2 3 1 1 1 1 v 1 2 Lo 3 1 1 1 11 1 2 3 35 1 1 al ID 3 1 3e 2 52 1 1 2 1 1 i 1 Ju mA 13 1 2 2 il 1 ii 1 snb 1 1 2 1 1 a POPGENE VERSION 1 31 26 Input file format for diploid data co dominant marker Diploid alphabetic data of 3 populations each with varying records genotypes amp 21 loci Number of populations 3 Number of loci 21 Locus name AAT 1 AAT 2 AAT 3 ACO ADH DIA 1 DIA 3 EST 2 GDH G6P HA IDH MDH 1 MDH 2 MDH 3 MDH 4 PEP 1 PEP 2 PGI 2 PGM SPG 2 AA AA AA AA AA AA AA BB AA AA AA AA AA AA AA AA AA AA AA AA AA AA AB BB AA AB BB AA AA AA AA AA AA AB AA AA AA AA AA AA BC AC AA AB AB AB AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA BB CC AA BB AB AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AB AC AA BB AA AA AA AA AA AA AA AC AA AA AA AB AA AA AA AA AB AB AC AA AB AB AA AA AA AA AA AA AB AA AA AA AA AA AB AA AA AA BC AC AA AB AB AA AA AA AA AB AA AB AA AA AA AA AA AA AA AA AA BB AA AA AA AA AA AA AA AA AA AA AA AA AC AA AA AA AA AA AA AA BC AA AB AA AA AA AA AA AA AA AB AA AA AA AA AA AA AA AB BC BC AA BB
7. AB AA AA AA AA AB AA AA AA AA AA AC AA AA AA AA AB AC AB AA BB BB AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AB AC AA BB AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA BC AB AB AA AA AA AA AA AA AA AA AA AA AC AA AA AA AA AA AB AA AC AA AB AB AA AA AA AA AA AA AB AA AA AA AA AA AA AA AA AA BB BB AA AA AA AA AA AA AA AA AB AA AA AA AA AA AA AA AA AC AC AA BB AB AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AB BB BC AA BB AA AC AA AA AA AA AA AA AA AA AC AD AA AA AA AA AA AB BC AA AB AB AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA BB BC AA BB AA AA AA AA AA AA AA AB AA AA AA AC AA AA AA AA AA AA BC AA AB AB AA AA AA AA AA AA BC AA AA AA AA AA AA AA AA AA AB BC AA AB AB AA AA AA AA AA AA AB AA AA AA AA AA AA AA AA AA BD BC AA AB AB AA AA AA AA AA AA AA AA AA AC AE AA AA AA AA AB CC BB AA BB AA AA AA AA AA AA AA AA AA AB AA AA AA AA AA BC BB AA AB AB AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AB CC BB AA AA AB AA AA AA AA AA AA AA AA AA AA AB AA AA AA AA AA BB BC AA AB AB AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AB BC AA AA BB AA AA AA AA AA AA AB AA AA AA AA AA AA AA AA AB AC AB AA BB AA AA AA AA AA AA AA AA AA AA AA AB AA AA AA AA AB BC BB AA BB BB AA AA AA AA AA AA AB AA AA AA AA AA AA AB AB AB AA BB AA AA AA AA AA AA AA AA AA AA AA Ies AA AA AA AA BB AB AA AA AA AA AA AA AA AA BB AA AA AA AA AA AA AA AC BC AA BB AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA
8. CC BC AA AB AB AA AA AA AA AA AA AB AA AA AA AC AA AA AA AA AA BB AC AA BB AA AA AA AA AA AA AA AA AA AA CC AA AA AA AB AA BE BB AA BB AB AA AA AA AA AA AA AB AA AA AA AA AA AA AA AB BC AA AB AA AA AA AA AA AA AA AB AA AA AA AA AA AA AA AA BB AA CC AA AB AB AA AA AA AA AA AA BC AA AA AA AA AA AA AA AA AB AC BC AA BB BB AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AB AA BB AA AC AB AA AA AA AA AA AA AB AA AA AA AA AA AA AA AA AB BB AC AA BB AA AA AA AA AA AA AA AB AA AA AA AA AA AA AA AA AA AC AC AA AA AB AA AA AA AB AA AA AB AA AA AA AA AA AA AA AA AA AB AB AA AA AA AA AA AB AA AA AA AA AA AA AA AA BC BC AA BB AB AA AA AA AA AA AA AB AA AA AA AC AA 52 27 POPGENE VERSION 1 31 Input file format for diploid data dominant marker 04 2 04 3 04 4 04 5 04 6 04 7 0 0 I I lt lt A A o 1 10 10 L dg I lt lt lt CV CV A C oed cd ond cd on oc on n d n d ec C oed cd oed cd cd oc oc on oc on c dH dO d d ud sr s si 22 53 22 291 iHd 1O O 1 1 1C HEDE ED 163 9 CI O1O OO ooo CCC CCCCCCC CCCC CC0C O O O n O n O
