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dc.contributor.advisorDavid Altshuler.en_US
dc.contributor.authorLiu, Brendan Fen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2014-11-24T18:38:51Z
dc.date.available2014-11-24T18:38:51Z
dc.date.copyright2014en_US
dc.date.issued2014en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/91838
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 69-71).en_US
dc.description.abstractThe genetic architecture, the number, frequency, and effect size of disease causing alleles for many common diseases including Type 2 Diabetes is not fully understood. Genetic simulations can be used to make predictions under specified genetic architecture models. Models whose predictions are inconsistent with empirical data can be rejected. We extended a gene simulation model previously published by our lab. The distribution of number and length of coding and intron regions of each simulated gene was consistent with the distribution in the human genome. Selection pressure against mutations was modeled by utilizing the cross-species conservation of each region. The combined distribution of variants by their frequency over 500 genes was compared between the simulated genes and the corresponding empirical data. This distribution of variants between the simulated and empirical data was found to be consistent.en_US
dc.description.statementofresponsibilityby Brendan F. Liu.en_US
dc.format.extent71 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleDeveloping a gene model for simulations that incorporates multi-species conservationen_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc894242021en_US


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