BC1501
POPULATION GENETICS SIMULATION EXPERIMENTS
OVERVIEW
The Big Picture: There are two main parts to lab today. First, we will use ANOVA to analyze
our pooled class data from the corn embryo germination experiment to investigate the effects of
GA, ABA, and GLUC on shoot and root length in these embryos. You will then write a lab report
interpreting this data. Second, we will investigate the process of evolution. Because evolution
occurs in populations of organisms, rather than in individual organisms, and it typically takes a
long time, from decades to millions of years, it is difficult to study in an introductory biology
class. Thus in lab, we will use the important technique of simulation modeling to study
evolutionary change.
•
BEFORE LAB
DURING LAB
ASSIGNMENTS
(DUE AT THE
BEGINNING OF
NEXT WEEK’S
LAB)
Background Information
o
Russell et al., CH 20
•
Take-Home Quiz: Due at the BEGINNING of Lab Today
•
Part A: Analyzing corn embryo germination data using ANOVA
•
Part B: Modeling the Hardy-Weinberg Theorem*
•
Corn Embryo Germination Data Analysis Worksheet
(keep for lab report; do not turn in)
•
Corn Embryo Germination Lab Report
•
Worksheet 9: Population Genetics (group assignment):
* PART B is modified from Palladino MA, Desharnais R, and Bell J “Lab Manual for BiologyLabs OnLine; Population Genetics Lab”.
Thought and Discussion Questions:
•
The Hardy-Weinberg may seem over-simplistic or unrealistic. What are the pros and cons of
a simple versus a complex model? A realistic versus an unrealistic model?
•
If you know the genotype frequency in a population, can you predict the allele frequency? Do
you need to make any assumptions to make this prediction?
•
If you know the allele frequency in a population, can you predict the genotype frequency? Do
you need to make any assumptions to make such a prediction?
•
Sometimes conditions for Hardy-Weinberg equilibrium are disrupted temporarily:
o
What trends in allele frequencies or genotype frequencies would indicate that such a
disruption is happening?
o
During such a disruption, what might happen to the relationship between allele and
genotype frequencies?
o
Once all of the conditions for Hardy-Weinberg equilibrium are restored, what will
happen to allele frequencies? To genotype frequencies (and how fast)?
Learning Objectives:
(1) Know how to perform and interpret a 2-way ANOVA
(2) Review the Hardy-Weinberg theorem, and the conditions that must be met for the theorem to
predict allele frequencies and genotype frequencies.
(3) Demonstrate important factors that influence population genetics in a simulated population of
moths.
(4) Simulate the effects of changing conditions such as
•
•
genetic drift
modes of natural selection
• directional selection
• stabilizing selection
• disruptive selection
PART A: CORN EMBRYO GERMINATION DATA ANALYSIS
Last week, you collected data on shoot and root length in corn embryos grown for one week on media
with different combinations of glucose, GA, and ABA. This data has been pooled, and entered into SPSS
as two different data files called “GA ABA 2009” and “GA GLUC 2009”. This week, you will use SPSS
to perform statistical tests to investigate the effects of glucose, GA, and ABA on corn embryos.
As you have learned, ANOVA is a robust and powerful statistical method for comparing the response of a
single dependent variable to two or more levels (or treatments) of a categorical variable (also known as a
factor). This week, we will use ANOVA, not only to compare two groups, but also to investigate
potential interactions among different factors (independent variables) in their influence on the dependent
variable. Interactions are when one factor (independent variable) influences how a second factor
(independent variable) affects the dependent variable.
For instance, let’s examine the effect of two independent variables (or factors) on the abundance of an
estuary-dwelling plant that has a limited tolerance for salt water. In this case, the two factors are: (1)
environmental salinity, and (2) saltmarsh cordgrass presence, and the dependent variable is the abundance
of the estuary-dwelling plant. In low salinity areas, the estuary dwelling plant may suffer from
competition when saltmarsh cordgrass is present, and therefore display a low abundance. Yet in high
salinity areas, the plant may actually benefit from the de-salinating effect of cordgrass. Thus, the effect of
cordgrass presence (a factor) on the plant’s abundance (the dependent variable) differs with
environmental salinity (another factor).
To further explore the concept of an interaction, let’s return to our example from earlier in the semester,
in which we were examining the risk that Danish smokers face of developing chronic obstructive
pulmonary disease (COPD). Recall that heavy smokers appeared to face a greater risk than light smokers,
as indicated in Figure 1 below.
100
Among
group
variance
(between
means)
COPD Risk
80
60
40
Within
group
variance
(pooled)
20
0
Light
Heavy
Smoking exposure
Figure 1. Risk of Danish smokers for chronic obstructive pulmonary disease (COPD) with light (1-30 pack-years)
and heavy (>30 pack-years) smoking exposure. After Prescott et al. (1997). [Note that the middle line of the box is
the median (the mid-point of the data), the top and bottom of the box are the 25th and 75th percentiles, and the lines
are 10 and 90%.]