9. Nei s 1978 unbiased heterozygosity Shannon Index Estimates Shannon s 1949 information index as a measure of gene diversity Homogeneity Test Constructs two way contingency tables and carries out chi square 2 and likelihood ratio G tests for homogeneity of gene frequencies across populations The tests are carried out for Groups or Multiple Populations F Statistics Estimates F statistics Frr and Fis for Groups or Multiple Populations Hartl and Clark 1989 but estimates F statistics for a three level sampling hierarchy in random populations using a quite different approach by Weir 1990 for both Groups and Multiple Populations Gene Flow Estimates gene flow from the estimate of or Fsr Slatkin and Barton 1989 The estimation is made for Groups or Multiple Populations Genetic Distance Estimates Nei s 1972 genetic identity and genetic distance and Nei s 1978 unbiased genetic identity and genetic distance The estimation is made for Groups or Multiple Populations Dendrogram Draws a dendrogram based on Nei s genetic distances using UPGMA This program is an adoption of program NEIGHBOR of PHYLIP version 3 5c by Joe Felsenstein The drawing is executed for Groups or Multiple Populations Neutrality Test Performs the Ewens Watterson test for neutrality using the algorithm given in Manly 1985 POPGENE VERSION 1 31 8 Two locus LD Estimates Burrows composite measure of linkage disequil
10. have Frs value for each population but select HW equilibrium the program will ignore your value and assume HW equilibrium 223 Population Genetics Analysis Edit Search Co Dominant Dominant Quantitative Window Help bel ata 9 Seer rte Diploid Data Analysis for Dominant Markers D popgene rapddip dat r Data Format Diploid RAPD Data Set ipud Number of populations Variable as column C Record as column Number of loci 28 Locus name Assumption 01 1 01 2 01 3 03 1 03 2 03 3 4 1 4 2 4 3 OPA07 1 07 2 OPAD7 3 Hirarchical Structure 11 1 11 2 11 3 Hardy Weinberg Equilibrium Hardy Weinberg Disequilibrium Single Populations Multiple Populations name Slave Lake fis 0 238 Estimation 11101 100100 0011010 001 Gene Frequency IV Allele Number Effective Allele Number 11111 100100 0011010 001 11101 100101 0011010 11 Polymorphic Loci Gene Diversity Shannon Index 11101 111000 0011011 11C 11000 100000 0011010 001 Homogeneity Test F Statistics Gene Flow 10101 111100 1111010 001 Genetic Distance Dendrogram Neutrality Test 11101 100110 0001010 001 11001 100100 NANNININ NANI Check Cancel _ Hep other aspects of data analysis for dominant markers similar to that described co domi
11. n n d Oud Q A CV A A CCCC C CC C CCCC C0C Ore Or aps PD 6909 p C cd cd n oc n n d d n du C oed cd oed cd cd ond c n dn dH dn d do I om Or Or Orr Or Orr OOO C gt 1 1 1 jet PEDRI 15 LC c1 r3 n I OOOoOoO0o0O0nuooduooooo QUQOUO QOO lt QUO QOO QOO OO O60 O5 Oo Oo i PON lt lt lt lt lt 0d cd ed cd cd cd oed oc oc oce oc oc oc c gt on on n n 4 0 4 2H dH m d dH 0 d A A A A CO CI CI OO lt gt CI HOO CC E GAS OOO CI CX OOO icieciedeudududoduduoudodudoudooo 0 AA Add n n n n d NN NN 1o 1 1o 1o 2 OO O OO O OO O 0 CI AY Ded y vil 1261 OOdoooooooorduocoormududoocooo lt CC m OoOo
12. or Multiple Populations Appropriate Single Locus summary statistics Appropriate Multilocus summary statistics POPGENE VERSION 1 31 13 This is Haploid Data Analysis dialog box Notice that Check was activated Population Genetics Analysis File Edit Search Co Dominant Dominant Quantitative Window Haploid Data Analysis Data Format Variable as column Record as column Hirarchical Structure Single Populations Groups M Multiple Populations Single Locus Gene Frequency Allele Number Effective Allele Number Polymorphic Loci Gene Diversity Shannon Index Homogeneity Test F Statistics Iv Gene Flow Genetic Distance Dendrogram Neutrality Test Multilocus Two locus LD Brown Smouse Check all if you are not sure what specific analysis you want to carry out and then click OK POPGENE VERSION 1 31 14 This is Diploid Data Analysis dialog box for co dominant markers Notice that only some analyses were selected Ezs Population Genetics Analysis File Edit Search Co Dominant Dominant Quantitative Window Help esee Cede SP 9 i Isozyme Diplc Diploid Data Analysis i Number of popul t Number of loci m Data Format Locus name Variable as column C Record as column 1 2 IDH MDH 1 MDE Hirarchical Structure AA AA BB 2 n Si
