Go to Allele Server
In the upper right corner, click on the 'log in' link.
Click on 'REGISTER'
Choose a username and password, then click on 'register.'
After registering, you will automatically return to the Sequence Server
(it's a bug, we're working on it). Type http://www.bioservers.org/sad/
to return to the Allele Server.
Click on 'MANAGE GROUPS.' When the window opens, use the pull-down menu
to select 'Your Groups.' Then click on 'ADD GROUP' to create a database
for your class.
Name your group and select the private or public option. BEFORE YOU
CLICK 'OK,' scroll down to see the remaining two entry fields. You must
enter a password and the number of students in your database if you
wish to edit the group at a later date. We suggest you do this anyway
in case you make a mistake in data entry.
Click on 'EDIT GROUP' to add your data. Click on the INDIVIDUALS tab
at the top of the window. Use the pull-down menus to select the correct
data for the first individual (i.e. haplotype, sex, label). When you
are done, click 'Add.'
Repeat step h until you have finished entering your data. Click 'Done.'
Click on 'OK' in the 'MANAGE GROUPS' window.
Now you need to get your group into the workspace. Click on 'MANAGE
GROUPS.' Use the pull-down menu to select 'Your Groups.' Click on the
checkbox to the left of your group. Click 'OK.'
To see if your group is in Hardy-Weinberg equilibrium, choose 'Chi-Square'
from the pull-down menu on the RIGHT SIDE of the screen. Click on the
round button beneath this menu to select your group. Click 'ANALYZE.'
The Allele Server will perform a Chi-Square analysis using your observed
genotype frequencies and the genotype frequencies predicted from a population
in Hardy-Weinberg equilibrium. The predicted genotype frequencies are
calculated using the same approach you used in Part I.
The bottom of the analysis page shows pie charts for the observed and
expected genotype frequencies. Do these pie charts look substantially
different? The Chi-Square test provides a statistical measure for the
difference between the two sets of frequencies. In general, the higher
the Chi-Square value, the greater the difference between the observed
versus the expected frequencies.
Chi-Square test for Hardy-Weinberg equilibrium assumes the "null hypothesis"
- that is, the observed genotype frequencies are not significantly different
from those predicted for a population in equilibrium. As with any theoretical
value, the genotype frequencies predicted by the equilibrium equation
almost always differ from the frequencies observed in a real population.
The problem is to discern when the observed versus expected values differ
due to chance and when they are truly different.
probability value, or p-value, is used to evaluate the significance
of a Chi-Square. Scientists give a wide margin for differences that
may occur by chance by setting the cutoff for significance at p-value
of 0.05 (5%) or less. This means that one may expect a Chi-Square of
this value to occur by chance in 5% of genotype comparisons. Conversely,
there is a 95% probability that the differences between observed versus
expected genotype frequencies are "real." Social scientists expand the
probability window by saying that p-values between 0.5 and 0.10 "approach
example, a p-value of 0.34 means that there is a 34% probability that
the genotype differences are due to chance and 66% chance that they
are not due to chance. This p-value is not significant, the null hypothesis
is upheld, and we say that the population is in Hardy-Weinberg equilibrium.
A p-value of 0.02 means that there is a 2% probability that the genotype
differences are due to chance and 98% chance that they are not due to
chance. This p-value is significant, the null hypothesis is rejected,
and we say that the population is not in Hardy-Weinberg equilibrium.
is the p-value for your Chi-Square? Is it less than .05? If so, can
you suggest any factors that might account for why your observed population
is not in Hardy-Weinberg equilibrium?