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The Genetics of Multiple Sclerosis: How do Genes affect Prognosis? Project Summary Multiple Sclerosis is a disorder that affects the central nervous system and seems to have a genetic link. This study proposes to take a look at chromosomes 1, 3, 6, 7 and X and see how they affect the prognosis of the patient. The overall goal of this study is to see if a different numbers of mutations affect the prognosis of the patient. This will be done by determining how many mutations each patient has and then tracking their progress over the course of 10 years. This study hopes to yield a new way to test for MS, or allow recently diagnosed patients a way to better prepare for what may be in their future. http://www.livenet.ch/Service/images-cards/dna-.jpg Introduction MS is a progressive disorder that affects the central nervous system by degenerating the myelin sheath around nerves. (Multiple Sclerosis 1990). Both symptoms and treatments of MS vary due to which nerves are affected, and how frequent the attacks on the myelin sheath happen. In most cases, the disease starts with alternating episodes and recovery, then progressing into a phase where there is no recovery. Many theories exist as to what exactly causes MS, but no definite correlation has been found. Genetics is the strongest correlation thus far. A number of chromosomes have been linked to causing MS, but exactly which loci have not yet been determined. Andrea Child Department of Biological Sciences Objective To determine if more gene polymorphisms indicate a worse prognosis for patients. http://www.mspathways.ca/en/images/myelin.jpg Review of Literature Studies have shown that there appears to be a genetic link to this disease. There is a familial recurrence rate of approximately 15% (Compston 1990). Studies have also shown that fraternal twins have a 3-5% chance of developing MS after the first twin, but identical twins have a 25% chance of developing it (Aaseng 2000). Genetics is not the only predictor of prognosis of MS; however it is the most prominent. Pregnancy, virus infections and trauma have all been linked to more frequent attacks to the myelin sheath. These all must be taken into account when observing the prognosis of MS. However an estimation on the course of MS can be made by genetic factors, age, and year of onset in relatives (Compston 1999). There are many treatments for MS, but they all fall under two main categories. One strategy is to attempt to reduce the attack on the CNS. The other focuses on relief of symptoms (Multiple Sclerosis 1990). Both of these treatments are heavy in different types of drugs. Research Design My thesis proposal is an original idea with considerations taken from Compston (1999) and Risch (1990). Gene loci used in the study were decided upon by past studies, including such done by Ebers et al (1996) and Chataway et al (1998). They were decided on by relevancy found in studies, maximum lod scores from a combination of studies, and how many times the loci were mentioned in separate studies. Actual DNA isolation and amplification methods came from Ebers et al (1996) and He et al (1998). 1) Patients are gathered with the cooperation of doctors. All will be females ranging in age from 20-40 from the same states in Canada who have all been recently diagnosed. 2) A gene screen is performed to determine the number of mutations in each patient. (PCR Assay) From this, patients will be put into groups, as shown by the figure below. Within the groups of MS patients, sub-groups will be made based on the type of treatment the patient is receiving. Expected Results If genes are correlated, more mutations should lead to a worse prognosis. The group with 1-2 mutations should progress the slowest through the stages of MS, have very few plaques, and score low on the Incapacity Scale and high on Self Rated and Quality of Life scales. The group with 5 mutations should progress quickly and have more plaques on the MRI’s and have higher scores on the Incapacity Scale and lower scores on the Self Rated and Quality of Life scales. The group with 3-4 mutations should fall somewhere within the middle of these two groups. http://www.remicade-lawyer.com/images/wheelchair.png Literature Cited Aaseng, Nathan. 2000. Multiple Sclerosis. New York: Franklin Watts Chataway, Jeremy, Feakes, Robert, Coraddu, Francesca, Gray, Julia, Deans, Jackie, Fraser, Mary, Robertson, Neil, Broadley, Simon, Jones, Hywel, Clayton, David, Goodfellow, Peter, Sawcer, Stephen, and Compston, Alastair. 1998. The genetics of multiple sclerosis: principles, background and updated results of the United Kingdom systematic genome screen. Brain 121:1869-1887 Compston, Alastair. 1990. The Genetic Epidemiology of Multiple Sclerosis. Philosophical Transactions: Biological Sciences 354:1623-1635 Ebers, Geroge C., Kukay, Kim, Bulman, Dennis E., Anderson, Carol, Armstrong, Holly, Cousin, Kieth, Bell, Robert B., Hader, Walter, Paty, Donald W., Hashimoto, Stanley, Oger, Joel, Duquette, Pierre, Warren, Sharon, Gray, Trevor, O’Connor, Paul, Nath, Avindra, Auty, Anthony, Metz, Luanne, Francis, Gordon, Paulseth, John E., Murray, T. John, Pryse-Phillips, William, Nelson, Robert, Freedman, Mark, Brunet, Donald, Bouchard, Jean-Pierre, Hinds, David, and Risch, Neil. 1996. A full genome search in multiple sclerosis. Nature Genetics 13:472-476 3) Study: Runs for 10 years. Data collected every year via MRI’s and questionnaires. Data analyses will be done at the 2, 5 and 10 year marks. MRI’s are done to scan for plaques. Questionnaires used include Self Rated Abilities Scale, Incapacity Status Scale, and Quality of life index: MS Version. He, Bing, Xu, Chun, Yang, Bei, Lantiblom, Anne-Marie, Fredrikson, Sten, and Hillert, Jan. 1998. Linkage and association analysis of genes encoding cytokines and myelin proteins in multiple sclerosis. Journal of Neuroimmunology. 86:13-19 Risch, Neil. 1990. Linkage Strategies for Genetically Complex Traits. I Multilocus Models. American Journal of Human Genetics. 46:222-228 U.S. Department of Health and Human Services. 1990. Multiple Sclerosis. Washington DC: U.S. Government Printing Office Acknowledgements Dr. Kaltreider for all of his time helping me bounce ideas back and forth and Dr. Rehnberg for giving wonderful feedback.