13. A AA AA AA AA AA AB AA AA AA AA AA AA AA AA AA AA AA AA EE EA Enter the level for LD test EEREEEEEEEE EEEEEEEEEREEE EEEEEEEEEEEE EEEEEEEZEGEEEL PEREEEEEEEEE AAA ARR BA AA AA AA AA AM EI AA AA AA AA AB AA AA AA 4 POPGENE VERSION 1 31 22 8 If Neutrality Test was checked at step 3 then you need to select the number of simulations for computing 95 lower and upper confidence limits used to test for neutrality We recommend 500 1000 simulations for a reliable estimation of these confidence limits Ezs Population Ge sis File Edit Search CoDomi Dominant Quantitative window Help EST 2 GDH G6P EEEEEEEEEEEE EEEEEEEEEEEE EEEEEEEESGEEE EEEEEEEEEEEE EEEEEEEEEEEE EEEEEEEEEEEE EEEEEEEEEEEE EEEEBEREEEEE EEEEEEEEZEEE EEEEEEEEEEEE EEEREEEEZEBE EEREEEEEEEEEE gt gt gt gt gt gt gt AA AA AA AA AA AA AN 4 POPGENE VERSION 1 31 23 9 If you have correctly completed steps 1 to 8 then you prompted with result dat output Window displaying the results from the analysis you Just chose Ac
14. A AA AA AA AA AB AA AA AA AA AA AB AA AA AA BC AC AA AB AB AA AA AA AA AB AA AB AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA BC AA AB AA AA AA AA AA AA AA AB AA AA AA AA AA AA BC BC AA BB AA AA AA AA AA AA AC AA AA AA AB AC AB AA BB BB AA AA AA AA AA AA AA AA AA AA AA AA AA BB AA AA AA AA AA AA AA AA AA AA AA AA 7 Do you wantto retain all populations for further analysis AA AA AA AA AA AB AB AA AA AA AA AA AA AA AA AA AA AA AA AA AA m AA AA AA AA AA BB AA AB AA AA AA AA AA AA AA AB AA AA AA AA AA AA AA AA BC AA AA AB AA AA AA AA AA AA RR RR AA AA AR AA AA AA POPGENE VERSION 1 31 18 If your answer is No then Delete Populations dialog box pops up for your selection of populations to be deleted Populations A POPGENE VERSION 1 31 19 6 If you selected Groups at step 3 enter the number of groups in enter the number of group
15. POPGENE VERSION 1 31 Microsoft Window based Freeware for Population Genetic Analysis Quick User Guide A joint Project Development by Francis C Yeh Rong cai Yang University of Alberta And Tim Boyle Centre for International Forestry Research August 1999 POPGENE VERSION 1 31 1 New Feature in Current Version For Windows 95 08 and NT Users 32 bit version This version 1 31 has a new graphics interface that produces publication quality dendrograms POPGENE generates two dendrograms for each populations analysis base on Nei s regular and unbiased genetic distance measures Immediately after each of these dendrograms in the output file you will now see File Name dgraml plt or File Name dgram2 plt These two files are stored in the same directory as your output file They are the files you use for printing publication quality dendrogram Now open your word processing package such as Microsoft Word or Corel WordPerfect Use the statements commands insert picture file from to bring these files dgraml plt or dgram2 plt into your word processor For Windows 3 11 Users 16 bit version Users must first download and install the following file ftp ftp microsoft com Softlib MSLFILES HPGL EXE Then follow the procedures detailed above for Windows 95 98 and NT POPGENE VERSION 1 31 2 Installing POPGENE from diskette CD 1 Start Microsoft Windows Windows 3 11 Windows 95 98 and
16. e 223 Population Geneti Dominant Quantitative Window Help Diploid Data D popgene diptest dat Isozyme Diploid Test Data Number of populations 4 Number of loci 21 Locus name 1 2 AAT 3 ACO ADH DIA 1 DIA 3 2 GDH G6P HA IDH MDH 1 MDH 2 MDH 3 MDH 4 1 2 PGI 2 SPG 2 AA AA AA AA BB AB AA AB BB AA AA AA AA AA AA AA AA AA AB AA AA AA AA AA AA AA AA AA BB AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AB BB AC AA AB BB AA AA AA AA AA AA AA AA AA AB AA AA AA AA AA AA BC AC AA AB AB AB AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA BB CC AA BB AB AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AB AC AA BB AA AA AA AA AA AA AA AC AA AA AA AB AA AA AA AA AB AB AC AA AB AB AA AA AA AA AA AA AB AA AA AA AA AA AB AA AA AA BC AC AA AB AB AA AA AA AA AB AA AB AA AA AA AA AA AA AA AA AA BB AA AA AA AA AA AA AA AA AA AA AA AA AA AC AA AA AA AA AA AA AA BC AA AB AA AA AA AA AA AA AA AB AA AA AA AA AA AA AA AA AB BC BC AA BB AB AA AA AA AA AB AA AA AA AA AA AC AA AA AA AA AB AC AB AA BB BB AA AA AA AA AA AA AA AA AA AA AA AA AA AR AA PR AA AA AA AA AA AA AA AA AA AA AA AA AA Analyze isozymes diploid data 3 Open Haploid Data Analysis or Diploid Data Analysis dialog box to check If variables marker loci or records individual organisms are entered as columns If your analysis will be carried out for Single Populations Groups and
