Classifying Rheumatoid Arthritis Risk with Genetic Subgroups Using Genome-Wide Association

Hdl Handle:
http://hdl.handle.net/10675.2/318834
Title:
Classifying Rheumatoid Arthritis Risk with Genetic Subgroups Using Genome-Wide Association
Authors:
Letter, Abraham J.
Abstract:
Structured genome-wide association methods can be used to find population substructure, determine significant SNPs, and subsequently narrow down the field of SNPs to those most significant for determining disease risk. Beginning with more than 500,000 SNPs and rheumatoid arthritis (RA) phenotype data for cases and controls, we used a three-part clustering approach that found 684 SNPs significant for determining RA after accounting for clusters, and of those, 168 SNPs with differing odds across clusters. These 168 SNPs were used to create 16 population subgroups, each revealing a unique pattern of minor allele frequencies. The subgroups showed some commonality in multi-dimensional scaling plots, however, and were combined into five RA risk categories, each with odds differing from the other categories with pvalues less than 0.0001. Thus, based on SNP information from 168 SNPs it may be possible to assign an individual into one of five distinct RA risk categories.
Affiliation:
Department of Biostatistics and Epidemiology
Issue Date:
Apr-2010
URI:
http://hdl.handle.net/10675.2/318834
Type:
Thesis
Language:
en
Description:
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Appears in Collections:
Department of Biostatistics and Epidemiology Theses and Dissertations; Theses and Dissertations

Full metadata record

DC FieldValue Language
dc.contributor.authorLetter, Abraham J.en
dc.date.accessioned2014-06-03T22:33:15Z-
dc.date.available2014-06-03T22:33:15Z-
dc.date.issued2010-04-
dc.identifier.urihttp://hdl.handle.net/10675.2/318834-
dc.descriptionThe file you are attempting to access is currently restricted to Augusta University. Please log in with your NetID if off campus.en
dc.description.abstractStructured genome-wide association methods can be used to find population substructure, determine significant SNPs, and subsequently narrow down the field of SNPs to those most significant for determining disease risk. Beginning with more than 500,000 SNPs and rheumatoid arthritis (RA) phenotype data for cases and controls, we used a three-part clustering approach that found 684 SNPs significant for determining RA after accounting for clusters, and of those, 168 SNPs with differing odds across clusters. These 168 SNPs were used to create 16 population subgroups, each revealing a unique pattern of minor allele frequencies. The subgroups showed some commonality in multi-dimensional scaling plots, however, and were combined into five RA risk categories, each with odds differing from the other categories with pvalues less than 0.0001. Thus, based on SNP information from 168 SNPs it may be possible to assign an individual into one of five distinct RA risk categories.en
dc.language.isoenen
dc.rightsCopyright protected. Unauthorized reproduction or use beyond the exceptions granted by the Fair Use clause of U.S. Copyright law may violate federal law.-
dc.subjectRheumatoid Arthritisen
dc.subjectPopulation Substructureen
dc.subjectStructured Associationen
dc.subjectGenome-Wide Associationen
dc.subjectPLINKen
dc.titleClassifying Rheumatoid Arthritis Risk with Genetic Subgroups Using Genome-Wide Associationen
dc.typeThesisen
dc.contributor.departmentDepartment of Biostatistics and Epidemiologyen
dc.description.advisorXu, Hongyan Nathanen
dc.description.degreeMaster of Science (M.S.)en
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