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dc.contributor.authorLewis, Hannah
dc.contributor.authorMallmann, Eric
dc.contributor.authorBrisbin, Abra
dc.descriptionColor poster with text, images, charts, and graphs.en_US
dc.description.abstractNineteen genetic variants have been well-established as associated with risk for AMD; many of these are in genes related to atherosclerosis, angiogenesis, and the innate immune system. These known variants explain approximately 65% of the genetic basis of the disease [2].There is a need to develop statistical methods to identify additional genetic variants that contribute to AMD, to improve understanding of the disease etiology and enable treatments to be targeted to specific versions of the disease. For example, some evidence suggests that a treatment of antioxidants with zinc slows the progression of AMD in patients with the low-risk variant of the CFH gene, but not in patients with the high-risk variant. Our research goal is to find undiscovered genetic variants which may be associated with AMD using a two-phase approach. In phase 1 we will do a regression on previously known associated SNPs, and in phase 2 we will do a random forest on a different set of SNPs using residuals from the regression to find potential new associations. We will perform our statistical analysis first on a simulated data set and then apply this analysis to a set of real genetic data.en_US
dc.description.sponsorshipUniversity of Wisconsin--Eau Claire Office of Research and Sponsored Programsen_US
dc.relation.ispartofseriesUSGZE AS589;
dc.subjectMacular degenerationen_US
dc.subjectRandom foresten_US
dc.subjectGenetic diseasesen_US
dc.subjectDepartment of Mathematicsen_US
dc.titleRandom Forest Analysis of Age-Related Macular Degenerationen_US

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