On Tuesday you collected phenotype data on about 100 cats from one location. Today you'll use the phenotype data to estimate allele frequencies using the Hardy-Weinberg relationship, test the fit of observed genotype frequencies to those expected from Hardy-Weinberg, and see whether cats from your city are different from cats from the city you matched it to.

For the *longhair* and *white* loci, you only have two phenotypes, one caused by a recessive allele and one caused by a dominant allele. You will therefore need to estimate the allele frequencies using the Hardy-Weinberg relationship, which says that the frequency of a recessive homozygote is equal to the frequency of the recessive allele squared. Therefore, to estimate the frequency of the recessive *l* allele at the *longhair* locus, take the square root of the proportion of longhaired cats. For example, if 9% of the cats in your sample have long hair, you'd take the square root of 0.09, which equals 0.30. You'd estimate that the allele frequencies are 0.30 *l* and 0.70 *S*. Of course, you don't know this for sure, as you don't know how many of the short-haired cats are *Sl* and how many are *SS*. Do this calculation for your data for *longhair* and for *white*. Remember that the *W* allele, which causes white hair, is dominant. This is a good reminder that "dominant" does not refer to how common an allele is, it tells you what phenotype the heterozygous genotype has.

For the *Orange* locus, you can distinguish all the genotypes, so you don't need to estimate the allele frequency in your sample, you can count it directly. Males are either *OY*, an orange or cream colored cat, or *oY*, which is black, brown or gray. Count the number of *O* alleles and the number of *o* alleles in the males.

Females are either *OO* (orange or cream), *oo* (black, brown or gray), or *Oo* (calico or tortoiseshell). Count the number of *O* and *o* alleles in females, too. Add the total number of each allele in males and females to get your estimate of the allele frequencies.

For the *spotting* locus, use two different methods of estimating the allele frequency. First, lump together those with less than and greater than 50% white into one category. Treat the absence of white as a recessive phenotype caused by the *s* allele, and estimate the allele frequencies the same way you did for *longhair* and *white*. Next, count those cats with more than 50% white as *SS*, those with some white but less than 50% as *Ss*, and those with no white as *ss*. Count the number of alleles the same way you did for *Orange* in females.

For *Orange* in females and *spotting* in all cats, use the Hardy-Weinberg relationship to predict the proportion of each homozygous genotype and the heterozygous genotype. Read about the chi-square test of goodness-of-fit, download the spreadsheet from that page, and test how well your observed data fit the expected. Note that your "degrees of freedom" is based on an intrinsic hypothesis, so enter "1" under "intrinsic." Record the P-value.

Compare the phenotype frequencies (for the *longhair* and *white* loci) and the allele frequencies (for *spotting* and *Orange*) between your city and the city you paired it with, using Fisher’s exact test of goodness-of-fit.

On Tuesday, Sept. 2, you must turn in a lab report on this week's work. It must be typed. It should have the following information in one or more tables:

- The phenotype frequencies you counted.
- The allele frequencies you counted.
- Estimated genotype frequencies for
*Orange*and*spotting*, along with the observed genotype frequencies and the P-value for the difference between expcted and observed. - The phenotype frequencies (for the
*longhair*and*white*loci) and the allele frequencies (for*spotting*and*Orange*) for your city and your paired city, along with the P-values for the difference between them.

Your report should also contain the following:

- A description of any methods you used when collecting the data that went beyond what was in the instructions for the lab. This will primarily consist of how you dealt with questions like bad photos, kittens, siblings, etc.
- A description of your ideas for better ways to collect accurate data. This can include both better ways to use pet shelter photos, plus other ways you can think of to collect cat data from different places.
- A description of your most interesting result. "Interesting" in this case could mean the goodness of fit test with the lowest P-value, the genetic differences between cities, or something else you found interesting. Pick something that is interesting, even if none of your P-values are significant.
- Ideas for further research on your most interesting result. Suggest every possible explanation for the interesting result that you can think of, and describe further experiments someone could do to test those explanations.

There is no minimum or maximum length for the lab report, but you must include everything listed above.

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This page was last revised August 28, 2014. Its URL is http://udel.edu/~mcdonald/geneticslab2.html