Key result
Logic regression identifies gene-environment interactions linked to risk of myocardial infarction and stroke.
Why the study?
Few methods exist for identifying interaction effects among multiple SNPs or between SNPs and environmental factors in genetic association studies of myocardial infarction and stroke.
Case-Control (n=1,614)
Answers are grounded in this paper and cite their sources.
May support exploratory gene-environment interaction analyses in MI and stroke; leaves open clinical translation pending validation.
Recent developments in genetic sequencing technology now make it possible to genotype large numbers of single nucleotide polymorphisms (SNPs) in large samples. Many association studies using SNP data are now being carried out. Typically, these observational studies establish whether certain haplotypes or individual SNPs are associated with a health outcome. Few methods exist for finding interaction effects among multiple SNPs or between SNPs and environmental factors. In this paper, the authors describe logic regression, an exploratory method with which to identify interactions for further research. They illustrate this method using data from a US case-control study of myocardial infarction and stroke (1995-1999) carried out among 1,614 persons in Washington State who were genotyped for 32 SNPs on five genes in the renin-angiotensin system.
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Kooperberg et al. (2006) conducted a case-control in Myocardial infarction and stroke (n=1,614). Genetic variation in the renin-angiotensin system (32 SNPs) was evaluated on Myocardial infarction or stroke. Logic regression was introduced as an exploratory method to identify interaction effects among multiple genetic variants and environmental factors for outcomes like myocardial infarction and stroke.
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