Thèse (M.A.)--Université Laval, 1997. / Comprend des réf. bibliogr.

No description available.

Thesis (B.A. (Hons)) -- University of Adelaide, 1966. / [Typescript]. Includes bibliographical references.

Thesis (M.A.)--Guo li Taiwan da xue. / Includes bibliographical references and indexes.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1969. / Vita. Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

Thesis (M.S.)--Ball State University, 2003. / Includes bibliographical references (leaves 53-57).

Many complex diseases are known to have a substantial genetically heritable component. Elucidation of these genetic risk factors provides increased knowledge of the biological mechanisms that result in the diseases while also presenting new potential targets for therapy. This thesis explores the methodology of mapping genetic loci using isolated populations in the context of quantitative trait analysis. Chapter 1 explores the rational for the project, discussing the benefits of using quantitative traits rather than binary disease status and the pros and cons of using isolated populations. This is followed by a brief history of genetic mapping with reference to type 2 diabetes mellitus (T2D) and related quantitative traits. Chapter 2 introduces the methods used in this thesis. This includes strategies to deal with medication, methods to determine kinship between individuals, linkage analysis, association analysis and meta‐analysis of multiple studies. Chapter 3 presents linkage analysis of T2D related traits carried out in 2 – 4 populations depending on availability of the traits and appropriate marker data. Chapter 4 presents the results of association analysis for T2D related traits in 3 – 5 populations using genome‐wide SNP data. The results using the alternate methods described in chapter 2 are compared using fasting glucose as this was the most widely measured phenotype. Chapter 5 introduces additional traits derived by pulse wave analysis and discusses their relevance to metabolic disease before presenting association analysis using the preferred method from chapter 4. An overall discussion of the strengths and weaknesses of the analysis is given in chapter 6.

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No description available.

To dissect the genetic architecture of blood pressure and assess effects on target organ damage, we analyzed 128,272 SNPs from targeted and genome-wide arrays in 201,529 individuals of European ancestry, and genotypes from an additional 140,886 individuals were used for validation. We identified 66 blood pressure-associated loci, of which 17 were new; 15 harbored multiple distinct association signals. The 66 index SNPs were enriched for cis-regulatory elements, particularly in vascular endothelial cells, consistent with a primary role in blood pressure control through modulation of vascular tone across multiple tissues. The 66 index SNPs combined in a risk score showed comparable effects in 64,421 individuals of non-European descent. The 66-SNP blood pressure risk score was significantly associated with target organ damage in multiple tissues but with minor effects in the kidney. Our findings expand current knowledge of blood pressure-related pathways and highlight tissues beyond the classical renal system in blood pressure regulation.