Lab #1: Population Genetics by Parker Lehman, Samuel Robinson, David Rowlett, and Janessa Wangadi.
Part A. Distribution of Genes in a Population
Hypothesis:
If we randomly select pairs of beans from a cup with an equal number of white and speckled beans, then we will get 25 heterozygous pairs, 12.5 homozygous white pairs, and 12.5 homozygous speckled pairs because this experiment represents the combination of 2 heterozygous organisms and the Mendelian ratio for combining 2 heterozygous organisms is 1:2:1.
Predictions:
We predicted the genotypes to be 25 heterozygote, 12.5 homozygous white, and 12.5 homozygous speckle using the Mendelian ratio.
Results:
Figure 1. Results and prediction from the simulation of gene distribution in a population combining heterozygous white and speckled beans.
Chi-square Calculations:
Samuel & Janessa
Genotype
|
Observed, O
|
Expected, E
|
O - E
|
((O - E)2) / E
|
Heterozygote (White & Speckled)
|
18
|
25
|
7
|
1.96
|
Homozygote (Speckled)
|
16
|
12.5
|
3.5
|
0.98
|
Homozygote (White)
|
16
|
12.5
|
3.5
|
0.98
|
Chi-square =
|
3.92
|
Table 1. Chi-square calculations for Samuel and Janessa’s group.
Parker & David
Genotype
|
Observed, O
|
Expected, E
|
O - E
|
((O - E)2) / E
|
Heterozygote (White & Speckled)
|
22
|
25
|
3
|
0.36
|
Homozygote (Speckled)
|
14
|
12.5
|
1.5
|
0.18
|
Homozygote (White)
|
14
|
12.5
|
1.5
|
0.18
|
Chi-square =
|
0.72
|
Table 2. Chi-square calculations for Parker and David’s group.
Discussion:
In this experiment, as shown in Figure 1, Parker and David’s data showed 22 heterozygotes, 14 homozygous white and 14 homozygous speckled. Figure 1 also shows Samuel and Janessa’s data to be 18 heterozygotes, 16 homozygous white and 16 homozygous speckled. When we tested our data as shown in Table 1 and 2 with Chi-squared statistical analysis using 2 degrees of freedom and a p-value of 0.05, we found Samuel and Janessa’s data had a value of 3.92 and Parker and David’s data had a value of 0.72 (McFarland, J and Shlichta, G 2017). Both these values were less than the critical value of 5.99, which supports the null hypothesis that our data was not significantly different from our predictions and the Mendelian ratio of 1:2:1 for combining 2 heterozygous organisms was supported.
Part B. Genetic Drift
Hypothesis:
With a limited gene pool we can predict that certain allele frequencies will decrease drastically, even to point of non-existence, and that other allele frequencies will increase rapidly.
Null Hypothesis:
With a limited gene pool we can predict that certain allele frequencies will not decrease drastically and that other allele frequencies will not increase rapidly.
Predictions:
An allele frequency, simulated with beads, will be either greatly reduced or eliminated, and another will become more prominent.
Results:
Figure 2. Results from the simulation of allele frequency and genetic drift in Population A over ten generations using different colored beads.
Population A
Generation
|
1
|
2
|
3
|
4
|
5
|
6
|
7
|
8
|
9
|
10
|
Red
|
13
|
13
|
15
|
16
|
17
|
18
|
15
|
15
|
15
|
12
|
White
|
14
|
15
|
17
|
19
|
18
|
17
|
17
|
16
|
12
|
10
|
Blue
|
13
|
10
|
6
|
6
|
8
|
6
|
7
|
5
|
8
|
7
|
Clear
|
10
|
12
|
11
|
9
|
7
|
9
|
13
|
14
|
15
|
21
|
Table 3. Data from the simulation of allele frequency and genetic drift in Population A over ten generations using four different colored beads as alleles.
Figure 3. Results from the simulation of allele frequency and genetic drift in Population B over ten generations using different colored beads.
Population B
Generation
|
1
|
2
|
3
|
4
|
5
|
6
|
7
|
8
|
9
|
10
|
Red
|
12
|
11
|
14
|
12
|
16
|
17
|
16
|
15
|
16
|
16
|
White
|
11
|
10
|
7
|
8
|
4
|
2
|
3
|
2
|
1
|
0
|
Blue
|
12
|
14
|
11
|
12
|
15
|
16
|
11
|
12
|
15
|
19
|
Clear
|
15
|
15
|
18
|
18
|
15
|
15
|
20
|
21
|
18
|
15
|
Table 4. Data from the simulation of allele frequency and genetic drift in Population B over ten generations using four different colored beads as alleles.
