Genetic Variants and Their Interactions in the Prediction of Increased Pre-Clinical Carotid Atherosclerosis: The Cardiovascular Risk in Young Finns Study

Abstract

The relative contribution of genetic risk factors to the progression of subclinical atherosclerosis is poorly understood. It is likely that multiple variants are implicated in the development of atherosclerosis, but the subtle genotypic and phenotypic differences are beyond the reach of the conventional case-control designs and the statistical significance testing procedures being used in most association studies. Our objective here was to investigate whether an alternative approach—in which common disorders are treated as quantitative phenotypes that are continuously distributed over a population—can reveal predictive insights into the early atherosclerosis, as assessed using ultrasound imaging-based quantitative measurement of carotid artery intima-media thickness (IMT). Using our population-based follow-up study of atherosclerosis precursors as a basis for sampling subjects with gradually increasing IMT levels, we searched for such subsets of genetic variants and their interactions that are the most predictive of the various risk classes, rather than using exclusively those variants meeting a stringent level of statistical significance. The area under the receiver operating characteristic curve (AUC) was used to evaluate the predictive value of the variants, and cross-validation was used to assess how well the predictive models will generalize to other subsets of subjects. By means of our predictive modeling framework with machine learning-based SNP selection, we could improve the prediction of the extreme classes of atherosclerosis risk and progression over a 6-year period (average AUC 0.844 and 0.761), compared to that of using conventional cardiovascular risk factors alone (average AUC 0.741 and 0.629), or when combined with the statistically significant variants (average AUC 0.762 and 0.651). The predictive accuracy remained relatively high in an independent validation set of subjects (average decrease of 0.043). These results demonstrate that the modeling framework can utilize the “gray zone” of genetic variation in the classification of subjects with different degrees of risk of developing atherosclerosis.

GCKR gene functional variants in type 2 diabetes and metabolic syndrome: do the rare variants associate with increased carotid intima-media thickness?

“…N-epsilon-carboxymethyllysine) confer an independent significant risk for cardiovascular diseases and associated with atherosclerotic lesions not only in diabetic but in normoglycemic subjects [29-34]. …”

Multidimensional Prognostic Risk Assessment Identifies Association Between IL12B Variation and Surgery in Crohn’s Disease

“…Recent studies have extended the number of CD loci achieving association at a genome-wide level to 71 (20–22). In looking for genes that predict natural history in CD, a logical place to start would be with the known susceptibility loci, but it is also quite possible that non-susceptibility loci contribute to disease behavior, as has been demonstrated in coronary artery disease (23, 24). These concepts justify testing associations with known CD loci and identifying novel loci using a whole genome approach for finding loci that influence the development of complications and surgery.…”

Fast and parallelized greedy forward selection of genetic variants in Genome-wide association studies

“…As genetic changes typically take long periods of time to manifest themselves, it is believed that GWAS can not only yield the power to determine an individual's present disease status, but also the future class status of subjects [1]- [3]. While these studies have yielded numerous significant loci with positive relationships to various disease phenotypes, it has often been the case that these variants have only explained a limited amount of the heritability of the disease.…”

“…The fundamental flaw in this approach is that it does not account for epistasis interactions amongst the genetic loci, but rather assumes that the interactions between SNPs are negligible in regards to disease onset [3]. To help account for these synergistic SNPs, recent studies have begun to make use of more advanced machine learning algorithms that can account for the manner in which multiple polymorphisms work together to help express or repress an individual's disease phenotype.…”

“…These methods select the subset of features optimal for the classification method. This approach has been shown to select better subsets of SNPs, though they have not previously been thought to be a plausible alternative for GWAS because of the computational intensity of the algorithms [3].…”