17. e or more Single Locus and Multilocus check boxes HAPLOID DATA ANALYSIS Gene Frequency Estimates gene frequencies at each locus from raw data Missing values are excluded from such estimation Allele Number Counts the number of alleles with nonzero frequency Effective Allele Number Estimates the reciprocal of homozygosity Hartl and Clark 1989 125 Polymorphic Loci Percentage of all loci that are polymorphic regardless of allele frequencies Gene Diversity Estimates Nei s 1973 gene diversity Shannon Index Estimates Shannon s information index as a measure of gene diversity Homogeneity Test Constructs two way contingency tables and carries out chi square 2 and likelihood ratio G tests for homogeneity of gene frequencies across populations The tests are carried out for Groups or Multiple Populations F Statistics Estimates Nei s 1973 Gsr for Groups or Multiple Populations and estimates both and Gcs for Groups and Multiple Populations Gene Flow Estimates gene flow from the estimate of or Fsr Slatkin and Barton 1989 The estimation is made for Groups or Multiple Populations POPGENE VERSION 1 31 6 Genetic Distance Estimates Nei s 1972 genetic identity and genetic distance and Nei s 1978 unbiased genetic identity and genetic distance The estimation is made for Groups or Multiple Populations Dendrogram Draws a dendrogram based on Nei s genetic distances using UPGMA T
18. his program is an adoption of program NEIGHBOR of PHYLIP version 3 5c by Joe Felsenstein The drawing is executed for Groups or Multiple Populations Neutrality Test Performs the Ewens Watterson test for neutrality using the algorithm given in Manly 1985 Two locus LD Estimates gametic disequilibria between pairs of loci and E tests for significance Weir 1979 for Single Populations and performs Ohta s 1982a b two locus analysis of population subdivision D Statistics for Multiple Populations Brown Compute observed and expected moments of K the number of heterozygous loci between two randomly chosen gametes in a population as well as multilocus indices and 95 confidence limits from these moments Brown et al 1980 for Single Populations and partition the total and average variances of K in a mixed pool of several populations into single locus and two locus components Brown and Feldman 1981 for Multiple Populations Smouse Codes the most frequent allele as one 1 and a synthetic allele consisting of all the other alleles combined as zero 0 Yang and Yeh 1993 Estimates average interlocus correlation based on the coded data in a population Smouse and Neel 1977 for Single Populations and estimates among and within population interlocus correlations for Multiple Populations DIPLOID DATA ANALYSIS Genotypic Frequency Estimates genotypic frequencies observed at each locus from raw data only for co dominant markers Mi
19. i M 1972 Genetic distance between populations Am Nat 106 283 292 Nei M 1973 Analysis of gene diversity in subdivided populations Proc Natl Acad Sci USA 70 3321 3323 Nei M 1978 Estimation of average heterozygosity and genetic distance from a small number of individuals Genetics 89 583 590 Ohta T 1982a Linkage disequilibrium with the island model Genetics 101 139 155 Ohta T 1982b Linkage disequilibrium due to random drift in finite subdivided populations Proc Natl Acad Sci USA 79 1940 1944 POPGENE VERSION 1 31 9 Shannon CE Weaver W 1949 The mathematical theory of communication Univ of Illinois Press Urbana Slatkin M Barton NH 1989 A comparison of three indirect methods for estimating average levels of gene flow Evolution 43 1349 1368 Smouse PE Neel JV 1977 Multivariate analysis of gametic disequilibrium in the Yanomama Genetics 85 733 752 Smouse PE Neel JV Liu W 1983 Multiple locus departures from panmictic equilibrium within and between village gene pools of Amerindian Tribes at different stages of agglomeration Genetics 104 133 153 Weir BS 1979 Inferences about linkage disequilibrium Biometrics 35 235 254 Weir BS 1990 Genetic Data Analysis Sinauer Associates Sunderland MA Wright S 1978 Variability within and among natural populations Vol 4 The Univ Of Chicago Press Chicago Yang R C Yeh FC 1993 Multilocus structure in Pinus contorta Dougl Theor Appl Genet 87 568 576
