| fgendist

## Function

Compute genetic distances from gene frequencies

## Description

Computes one of three different genetic distance formulas from gene frequency data. The formulas are Nei's genetic distance, the Cavalli-Sforza chord measure, and the genetic distance of Reynolds et. al. The former is appropriate for data in which new mutations occur in an infinite isoalleles neutral mutation model, the latter two for a model without mutation and with pure genetic drift. The distances are written to a file in a format appropriate for input to the distance matrix programs.

## Algorithm

This program computes any one of three measures of genetic distance from a set of gene frequencies in different populations (or species). The three are Nei's genetic distance (Nei, 1972), Cavalli-Sforza's chord measure (Cavalli- Sforza and Edwards, 1967) and Reynolds, Weir, and Cockerham's (1983) genetic distance. These are written to an output file in a format that can be read by the distance matrix phylogeny programs FITCH and KITSCH.

The three measures have somewhat different assumptions. All assume that all differences between populations arise from genetic drift. Nei's distance is formulated for an infinite isoalleles model of mutation, in which there is a rate of neutral mutation and each mutant is to a completely new alleles. It is assumed that all loci have the same rate of neutral mutation, and that the genetic variability initially in the population is at equilibrium between mutation and genetic drift, with the effective population size of each population remaining constant.

Nei's distance is:

                                          \   \
                                          /_  /_  p1mi   p2mi
                                           m   i
               D  =  - ln  ( ------------------------------------- ).

                               \   \                \   \
                             [ /_  /_  p1mi2]1/2   [ /_  /_  p2mi2]1/2
                                m   i                m   i

where m is summed over loci, i over alleles at the m-th locus, and where

         p1mi

is the frequency of the i-th allele at the m-th locus in population 1. Subject to the above assumptions, Nei's genetic distance is expected, for a sample of sufficiently many equivalent loci, to rise linearly with time.

The other two genetic distances assume that there is no mutation, and that all gene frequency changes are by genetic drift alone. However they do not assume that population sizes have remained constant and equal in all populations. They cope with changing population size by having expectations that rise linearly not with time, but with the sum over time of 1/N, where N is the effective population size. Thus if population size doubles, genetic drift will be taking place more slowly, and the genetic distance will be expected to be rising only half as fast with respect to time. Both genetic distances are different estimators of the same quantity under the same model.

Cavalli-Sforza's chord distance is given by

                       \               \                        \
         D2    =    4  /_  [  1   -    /_   p1mi1/2 p 2mi1/2]  /  /_  (am  - 1)
                        m               i                        m

where m indexes the loci, where i is summed over the alleles at the m-th locus, and where a is the number of alleles at the m-th locus. It can be shown that this distance always satisfies the triangle inequality. Note that as given here it is divided by the number of degrees of freedom, the sum of the numbers of alleles minus one. The quantity which is expected to rise linearly with amount of genetic drift (sum of 1/N over time) is D squared, the quantity computed above, and that is what is written out into the distance matrix.

Reynolds, Weir, and Cockerham's (1983) genetic distance is

                           \    \
                           /_   /_  [ p1mi     -  p2mi]2
                            m    i
           D2     =      --------------------------------------

                             \               \
                          2  /_   [  1   -   /_  p1mi    p2mi ]
                              m               i

where the notation is as before and D2 is the quantity that is expected to rise linearly with cumulated genetic drift.

Having computed one of these genetic distances, one which you feel is appropriate to the biology of the situation, you can use it as the input to the programs FITCH, KITSCH or NEIGHBOR. Keep in mind that the statistical model in those programs implicitly assumes that the distances in the input table have independent errors. For any measure of genetic distance this will not be true, as bursts of random genetic drift, or sampling events in drawing the sample of individuals from each population, cause fluctuations of gene frequency that affect many distances simultaneously. While this is not expected to bias the estimate of the phylogeny, it does mean that the weighing of evidence from all the different distances in the table will not be done with maximal efficiency. One issue is which value of the P (Power) parameter should be used. This depends on how the variance of a distance rises with its expectation. For Cavalli-Sforza's chord distance, and for the Reynolds et. al. distance it can be shown that the variance of the distance will be proportional to the square of its expectation; this suggests a value of 2 for P, which the default value for FITCH and KITSCH (there is no P option in NEIGHBOR).

