Genetics

Corn Genetics

Corn Lab Background:

Gregor Mendel was the first to describe the principles of inheritance that are the basis for classical genetics and the development of modern genetics. The purpose of this lab is to demonstrate Mendelian patterns and principles of inheritance.

 

okatee corn

 

You will use corn as the model organism in this lab. Corn was one of the first model organisms to be used in genetics, but it is not often used today due to the long (one year) generation time. We will use corn to demonstrate Mendelian principles of genetics. However, corn is also the model organism that was first used to demonstrate that some genes do not behave as predicted by Mendelian principles. These genes are called transposable elements and are capable of jumping from one place to another in the genome. They can also cause chromosome breakage. They were discovered by Babara McClintock, shown right, in the 1940's. She also was involved in the discovery of genetic crossing over. In 1983 she received a Nobel Prize for her work on transposable elements. For more about transposable elements in corn see McClintock and the Ac/Ds Transposable Elements of Corn.

Corn Lab Instructions:

The characters under investigation will be kernel color, which can be either purple or yellow in some lines or red and white in others, and kernel shape, which can be either round or wrinkled. Purple/red are due to coloration of the aleurone, a thin skin that covers the kernel. Yellow is due to a colorless (clear) aleurone, which exposes the endosperm below. Round is due to a starchy endosperm, which retains a round shape as it dries. Wrinkled is due to a sugary endosperm, which is more condensed and causes the kernel to shrink and acquire a wrinkled shape when it dries.

This exercise will involve a lot of counting (Mendelian genetics always involves a lot of counting). You will need to work in groups, with one member calling out the phenotypes of the kernels and the other keeping a running tabulation of the counts. If there are more than two in the group, alternate so that all group members gain experience in this exercise.

There will be two types of corn - boxed and bagged. The boxed corn shows the parents, first generation and second generation of a Mendelian cross. It also shows the genotypes of each. The bagged corn shows only the second generation phenotypes.

Start with the boxed corn. There are two types of crosses: monohybrid, which is a cross involving only one characteristic (kernel color), and dihybrid, which involves two characteristics (kernel color and kernel shape). For each type of cross, examine the parents and the first generation to determine dominance relationships between the phenotypes. There is no need to count individuals in the parental or first generation because all kernels on one ear will be the same genotype, and there will be little phenotypic variation, if any. However, you do need to keep a record of the parental and first generation phenotypes for your report. Then examine the second generation. In the monohybrid cross there will be two phenotypes, in the dihybrid there will be four. For each type of cross, determine what these phenotypes look like.

When you have established the appearance of each phenotype or combination of phenotypes, it is time to start counting. To count, choose a spot at random on the sample ear, then include all kernels after that, counting right to left and moving down a row when you reach the end. As you count, use some object, such as a toothpick, as a pointer to keep track of where you are. Do NOT use any object that will leave a mark. You should count about 75 kernels in the monohybrid cross and 100 in the dihybrid cross, keeping a record of how many of each phenotype there are.

For kernel color, you may see some that are light purple. Is this a genetic effect, perhaps due to intermediate inheritance? For a hint, look at the phenotypes of the first generation, or look to see what the phenotypes of other ears are. Did other groups make the same observation?

A monohybrid cross is expected to produce a 3:1 ratio and the dihybrid cross a 9:3:3:1 ratio. For an explanation of this, see Introduction to Mendelian Genetics. Analyze your data with a chi-square test to determine whether they fall within the range of normal variation for these expected ratios (see "The Chi-Square Test" below for instrucion on how to calculate and interpret a chi-square). Are your results consistent with the expected ratios? How would this outcome be explained according to Mendelian principles?

When you have completed the boxed corn, go to the bagged corn. There should be seven different ears in each bag. Some ears have reddish and white kernels, others yellow and purple. Some are all yellow. You will need to make counts for all seven phenotypically different ears in the bag you receive. For each of your ears, determine whether it is a monohybrid or dihybrid cross and be able to explain why you think so. Then count about 75 kernels if your ear is a monohybrid cross and 100 for dihybrid, tallying them by phenotype as you did with the boxed corn. Test the results for consistency with Mendelian principles with a chi-square.