20. ibria between pairs of loci and 2 tests for significance Weir 1979 for Single Populations performs Ohta s 1982a b two locus analysis of population subdivision D Statistics for Multiple Populations Only for co dominant markers Smouse Codes a homozygote for the most frequent allele as one 1 a homozygote for the synthetic allele consisting of all the other alleles combined as zero 0 and their heterozygote as one half 1 2 Estimates average interlocus correlation based on the coded data in a population and test for both Hardy Weinberg and linkage disequilibria Smouse et al 1983 for Single Populations and estimates among and within population interlocus correlations for Multiple Populations In Multiple Populations case no attempt was given to estimate Hardy Weinberg disequilibrium in a subdivided population as these estimates would be equivalent to F statistics given above Only for co dominant markers References Brown AHD Feldman MW Nevo E 1980 Multilocus structure in natural populations of Hordeum spontaneum Genetics 96 523 536 Brown AHD Feldman MW 1981 Population structure of multilocus associations Proc Natl Acad Sci USA 78 5913 5916 Hartl DL Clark AG 1989 Principles of population genetics 2nd ed Sinauer Associates Sunderland MA Levene H 1949 On a matching problem in genetics Ann Math Stat 20 91 94 Manly BFJ 1985 The statistics of natural selection Chapman and Hall London Ne
21. nant markers POPGENE VERSION 1 31 25 Input File Format Input file for POPGENE analysis consists of the header section and the data The header section specifies 1 a job title delimited by 2 number of populations 3 number of loci and 4 locus names The body of data starts with for each population population ID optional population name optional If you do not give population ID or population name you must leave at least one blank line between populations and will generate population ID automatically for you But if you do your population ID and population name must be unique for each population The raw data in free format and with or without one or more spaces between columns immediately follow without blank lines in between Missing values must be set to for haploids and dominant markers such as RAPDs i e one digit to score for presence or absence of allele and 1 two digits for diploids co dominant markers in your input file are three examples of data The first two datasets have space between columns and the third dataset has mixture of space and without space to illustrate flexibility of data input and how input file for haploid and diploid should be prepared Input file format for haploid data Haploid numeric data of 3 populations each with 3 records gametes amp 19 loci x Number of populations 3 Number of loci 19 locus name
22. ncis C Yeh Department of Renewable Resources University of Alberta Edmonton AB Canada T6G 2 1 Tel 780 492 3902 780 492 4323 Email francis yeh ualberta ca Home Page http www ualberta ca fyeh fyeh Credits Dr Francis C Yeh Dr Timothy Boyle Dr Yang Rongcai Zhihong Ye Judy Mao Xiyan Acknowledgements POPGENE cannot be initiated without financial support We thank the Natural Sciences and Engineering Council of Canada for an operating grant A2282 to FCY for population genetics research the Canadian Forestry Service for a Green Plan Grant to FCY for studying multilocus genetic structure and the Centre for International Forestry for a grant to FCY to support the analysis of multilocus variation C programming and Microsoft Window interface
23. ngle Populations Groups M Multiple Populations AA AA AA AA AA 1 i AB BB 2 Single locus AA AA AA AA BC 2 Genotypic Frequency HW Test AA AA C AA AA Allele Frequency Allele Number Effective Allele Number AB 2 5 AA AA AA BC 2 Polymorphic Loci Obs Homozygosity Exp Homozygosity AA AA AA AA BB Shannon Index Obs Heterozygosity Exp Heterozygosity AA AA I AA BC I Homogeneity Test F Statistics Gene Flow AC 1 AM AA AA ARO Genetic Distance Dendrogram Neutrality Test 4 Multilocus Two locus LD Smouse Check All Hee POPGENE VERSION 1 31 15 4 You are now prompted a Query Do you want to retain all loci for further analysis 2 Click on Yes or No to answer the question Ezs Population Genetics Analysis Edit Search Co Dominant Dominant Quantitative Window Help 3 5 TJ D popgene diptest Isozyme Diploid Test Data Number of populations 4 Number of loci 21 Locus name 1 2 3 ACO ADH DIA 1 DIA 3 EST 2 GDH G6P HA IDH MDH 1 MDH 2 MDH 3 MDH 4 1 PEP 2 PGI 2 PGM SPG 2 BB AB BB BB BB EE E E E 5 E gt gt gt
