LAB 2 Assignment: Autosomal vs. X-Linked
Independent Assortment vs. Linked Data Analysis (25 pts)
Due at the beginning of Lab 4.
Your assignment will be to show, in figures or tables and a narrative, how your data addresses our topic, the investigation of patterns of inheritance in C. elegans and how your data answer all parts of 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 are on autosomes, are those genes inherited independently or are they on the same linkage group?" . You will write in the form of a Results section in a scientific paper. In other words, you will construct the results section of a research report on our Series 1 topic: Investigating Patterns of Inheritance in C. elegans. You began with three phenotypically identical strains of true breeding worms with two defects. They were all dumpy and uncoordinated. You job was to ascertain the relative position of the genes responsible for these two aberrant phenotypes in each strain. You set up some very specific kinds of crosses based on knowledge of typical Medelian patterns of inheritance. You used that knowledge and the observation and scoring of the progeny of your crosses to differentiate which strains had autosomal vs. sex-linked mutations and, between the two strains with autosomal mutations, which had both mutations on the same linkage group and which did not. We began with the hypothesis that on one strain the genes responsible for the defects were 1) autosomal and linked (meaning they are on the same autosomal chromosome or linkage group); on another strain the genes were 2) on two different autosomes (autosomal unlinked); and on another strain the genes 3) were also on two different chromosomes (unlinked)- but one of them was sex-linked, on the X, and the other on an autosome. You should have sufficient course data to sort out these strains and achieve our goals.
Scientific writing uses data from experimentation to answer questions and shed some light on broader topics. 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 or results. There are two equally important parts to a data analysis: figures (graphs, drawings, or photos) and/or tables, WITH a thorough explanatory narrative that begins with the overall topic, the experiment goal(s) and a brief summary of the methods---"In order to find out_________, we did__________."
Before you begin to write, you should objectively analyze the data collected by the whole course (combined 5 lab sections). You learned from the phenotype of the heterozygous male progeny (from the cross between N2 wild type males and the Dpy Unc hermaphrodites) which strain has the x-linked mutation. Do the data from the scoring, support that conclusion that the mutations are unlinked in this strain? Note that the expected 9:3:3:1 ratio of F2 progeny of the dihybrid selfing in this strain isn't perfect. Does that mean that the two genes responsible for the two phenotypic defects aren't really on different chromosomes? Do these data better support linkage? You could do a statistical test for goodness of fit (such as a Chi square) to see if it is likely that the differences between what we observed and what we expected are due to chance and, if so, that we can accept our conclusion with more confidence. Unfortunately, the Chi square test is used with a specific kind of cross that we didn't do, so we can't use that statistical test to help us assess our data. There are other tests that we could use but, for now, we will not use statistical tests for goodness of fit. You should use ALL the information you have: a combination of our data and our knowledge about the strains. If only one strain had male F1 progeny that showed only one, and not both, mutant phenotypes, what is the only conclusion possible about linkage?
If you didn't see mutant males when you created heterozygotes by crossing N2s with DpyUnc hermaphrodites, you have ruled out X-linkage and, therefore, know something else important: that the genes responsible for both mutations in the other two strains are on autsosomes. Now you need to show, from the dihybrid self crosses, whether or not the mutations are on the same or different autosomes. Since we know that one strain has two linked mutated genes and the other has unlinked mutations, which of the remaining strains shows progeny that better fit independent assortment than linkage? How so, explain!! Remember that your general audience is unlikely to know what 9:3:3:1 means or from where those ratios come. You should not just state the expected progeny ratios for independent assortment and complete linkage but include a figure or table with Punett squares showing exactly how those progeny numbers were derived.
Your data is likely to be far from a perfect fit to any of Mendel's rules of segregation but that does not mean that you can't make conclusions and answer your experimental question with confidence. DO NOT trash your data or your experiment in your narrative!!! Do not have a "sources of error" section where you stress all the difficulties or the things you did wrong. This is not a lab report. We don't write lab reports in BISC courses; rather, think of this as part of a research report that might, eventually, be sent to a journal for publication. No one wants to publish (or read) a report of a lousy set of experiments that couldn't conclude anything. Realize that all experiments have their problems and no data are perfect. Our goal in science is NOT to "prove" a hypothesis but to objectively see whether or not our experiments and our data from them allow us to form some answers to the questions that drove the investigation. If we don't have perfect confidence in our conclusions because our experiments or our data collection was imperfect, that's pretty much the universal situation in science. Your narrative explains how you used what you know and found out experimentally to answer your question(s), using key terms that indicate your level of confidence in your conclusions (such as ,"it is likely that...", "the data fit _____ better than _____"; "it is more likely that _____ is _____ than _____" because____", etc.")
Visual information is crucial in a Results section. In science, you must have both visual presentations of, usually, processed rather than raw data, AND a narrative that refers to that visual data found in figures or tables. The narrative must include direct references to those figures or tables. The narrative points out the most salient information before giving the conclusions gleaned. Remember that your reader doesn’t necessarily know Mendel's carefully worked out progeny ratios. Therefore, you will need to both show AND explain (figures/tables and text), not only how your results compare or don't compare to expected ratios, but also include a brief explanation of those expected ratios.
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 or knows much about C. elegans or genetics, you will have to include the essential information on which your investigation and the data interpretation is based. You can distill the basics from the introductory material provided in this wiki, your textbook or from your lecture notes, but be careful not to plagiarize and not to include too much general information. This data analysis/results section must be written completely in your own words and it should include only what is necessary to follow your data analysis from question to conclusion. Because Mendel's laws are considered "common knowledge", you need not cite your textbook or other sources of this information.
There are many ways to write a good Results analysis. Some suggestions (these are ONLY suggestions; you may have other better ideas) for tables or figures to adequately, visually support your results narrative:
- The diagram of each of the three crosses, including the identity of the strains
- A table of processed data showing your observed phenotype scores for the F2 of each of the autosomal strains compared to the expected scores for independent assortment.
- Punett squares or some other way of showing how independent segregation and complete linkage progeny ratios are derived.
- Whatever else you feel is appropriate or useful for your audience. DO NOT include tables of raw, unprocessed data.
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 data.
Remember that the Resources section of the wiki has a lot of valuable information on every section of a research report. You should read carefully the information there on the Results section, including on effective figure and table design.
Data Analysis Rubric- Sex Linkage & Independent Assortment – 25 points
||At or Above Standard
|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 or the legend to see the data’s main meaning. All data adequately identified; correct units included; labeling appropriate.
||Figure(s) or table(s) not well designed to illustrate main points visually, clearly, or in most direct and simple way or missing essential information needed for understanding.
||Figure legend is below figure & includes a number. Table # & title is above table, legend info below. Tables numbered sequentially, independent of figure numbers. All legends include all essential information and no unnecessary detail about how data shown 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 more appropriate for the narrative data analysis. All data adequately identified and parameters, ambiguous symbols or terms 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.
||Narrative is structured appropriately: begins with a concise description of topic, experimental goals and experimental design. Narrative references figures & tables directly and describes key findings accurately, concisely and clearly & includes only relevant information. Data analysis is thorough and leads incrementally & clearly to appropriate conclusions to experimental question and addresses topic’s goals. Analysis is understandable to an audience unfamiliar with topic and principles used in the experimental design.
||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. Data analysis requires background knowledge that general audience may not have.