Linkage Disequilibrium Analysis of Quantitative Trait Locus Associated with Lipid Profiles

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.

Genetic Association Analysis of Lipid Profiles Using Linear Mixed Model

BACKGROUND AND OBJECTIVES: Analyzing the association between multiple SNPs and the disease outcomes will provide new insight into the disease's etiology. However, this presents an analytic difficulty due to the large number of SNPs and the complex relationships among them. We proposed using the mixed model approach to identify the significant multi-locus genotypes and the high-order gene-to-gene interactions. SUBJECTS AND METHODS: We described the mixed effects model and applied this approach to real world data. For the purpose of these analyses, we examine the association of four types of SNPs (AGT5, APOB, CETP3 and ACE6) with the lipid profiles and the measures related with cardiovascular disease. We used data from 672 healthy individuals (283 males and 389 females) who were without cardiovascular diseases. RESULTS: The results of our analysis suggested that there were significant random genotype patterns and genotype groups according to the gender effect on the lipid profiles. In other words, there was significant variability across the genotype groups because of the effect of gender on the lipid profiles. CONCLUSION: The mixed model approach provided a flexible statistical framework for controlling potential confounding variables and for identifying a significant genetic contributions that may come about through the effects of multi-locus genotypes or through an interaction between the genotype and environmental variables (e.g. gender) with the variations in quantitative traits (e.g. lipid profiles). There were significant genetic contributions to the variability in the lipid profiles, and these were explained by the 4 SNPs described in our real data.

Clinical Research Design and Biostatistical Methods / 대한비뇨기과학회지

Purpose: To proceed effectively with clinical research requires an understanding of the fundamental principles of study design and biostatistical methods. In this article, we identified and summarized basic clinical research designs and some of the key biostatistical methods that have been commonly used in clinical research. Materials and Methods: In an observational study, cross-sectional, case- control and Cohort designs were illustrated and compared. In a clinical trial study, parallel group design and cross-over designs were described according to their characteristics. Also, the biostatistical methods for their usages classified and summarized. Results: Understanding and evaluating research design are part of the process researchers must use to determine both the quality and usefulness of their research. Adequate applications to biostatistical methods are need; i.e., descriptive statistics, Student's t-test, ANOVA, nonparametrics, categorical data analysis, correlation and regression, and survival analysis. Conclusions: Research findings are used by clinical researcher to guide their practice and reduce their uncertainty in clinical decision making. However, to understand how to interpret research results, it is important to be able to understand basic statistical concepts and types of study design. Clinicians should also appropriately choose the biostatistical methods to suit their purposes.

Analysis of the Relative Effects of SNPs within a Gene to Serum Lipid Profiles Using Stepwise Linear Regression

BACKGROUND AND OBJECTIVES: It is very important to distinguish between the primary and secondary genetic effects at different sites within a small genetic region. Therefore, we evaluated the relative effects of single nucleotide polymorphisms (SNPs) within a gene on the serum lipid profiles by using individual data. SUBJECTS AND METHODS: To evaluate the contributions of SNPs in a region to the serum lipid profiles (total cholesterol, triglyceride, low density lipoprotein, high density lipoprotein), we used data that consisted of 808 individuals (327 males and 481 females) who did not have cardiovascular disease. In this study, we used a stepwise regression procedure to analyze the relative effects of four single nucleotide polymorphisms (ACE6, ACE7, ACE8, ACE10) in a gene region on the development of the serum lipid profiles in each gender group. RESULTS: In the males, there were epistatic interaction effects between two loci (ACE6xACE7, ACE6xACE8, ACE6xACE10, ACE8xACE10 and ACE7xACE8) and among three loci (ACE6xACE7xACE8, ACE6xACE7xACE10 and ACE6xACE8xACE10). Also, there are interaction effects between two loci (ACE6xACE7, ACE6xACE8, ACE6xACE10, ACE7xACE10 and ACE8xACE10) and among three loci (ACE6xACE7xACE8, ACE6xACE7xACE10, ACE6xACE8xACE10 and ACE7xACE8xACE10) in the females. CONCLUSION: The results suggested that each of these loci is important in causing a relative change of the serum lipid profiles, even with simultaneously accounting for the effects at the other loci. In the results of the analysis, there existed the effects of individual loci and significant interaction between the loci on the serum lipid profiles in each gender group. It was confirmed that this stepwise regression method can be suitable for evaluating the relative effects of SNPs and it is easily performed.