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

Abstract

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.

Simple, standardized incorporation of genetic risk into non-genetic risk prediction tools for complex traits: coronary heart disease as an example

“…Thus, our approach appears robust to small violations of this assumption. This confirms others' and our experiences with GRSs that they are fairly robust to alternative constructions (Purcell et al, 2009; Simonson et al, 2011). …”

A Polygenic Risk Score Associated with Measures of Depressive Symptoms Among Older Adults

“…In recent years however, many studies, have begun to use polygenic methods to examine complex traits, with relatively good success (Morrison et al, 2007; Kathiresan et al, 2008; Purcell et al,2009; Bush et al, 2010; Lango et al, 2010; Simonson et al, 2011; Belsky et al, 2013). In the current study, none of our SNPs met genome-wide significance, but 14 (11 after grouping SNPs by LD) met benchmarks for having a “suggested association”.…”

Tooth Agenesis; Aetiological Factors

“…[57] This observation has been evidenced in monozygotic twins and triplets . There is no dominance or recessivity in the polygenic model.…”