MENDELIAN GENETICS: Corn

Adapted from: http://au.frcc.cccoes.edu/~sciencedepbc/Science%20Dept%20Site/biology/labindex.htm

Introduction:

The first scientist to understand and document the basic laws that govern heredity was Gregor Mendel. He understood the principals of probability and statistics and the results of fertilization. Through numerous trials of experimentation, he was able to work out basic principals of heredity.

Before Mendel, it was thought that offspring characteristics were the results of blends from parents. It was Mendel who discovered that the characteristics of offspring are not a blending of characteristics of their parents. Instead, the hereditary material is passed as genes from parents to offspring.

In this investigation, you will use hybrid corn to study the inheritance of corn kernel shape and color.

Objectives:

1. Predict ratios (phenotypic, genotypic) based on data.

2. Construct Punnett squares for one and two loci inheritance.

Calculate chi-square values and determine if the hypothesis is supported data.

Materials:

Corn labeled (9:3:3:1),

Chi-square values chart

Making Predictions:

If the genotypes of parents are known, it is possible to make predictions about the phenotype(s) of their offspring.

Monohybrid Cross:

A monohybrid cross is one in which parents are both heterozygous for a gene that controls a trait (Tt x Tt). The genotypes and phenotypes of the offspring can be predicted as shown below:

 

T

T

T

TT

Tt

t

Tt

tt

The resulting genotypic ratio is (1:2:1). If T is dominant over t, a TT individual will appear the same as a Tt individual.

Therefore, the phenotypic ratio resulting from a monohybrid cross is (3:1).

Dihybrid Cross:

It is also possible to predict the results of a cross involving parents who are heterozygous for two traits that are controlled by two different genes (TtSs x TtSs). The number can go on and on (3 or more). The predicted outcome from a cross between two dihybrid parents (TtSs x TtSs) is shown below:

 

 

TS

Ts

tS

ts

TS

TTSS

TTSs

TtSS

TtSs

Ts

TTSs

TTss

TtSs

Ttss

tS

TtSS

TtSs

ttSS

ttSs

ts

TtSs

Ttss

ttSs

ttss

A dihybrid cross will produce offspring of nine (9) different classes with the dominant trait at both loci, three (3) with one dominant trait at one locus, three (3) with the dominant trait at the other locus, and one (1) with neither dominant trait at either locus. The phenotypic ratio is therefore (9:3:3:1).

Procedure:

Obtain an ear of corn labeled (9:3:3:1). Sweet kernels are wrinkled and starchy kernels are smooth. Randomly pick a few rows of kernels and count all the visible kernels (ignore the ones under the label), noting the number of yellow, sweet, yellow, starchy, purple, sweet, and purple starchy (some of the purple kernels may look reddish or bluish). If the ratio is 9:3:3:1, you would expect to count 90 of the double dominant trait and 30 of each of the intermediate types and 10 of the double recessive types. Record your actual numbers in your journal:

Yellow, Sweet Kernels

Yellow, Starchy Kernels

Purple, Sweet Kernels

Purple, Starchy Kernels

Do your results exactly match your predicted ratio? ______________

Usually your results will not match the predicted ratio exactly. The important question is, are your (observed) results close enough to the expected values that you feel comfortable with the predicted mode of inheritance and ratio (9:3:3:1)? What if your results were 100:20: 25: 15, 30:30:30:3), or 40:80? Since data are rarely perfect, we rely on statistics to confirm that the observed numbers are close enough to the expected numbers that the differences could be due to a small sampling error.

Chi-Square Analysis:

A statistical test that can be used for the kind of data presented in this lab is called a

Chi-square test. Chance alone can cause variations from expected results. This test can be used to determine how probable it is that data with large variations are due to more than just chance.

The formula for the Chi-Square test is:

X2 = Σ { (o – e)2 }

                    e

X2 = chi-square

Σ = sum of

o = observed results

e = expected results

Your instructor will work and example of the chi-square test for a 9:3:3:1 ratio.

**Carry out the chi-square test for your data from the corn labeled 9:3:3:1 in your journal.

Once you have calculated a chi-square value for your data, the chi-square probability table below should be used to see where your calculated value falls. Degrees of freedom (df) is calculated by subtracting one from the total number of observed phenotypes. There were four phenotypes (purple/sweet, purple/starchy, yellow/sweet, yellow/starchy).

The table indicates whether the deviation of the observed values from the expected values is due to chance. For example, for df = 3, the number 7.81 appears under column 0.05. If the calculated chi-square value is as large or larger that 7.81 then that would be expected by chance in just 5% of all possible samples. If your calculated chi-square value is less than 7.81, your data match the expected value close enough such that the 3:1 ratio hypothesis can be accepted.

Values of Chi-Square

----------Hypothesis is Supported-------------

Hypothesis not Supported

Difference Insignificant

Difference Significant

df

.99

.95

.80

.50

.30

.20

.10

.05

.02

.01

1

.00016

.0039

.064

.455

1.07

1.64

2.70

3.841

5.41

6.63

2

.0201

.103

.446

1.38

2.0

3.21

4.60

5.99

7.82

9.21

3

.115

.352

1.00

2.36

3.66

4.64

6.25

7.81

9.83

11.34

4

.297

.711

1.64

3.35

4.87

5.98

7.77

9.48

11.66

13.27

5

.554

1.14

2.34

4.35

6.06

7.28

9.23

11.07

13.38

15.08

 

Post-lab Questions

1. Show the Punnett Square for the parents, include the genotypic and phenotypic expectations.

2. Make a table of your results. (yours and the class)  (computer typed) don't forget only one horizontal line, title and any units.

3.Determine the expected results of your corn (you and  your partner) and the class.

4.  Calculate the chi-square value for your results and the class. Show your work.  (Must be handwritten)

5. How many degrees of freedom are appropriate for your chi-square value? How do you calculate this value? Show your work.

6. Do you accept or reject your hypothesis? Explain why or why not. (remember your Chi-square value). Would you consider using other students’ data from this lab activity? How would adding the extra data affect your chi-square calculation?