If you think that the pure genetic drift model is appropriate, and are thus tempted to use the Cavalli-Sforza or Reynolds et. al. distances, you might consider using the maximum likelihood program CONTML instead. It will correctly weigh the evidence in that case. Like those genetic distances, it uses approximations that break down as loci start to drift all the way to fixation. Although Nei's distance will not break down in that case, it makes other assumptions about equality of substitution rates at all loci and constancy of population sizes.

qThe most important thing to remember is that genetic distance is not an abstract, idealized measure of "differentness". It is an estimate of a parameter (time or cumulated inverse effective population size) of the model which is thought to have generated the differences we see. As an estimate, it has statistical properties that can be assessed, and we should never have to choose between genetic distances based on their aesthetic properties, or on the personal prestige of their originators. Considering them as estimates focuses us on the questions which genetic distances are intended to answer, for if there are none there is no reason to compute them. For further perspective on genetic distances, I recommend my own paper evaluating Reynolds, Weir, and Cockerham (1983), and the material in Nei's book (Nei, 1987).

## Usage

Here is a sample session with fgendist

    % fgendist
    Compute genetic distances from gene frequencies
    Phylip gendist program input file: gendist.dat
    Phylip gendist program output file [gendist.fgendist]:

    Distances calculated for species
        European     .
        African      ..
        Chinese      ...
        American     ....
        Australian   .....

    Distances written to file "gendist.fgendist"

    Done.

Go to the input files for this example
Go to the output files for this example

## Command line arguments

       Standard (Mandatory) qualifiers:
      [-infile]            frequencies File containing one or more sets of data
      [-outfile]           outfile    [*.fgendist] Phylip gendist program output
                                      file

       Additional (Optional) qualifiers:
       -method             menu       [n] Which method to use (Values: n (Nei
                                      genetic distance); c (Cavalli-Sforza chord
                                      measure); r (Reynolds genetic distance))
       -[no]progress       boolean    [Y] Print indications of progress of run
       -lower              boolean    [N] Lower triangular distance matrix

       Advanced (Unprompted) qualifiers: (none)
       Associated qualifiers:

       "-outfile" associated qualifiers
       -odirectory2        string     Output directory

       General qualifiers:
       -auto               boolean    Turn off prompts
       -stdout             boolean    Write standard output
       -filter             boolean    Read standard input, write standard output
       -options            boolean    Prompt for standard and additional values
       -debug              boolean    Write debug output to program.dbg
       -verbose            boolean    Report some/full command line options
       -help               boolean    Report command line options. More
                                      information on associated and general
                                      qualifiers can be found with -help -verbose
       -warning            boolean    Report warnings
       -error              boolean    Report errors
       -fatal              boolean    Report fatal errors
       -die                boolean    Report dying program messages

    acdDelPhyloFreq '0'

Standard (Mandatory) qualifiers | Allowed values | Default
--- | --- | ---
[-infile] (Parameter 1) | File containing one or more sets of data | Frequency value(s) |
[-outfile] (Parameter 2) | Phylip gendist program output file | Output file | <*>.fgendist
Additional (Optional) qualifiers | Allowed values | Default

-method

Which method to use

n | (Nei genetic distance)
c | (Cavalli-Sforza chord measure)
r | (Reynolds genetic distance)

n

-[no]progress | Print indications of progress of run | Boolean value Yes/No | Yes
-lower | Lower triangular distance matrix | Boolean value Yes/No | No
Advanced (Unprompted) qualifiers | Allowed values | Default
(none)

acdDelPhyloFreq '0'

## Input file format

fgendist reads continuous character data

### Continuous character data

The programs in this group use gene frequencies and quantitative character values. One (CONTML) constructs maximum likelihood estimates of the phylogeny, another (GENDIST) computes genetic distances for use in the distance matrix programs, and the third (CONTRAST) examines correlation of traits as they evolve along a given phylogeny.

