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Concept, design and implementation of a cardiovascular gene-centric 50 k SNP array for large-scale genomic association studies.

Keating BJ, Tischfield S, Murray SS, Bhangale T, Price TS, Glessner JT, Galver L, Barrett JC, Grant SF, Farlow DN, Chandrupatla HR, Hansen M, Ajmal S, Papanicolaou GJ, Guo Y, Li M, Derohannessian S, de Bakker PI, Bailey SD, Montpetit A, Edmondson AC, Taylor K, Gai X, Wang SS, Fornage M, Shaikh T, Groop L, Boehnke M, Hall AS, Hattersley AT, Frackelton E, Patterson N, Chiang CW, Kim CE, Fabsitz RR, Ouwehand W, Price AL, Munroe P, Caulfield M, Drake T, Boerwinkle E, Reich D, Whitehead AS, Cappola TP, Samani NJ, Lusis AJ, Schadt E, Wilson JG, Koenig W, McCarthy MI, Kathiresan S, Gabriel SB, Hakonarson H, Anand SS, Reilly M, Engert JC, Nickerson DA, Rader DJ, Hirschhorn JN, Fitzgerald GA - PLoS ONE (2008)

Bottom Line: True complex disease-associated loci often exert modest effects, so their delineation currently requires integration of diverse phenotypic data from large studies to ensure robust meta-analyses.The custom flexibility of the array platform facilitated interrogation of loci at differing stringencies, according to a gene prioritization strategy that allows saturation of high priority loci with a greater density of markers than the existing GWAS tools, particularly in African HapMap samples.DNA from over 200,000 extensively phenotyped individuals will be genotyped with this array with a significant portion of the generated data being released into the academic domain facilitating in silico replication attempts, analyses of rare variants and cross-cohort meta-analyses in diverse populations.

View Article: PubMed Central - PubMed

Affiliation: The Institute for Translational Medicine and Therapeutics, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvannia, USA.

ABSTRACT
A wealth of genetic associations for cardiovascular and metabolic phenotypes in humans has been accumulating over the last decade, in particular a large number of loci derived from recent genome wide association studies (GWAS). True complex disease-associated loci often exert modest effects, so their delineation currently requires integration of diverse phenotypic data from large studies to ensure robust meta-analyses. We have designed a gene-centric 50 K single nucleotide polymorphism (SNP) array to assess potentially relevant loci across a range of cardiovascular, metabolic and inflammatory syndromes. The array utilizes a "cosmopolitan" tagging approach to capture the genetic diversity across approximately 2,000 loci in populations represented in the HapMap and SeattleSNPs projects. The array content is informed by GWAS of vascular and inflammatory disease, expression quantitative trait loci implicated in atherosclerosis, pathway based approaches and comprehensive literature searching. The custom flexibility of the array platform facilitated interrogation of loci at differing stringencies, according to a gene prioritization strategy that allows saturation of high priority loci with a greater density of markers than the existing GWAS tools, particularly in African HapMap samples. We also demonstrate that the IBC array can be used to complement GWAS, increasing coverage in high priority CVD-related loci across all major HapMap populations. DNA from over 200,000 extensively phenotyped individuals will be genotyped with this array with a significant portion of the generated data being released into the academic domain facilitating in silico replication attempts, analyses of rare variants and cross-cohort meta-analyses in diverse populations. These datasets will also facilitate more robust secondary analyses, such as explorations with alternative genetic models, epistasis and gene-environment interactions.

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Power to detect main effect with 550 K SNPs using various case control sizes & MAFs.Genome wide association power calculated based on n unrelated cases and n unrelated controls. The disease model is assumed to be multiplicative with disease minor allele frequency (MAF) = 0.05, 0.1, and 0.2, and the odds ratio = 1.2, 1.4, and 1.6. Significance is assessed at the 5% level using Bonferroni correction assuming 550 K tests.
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pone-0003583-g001: Power to detect main effect with 550 K SNPs using various case control sizes & MAFs.Genome wide association power calculated based on n unrelated cases and n unrelated controls. The disease model is assumed to be multiplicative with disease minor allele frequency (MAF) = 0.05, 0.1, and 0.2, and the odds ratio = 1.2, 1.4, and 1.6. Significance is assessed at the 5% level using Bonferroni correction assuming 550 K tests.

Mentions: Cardiovascular disease (CVD), the leading cause of death in the developed world [1], has been shown to have significant heritability [2]–[6]. The pattern of CVD in developed countries has changed as the detection and management of risk factors such as hypertension, hypercholesterolemia and predisposition to thrombosis has coincided with a decline in the incidence of myocardial infarction (MI) and stroke [7]. Efforts to discover genetic determinants of complex disease have included analyses of genetic variation, using SNPs, between populations of individuals differing in incident or prevalent disease traits and/or clinical events. However, many apparent associations have not replicated for reasons including inadequate sample size, imprecise or inaccurate phenotyping, insufficiently stringent statistical thresholds, genuine heterogeneity of causality and population stratification [8], [9]. The International HapMap Project [10], combined with advances in genotyping technologies, has led to the generation of multiple array-based SNP genotyping products for GWAS. These developments enable reasonably dense and unbiased global scans of the human genome which have already identified novel loci associated with CVD [11]–[14]. Despite the value of the GWAS approach, a number of limitations exist, including cost and incomplete coverage in the HapMap samples. GWAS also have relatively low power to detect subtle, but potentially important effects, in studies of “typical” sample sizes. For example, calculations of the general power to detect a primary effect using an array with >500 K SNPs are depicted in Figure 1.


