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| Description
| - Background: Myocardial infarction and stroke represent a major public health problem in most developing countries. This study explores genetic predisposition of acute myocardial infarction in the Czech population. Methods and Results: Genome-wide expression study used matched case-control design. Peripheral blood samples of the controls were matched to those of cases based on gender, age, status of diabetes mellitus and smoking status. Six months cardiovascular survival status of the cases was used to identify two distinct subgroups among the cases. Linear models for microarray data were employed to identify differential gene expression. Shrunken centroids technique helped in identifying the subsets of differentially expressed genes with predictive properties in independent samples. Predictive properties were evaluated using bootstrap sampling. Sixty transcripts were found to be both clinically and statistically differentially expressed among the cases not surviving the six months follow-up period relative to controls, while no such transcripts were observed among other surviving cases. The two subgroups of cases exhibited fourteen differentially expressed transcripts. Predictive modeling indicated sixteen out of sixty transcripts to best discriminate between the controls and cases that died during the follow-up period from cardiovascular causes, while for the surviving cases the already non-significant set of transcripts could not be further reduced. Eleven out of fourteen transcripts were found to best discriminate between the two groups of cases using shrunken centroids. Conclusions: The study identified genes associated with excess genetic risk of acute myocardial infarction, including those associated with the six months fatality of the cases.
- Background: Myocardial infarction and stroke represent a major public health problem in most developing countries. This study explores genetic predisposition of acute myocardial infarction in the Czech population. Methods and Results: Genome-wide expression study used matched case-control design. Peripheral blood samples of the controls were matched to those of cases based on gender, age, status of diabetes mellitus and smoking status. Six months cardiovascular survival status of the cases was used to identify two distinct subgroups among the cases. Linear models for microarray data were employed to identify differential gene expression. Shrunken centroids technique helped in identifying the subsets of differentially expressed genes with predictive properties in independent samples. Predictive properties were evaluated using bootstrap sampling. Sixty transcripts were found to be both clinically and statistically differentially expressed among the cases not surviving the six months follow-up period relative to controls, while no such transcripts were observed among other surviving cases. The two subgroups of cases exhibited fourteen differentially expressed transcripts. Predictive modeling indicated sixteen out of sixty transcripts to best discriminate between the controls and cases that died during the follow-up period from cardiovascular causes, while for the surviving cases the already non-significant set of transcripts could not be further reduced. Eleven out of fourteen transcripts were found to best discriminate between the two groups of cases using shrunken centroids. Conclusions: The study identified genes associated with excess genetic risk of acute myocardial infarction, including those associated with the six months fatality of the cases. (en)
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| Title
| - Determinants of Excess Genetic Risk of Acute Myocardial Infarction - A Matched Case-Control Study
- Determinants of Excess Genetic Risk of Acute Myocardial Infarction - A Matched Case-Control Study (en)
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| skos:prefLabel
| - Determinants of Excess Genetic Risk of Acute Myocardial Infarction - A Matched Case-Control Study
- Determinants of Excess Genetic Risk of Acute Myocardial Infarction - A Matched Case-Control Study (en)
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| skos:notation
| - RIV/67985807:_____/12:00427421!RIV15-AV0-67985807
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| http://linked.open...avai/riv/aktivita
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| http://linked.open...avai/riv/aktivity
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| http://linked.open...iv/cisloPeriodika
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| http://linked.open...vai/riv/dodaniDat
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| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
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| http://linked.open...iv/duvernostUdaju
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| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
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| http://linked.open...ai/riv/idVysledku
| - RIV/67985807:_____/12:00427421
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| http://linked.open...riv/jazykVysledku
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| http://linked.open.../riv/klicovaSlova
| - genome-wide association study; gene expression; myocardial infarction; genetic predisposition; predictive modeling (en)
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| http://linked.open.../riv/klicoveSlovo
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| http://linked.open...odStatuVydavatele
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| http://linked.open...ontrolniKodProRIV
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| http://linked.open...i/riv/nazevZdroje
| - European Journal for Biomedical Informatics
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| http://linked.open...in/vavai/riv/obor
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| http://linked.open...ichTvurcuVysledku
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| http://linked.open...cetTvurcuVysledku
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| http://linked.open...vavai/riv/projekt
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| http://linked.open...UplatneniVysledku
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| http://linked.open...v/svazekPeriodika
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| http://linked.open...iv/tvurceVysledku
| - Kolář, M.
- Peleška, Jan
- Slovák, Dalibor
- Tomečková, Marie
- Zvárová, Jana
- Kalina, Jan
- Valenta, Zdeněk
- Mazura, Ivan
- Feglarová, Petra
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| issn
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| number of pages
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