A few of your ears will have an unexpected result. Look closely and you will see some kernels that are spotted. If your cross is a monohybrid, keep a record of the number of spotted kernels - for this data set you will have three phenotypic categories. You will have to decide whether to count the spotted kernels as purple, yellow or a separate category altogether, perhaps an intermediate category. A series of chi-square tests on data from the two ears will allow you to determine the best hypothesis. Do an internet search for spotted corn kernels and find out what causes the spotting. Keep this because you will need to explain the spotting in your written report.

If you obtain a chi-square that requires you to reject your null hypothesis for any of the boxed or bagged corn, try to derive another null hypothesis to test. For example, a different phenotype may be dominant than the one you expected, or both parents may not have been heterozygous. Some of your monohybrid bagged ears may have a ratio that appears to be closer to 1:1 than to 3:1, or a dihybrid might have a ratio that appears to be 1:1:1:1. What would account for a 1:1 or a 1:1:1:1 ratio? If you have an unexpected result, you should talk to members of another group and find out if they have the same result. Compare your results to determine whether the two results are similar, and if not, which is more likely to be representative of all samples.

Keep your results - you will need them for a written report on this lab. In addition to your results, obtain class results or a large sample of results presented during the class discussion of these crosses. You will need all of this data for your report.

Format for Written Report on the Corn Lab:

Start your report with the title, followed by the group members' names. Then begin the body of the report. Do not include a cover sheet..

Introduction: This section provides the background and purpose for your project. For this report, you should briefly describe Mendel's results and his principles or laws that were derived from them, for both mono and dihybrid crosses and the respective test crosses. Specifically, for each type of cross you should include a description of Mendel's cross, his results (the phenotypic ratio) , his conclusions and the Mendelian law that resulted. Do not include biographical information on Mendel. Then state the purpose: the purpose of this exercise is to demonstrate the inheritance patterns that result from monohybrid and dihybrid crosses. This section should be about three quarters to one page long, and not longer than one page.

Methods: Here, you report the methods you used in sufficient detail that someone else could repeat your work. Describe the types of corn you examined (boxed and bagged), the phenotypes of the corn, and the method you used for counting. You should also describe the method of statistical analysis (chi-square) and the formula you used to calculate a chi-square in this section. Describe what you did, rather than what someone else should do. In other words, use a narrative format instead of a recipe format. This section should be about half to three quarters of a page. You may use the information in the instructions given above, but rephrase it in your own words.

Results: This section should start with the boxed corn first, then the bagged corn, and should contain a general written summary of your results as well as tables showing your actual data sets. The written summary should not give specific data (that will be in the tables) but should provide enough detail that a reader would know what your general qualitative results were without referring to tables. Some points to include in the written summary of the boxed corn results are the phenotypes of parents and F1, how this result is interpreted and a brief summary of the data analysis for the F2. Include a verbal description of why you would accept or reject the null hypothesis for each cross and what this means. For the bagged corn, describe whether the cross is monohybrid or dihybrid and whether it is a testcross, and how you know. Determine what the original parents and the first generation could have been. Then give the phenotypic ratio that you tested, the results of the chi-square and what this ratio means. Refer to data tables in your summary. The tables provide the specific quantitative details of your results. Arrange tables for the boxed corn to have four columns with the following headings: Phenotype, Observed, Expected and (E-O)2/E. The number of rows will vary depending on the type of cross, but the last row should give the totals. At the bottom of the last column, show the total chi-square. Directly below the chi-square table, give the chi-square, degrees of freedom (df) and state whether the null hypothesis will be accepted or rejected (for chi-square calculation and interpretation see below). For the corn in bags, you may use tables with three columns: phenotype, observed counts and expected. The chi-square information (chi-square value, df, accept or reject) should be placed in a merged cell below the columns.

There should be one table for each of the crosses you have analyzed (boxed monohybrid, boxed dihybrid, different kinds of bagged corn).

