A quantitative trait locus influences coordinated variation in measures of ApoB-containing lipoproteins

PCA-based GRS analysis enhances the effectiveness for genetic correlation detection

Genetic risk score (GRS, also known as polygenic risk score) analysis is an increasingly popular method for exploring genetic architectures and relationships of complex diseases. However, complex diseases are usually measured by multiple correlated phenotypes. Analyzing each disease phenotype individually is likely to reduce statistical power due to multiple testing correction. In order to conquer the disadvantage, we proposed a principal component analysis (PCA)-based GRS analysis approach. Extensive simulation studies were conducted to compare the performance of PCA-based GRS analysis and traditional GRS analysis approach. Simulation results observed significantly improved performance of PCA-based GRS analysis compared to traditional GRS analysis under various scenarios. For the sake of verification, we also applied both PCA-based GRS analysis and traditional GRS analysis to a real Caucasian genome-wide association study (GWAS) data of bone geometry. Real data analysis results further confirmed the improved performance of PCA-based GRS analysis. Given that GWAS have flourished in the past decades, our approach may help researchers to explore the genetic architectures and relationships of complex diseases or traits.

Maximizing the power of principal-component analysis of correlated phenotypes in genome-wide association studies

Many human traits are highly correlated. This correlation can be leveraged to improve the power of genetic association tests to identify markers associated with one or more of the traits. Principal component analysis (PCA) is a useful tool that has been widely used for the multivariate analysis of correlated variables. PCA is usually applied as a dimension reduction method: the few top principal components (PCs) explaining most of total trait variance are tested for association with a predictor of interest, and the remaining components are not analyzed. In this study we review the theoretical basis of PCA and describe the behavior of PCA when testing for association between a SNP and correlated traits. We then use simulation to compare the power of various PCA-based strategies when analyzing up to 100 correlated traits. We show that contrary to widespread practice, testing only the top PCs often has low power, whereas combining signal across all PCs can have greater power. This power gain is primarily due to increased power to detect genetic variants with opposite effects on positively correlated traits and variants that are exclusively associated with a single trait. Relative to other methods, the combined-PC approach has close to optimal power in all scenarios considered while offering more flexibility and more robustness to potential confounders. Finally, we apply the proposed PCA strategy to the genome-wide association study of five correlated coagulation traits where we identify two candidate SNPs that were not found by the standard approach.

Determinants of variation in human serum paraoxonase activity

Paraoxonase-1 (PON1) is associated with high-density lipoprotein (HDL) particles and is believed to contribute to antiatherogenic properties of HDLs. We assessed the determinants of PON1 activity variation using different substrates of the enzyme. PON1 activity in serum samples from 922 participants in the San Antonio Family Heart Study was assayed using a reliable microplate format with three substrates: paraoxon, phenyl acetate and the lactone dihydrocoumarin. There were major differences among results from the three substrates in degree of effect by various environmental and genetic factors, suggesting that knowledge of one substrate activity alone may not provide a complete sense of PON1 metabolism. Three significant demographic covariates (age, smoking status and contraceptive usage) together explained 1-6% of phenotypic variance, whereas four metabolic covariates representing lipoprotein metabolism (apoAII, apoAI, triglycerides and non-HDL cholesterol) explained 4-19%. Genes explained 65-92% of phenotypic variance and the dominant genetic effect was exerted by a locus mapping at or near the protein structural locus (PON1) on chromosome 7. Additional genes influencing PON1 activity were localized to chromosomes 3 and 14. Our study identified environmental and genetic determinants of PON1 activity that accounted for 88-97% of total phenotypic variance, suggesting that few, if any, major biological determinants are unrepresented in the models.

Dietary and genetic probes of atherogenic dyslipidemia

A goal of dietary management of cardiovascular disease risk in patients with obesity and metabolic syndrome is improvement in the atherogenic dyslipidemia comprising elevated triglyceride, reduced high-density lipoprotein (HDL) cholesterol, and increased numbers of small, dense low-density lipoprotein (LDL) particles. Individuals with a genetically influenced trait characterized by a high proportion of small, dense LDL (phenotype B) respond to a low-fat, high-carbohydrate diet with greater reduction of LDL cholesterol, apoprotein B, and mid-sized LDL2 particles than unaffected subjects (phenotype A). In contrast, in phenotype A subjects there is a reciprocal shift from large LDL1 to small LDL3 such that a high proportion convert to phenotype B. There is evidence for heritable effects on these diet-induced subclass changes and for the involvement of specific genes. For example, a haplotype of the APOA5 gene associated with increased plasma triglyceride and small, dense LDL predicts greater diet-induced reduction of LDL2, a haplotype-specific effect that is strongly correlated with both increased VLDL precursors and LDL4 products. Understanding of such diet-genotype interactions may help to elucidate mechanisms that are responsible for phenotype B and for its differential dietary responsiveness. This information may also ultimately help in identifying those individuals who are most likely to achieve cardiovascular risk benefit from specific dietary interventions.