title: Evaluation of genetic risk scores for lipid levels using genome-wide markers in the Framingham Heart Study.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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).
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Biomedical Informatics, School of Medicine, University of Utah, 26 South 2000 East, Salt Lake City, Utah 84112, USA. stephen.piccolo@hsc.utah.edu.
authors.forename: Stephen R
authors.initials: SR
authors.lastname: Piccolo
authors.forename: Ryan P
authors.initials: RP
authors.lastname: Abo
authors.forename: Kristina
authors.initials: K
authors.lastname: Allen-Brady
authors.forename: Nicola J
authors.initials: NJ
authors.lastname: Camp
authors.forename: Stacey
authors.initials: S
authors.lastname: Knight
authors.forename: Jeffrey L
authors.initials: JL
authors.lastname: Anderson
authors.forename: Benjamin D
authors.initials: BD
authors.lastname: Horne