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Genetics of Corn
Corn (Zea maize) is one of the worlds most important crop plants. Because of its economic importance, corn genetics have been extensively studied. Some aspects of corn make it a difficult plant to work with, these include: relatively long generation time, phenotypic traits that may be influenced by the environment, large size of individual plants, and the corn genome is massive. Some of these limitations can be overcome by observing phenotypic traits of corn kernels (coincidentally the most economically important part of the plant). As each kernel represents the next generation, a whole year of growing to get the next generation can be eliminated, the kernels parents can usually be easily identified, and a large number of individual kernels take up very little space. Some of the great advances in understanding of genetics have been made by corn geneticists including Nobel prize winner Barbara McClintok who discovered transposons. Many of the techniques used in corn genetics are used on a large scale by seed corn companies to produce the hybrid strains grown by most farmers in the United States. Typically crosses are controlled by placing plastic bags over unfertilized female flowers, and then artificially fertilizing them by transferring pollen with a paint brush. In this experiment, you will be examining various cross in corn. The traits you will be looking at are kernel texture and kernel color. In formulating your hypotheses, you may want to take into consideration that more primitive strains of corn tend to have purple colored smooth textured kernels. You will be provided with corn cobs containing kernels produced using a variety of crossing techniques. It is your job to first collect data on the number of kernels exhibiting various phenotypes, use inductive reasoning to hypothesize what kind of cross was done on the corn and finally to test your hypothesis using the Chi square (X2) statistic to determine the probability your hypothesis is correct. The questions you will be attempting to address in this experiment are:
1 Which alleles are dominant and which are recessive?
2 Do these traits behave as would be expected based on Mendel's law of independent assortment?
In answering both questions, you will want to determine what phenotypic classes exist relative to the two traits being examined, and then classify each kernel on your cob, recording the number of kernels falling into each phenotypic category, for example, how many kernels on your cob are purple and wrinkled? Once you have the raw data, you will have to determine whether the kernels on your cob fall within the expected phenotypic ratios predicted by Mendel's laws. This is best achieved by using a statistical test called the Chi Square. This test helps establish if the data you have collected is close enough to the expected ratio to retain your hypothesis, or alternatively whether your data is so far from what would be expected based on your hypothesis that it should be rejected. A standard rule used by statisticians to decide on whether a hypothesis should be rejected or not is the 0.05 rule. This rule says that if there is only a 5% chance that the data obtained differs from what would be expected due to random events, the hypothesis should be rejected. In other words, if the hypothesis you uses has a 95% chance of being wrong, you -2 should reject it. This is very important, as it means that, when statistics are used, a perfectly correct hypothesis would be expected to be rejected 1 out of every 20 times the test was used.
d 2 2 = e The formula for the Chi Square is: Where d = the deviation between observed values and expected values, and e = the expected values. Once the Chi square value is known, it can be compared to a table of Chi square values which can be used to determine the p value, or probability the hypothesis being tested, some times called the null (Ho) hypothesis, is correct based on your data. Chi square results that give a p value of greater than 0.05 are not considered to "significantly" deviate from what would be expected based on the hypothesis so the hypothesis should be retained. Values for p of 0.05 or less are considered "significant" and should result in rejection of the hypothesis. One more number is necessary if the p value is to be determined from the Chi square, and that is the degrees if freedom. For the purposes of this experiment, the degrees of freedom is the number of phenotype classes minus one. After completing a Chi square on your own data, pool it with data from others in the class who were testing the same hypothesis to see if the combined data yields a different conclusion. You may want to address the question of the sensitivity of Chi squares to small sample sizes.
TABLE OF CHI SQUARE VALUES
p Values Deg. Sig. H Sig. Free. 0.99 0.95 0.90 0.80 0.70 0.50 0.30 0.20 0.10 0.05 0.01 1 0.0002 0.0039 0.0158 0.0642 0.148 0.455 1.074 1.624 2.706 3.841 6.635 2 0.0210 0.103 0.211 0.466 0.713 1.386 2.408 3.219 4.605 5.991 9.210 3 0.155 0.352 0.584 1.005 1.424 2.366 3.665 4.642 6.251 7.816 11.345 4 0.297 0.711 1.064 1.649 2.195 3.357 4.878 5.989 7.779 9.488 13.277 5 0.554 1.145 1.610 2.343 3.000 4.351 6.064 7.189 9.236 11.070 15.086 6 0.872 1.635 2.204 3.070 3.828 5.348 7.231 8.558 10.645 12.592 16.812 7 1.239 2.167 2.833 3.822 4.671 6.346 8.383 9.803 12.017 14.067 18.47 8 1.646 2.733 3.490 4.594 5.527 7.344 9.524 11.03 13.362 15.507 20.09
Supplies:
Corn samples resulting from various crosses.