24. ode your alleles or to 52 if you use the alphabetic letters respectively capital alphabet A Z for alleles 1 to 26 and lower alphabet a z for alleles 27 52 POPGENE VERSION 1 31 11 Getting Started Before being able to conduct any analysis with POPGENE you will have to prepare an ASCII input file see Input File Format for different formats required for your input data files Any commonly used text editor of your preference can be used for this purpose or you can bring data from other Window packages e g Microsoft Excel into the POPGENE text editor using cut and paste Alternatively you can use POPGENE Window based text editor to prepare the required data file Save your file before proceed further To carry out the analysis with POPGENE proceed with the following steps 1 Click on File on the main menu bar and Load Data on the File menu to select appropriate data sets to be analyzed in this version Co Dominant and Dominant Data can be selected for further analysis Population Genetics Analysis im Edit Search Co Dominant Dominant Quantitative Window Help Dominant Marker Data Quantitative Trait Data Open 272 Gaye Revert Save Print Rreview Eine Fant Setup Load isozymes data file from disk POPGENE VERSION 1 31 12 2 Upon loading your data file click Co Dominant the main menu bar to select Haploid or Diploid analysis depending on your data typ
25. pulation genetics analysis using quantitative traits Window Use the Window menu to arrange select and control the attributes of different windows Help Use the Help menu to open a standard Microsoft Help window containing information on how to use different features of the POPGENE package At the top of the data window there is a Tool Bar to provide quick easy access to the special features of the window At the bottom of the POPGENE application window there is a Status Bar to indicate the current status of the window including the cursor position and the size of the input data file in bytes POPGENE VERSION 1 31 5 Overview of POPGENE Computing Programs Below is a brief description of each of the programs developed for POPGENE Co Dominant and Dominant markers For more detailed discussion on the algorithms from which the programs have been developed you should consult with the original references cited at the end of this section These references will be also helpful in assisting you to interpret the outputs from the programs POPGENE Co Dominant and Dominant markers have two dialog boxes Haploid Data Analysis and Diploid Data Analysis In each of these boxes there are three levels of Hierarchical Structure given as three check boxes Single Populations Groups and Multiple populations Estimation of Single Locus and Multilocus genetic parameters is carried out by clicking one or more Hierarchical Structure check boxes and on
26. s dialog box Dominant Quantitative Window Help 2 EST 2 GDH PGI 2 PGM p EEEEEEEEEEEE EEEEEEEEEEELE EEEEEEEEGSEEE EEEEEEEEEEEE E E E E E E E gt P E EEEEEEEEEEEE E 5 E E b F P F P EEEEEEEEEEEE EEEEEEEEEEELE E E E E gt EEEEEEEEEEEE AA AA AA AA AA AB AA AA AA AA AA AA AA AB AA AA AA AA AA AA AA AB AA AB AA EEEREEEEE E gt POPGENE VERSION 1 31 20 Click OK to open Group Populations dialog box for grouping of populations select appropriate populations for each group roup Populations Population List Group Number List group 1 Group Population List pop 1 pop 3 A A IA d POPGENE VERSION 1 31 21 7 If Two locus LD was checked at step 3 then you need to select a significance level P to test for linkage disequilibria between pairs of loci Important A high P value can result in an extremely large output when you have a large number of alleles locus loci and populations In most cases P lt 0 05 should be used Dominant Quantitative window Help LEANAR opgene diptest dat Isozyme Diploid Test Data Number of populations 4 Number of loci 21 Locus name 1 2 AAT 3 ACO ADH DIA 1 DIA 3 5 2 GDH G6P HA IDH MDH 1 MDH 2 MDH 3 MDH 4 PEP 1 2 PGI 2 PGM SPG 2 BB A