Concept, design and implementation of a cardiovascular gene-centric 50 k SNP array for large-scale genomic association studies.

Keating BJ, Tischfield S, Murray SS, Bhangale T, Price TS, Glessner JT, Galver L, Barrett JC, Grant SF, Farlow DN, Chandrupatla HR, Hansen M, Ajmal S, Papanicolaou GJ, Guo Y, Li M, Derohannessian S, de Bakker PI, Bailey SD, Montpetit A, Edmondson AC, Taylor K, Gai X, Wang SS, Fornage M, Shaikh T, Groop L, Boehnke M, Hall AS, Hattersley AT, Frackelton E, Patterson N, Chiang CW, Kim CE, Fabsitz RR, Ouwehand W, Price AL, Munroe P, Caulfield M, Drake T, Boerwinkle E, Reich D, Whitehead AS, Cappola TP, Samani NJ, Lusis AJ, Schadt E, Wilson JG, Koenig W, McCarthy MI, Kathiresan S, Gabriel SB, Hakonarson H, Anand SS, Reilly M, Engert JC, Nickerson DA, Rader DJ, Hirschhorn JN, Fitzgerald GA - PLoS ONE (2008)

Power to detect main effect with 550 K SNPs using various case control sizes & MAFs.Genome wide association power calculated based on n unrelated cases and n unrelated controls. The disease model is assumed to be multiplicative with disease minor allele frequency (MAF) = 0.05, 0.1, and 0.2, and the odds ratio = 1.2, 1.4, and 1.6. Significance is assessed at the 5% level using Bonferroni correction assuming 550 K tests.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2571995&req=5

pone-0003583-g001: Power to detect main effect with 550 K SNPs using various case control sizes & MAFs.Genome wide association power calculated based on n unrelated cases and n unrelated controls. The disease model is assumed to be multiplicative with disease minor allele frequency (MAF) = 0.05, 0.1, and 0.2, and the odds ratio = 1.2, 1.4, and 1.6. Significance is assessed at the 5% level using Bonferroni correction assuming 550 K tests.
Mentions: Cardiovascular disease (CVD), the leading cause of death in the developed world [1], has been shown to have significant heritability [2]–[6]. The pattern of CVD in developed countries has changed as the detection and management of risk factors such as hypertension, hypercholesterolemia and predisposition to thrombosis has coincided with a decline in the incidence of myocardial infarction (MI) and stroke [7]. Efforts to discover genetic determinants of complex disease have included analyses of genetic variation, using SNPs, between populations of individuals differing in incident or prevalent disease traits and/or clinical events. However, many apparent associations have not replicated for reasons including inadequate sample size, imprecise or inaccurate phenotyping, insufficiently stringent statistical thresholds, genuine heterogeneity of causality and population stratification [8], [9]. The International HapMap Project [10], combined with advances in genotyping technologies, has led to the generation of multiple array-based SNP genotyping products for GWAS. These developments enable reasonably dense and unbiased global scans of the human genome which have already identified novel loci associated with CVD [11]–[14]. Despite the value of the GWAS approach, a number of limitations exist, including cost and incomplete coverage in the HapMap samples. GWAS also have relatively low power to detect subtle, but potentially important effects, in studies of “typical” sample sizes. For example, calculations of the general power to detect a primary effect using an array with >500 K SNPs are depicted in Figure 1.

Bottom Line: True complex disease-associated loci often exert modest effects, so their delineation currently requires integration of diverse phenotypic data from large studies to ensure robust meta-analyses.The custom flexibility of the array platform facilitated interrogation of loci at differing stringencies, according to a gene prioritization strategy that allows saturation of high priority loci with a greater density of markers than the existing GWAS tools, particularly in African HapMap samples.DNA from over 200,000 extensively phenotyped individuals will be genotyped with this array with a significant portion of the generated data being released into the academic domain facilitating in silico replication attempts, analyses of rare variants and cross-cohort meta-analyses in diverse populations.

View Article: PubMed Central - PubMed

Affiliation: The Institute for Translational Medicine and Therapeutics, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvannia, USA.

ABSTRACT
A wealth of genetic associations for cardiovascular and metabolic phenotypes in humans has been accumulating over the last decade, in particular a large number of loci derived from recent genome wide association studies (GWAS). True complex disease-associated loci often exert modest effects, so their delineation currently requires integration of diverse phenotypic data from large studies to ensure robust meta-analyses. We have designed a gene-centric 50 K single nucleotide polymorphism (SNP) array to assess potentially relevant loci across a range of cardiovascular, metabolic and inflammatory syndromes. The array utilizes a "cosmopolitan" tagging approach to capture the genetic diversity across approximately 2,000 loci in populations represented in the HapMap and SeattleSNPs projects. The array content is informed by GWAS of vascular and inflammatory disease, expression quantitative trait loci implicated in atherosclerosis, pathway based approaches and comprehensive literature searching. The custom flexibility of the array platform facilitated interrogation of loci at differing stringencies, according to a gene prioritization strategy that allows saturation of high priority loci with a greater density of markers than the existing GWAS tools, particularly in African HapMap samples. We also demonstrate that the IBC array can be used to complement GWAS, increasing coverage in high priority CVD-related loci across all major HapMap populations. DNA from over 200,000 extensively phenotyped individuals will be genotyped with this array with a significant portion of the generated data being released into the academic domain facilitating in silico replication attempts, analyses of rare variants and cross-cohort meta-analyses in diverse populations. These datasets will also facilitate more robust secondary analyses, such as explorations with alternative genetic models, epistasis and gene-environment interactions.

Show MeSH
Related in: MedlinePlus