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SNP set association analysis for genome-wide association studies.

Cai M, Dai H, Qiu Y, Zhao Y, Zhang R, Chu M, Dai J, Hu Z, Shen H, Chen F - PLoS ONE (2013)

Bottom Line: Genome-wide association study (GWAS) is a promising approach for identifying common genetic variants of the diseases on the basis of millions of single nucleotide polymorphisms (SNPs).Simulated SNP sets are generated under scenarios of 0, 1 and ≥ 2 causal SNPs model.We also apply these four methods to a real GWAS of non-small cell lung cancer (NSCLC) in Han Chinese population.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China.

ABSTRACT
Genome-wide association study (GWAS) is a promising approach for identifying common genetic variants of the diseases on the basis of millions of single nucleotide polymorphisms (SNPs). In order to avoid low power caused by overmuch correction for multiple comparisons in single locus association study, some methods have been proposed by grouping SNPs together into a SNP set based on genomic features, then testing the joint effect of the SNP set. We compare the performances of principal component analysis (PCA), supervised principal component analysis (SPCA), kernel principal component analysis (KPCA), and sliced inverse regression (SIR). Simulated SNP sets are generated under scenarios of 0, 1 and ≥ 2 causal SNPs model. Our simulation results show that all of these methods can control the type I error at the nominal significance level. SPCA is always more powerful than the other methods at different settings of linkage disequilibrium structures and minor allele frequency of the simulated datasets. We also apply these four methods to a real GWAS of non-small cell lung cancer (NSCLC) in Han Chinese population.

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Related in: MedlinePlus

Test powers at single causal SNP model based on 10 SNPs.The plot shows the powers (y-axis) of each method over the different LD and MAF structures (x-axis). The first line of x-axis represents LD, and the bottom line is MAF.
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pone-0062495-g001: Test powers at single causal SNP model based on 10 SNPs.The plot shows the powers (y-axis) of each method over the different LD and MAF structures (x-axis). The first line of x-axis represents LD, and the bottom line is MAF.

Mentions: Results from the simulations on scenarios A4–A6 are presented by Figure 1, which shows that SPCA has the best power. As MAF is fixed as 0.05, 0.1 or 0.2 and LD is set as 0.2, powers of PCA,KPCA and SIR are approximate, which are respectively near 20%, 35% and 60%. At the same time, the power of SPCA is 22.6%, 45.1% and 69.2%. When LD is 0.5, KPCA is about 7% more powerful than PCA. When LD is strong, the power of KPCA is close to that of SPCA. The power of SIR is lower than the other methods in most scenarios.


SNP set association analysis for genome-wide association studies.

Cai M, Dai H, Qiu Y, Zhao Y, Zhang R, Chu M, Dai J, Hu Z, Shen H, Chen F - PLoS ONE (2013)

Test powers at single causal SNP model based on 10 SNPs.The plot shows the powers (y-axis) of each method over the different LD and MAF structures (x-axis). The first line of x-axis represents LD, and the bottom line is MAF.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3643925&req=5

pone-0062495-g001: Test powers at single causal SNP model based on 10 SNPs.The plot shows the powers (y-axis) of each method over the different LD and MAF structures (x-axis). The first line of x-axis represents LD, and the bottom line is MAF.
Mentions: Results from the simulations on scenarios A4–A6 are presented by Figure 1, which shows that SPCA has the best power. As MAF is fixed as 0.05, 0.1 or 0.2 and LD is set as 0.2, powers of PCA,KPCA and SIR are approximate, which are respectively near 20%, 35% and 60%. At the same time, the power of SPCA is 22.6%, 45.1% and 69.2%. When LD is 0.5, KPCA is about 7% more powerful than PCA. When LD is strong, the power of KPCA is close to that of SPCA. The power of SIR is lower than the other methods in most scenarios.

Bottom Line: Genome-wide association study (GWAS) is a promising approach for identifying common genetic variants of the diseases on the basis of millions of single nucleotide polymorphisms (SNPs).Simulated SNP sets are generated under scenarios of 0, 1 and ≥ 2 causal SNPs model.We also apply these four methods to a real GWAS of non-small cell lung cancer (NSCLC) in Han Chinese population.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China.

ABSTRACT
Genome-wide association study (GWAS) is a promising approach for identifying common genetic variants of the diseases on the basis of millions of single nucleotide polymorphisms (SNPs). In order to avoid low power caused by overmuch correction for multiple comparisons in single locus association study, some methods have been proposed by grouping SNPs together into a SNP set based on genomic features, then testing the joint effect of the SNP set. We compare the performances of principal component analysis (PCA), supervised principal component analysis (SPCA), kernel principal component analysis (KPCA), and sliced inverse regression (SIR). Simulated SNP sets are generated under scenarios of 0, 1 and ≥ 2 causal SNPs model. Our simulation results show that all of these methods can control the type I error at the nominal significance level. SPCA is always more powerful than the other methods at different settings of linkage disequilibrium structures and minor allele frequency of the simulated datasets. We also apply these four methods to a real GWAS of non-small cell lung cancer (NSCLC) in Han Chinese population.

Show MeSH
Related in: MedlinePlus