Autosomal vs. X-Linked
Independent Assortment vs. Linked Data Analysis (15pt)
You have scored your initial crosses of N2 (WT) males with three different mutant hermaphrodites of strains MB1, MB2, and MB3 and you should have come to some decision about whether or not the mutations observed in each strain are on the same autosomal chromosome (or linkage group:linked) or are on two different autosomes (autosomal unliked) or are on two different chromosomes, one of them on the X (unlinked between them but called X-linked).
You should have the data now to answer our experimental question,"Are the genes responsible for the dumpy and uncoordinated phenotype observed in MB1, MB2, and MB3 strains of C. elegans on autosomes or X-linked and, if both on autosomes, are those genes inherited independently or are they on the same linkage group?" Therefore, you are ready to practice writing about your experiments and your data in the form of a Results section in a scientific paper. Scientific writing uses data from experimentation to answer such questions as ours. You did the experiment (setting up and scoring your first set of crosses) and you figured out the answer from comparing your expectations (which are based on a hundred years of other genetic investigators combined wisdom) to your results. Now you need to present your evidence and your reasoning to an audience that doesn't know much about worms or genetics. In science, this part is called data analysis, written up as figures (graphs, drawings, or photos) and/or tables, with an explanatory narrative. It's called the Results section.
If you think that the mutations in a particular strain are autosomal, what is the evidence for that conclusion? Yes, evidence could be finding and citing a reliable, peer-reviewed published source that says so (if others have worked on these strains before you), but the Results section, generally, limits itself to experimental evidence, preferably data from the authors’ experiments. Is there data from your observations of the F1 progeny that allows the conclusion that the mutations in two of the strains are not sex linked? Is there data from your scoring of the F2 progeny from your crosses that allows the conclusion that the mutations in one of the strains are linked?Is there data from your scoring of the F2 progeny from your crosses that allows the conclusion that the mutations in one of the strains are on different autosomes (unlinked)? Will it be effective to write about those observations and to give the scoring results in text form only, or would it be helpful to add a table or figure (photo, graph, or drawing) as a visual aid to help your reader (who, you remember, doesn’t know a male from a hermaphrodite worm) understand the evidence?
Since you must write as though the reader and evaluator of this data analysis is NOT your lab instructor and is NOT another student in this class who has access to this wiki, you will have to start your data analysis (Results) by explaining the basics of your experiment and its goals, including crucial information about C. elegans and about classical genetics rules of inheritance. You can distill the essentials from the introductory material provided in this wiki, but be careful not to plagiarize and not to include too much general information. Your submitted data analysis must be written completely in your own words and it should include only essential, briefly described background information.
It is often easier to consider each part of the experimental goals you have addressed separately, but there are many ways to write a good Results analysis. Just don’t forget to be clear about which observations serve as evidence for the autosomal or sex/linked question and which crosses and scoring answers the linked or independent assortment question that we also addressed. If the genes were sorted independently, the F2 ratios would be close to: 9/16 wild type (+/+;+/+); 3/16 Dpy(d/d;+/+); 3/16 Unc(+/+;u/u); 1/16 Dpy Unc (d/d;u/u)(9:3:3:1 ratio). If the genes were closely linked, your ratios would be very different from the 9:3:3:1 ratio. Don't fail to include a conclusion. If your data allows you to answer the experimental question then make that conclusion part of your data analysis. br>
To be useful as evidence for a conclusion, your observed ratios should include a more objective evaluation than a subjective assessment of “close or not close” to expected values for independent assortment. It is common to perform some objective "goodness of fit" analysis, such as a chi square, to see if the deviation your data shows from the expected ratios is likely to be due to chance alone or if the deviation seen from the expected is because your genes are not sorting independently because of linkage. However, chi square tests for linkage are performed comparing to expected ratios from a test cross (1:1:1:1) rather than ratios from a dihybrid cross (9:3:3:1 ratio), so we can't use a statistical tool this time.
Some suggestions for tables or figures to adequately, visually support your results narrative:
- The diagram of all three crosses, including the identity of the strains
- A table with your observed phenotype scores for the F2 of each of the autosomal strains.
- Data processed to show a clear comparison to the expected unlinked phenotypic ratio for the two autosomal strains
To get a feel for how a data analysis is written as a Results section in a scientific paper, take a look at the results section in a variety of published science journals, such as Cell or Genetics. The Wellesley library has electronic subscriptions to many of the journals that model this concept well. Also refer to the “How to Write a Scientific Paper” section in the Resources section of this wiki. There you will find valuable information on how to format figures/ tables with proper legends and the basics of how to write about your data.
Data Analysis Rubric- Sex Linkage & Independent Assortment – 15 points
| || At or Above Standard || Below Standard || Possible|
| Table(s) and/or Figure(s) well designed to illustrate conclusions. Included all crucial information that allows the figure or table to make the main points visually and to “stand alone”: novice reader does not need to read the narrative to see the data’s meaning. All data adequately identified, correct units included, labeling appropriate.
|| Figure(s) or table(s) not well designed to illustrate main points or missing essential information needed for understanding.
|| Figure legend is below figure & includes a number. Table legend is above table and includes sequential numbers independent of figure numbers. All legends include all essential information and no unnecessary detail about how data included was generated. Figure or table title gives the main point of the figure or table. Body of legend does not summarize main conclusions or include other material appropriate for the narrative data analysis. All data adequately identified and parameters defined.
|| Missing figure or table#, title, or legend. Legend (or title) is in wrong place or does not include appropriate numbering. Missing information about how data was generated. Missing part or all of key to symbols/ colors or other ambiguous information. Missing part or all crucial information that helps the figure or table to “stand alone”. Legend includes unimportant detail or includes a summary of the findings that is more appropriate for the narrative portion of the data analysis.
| Data Analysis
|| Narrative begins with an appropriately concise description of both experimental goals and experimental design. Narrative includes key findings, describes the data accurately, concisely and clearly, & includes only relevant information. Data analysis leads incrementally & clearly from data to appropriate conclusions to experimental question. Specific figure and table numbers for data that supports conclusions are cited in the narrative.
|| Narrative doesn't begin with an appropriately concise description of the experimental goals and experimental design. Narrative omits key findings, describes the data inaccurately or unclearly, includes irrelevant information, or is repetitive. Narrative fails to give appropriate conclusions to the experimental questions or fails to show how the experimental data allow the conclusions. Specific figure and table numbers for data that support conclusions is not cited in the narrative.