Chi-square Calculations:
Population A
Genotype
|
Observed, O
(Generation 10)
|
Expected, E (Generation 1)
|
O - E
|
((O - E)2) / E
|
Red
|
12
|
13
|
1
|
0.077
|
White
|
10
|
14
|
4
|
1.14
|
Blue
|
7
|
13
|
6
|
2.77
|
Clear
|
21
|
10
|
11
|
12.1
|
Chi-square =
|
16.09
|
Table 5. Chi-square calculations for Population A.
Population B
Genotype
|
Observed, O
(Generation 10)
|
Expected, E (Generation 1)
|
O - E
|
((O - E)2) / E
|
Red
|
16
|
12
|
4
|
1.33
|
White
|
0
|
11
|
11
|
11
|
Blue
|
19
|
12
|
7
|
4.08
|
Clear
|
15
|
15
|
0
|
0
|
Chi-square =
|
16.42
|
Table 6. Chi-square calculations for Population B.
Discussion/Conclusion:
Our data supported our hypothesis that allele frequencies will change more dramatically in smaller populations due to genetic drift. Genetic drift is a change in allele frequency in a population due to random chance. Therefore, we also concluded that our simulation was of genetic drift because the sizes of the beads were the same. In Population B, as shown in Figure 3 and Table 4, no white beads were left by the tenth generation, resulting in only three beads, or three alleles, left in the population. While in Population A, as shown in Figure 2 and Table 3, the clear beads have almost doubled in size by the tenth generation. We also tested our data with the Chi-square statistical analysis using three degrees of freedom and a p-value of 0.05 (McFarland, J and Shlichta, G 2017). As mentioned in Tables 5 and 6, Population A had a value of 16.09 and Population B had a value of 16.42, while the critical value from our conditions were 7.82. Both populations has a Chi-square value that are significantly larger than the critical value, which refuted our null hypothesis that a significant change has not occurred to both populations.
Reference:
McFarland, J and Shlichta, G. 2017. Lab 2. Chi square Statistics (BIOL& 211 Majors Cellular
Laboratory) Manual [Lab Manual]. Edmonds Community College.
I find it interesting that the blue allele frequency was cut almost in half in your population A data. In my group's data, our blue allele for population A increased pretty dramatically. Your population B data is a great example of genetic drift since the white allele went completely extinct. It makes me wonder if you guys had any bias in your sampling that may have caused the white allele to go to extinction or whether it was truly random? The population B data for my group barely changed at all over time. Ultimately, the diverse data between my group and yours really showcases the randomness of genetic drift.
ReplyDeleteI'm wondering what tactic you guys used to split your populations up before you doubled them. My partner and I just closed our eyes and split them evenly between two cups and just picked one of the cups, making sure it had 50. It seems like your population A had a big decline in blue, which happened to our population A as well, it makes me think what happened when splitting the alleles apart...was there unknown bias or just a genetic drift? Your population B had a major decrease in white beads too, while our population B white beads actually stayed the same for four generations and then hiked up to increasing numbers- it seems like overtime when the decline of alleles just keeps going down, it sometimes has the chance of not increasing or to stay extant anymore and just falls extinct. But great data & good job.
ReplyDeleteI liked comparing our groups' genetic drift data with yours as we used different materials for the second experiment: beans instead of beads. We found it difficult to randomly select beans out of the cup because they were different sizes and shapes, making it very hard to pick up some of the beans. Were all of your beads the same size? I noticed that the blue beads decreased the most out of all the colors in the population A run, while the white beads decreased rapidly in population B. In fact your results from the two populations differ a great deal. I feel like this is pretty good proof that your bead selection during the experiment was random.
ReplyDeleteI think your graphs & tables look great and your writing is well organized and easy to follow. Great job!
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ReplyDeleteIt's interesting that your guys Chi-square values were very high, and it refuted the hypothesis. Comparing with our graphs in population A, our red, white and clear beads were tangling together and higher value than the blue beads. On another hand, your guys graph showed that the tangling red, white and clear were higher than the blue beads on the population A graph. Comparing between the Population A and B from your date, we see that the white beads in the A is much higher than the bead in B through 10 generations. It's a very good proof of randomness of the beads over time. Great datas and it's very easy to follow through out the report, good job!
ReplyDeleteGreat post! Observing everyone around me, it seemed that everyone tried to keep it random as possible just by closing their eyes and separate the beans. it is very amusing as to how we could all get different results. During my group's experiment, our black beads completely disappeared while our red beans continuously grew in numbers as each generation proceeded. It is definitely easier to have no bias towards any colored beads compared to using those beans as you could feel which one is bigger and smaller.
ReplyDeleteI agree, great post! It reads very well and is well-organized. We had similar results in our part 1. Instead of the predicted 50%, 25%, 25% distribution we ended up with something closer to 33%, 33% and 33%.
ReplyDelete