Abstract
Multiple single-nucleotide polymorphisms have been associated with low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) levels. In this paper, we evaluate a weighted and an unweighted approach for estimating the combined effect of multiple markers (using genotypes and haplotypes) on lipid levels for a given individual. Using data from the Framingham Heart Study SHARe genome-wide association study, we tested genome-wide genotypes and haplotypes for association with lipid levels and constructed genetic risk scores (GRS) based on multiple markers that were weighted according to their estimated effects on LDL-C, HDL-C, and TG. These scores (GRS-LDL, GRS-HDL, and GRS-TG) were then evaluated for associations with LDL-C, HDL-C, and TG, and compared with results of an unweighted method based on risk-allele counts. For comparability of metrics, GRS variables were divided into quartiles. GRS-LDL quartiles were associated with LDL-C levels (p = 2.1 x 10-24), GRS-HDL quartiles with HDL-C (p = 5.9 x 10-22), and GRS-TG quartiles with TG (p = 5.4 x 10-25). In comparison, these p-values were considerably lower than those for the associations of the unweighted GRS quartiles for LDL-C (p = 3.6 x 10-7), HDL-C (p = 6.4 x 10-16), and TG (p = 4.1 x 10-10). GRS variables were highly predictive of LDL-C, HDL-C, and TG measurements, especially when weighted based on each marker's individual association with those intermediate risk phenotypes. The allele-count GRS approach that does not weight the GRS by individual marker associations was considerably less predictive of lipid and lipoprotein measures when the same genetic markers were utilized, suggesting that substantially more risk-associated genetic marker information is encapsulated by the weighted GRS variables.
Highlights
Multiple single-nucleotide polymorphisms have been associated with low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) levels
We previously introduced the concept of aggregating polygenic information in a genetic risk score (GRS) weighted by the estimated effect of each marker on intermediate risk phenotypes [7]
We hypothesized that the weighted GRS approach would be more powerful than an integer-based GRS because it accounts for variability in the effect of each marker on the phenotype and may better represent the complex physiology that drives changes in lipid and lipoprotein levels
Summary
Multiple single-nucleotide polymorphisms have been associated with low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) levels. Published genome-wide association studies reported novel loci for LDL-C, HDLC, and TG, while confirming several previously described loci [3,4,5] Those studies replicate each other for many of the loci; it is unclear how the combined effect of these markers for a given patient should be estimated most effectively. We previously introduced the concept of aggregating polygenic information in a GRS weighted by the estimated effect of each marker on intermediate risk phenotypes [7] For this investigation, we hypothesized that the weighted GRS approach would be more powerful than an integer-based GRS because it accounts for variability in the effect of each marker on the phenotype and may better represent the complex physiology that drives changes in lipid and lipoprotein levels
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