The following hypotheses are tested: 1) The risk of developing Type 2 diabetes-associated cardiovascular disease (CVD) has a significant heritable component that can be measured, and 2) The chromosomal locations of genes contributing to CVD in Type 2 diabetes can be determined and the genes identified using modern molecular genetic approaches. The investigators predict that these genetic factors can be detected in studies of sibling pairs with Type 2 diabetes through genetic epidemiology methods and linkage analysis. Type 2 diabetes-affected sibling pairs, unaffected siblings, and parents, if available, will be recruited and multiple clinical and subclinical measures of subclinical CVD risk will be assessed, including coronary artery calcification (CAC), carotid arterial wall thickness (IMT), ECG variables, and prevalent CVD. Data on the patients are collected in one visit to the General Clinical Research Center (GCRC) which includes an interview and physical examination, a resting 12-lead electrocardiogram (ECG), B-mode ultrasound of the carotid arteries, retrospectively gated helical CT (RGHCT), and a spectrum of clinical laboratory measures. Genetic and epidemiological methods will be used to evaluate the familial aggregation of subclinical CVD taking into consideration the effects of shared environmental exposures (e.g. smoking, diet, alcohol intake and physical activity) and clinical measures (e.g., body mass index, blood pressure, lipids, age, sex, etc.). Initial estimates of heritability suggest a significant heritable component to subclinical CVD. Clinical evaluation will be followed by a comprehensive molecular genetic analysis of the sib pairs/families including a genome wide screen, which will be followed by a focused effort to create a high quality dataset by regenotyping or replacing problem markers. Evidence for linkage to quantitative trait loci (QTLs) influencing CAC and IMT will be pursued in those chromosomal regions showing suggestive evidence for linkage and then performing further analyses to detect associations with these "saturation" markers.