Much work has targeted the detection of disease genes through genetic mapping for metabolic diseases such as type 2 diabetes (T2D), cardiovascular diseases (CVD), and other diabetes-related traits such as body mass index (BMI) and hemoglobin (HbA1C) levels. However, the etiology of metabolic diseases remains partially understood hampering the development of more personalized diagnosis, treatment and prevention strategies.
This dissertation examines the association between genetic variants with risk of metabolic diseases and diabetes-related quantitative traits in both candidate gene and genome-wide scan settings. In particular, we assessed the association of genetic loci related to adiposity, inflammation, and lipid storage, with the risk of diabetes using a candidate gene approach. We also investigated biological pathways that may give rise to the development of vascular disease (T2D and/or CVD) and also further investigated genetic variants related to BMI and HbA1C levels using a genome-wide approach. Chapter 1 introduces general background on the evolution of genetic research in the arena of metabolic diseases. Chapter 2 investigates common variants in the genomic region of FABP4, CRP, TNF, IL6 and PPARG in relation to diabetes risk among postmenopausal women enrolled in the Women's Health Initiative Observational Study (WHI-OS). Chapter 3 examines whether common variants involved in vascular disease risk are clustered in multiple pathways among African and Hispanic American participants in the WHI SNP Health Association Resource (SHARe) cohort. Chapter 4 examines the association between genetic variants with BMI and HbA1C levels using a family-based genome-wide association approach among participants in the Framingham Heart Study (FHS).
Our main findings are: 1) Candidate gene-based studies indicate that variation exists across even the candidate gene regions. FABP4 genotypes were associated with reduced VCAM-1 levels, though none of the common genetic variants in the FABP4 gene examined were associated with risk of T2D. We also observed modest associations between TNF genetic variants and circulating concentrations of TNF-α-R2, although common variants of CRP, TNF, and IL6 genes were not associated with T2D risk. Using the example of the PPARG gene with T2D risk, however, we replicated the association between the PPARG Pro12Ala genetic variant with diabetes risk and found that haplotype-based analysis is more powerful than single-SNP analysis for identifying genetic variants. 2) Using a pathway-based analytical approach and genome-wide scan data collected among African and Hispanic American postmenopausal women, we observed that genetic variants associated with vascular disease appeared to cluster into several biological pathways including the glycerolipid metabolism and PPAR signaling pathways. 3) We found strong associations between SNPs near the LOC100507205 locus and BMI in the family-based Framingham Heart Study with three generations. We also replicated five well-validated genes that have been previously reported to be significantly associated with the BMI trait. These findings contribute to the growing body of literature in identifying genetic variants in the development of metabolic disease, further future work (e.g. in the area of structure and functional variants) are warranted to improve understanding of the genetic architecture for metabolic outcomes. Increasing integration of cutting edge genomic science into population-based epidemiologic investigation will accelerate and improve our understanding of the genetic susceptibility of complex diseases. The work described in this dissertation represents a tip of our effort toward the ultimate improvement of the diagnosis, treatment and prevention of metabolic diseases in human populations.