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

Sebastian Okser, Terho Lehtimäki, Laura L. Elo, Nina Mononen, Nina Peltonen, Mika Kähönen, Markus Juonala, Yue-Mei Fan, Jussi A. Hernesniemi, Tomi Laitinen, Leo-Pekka Lyytikäinen, Riikka Rontu, Carit Eklund, Genetic Variants and Their Interactions in the Prediction of Increased Pre-Clinical Carotid Atherosclerosis: The Cardiovascular Risk in Young Finns Study. PLoS Genetics , 2010.

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.

BibTeX entry:

@ARTICLE{jOkLeElMoPeKaJuFaHeLaLyRoEkHuTaHuViRaAi10a,
  title = {Genetic Variants and Their Interactions in the Prediction of Increased Pre-Clinical Carotid Atherosclerosis: The Cardiovascular Risk in Young Finns Study},
  author = {Okser, Sebastian and Lehtimäki, Terho and Elo, Laura L. and Mononen, Nina and Peltonen, Nina and Kähönen, Mika and Juonala, Markus and Fan, Yue-Mei and Hernesniemi, Jussi A. and Laitinen, Tomi and Lyytikäinen, Leo-Pekka and Rontu, Riikka and Eklund, Carit},
  journal = {PLoS Genetics},
  year = {2010},
}

Belongs to TUCS Research Unit(s): Turku BioNLP Group, Biomathematics Research Unit (BIOMATH)

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