When the gene frequencies data are used in CONTML or GENDIST, this involves the following assumptions:

1. Different lineages evolve independently.
2. After two lineages split, their characters change independently.
3. Each gene frequency changes by genetic drift, with or without mutation (this varies from method to method).
4. Different loci or characters drift independently.

How these assumptions affect the methods will be seen in my papers on inference of phylogenies from gene frequency and continuous character data (Felsenstein, 1973b, 1981c, 1985c).

The input formats are fairly similar to the discrete-character programs, but with one difference. When CONTML is used in the gene-frequency mode (its usual, default mode), or when GENDIST is used, the first line contains the number of species (or populations) and the number of loci and the options information. There then follows a line which gives the numbers of alleles at each locus, in order. This must be the full number of alleles, not the number of alleles which will be input: i. e. for a two-allele locus the number should be 2, not 1. There then follow the species (population) data, each species beginning on a new line. The first 10 characters are taken as the name, and thereafter the values of the individual characters are read free-format, preceded and separated by blanks. They can go to a new line if desired, though of course not in the middle of a number. Missing data is not allowed - an important limitation. In the default configuration, for each locus, the numbers should be the frequencies of all but one allele. The menu option A (All) signals that the frequencies of all alleles are provided in the input data -- the program will then automatically ignore the last of them. So without the A option, for a three-allele locus there should be two numbers, the frequencies of two of the alleles (and of course it must always be the same two!). Here is a typical data set without the A option:

         5    3
    2 3 2
    Alpha      0.90 0.80 0.10 0.56
    Beta       0.72 0.54 0.30 0.20
    Gamma      0.38 0.10 0.05  0.98
    Delta      0.42 0.40 0.43 0.97
    Epsilon    0.10 0.30 0.70 0.62

whereas here is what it would have to look like if the A option were invoked:

         5    3
    2 3 2
    Alpha      0.90 0.10 0.80 0.10 0.10 0.56 0.44
    Beta       0.72 0.28 0.54 0.30 0.16 0.20 0.80
    Gamma      0.38 0.62 0.10 0.05 0.85  0.98 0.02
    Delta      0.42 0.58 0.40 0.43 0.17 0.97 0.03
    Epsilon    0.10 0.90 0.30 0.70 0.00 0.62 0.38

The first line has the number of species (or populations) and the number of loci. The second line has the number of alleles for each of the 3 loci. The species lines have names (filled out to 10 characters with blanks) followed by the gene frequencies of the 2 alleles for the first locus, the 3 alleles for the second locus, and the 2 alleles for the third locus. You can start a new line after any of these allele frequencies, and continue to give the frequencies on that line (without repeating the species name).

If all alleles of a locus are given, it is important to have them add up to 1. Roundoff of the frequencies may cause the program to conclude that the numbers do not sum to 1, and stop with an error message.

While many compilers may be more tolerant, it is probably wise to make sure that each number, including the first, is preceded by a blank, and that there are digits both preceding and following any decimal points.

CONTML and CONTRAST also treat quantitative characters (the continuous-characters mode in CONTML, which is option C). It is assumed that each character is evolving according to a Brownian motion model, at the same rate, and independently. In reality it is almost always impossible to guarantee this. The issue is discussed at length in my review article in Annual Review of Ecology and Systematics (Felsenstein, 1988a), where I point out the difficulty of transforming the characters so that they are not only genetically independent but have independent selection acting on them. If you are going to use CONTML to model evolution of continuous characters, then you should at least make some attempt to remove genetic correlations between the characters (usually all one can do is remove phenotypic correlations by transforming the characters so that there is no within-population covariance and so that the within-population variances of the characters are equal -- this is equivalent to using Canonical Variates). However, this will only guarantee that one has removed phenotypic covariances between characters. Genetic covariances could only be removed by knowing the coheritabilities of the characters, which would require genetic experiments, and selective covariances (covariances due to covariation of selection pressures) would require knowledge of the sources and extent of selection pressure in all variables.

