Prediction of Coronary Heart Disease Risk using a Genetic Risk Score: The Atherosclerosis Risk in Communities Study

University of California, San Francisco, San Francisco, California, United States
American Journal of Epidemiology (Impact Factor: 5.23). 08/2007; 166(1):28-35. DOI: 10.1093/aje/kwm060
Source: PubMed


Recent studies have evaluated whether incorporating nontraditional risk factors improves coronary heart disease (CHD) prediction models. This 1986-2001 US study aggregated the contribution of multiple single nucleotide polymorphisms into a genetic risk score (GRS) and assessed whether the GRS plus traditional risk factors predict CHD better than traditional risk factors alone. The Atherosclerosis Risk in Communities (ARIC) cohort was followed for a median of 13 years for CHD events (n = 1,452). Individuals were genotyped for 116 single nucleotide polymorphisms associated with CHD in multiple case-control studies. Single nucleotide polymorphisms nominally predicting incident CHD in the ARIC study were included in the GRS. The GRS was significantly associated with incident CHD in Blacks (hazard rate ratio = 1.20, 95% confidence interval: 1.11, 1.29) and Whites (hazard rate ratio = 1.10, 95% confidence interval: 1.06, 1.14) as well as in each tertile defined by the traditional cardiovascular risk score (p < or = 0.02). When receiver operating characteristic curves based on traditional risk factors were recalculated after the GRS was added, the increase in the area under the receiver operating characteristic curve was statistically significant for Blacks and suggestive of improved CHD prediction for Whites. This study demonstrates the concept of aggregating information from multiple single nucleotide polymorphisms into a risk score and indicates that it can improve prediction of incident CHD in the ARIC study.

9 Reads
  • Source
    • "Meta-regression analysis on the association between KIF6 719Arg allele, LDL cholesterol and the risk of CAD involving 144,931 participants have shown that the 'Arg' allele increases vulnerability to LDL cholesterol and thereby influences the expected clinical benefit of statin therapy (Ference et al., 2011). Till date, many large-scale clinical studies have reported the association of KIF6 719Arg allele with CAD risk and benefit over statin treatment (Iakoubova et al., 2008a,b; Li et al., 2010; Morrison et al., 2007; Shiffman et al., 2008a,b). A large scale meta-analysis suggested that KIF6 719Arg allele is a risk factor for CAD in Caucasians and carriers are benefitted from statin therapy (Peng et al., 2012). "
    [Show abstract] [Hide abstract]
    ABSTRACT: The KIF6 719Arg allele is an interesting genomic variant widely screened in various populations and is reported to be associated with the risk of Coronary Artery Disease (CAD) and statin treatment outcome. Recent population based clinical studies and large-scale meta-analyses pondered over the role of 719Arg variant in CAD risk and treatment response. We screened the KIF6 Trp719Arg polymorphism (rs20455) in south Indian CAD patients in a case–control approach. A total of 1042 samples (510 CAD patients and 532 controls) were screened for the KIF6 Trp719Arg SNP by TaqMan SNP genotyping assay, followed by meta-analysis of the genotype data of non-Europeans reports. The 719Arg risk genotype (GG) was observed in 29.6% of CAD cases and in 30.1% of controls with an odds ratio (OR) of 1.07 (95% CI: 0.76–1.50), p value = 0.709. No significant difference in the genotype frequency was observed between CAD and controls in both dominant model (AG + GG vs AA) and allelic model (719Arg vs 719Trp) with an OR of 1.11 (p = 0.491) and 1.03 (p = 0.767), respectively. The covariate analysis indicated that smoking & alcohol consumption increased the risk for MI among CAD patients. Meta-analysis showed that the KIF6 719Arg allele is not associated with CAD risk in both fixed effect (p = 0.515, OR = 1.023, 95% CI = 0.956–1.094) and random effect (p = 0.547, OR = 1.022, 95% CI = 0.953–1.096). The symmetrical shape of the Egger's funnel plots revealed that there is no publication bias. These results suggest that there is no association of KIF6 719Arg allele with CAD risk in South Indian population and the meta-analysis confirms the same among non-European population.
    Meta Gene 07/2015; 5. DOI:10.1016/j.mgene.2015.07.001
  • Source
    • "use polygenic methods to examine complex traits, with relatively good success (Morrison et al. 2007; Kathiresan et al. 2009; International Schizophrenia Consortium et al. 2009; International Multiple Sclerosis Genetics Consortium [IMSGC]et al. 2010; Lango Allen 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. "
    [Show abstract] [Hide abstract]
    ABSTRACT: It has been suggested that depression is a polygenic trait, arising from the influences of multiple loci with small individual effects. The aim of this study is to generate a polygenic risk score (PRS) to examine the association between genetic variation and depressive symptoms. Our analytic sample included N = 10,091 participants aged 50 and older from the Health and Retirement Study (HRS). Depressive symptoms were measured by Center for Epidemiological Studies–Depression scale (CESD) scores assessed on up to nine occasions across 18 years. We conducted a genome-wide association analysis for a discovery set (n = 7,000) and used the top 11 single-nucleotide polymorphisms, all with p < 10−5 to generate a weighted PRS for our replication sample (n = 3,091). Results showed that the PRS was significantly associated with mean CESD score in the replication sample (β = .08, p = .002). The R2 change for the inclusion of the PRS was .003. Using a multinomial logistic regression model, we also examined the association between genetic risk and chronicity of high (4+) CESD scores. We found that a one-standard-deviation increase in PRS was associated with a 36 percent increase in the odds of having chronically high CESD scores relative to never having had high CESD scores. Our findings are consistent with depression being a polygenic trait and suggest that the cumulative influence of multiple variants increases an individual’s susceptibility for chronically experiencing high levels of depressive symptoms.
    Biodemography and Social Biology 10/2014; 20(2):199. DOI:10.1080/19485565.2014.952705 · 1.37 Impact Factor
  • Source
    • "Some genetic risk scores include thousands of variants only weakly linked with disease (Wray, Goddard, and Visscher 2007; Purcell et al. 2009; Evans, Visscher, and Wray 2009). Others are composed of a handful of markers with well-established evidence of association with a phenotype of interest (Morrison et al. 2007; Kathiresan et al. 2008; Ripatti et al. 2010). "
    [Show abstract] [Hide abstract]
    ABSTRACT: The sequencing of the human genome and the advent of low-cost genome-wide assays that generate millions of observations of individual genomes in a matter of hours constitute a disruptive innovation for social science. Many public use social science datasets have or will soon add genome-wide genetic data. With these new data come technical challenges, but also new possibilities. Among these, the lowest-hanging fruit and the most potentially disruptive to existing research programs is the ability to measure previously invisible contours of health and disease risk within populations. In this article, we outline why now is the time for social scientists to bring genetics into their research programs. We discuss how to select genetic variants to study. We explain how the polygenic architecture of complex traits and the low penetrance of individual genetic loci pose challenges to research integrating genetics and social science. We introduce genetic risk scores as a method of addressing these challenges and provide guidance on how genetic risk scores can be constructed. We conclude by outlining research questions that are ripe for social science inquiry.
    Biodemography and Social Biology 10/2014; 60(2):137-55. DOI:10.1080/19485565.2014.946591 · 1.37 Impact Factor
Show more


9 Reads