Consideration of more than one factor can often enhance our understanding of what controls the
dependent variable, here COPD risk. For example, examine Figure 2 below. Figure 2 shows the same
data as before, but with the effect of gender on COPD risk added to smoking exposure. Thus, we now
have two factors, ‘smoking exposure’ and ‘gender’. Each factor has two levels (smoking exposure has
“heavy” and “light”; gender has “male” and “female”). Do our conclusions change? Does smoking
exposure still have a significant effect? Is there an interaction between smoking exposure and gender?
100
COPD Risk
80
60
40
20
0
Male
Female
Light
Male
Female
Heavy
Smoking exposure
Figure 2. Risk of Danish smokers for chronic obstructive pulmonary disease (COPD) with light (1-30 pack-years)
and heavy (>30 pack-years) smoking exposure. After Prescott et al. (1997).
When we run a two-way (two-factor) ANOVA we essentially have three sub-tests: one for each of the
factors, and one for the interaction term – that is, the interaction between smoking exposure and gender.
The output is listed in Table 1 below.
Smoking exposure
Gender
Smoking exposure*Gender
F
158.421
339.976
100.910
p
< 0.001
< 0.001
< 0.001
Table 1. Summary statistics for two-way ANOVA on COPD
risk for Danish smokers.
The output in Table 1 tells us several things. First, there are significant differences in COPD risk between
the different levels of one factor (smoking exposure levels), and between the different levels of the other
factor (gender). By going back to Figure 2, we can see that smoking exposure increases COPD risk
(regardless of gender), and that female smokers face a higher risk of COPD than males (regardless of
smoking exposure).
But more importantly in this case, the significant ‘smoking exposure*gender’ interaction provides us with
a great deal of information. We can now see that in the first ANOVA (Figure 1) much of the trend of
increasing COPD risk with smoking exposure can actually be attributed to the increase in risk for women.
Risk increases for both men and women, but the jump in risk from light to heavy exposure is much
greater for women.
Statistical interactions are more difficult to interpret than the main factors, because if one finds a
significant statistical interaction, she must go back to graphs of the data to identify the nature of the effect.
In this case, heavy smoking exposure has a disproportionately greater effect on COPD risk among women
than among men. Other sorts of interactions are hypothetically possible; for instance women could face
greater risk at light exposure, and men could face higher risk at heavy exposure. This type of effect
reversal would be similar to the estuary-plant/cordgrass example described earlier.
In this lab, we will analyze our data from each experiment using ANOVA. First we will analyze the data
from experiment one: what are the effects of GA and ABA on corn embryo germination? We will
perform a two-way ANOVA because there are two different factors (GA and ABA). Each factor has two
levels (present or absent). We will investigate each factor’s effect, alone and in combination, on a
dependent variable (such as shoot length). Thus, we will be able to use the pooled class data to test for
the effects of the two factors, and the two-way interaction between them. Note that in this experiment
there are two different dependent variables (shoot length and root length). Thus, to analyze the data for
this experiment, we will perform two different two-way ANOVAs.
Next, we will analyze the data collected for experiment two: what are the effects of GA and GLUC on
corn embryo germination? Again, each factor has two levels (present or absent), and we will use the
pooled class data to test for the effects of the two factors, and the two-way interaction between them.
Again, we will perform two different two-way ANOVAs to investigate the effect of these factors on two
different dependent variables.
PROCEDURE PART A: TWO WAY ANOVA:
1. Work in the same groups as you did to set up the corn embryo germination experiment. Use the
directions hanging above the computers in the lab to use SPSS to perform ANOVAs.
2. As a group, answer the questions on the Corn Embryo Germination Data Analysis Worksheet.
You do not have to turn in the answer to these questions, but they will help you when writing
your lab report.
3. Before you leave lab, make sure that you have a copy of the Corn Embryo Germination Lab
Report Guidelines. You must follow these guidelines to write your lab report. Remember, you
should also refer to the handouts you received earlier this semester with general guidelines for
writing lab reports.
Experiment One: ABA + GA
1. According to the ANOVA, did either ABA or GA have a statistically significant effect on shoot
length? Was there a statistically significant interaction between the two hormones? Briefly describe
all three of these situations.
2. Did your data support your original hypothesis? Can you provide a biological rationale for these
findings?
3. According to the ANOVA, did either ABA or GA have a statistically significant effect on root
length? Was there a statistically significant interaction between the two hormones? Briefly describe
all three of these situations.
4. Did your data support your original hypothesis? Can you provide a biological rationale for these
findings?
Experiment Two: GLUC + GA
1. According to the ANOVA, did either GLUC or GA have a statistically significant effect on shoot
length? Was there a statistically significant interaction between the two hormones? Briefly
describe all three of these situations.
2. Did your data support your original hypothesis? Can you provide a biological rationale for these
findings?
3. According to the ANOVA, did either GLUC or GA have a statistically significant effect on root
length? Was there a statistically significant interaction between the two hormones? Briefly
describe all three of these situations.
4. Did your data support your original hypothesis? Can you provide a biological rationale for these
findings?