Recent methods for polygenic analysis of genome-wide data implicate an important effect of common variants on cardiovascular disease risk

BMC Medical Genetics

Research Article

Matthew B McQueen¹  Matthew A Simonson²  Matthew C Keller³  Amanda G Wills³ 

[1] Department of Integrative Physiology, University of Colorado Boulder, USA;Institute for Behavioral Genetics, University of Colorado at Boulder, 80303, Boulder, CO, USA;Department of Psychology, University of Colorado Boulder, USA;Department of Integrative Physiology, University of Colorado Boulder, USA;Institute for Behavioral Genetics, University of Colorado at Boulder, 80303, Boulder, CO, USA;Department of Psychology, University of Colorado Boulder, USA;Institute for Behavioral Genetics, University of Colorado at Boulder, 80303, Boulder, CO, USA;

关键词: Single Nucleotide Polymorphism;    Risk Score;    Framingham Heart Study;    Framingham Risk Score;    Genetic Risk Score;   

DOI  :  10.1186/1471-2350-12-146

 received in 2011-07-16, accepted in 2011-10-26,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundTraditional genome-wide association studies are generally limited in their ability explain a large portion of genetic risk for most common diseases. We sought to use both traditional GWAS methods, as well as more recently developed polygenic genome-wide analysis techniques to identify subsets of single-nucleotide polymorphisms (SNPs) that may be involved in risk of cardiovascular disease, as well as estimate the heritability explained by common SNPs.MethodsUsing data from the Framingham SNP Health Association Resource (SHARe), three complimentary methods were applied to examine the genetic factors associated with the Framingham Risk Score, a widely accepted indicator of underlying cardiovascular disease risk. The first method adopted a traditional GWAS approach - independently testing each SNP for association with the Framingham Risk Score. The second two approaches involved polygenic methods with the intention of providing estimates of aggregate genetic risk and heritability.ResultsWhile no SNPs were independently associated with the Framingham Risk Score based on the results of the traditional GWAS analysis, we were able to identify cardiovascular disease-related SNPs as reported by previous studies. A predictive polygenic analysis was only able to explain approximately 1% of the genetic variance when predicting the 10-year risk of general cardiovascular disease. However, 20% to 30% of the variation in the Framingham Risk Score was explained using a recently developed method that considers the joint effect of all SNPs simultaneously.ConclusionThe results of this study imply that common SNPs explain a large amount of the variation in the Framingham Risk Score and suggest that future, better-powered genome-wide association studies, possibly informed by knowledge of gene-pathways, will uncover more risk variants that will help to elucidate the genetic architecture of cardiovascular disease.

【 授权许可】

CC BY   
© Simonson et al; licensee BioMed Central Ltd. 2011