27. ssing values are excluded from such estimation HW Test Computes expected genotypic frequencies under random mating using the algorithm by Levene 1949 and perform chi square 2 and likelihood ratio tests for Hardy Weinberg equilibrium at each locus only for co dominant markers Fixation Index Estimates F s as a measure of heterozygote deficiency or excess Wright 1978 only for co dominant markers Allele Frequency Estimates gene frequencies at each locus from raw data Missing values are excluded from such estimation Allele Number Counts the number of alleles with nonzero frequency POPGENE VERSION 1 31 7 Effective Allele Number Estimates the reciprocal of homozygosity Hartl and Clark 1989 p 125 Polymorphic Loci Percentage of all loci that are polymorphic regardless of allele frequencies Obs Homozygosity Estimates proportion of observed homozygotes at a given locus Exp Homozygosity Estimates proportion of expected homozygotes under random mating see Exp Heterozygosity for appropriate references Obs Heterozygosity Estimates proportion of observed heterozygotes at a given locus only for co dominant markers Exp Heterozygosity Estimates proportion of expected heterozygotes under random mating only for co dominant markers Two estimates are given The first is Nei s 1973 heterozygosity The second the expected heterozygosity estimated using the algorithm of Levene 1949 which is the same as
28. thematical coprocessor is required to achieve a reasonable computing speed Software Windows 3 11 16 bit version or later version Windows 95 98 or NT 32 bit version APPLE computer users can run POPGENE on PowerPc or the new G3 but must first install a software such as Virtual PC or Soft Windows POPGENE runs effortlessly under Virtual PC and Soft Windows Neutrality tests and all multilocus genetic estimates are computationally demanding We strongly recommend running POPGENE on a Pentium based PC with 16 or more MB of RAM POPGENE VERSION 1 31 4 Overview of POPGENE Windows This package is written in Borland C 4 51 a powerful professional tool for creating Windows applications using the C and C languages The POPGENE Windows computing environment consists of two types of windows Data display windows and Dialog boxes POPGENE is menu driven Most features are accessed by making selections from the menus The main menu bar contains eight menus File Use the File menu to create a new data file or open an existing file Edit Use the Edit menu to modify or copy text from other Windows Search Use the Search menu to find and or replace selected text Co Dominant Use the Co Dominant menu to invoke population genetics analysis using co dominant markers Dominant Use the Dominant menu to invoke population genetics analysis using dominant markers Quantitative Use the Quantitative menu to invoke po
29. tivate the menu File Save as to save the output into an ASCII file for further use or cut and paste selected text directly into a Window based word processing package such as Microsoft Word WordPerfect ES Population Genetics Analysis ICI Edit Search Co Dominant Dominant Quantitative window Help I a 5 v 2 D popgene diptest dat ft C Nu Nui POPULATION GENETICS ANALYSIS B FL 4 4x x 6 amp 4 pate 1996 8 23 15 46 11 Description Isozyme Diploid Test AA 4 4 4 4 4 4 4 AR AA Multi populations Descriptive Statistics POPGENE VERSION 1 31 24 This is Diploid Data Analysis dialog box for dominant markers You prompted to choose between HW equilibrium i e Fis 0 HW disequilibrium i e Fis 0 Notice that in your data you can specify value for each population If you do not an input for F s the program will assume that the population is in HW equilibrium When you select HW disequilibrium at the prompt the program will read your Fis value and use it when estimating allele frequency If you
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