In monohybrid bagged corn with some spotted kernels, you will need to evaluate the basis for spotting by using different chi-square tests on each of the data sets with spotted kernels. The spots could be due to intermediate inheritance (incomplete dominance). In that case, the ratio of purple : spotted : yellow should be 1:2:1. Alternately, the spots could result from modification of kernels that should be purple or modification of kernels that should be yellow. For both monohybrid crosses, test for 3:1 and 1:1 ratios when spotted kernels are pooled with purple, then do the same when spotted kernels are pooled with yellow. If the chi-square tests are consistent with 3:1 and 1:1 ratios when spotted is pooled with purple, that will indicate that spotted is a modified form of purple. The opposite result will indicate spotted is a modified form of yellow. Check the link to Barbara McClintock's corn research to establish the basis for formation of spotted kernels.

Discussion: This should contain a brief summary of your general result, along with your conclusions and interpretations. Were all the results what you expected? If not, explain what could have caused the differences. How do you explain the light colored kernels in the monohybrid crosses? What caused the spotting on some of the kernels? Any unexpected or surprising results can be points for your discussion. This should between half a page and a page.

The Chi-Square Test:

Once data are collected, it is important to be able to determine whether they fall within the range of normal variation for the expected Mendelian ratios. To do this, a chi-square test is used. It will provide a guide as to whether to accept your null hypothesis (which will happen if the observed data fall within the normal variation for the expected ratios) or reject your null hypothesis (if the observed data do not fall within the range of normal variation). The chi-square value gives a measure of the extent to which your results differ from the expected - the larger the chi-square, the bigger the difference, and the less likely that the difference was due to random chance.

The Chi-square Formula:

       Χ2 = Σ(observed outcome - expected outcome)2 ÷ expected outcome

Translated, this means that the chi-square (Χ2) is the sum over all phenotypic groups (Σ), of the expected outcome minus the observed outcome (to measure how great the difference is) squared to make the difference positive, then divided by the expected outcome to adjust for sample size. For a particular dihybrid data set, the chi-square would be calculated as follows:

Data Table for a Dihybrid Cross

Observed Outcome
Expected Outcome
Phenotype
Count
  Calculation
Expected
Purple Round    
315
  (9/16)(556)
312.75
Purple Wrinkled
108
  (3/16)(556)
104.25
Yellow Round 
101
  (3/16)(556)
104.25
Yellow Wrinkled
32
  (1/16)(556)
34.75
Total
556
   
556.00

For this data set, Χ2 = ((315-312.75)2 ÷ 312.75) + ((108-104.25)2 ÷ 104.25) + ((101-104.25)2 ÷ 104.25) + ((32-34.75)2 ÷ 34.75) = 0.47

The Chi-Square Table:

To evaluate the chi-square calculation, you will also have to know the degrees of freedom (df) for the data set, which is the number of groups minus 1, which is: 4 - 1 = 3 df in this example.

You then use a chi-square table (below, in your lecture textbook or on the internet) to interpret the meaning of your chi-square value. If your value is less than or equal to the critical value for P=0.05, your result falls within the range of normal variation and should be accepted as consistent with the expected ratio. If your chi-square is larger than the critical value, your result falls outside the range of normal variation. In this case there is not a good enough fit between the expected and the observed, and another explanation for the observed outcome must be sought.

Critical values of the chi-square distribution

df P = 0.05 P = 0.01
  1 3.841   6.635
  2 5.991   9.210
  3 7.815 11.345
  4 9.488 13.277

The probability (P) for a particular critical value is the probability that a difference that large or larger between the expected and observed is due to random variation alone. P=0.05 is the normal cutoff for acceptance of a match between the expected and observed outcomes. P=0.05 means that there is a 5% probability that the difference between expected and observed is due to chance. For a chi-square value less than or equal to the critical value at P=0.05, the difference between observed and expected values is within the normal range of variation and the null hypothesis is accepted. The null hypothesis is that the observed outcome is consistent with the expected outcome and any difference is likely due to random variation. A chi-square value greater than the critical value means that the difference falls outside the range of normal variation (is not likely due to random chance) and the null hypothesis is rejected.