Background and Objectives : The common methods of genetic association analysis are sensitive to population stratification, which may easily lead to a spurious association result. We used a regression approach based for linkage disequilibrium to perform a high resolution genetic association analysis. Subjects and Methods : We applied a regression approach that can increase the resolution of quantitative traits that are related with cardiovascular diseases. The population data was composed of 543 males and 876 females without cardiovascular diseases, and it was obtained from a cardiovascular genome center. We used information about linkage disequilibrium between the marker and trait locus, and we added the covariates to model their effects. Results : We found that this regression approach has the merit of analyzing genetic association based on linkage disequilibrium. In the analysis of the male group, the total cholesterol was significantly in linkage disequilibrium with CETP3 (p=0.002), and triglyceride was significantly in linkage disequilibrium with ACE8 (p=0.037), APOA1-1 (p=0.031), APOA5-1 (p=0.001), APOA5-2 (p=0.001) and LIPC4 (p=0.022). HDL-cholesterol was significantly in linkage disequilibrium with ACE7 (p=0.002), ACE8 (p=0.008), ACE10 (p=0.003), APOA5-2 (p=0.022), and MTP1 (p=0.001). In the female group, total cholesterol was significantly associated with APOA5-1 (p=0.020), APOA5-2 (p=0.001), and LIPC1 (p=0.016), and triglyceride was significantly associated with APOA5-1 (p=0.009), APOA5-2 (p=0.001), and CETP5 (p=0.049). LDL-cholesterol was significantly associated with APOA5-2 (p=0.004), and HDL-cholesterol was significantly associated with LIPC1 (p=0.004). Conclusion ' We used a regression-based method to perform high resolution linkage disequilibrium analysis of a quantitative trait locus that's associated with lipid profiles. This method of using a single marker, as applied in this paper, was well suited for analysis of genetic association. Because of the simplicity, the method can also be easily performed by routine statistical analysis software.
All Science Journal Classification (ASJC) codes
- Internal Medicine
- Cardiology and Cardiovascular Medicine