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Using Rasch Model Bias Statistics to...

Fufaa, Gudeta D.

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Using Rasch Model Bias Statistics to Identify Genomic Markers that Influence Type 2 Diabetes Risk.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Using Rasch Model Bias Statistics to Identify Genomic Markers that Influence Type 2 Diabetes Risk./
Author:	Fufaa, Gudeta D.
Description:	316 p.
Notes:	Source: Dissertation Abstracts International, Volume: 73-03, Section: B, page: 1514.
Contained By:	Dissertation Abstracts International73-03B.
Subject:	Health Sciences, Public Health. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3482505
ISBN:	9781267040145

Using Rasch Model Bias Statistics to Identify Genomic Markers that Influence Type 2 Diabetes Risk.
Fufaa, Gudeta D.

Using Rasch Model Bias Statistics to Identify Genomic Markers that Influence Type 2 Diabetes Risk. - 316 p.

Source: Dissertation Abstracts International, Volume: 73-03, Section: B, page: 1514.

Thesis (Ph.D.)--Walden University, 2011.

The purpose of this study was to assess whether Rasch model bias detection statistics can be used to identify individual and combinations of genetic loci that significantly influence the risk of type 2 diabetes (T2D). Drawing on a conceptual framework founded in educational testing and psychometrics, the project utilized genome wide association study (GWAS) data that were generated by the Finland-United States Investigation of Non- Insulin-Dependent Diabetes Mellitus Genetics (FUSION) study and made available through the Database of Genotypes and Phenotypes at the National Center for Biotechnology Information. Alleles, genotypes, and phenotypes were iteratively scored, in the data and the dichotomous and partial credit Rasch models were used to conjointly scale phenotype and genotype data; to obtain model parameter estimates and fit statistics; and to generate locus- and gene-specific bias statistics. The results indicated that PPARD (OR = 1.015, 95% CI: 1.172-1.042), LMNA (OR = 1.083, 95% CI: 1.149-1.021), ENPP1 (OR = 1.061, 95% CI: 1.104-1.021), and CD36 (OR = 1.061, 95% CI: 1.104-1.021) were significantly associated with an increased risk of T2D at the haplotype level, while SLC2A4 (OR = 1.083, 95% CI: 1.149-1.021), TP53 (OR = 1.073, 95% CI: 1.137-1.011), NRF1 (OR = 1.051, 95% CI: 1.093-1.011), and CPE (OR = 1.041, 95% CI: 1.061-1.021) were significantly associated with T2D at the diplotype level. Multiple individual single nucleotide polymorphism loci were associated with an increased risk of T2D, as well. These findings demonstrate the validity of the conceptual framework and support an innovative use of the Rasch model. The study's primary implication for positive social change rests in its provision of a novel analytical approach to studying gene-disease associations that can be generally applied across disease areas and clinical specialties.

ISBN: 9781267040145Subjects--Topical Terms:

1017659
Health Sciences, Public Health.

Using Rasch Model Bias Statistics to Identify Genomic Markers that Influence Type 2 Diabetes Risk.
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500 $a Adviser: Shana Morrell.
502 $a Thesis (Ph.D.)--Walden University, 2011.
520 $a The purpose of this study was to assess whether Rasch model bias detection statistics can be used to identify individual and combinations of genetic loci that significantly influence the risk of type 2 diabetes (T2D). Drawing on a conceptual framework founded in educational testing and psychometrics, the project utilized genome wide association study (GWAS) data that were generated by the Finland-United States Investigation of Non- Insulin-Dependent Diabetes Mellitus Genetics (FUSION) study and made available through the Database of Genotypes and Phenotypes at the National Center for Biotechnology Information. Alleles, genotypes, and phenotypes were iteratively scored, in the data and the dichotomous and partial credit Rasch models were used to conjointly scale phenotype and genotype data; to obtain model parameter estimates and fit statistics; and to generate locus- and gene-specific bias statistics. The results indicated that PPARD (OR = 1.015, 95% CI: 1.172-1.042), LMNA (OR = 1.083, 95% CI: 1.149-1.021), ENPP1 (OR = 1.061, 95% CI: 1.104-1.021), and CD36 (OR = 1.061, 95% CI: 1.104-1.021) were significantly associated with an increased risk of T2D at the haplotype level, while SLC2A4 (OR = 1.083, 95% CI: 1.149-1.021), TP53 (OR = 1.073, 95% CI: 1.137-1.011), NRF1 (OR = 1.051, 95% CI: 1.093-1.011), and CPE (OR = 1.041, 95% CI: 1.061-1.021) were significantly associated with T2D at the diplotype level. Multiple individual single nucleotide polymorphism loci were associated with an increased risk of T2D, as well. These findings demonstrate the validity of the conceptual framework and support an innovative use of the Rasch model. The study's primary implication for positive social change rests in its provision of a novel analytical approach to studying gene-disease associations that can be generally applied across disease areas and clinical specialties.
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