CONTRAST is a program designed to infer, for a given phylogeny that is provided to the program, the covariation between characters in a data set. Thus we have a program in this set that allow us to take information about the covariation and rates of evolution of characters and make an estimate of the phylogeny (CONTML), and a program that takes an estimate of the phylogeny and infers the variances and covariances of the character changes. But we have no program that infers both the phylogenies and the character covariation from the same data set.

In the quantitative characters mode, a typical small data set would be:

         5   6
    Alpha      0.345 0.467 1.213  2.2  -1.2 1.0
    Beta       0.457 0.444 1.1    1.987 -0.2 2.678
    Gamma      0.6 0.12 0.97 2.3  -0.11 1.54
    Delta      0.68  0.203 0.888 2.0  1.67
    Epsilon    0.297  0.22 0.90 1.9 1.74

Note that in the latter case, there is no line giving the numbers of alleles at each locus. In this latter case no square-root transformation of the coordinates is done: each is assumed to give directly the position on the Brownian motion scale.

For further discussion of options and modifiable constants in CONTML, GENDIST, and CONTRAST see the documentation files for those programs.

### Input files for usage example

### File: gendist.dat

        5    10
    2 2 2 2 2 2 2 2 2 2
    European   0.2868 0.5684 0.4422 0.4286 0.3828 0.7285 0.6386 0.0205
    0.8055 0.5043
    African    0.1356 0.4840 0.0602 0.0397 0.5977 0.9675 0.9511 0.0600
    0.7582 0.6207
    Chinese    0.1628 0.5958 0.7298 1.0000 0.3811 0.7986 0.7782 0.0726
    0.7482 0.7334
    American   0.0144 0.6990 0.3280 0.7421 0.6606 0.8603 0.7924 0.0000
    0.8086 0.8636
    Australian 0.1211 0.2274 0.5821 1.0000 0.2018 0.9000 0.9837 0.0396
    0.9097 0.2976

## Output file format

fgendist output simply contains on its first line the number of species (or populations). Each species (or population) starts a new line, with its name printed out first, and then and there are up to nine genetic distances printed on each line, in the standard format used as input by the distance matrix programs. The output, in its default form, is ready to be used in the distance matrix programs.

### Output files for usage example

### File: gendist.fgendist

        5
    European    0.000000  0.078002  0.080749  0.066805  0.103014
    African     0.078002  0.000000  0.234698  0.104975  0.227281
    Chinese     0.080749  0.234698  0.000000  0.053879  0.063275
    American    0.066805  0.104975  0.053879  0.000000  0.134756
    Australian  0.103014  0.227281  0.063275  0.134756  0.000000

## Data files

None

## Notes

None.

## References

None.

## Warnings

None.

## Diagnostic Error Messages

None.

## Exit status

It always exits with status 0.

## Known bugs

None.

## See also

Program name | Description
--- | ---
egendist | Genetic Distance Matrix program
fcontml | Gene frequency and continuous character Maximum Likelihood

## Author(s)

This program is an EMBOSS conversion of a program written by Joe Felsenstein as part of his PHYLIP package.

Although we take every care to ensure that the results of the EMBOSS version are identical to those from the original package, we recommend that you check your inputs give the same results in both versions before publication.

Please report all bugs in the EMBOSS version to the EMBOSS bug team, not to the original author.

## History

Written (2004) - Joe Felsenstein, University of Washington.

Converted (August 2004) to an EMBASSY program by the EMBOSS team.

## Target users

This program is intended to be used by everyone and everything, from naive users to embedded scripts.