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Gene-gene and gene-environment interactions: new insights into the prevention, detection and management of coronary artery disease.

Lanktree MB, Hegele RA - Genome Med (2009)

Bottom Line: In addition, small-scale candidate gene association studies with functional hypotheses have identified gene-environment interactions.For future evaluation of gene-gene and gene-environment interactions to achieve the same success as the single gene associations reported in recent GWASs, it will be important to pre-specify agreed standards of study design and statistical power, environmental exposure measurement, phenomic characterization and analytical strategies.Here we discuss these issues, particularly in relation to the investigation and potential clinical utility of gene-gene and gene-environment interactions in CAD.

View Article: PubMed Central - HTML - PubMed

Affiliation: Departments of Medicine and Biochemistry, Blackburn Cardiovascular Genetics Laboratory, Robarts Research Institute, University of Western Ontario, London, Ontario N6A 5K8, Canada.

ABSTRACT
Despite the recent success of genome-wide association studies (GWASs) in identifying loci consistently associated with coronary artery disease (CAD), a large proportion of the genetic components of CAD and its metabolic risk factors, including plasma lipids, type 2 diabetes and body mass index, remain unattributed. Gene-gene and gene-environment interactions might produce a meaningful improvement in quantification of the genetic determinants of CAD. Testing for gene-gene and gene-environment interactions is thus a new frontier for large-scale GWASs of CAD. There are several anecdotal examples of monogenic susceptibility to CAD in which the phenotype was worsened by an adverse environment. In addition, small-scale candidate gene association studies with functional hypotheses have identified gene-environment interactions. For future evaluation of gene-gene and gene-environment interactions to achieve the same success as the single gene associations reported in recent GWASs, it will be important to pre-specify agreed standards of study design and statistical power, environmental exposure measurement, phenomic characterization and analytical strategies. Here we discuss these issues, particularly in relation to the investigation and potential clinical utility of gene-gene and gene-environment interactions in CAD.

No MeSH data available.


Related in: MedlinePlus

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Mentions: Statistical power is directly proportional to the number of study participants and to the size of the effect under study. Factors to be included in power calculations of all genetic investigations include the minor allele frequency, the degree of linkage disequilibrium between the queried marker and the hypothetical disease locus, the genotype error rate and the genetic or phenotypic heterogeneity (Box 2). Fortunately, high-throughput genotyping platforms have a negligible genotype error rate [37]. Correction for multiple comparisons and the measurement error of environmental exposures also influence study power [1,2]. As a result of the greater accuracy of genotyping compared with the measurement or report of environmental exposures, there is theoretically more power to detect a gene-gene interaction than a gene-environment interaction for the same sized sample. Studies with inaccurate or imprecise measurement of phenotype or environmental exposure may require up to 20 times larger samples to detect an association signal above background noise [36]. However, the power advantage of gene-gene investigations resulting from their higher measurement accuracy is diminished by the need to correct for multiple comparisons and by the potentially increased complexity of interactions compared with gene-environment investigations.


Gene-gene and gene-environment interactions: new insights into the prevention, detection and management of coronary artery disease.

Lanktree MB, Hegele RA - Genome Med (2009)

© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2664961&req=5

Mentions: Statistical power is directly proportional to the number of study participants and to the size of the effect under study. Factors to be included in power calculations of all genetic investigations include the minor allele frequency, the degree of linkage disequilibrium between the queried marker and the hypothetical disease locus, the genotype error rate and the genetic or phenotypic heterogeneity (Box 2). Fortunately, high-throughput genotyping platforms have a negligible genotype error rate [37]. Correction for multiple comparisons and the measurement error of environmental exposures also influence study power [1,2]. As a result of the greater accuracy of genotyping compared with the measurement or report of environmental exposures, there is theoretically more power to detect a gene-gene interaction than a gene-environment interaction for the same sized sample. Studies with inaccurate or imprecise measurement of phenotype or environmental exposure may require up to 20 times larger samples to detect an association signal above background noise [36]. However, the power advantage of gene-gene investigations resulting from their higher measurement accuracy is diminished by the need to correct for multiple comparisons and by the potentially increased complexity of interactions compared with gene-environment investigations.

Bottom Line: In addition, small-scale candidate gene association studies with functional hypotheses have identified gene-environment interactions.For future evaluation of gene-gene and gene-environment interactions to achieve the same success as the single gene associations reported in recent GWASs, it will be important to pre-specify agreed standards of study design and statistical power, environmental exposure measurement, phenomic characterization and analytical strategies.Here we discuss these issues, particularly in relation to the investigation and potential clinical utility of gene-gene and gene-environment interactions in CAD.

View Article: PubMed Central - HTML - PubMed

Affiliation: Departments of Medicine and Biochemistry, Blackburn Cardiovascular Genetics Laboratory, Robarts Research Institute, University of Western Ontario, London, Ontario N6A 5K8, Canada.

ABSTRACT
Despite the recent success of genome-wide association studies (GWASs) in identifying loci consistently associated with coronary artery disease (CAD), a large proportion of the genetic components of CAD and its metabolic risk factors, including plasma lipids, type 2 diabetes and body mass index, remain unattributed. Gene-gene and gene-environment interactions might produce a meaningful improvement in quantification of the genetic determinants of CAD. Testing for gene-gene and gene-environment interactions is thus a new frontier for large-scale GWASs of CAD. There are several anecdotal examples of monogenic susceptibility to CAD in which the phenotype was worsened by an adverse environment. In addition, small-scale candidate gene association studies with functional hypotheses have identified gene-environment interactions. For future evaluation of gene-gene and gene-environment interactions to achieve the same success as the single gene associations reported in recent GWASs, it will be important to pre-specify agreed standards of study design and statistical power, environmental exposure measurement, phenomic characterization and analytical strategies. Here we discuss these issues, particularly in relation to the investigation and potential clinical utility of gene-gene and gene-environment interactions in CAD.

No MeSH data available.


Related in: MedlinePlus