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B. Enter your total number of Cob 1 kernels of each colour in the 'Observed Number' column of the table below. Use your prediction above and your collected data to calculate the 'Expected Number'. Complete the table based on the calculations indicated in the column titles. Phenotype Observed (Class) Number (0) Purple Yellow TOTAL Expected Number (e) C. What is the p-value you obtained from the data for Cob 1? RESPONSE: D. Give an interpretation for this p-value. RESPONSE: E. Should your hypothesis be rejected or not? RESPONSE: (o - e) (o - e)² (o - e)² e x² = 2
One-Gene Cross Two true breeding strains of corn (the "parents") were crossed, one with purple kernels and one with yellow kernels. The F₁ offspring from this cross are found on Cob 4. Click on the Cob 4 thumbnail to view a high- resolution version of the entire cob. Cob 4 We want to determine the genotypes of the Cob 4 kernels. We will start by assuming that purple colour is dominant over yellow colour in corn. If we treat kernel colour as a single-gene trait, we can symbolize this relationship: D represents the allele associated with the dominant phenotype (purple), and d represents the allele associated with the recessive phenotype (yellow).
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side panel. Test Your Understanding 1. Using the data available to you (i.e., the photograph of Cob 4), is it possible to directly observe the genotypes of the Cob 4 kernels? Check your answer. No - it isn't possible to directly observe genotype from the photo, only phenotype. Phenotype is the appearance of the trait, while genotype refers to the individual's combination of alleles for that trait, which we cannot see by looking at the corn kernels. about the parents, els.
2. Based on the F₁ generation results observed on Cob 4 and the information given about the parents, state an appropriate hypothesis about the genotype of the F₁ (i.e., Cob 4) corn kernels. Check your answer. The genotype of all of the Cob 4 kernels is Dd. This is the only hypothesis that makes sense in light of the information about the parents. If one parent is true-breeding (i.e., homozygous) purple and the other is true-breeding yellow, then the "purple" parent can only contribute "purple" (D) alleles to the offspring, whicle the "yellow" parent can only contribute "yellow" alleles (d). All of the offspring must therefore be Dd, based on the information we have. If we test this hypothesis with an experiment and the outcome of the experiment doesn't match the predictions, then that's a clue that there are other mechanisms at work, and we need to look for more data and revise the hypothesis. derstanding Question 2, Cob 4 individuals. This re crossed each cross
The Experiment You now have a hypothesis about the genotype of the Cob 4 kernels (from Test Your Understanding Question 2, above). An appropriate test of your hypothesis is to conduct an experiment by crossing Cob 4 individuals. This cross will yield an F2 generation of offspring. Since you will need to study a large group of F2 offspring, six pairs of Cob 4 individuals are crossed, each cross yielding a 'Cob 1'. The results are pictured below. Click on the thumbnails to view high-resolution versions of whole cobs. Save a copy of each high-res photo on your computer. For each whole cob, use Fiji to count 5 rows of kernels and note the number of purple and yellow kernels (see instructions below). Print this page or draw the table below in your notes to record your observations. For kernels that appear to be a combination of purple and yellow, use your judgement as to whether the kernel is more purple or more yellow and count it accordingly. If you have any trouble with Fiji, you can download printable versions of Cobs 1_1 through 1_6 from the sidebar, compare them to the full-resolution photos, and count kernels manually. Cob 1 1 Cob 1_4 Phenotype purple yellow TOTAL Cob Cob 11 1_2 Cob 1_2 Cob 1_5 number of kernels Cob Cob Cob 1_3 Cob 1 3 Cob 1_6 Cob 1_4 1_5 1_6 TOTAL
Collecting Data with Fiji You used Fiji in a previous lab to measure diameters of red blood cells, and you will need it again in this lab to count corn kernels of different types (yellow, purple, smooth, wrinkled, etc.). If you have already downloaded, installed, and used Fiji, then skip to Step 3, below. 1. Start by going to https://imagej.net/Fiji/Downloads to download the program. 2. Download the correct Fiji package for your operation system and extract the program as instructed (see Lab 1). 3. Start the program; a small Fiji dialog box will appear on your screen (similar to the ImageJ dialog box in the screen cap below). 4. Return to this lab and right-click on the corn cob photos (on the following pages) to save copies of the images. Use ImageJ (File -> Open...) to open the photo that you want to count kernels on. 5. From the Plugins drop-down menu, go to Analyze --> Cell Counter. The Cell Counter dialog box will appear. Click on the Initialize button. (If you don't, you'll be prompted to do so.) 6. Choose the Type 1 counter, then move your mouse over to the corn photo and start clicking on the individual kernel types you want to count. You just have to click on kernels; the Cell Counter will keep a running tally in the box adjacent to 'Type 1'. When you're finished clicking on 5 rows' worth of kernels, note the total in the Cell Counter. 7. Switch to the Type 2 counter and count up the next type of kernels. Note the total in the Cell Counter dialog box. 8. Continue as necessary until you have counted all of the different types of kernels you need to. Counter Window Counter Window-co1101 2100-1503 pcs OU 1200 Image FeLdt Image Process Anyon Pugins Wedow Help CQBG X4+ AQ Macros Car pickar 0,30 Shortcuts Unites New Compile and Run Col Courtes Couutars Type1 Type 20 Type 30 от Type 50 Type 0170 Type Action Keep On Ince Ace Remove Dite Mode Reast Show Numbers sawa Savo Markos Load Markers Exporta Measur Analyze Examples riders Graphics Input-Output Macros 3 Toon Batch Measure Cat Counter Grid Measure And Labe Measure And Set Label Measure Ro
The Chi-Square Test: Comparing What You Observed to What You Predicted The numbers of kernels of each phenotype that you count will probably not match up with an exact Mendelian ratio. But what is the reason for this difference between what you actually observed and what you predicted that you would observe? Is this difference due to sampling "error" (more on that below), or is it due to your hypothesis being incorrect? If this difference between what was observed and what was expected is likely due to an incorrect hypothesis, then we will want to know that so we can reject our hypothesis. If the difference is likely due to sampling error, then we will not reject our hypothesis. Even if the hypothesis is correct, the fit won't be perfect: observed numbers will almost always differ from predicted numbers at least a little, due to the effects of sampling. For example: you counted five rows of kernels on six different cobs to get your results. What if you'd counted a different five rows? Your observed results would likely be a little bit different. This is what we mean by sampling "error" - not that there is a mistake in the procedure, but that there will always be some variability due to sampling. The chi-square test is a way to determine whether these differences between what you observed and what you expected are significant - that is, due to a problem with your hypothesis - or not significant that is, just due to sampling error. Complete the table in your assignment document. --
If you are not able to get Fiji to work for the 'One-Gene Cross' part of the lab, you can print this document and count kernels directly from these images. Cob 1_1 Cob 1_2 Cob 1_3
Cob 1_4 Cob 1_5 Cob 1_6