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Pathways of distinction analysis: a new technique for multi-SNP analysis of GWAS data.

Braun R, Buetow K - PLoS Genet. (2011)

Bottom Line: Importantly, PoDA improves on existing single-SNP and SNP-set enrichment analyses, in that it does not require the SNPs in a pathway to exhibit independent main effects.The results obtained strongly suggest that there exist pathway-wide genomic differences that contribute to disease susceptibility.PoDA thus provides an analytical tool that is complementary to existing techniques and has the power to enrich our understanding of disease genomics at the systems-level.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Population Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America.

ABSTRACT
Genome-wide association studies (GWAS) have become increasingly common due to advances in technology and have permitted the identification of differences in single nucleotide polymorphism (SNP) alleles that are associated with diseases. However, while typical GWAS analysis techniques treat markers individually, complex diseases (cancers, diabetes, and Alzheimers, amongst others) are unlikely to have a single causative gene. Thus, there is a pressing need for multi-SNP analysis methods that can reveal system-level differences in cases and controls. Here, we present a novel multi-SNP GWAS analysis method called Pathways of Distinction Analysis (PoDA). The method uses GWAS data and known pathway-gene and gene-SNP associations to identify pathways that permit, ideally, the distinction of cases from controls. The technique is based upon the hypothesis that, if a pathway is related to disease risk, cases will appear more similar to other cases than to controls (or vice versa) for the SNPs associated with that pathway. By systematically applying the method to all pathways of potential interest, we can identify those for which the hypothesis holds true, i.e., pathways containing SNPs for which the samples exhibit greater within-class similarity than across classes. Importantly, PoDA improves on existing single-SNP and SNP-set enrichment analyses, in that it does not require the SNPs in a pathway to exhibit independent main effects. This permits PoDA to reveal pathways in which epistatic interactions drive risk. In this paper, we detail the PoDA method and apply it to two GWAS: one of breast cancer and the other of liver cancer. The results obtained strongly suggest that there exist pathway-wide genomic differences that contribute to disease susceptibility. PoDA thus provides an analytical tool that is complementary to existing techniques and has the power to enrich our understanding of disease genomics at the systems-level.

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Related in: MedlinePlus

PoDA applied to simulated data.Alleles at 50 loci for 250 cases and 250 controls were simulated such that each SNP was in HWE and not associated with case status, but homozygous minor (red) at both loci 1 and 2 or 1 and 3 yielded a three-fold relative risk (a). A 12-SNP pathway comprising SNPs 1–12 shows differential  distributions (b); a random 12-SNP pathway does not (c). Boxplots are overlayed on the scatterplots of  for clarity.
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pgen-1002101-g001: PoDA applied to simulated data.Alleles at 50 loci for 250 cases and 250 controls were simulated such that each SNP was in HWE and not associated with case status, but homozygous minor (red) at both loci 1 and 2 or 1 and 3 yielded a three-fold relative risk (a). A 12-SNP pathway comprising SNPs 1–12 shows differential distributions (b); a random 12-SNP pathway does not (c). Boxplots are overlayed on the scatterplots of for clarity.

Mentions: To illustrate the above, we consider a simulated GWAS of 250 cases and 250 controls and 50 SNPs, shown in Figure 1, and ask whether we are able to detect a 12-SNP pathway in which a subset of SNPs appear to have an epistatic interaction. Alleles were simulated as binomial samples from a source population with MAFs ranging from 0.1 to 0.4 across the 50 SNPs, and case labels were assigned such that a combintion of homozygous minor alleles at SNPs 1 and 2 or 3 (i.e., ) conferred a three-fold relative risk, mimicking an epistatic interaction between SNPs 1 and 2 and SNPs 1 and 3 (Figure 1a). Alone, none of the 50 SNPs showed any association with case status, nor was any SNP significantly out of HWE in either cases or controls. However, the “shared pattern” in SNPs 1–3 is such that a 12 SNP pathway comprising SNPs 1–12 yields greater in cases than in controls as can been seen in Figure 1b, while a random 12 SNP pathway selected from the 50 SNPs (that includes SNP 3, but neither SNP 1 or 2) shows no difference in values as seen in Figure 1c.


Pathways of distinction analysis: a new technique for multi-SNP analysis of GWAS data.

Braun R, Buetow K - PLoS Genet. (2011)

PoDA applied to simulated data.Alleles at 50 loci for 250 cases and 250 controls were simulated such that each SNP was in HWE and not associated with case status, but homozygous minor (red) at both loci 1 and 2 or 1 and 3 yielded a three-fold relative risk (a). A 12-SNP pathway comprising SNPs 1–12 shows differential  distributions (b); a random 12-SNP pathway does not (c). Boxplots are overlayed on the scatterplots of  for clarity.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3111473&req=5

pgen-1002101-g001: PoDA applied to simulated data.Alleles at 50 loci for 250 cases and 250 controls were simulated such that each SNP was in HWE and not associated with case status, but homozygous minor (red) at both loci 1 and 2 or 1 and 3 yielded a three-fold relative risk (a). A 12-SNP pathway comprising SNPs 1–12 shows differential distributions (b); a random 12-SNP pathway does not (c). Boxplots are overlayed on the scatterplots of for clarity.
Mentions: To illustrate the above, we consider a simulated GWAS of 250 cases and 250 controls and 50 SNPs, shown in Figure 1, and ask whether we are able to detect a 12-SNP pathway in which a subset of SNPs appear to have an epistatic interaction. Alleles were simulated as binomial samples from a source population with MAFs ranging from 0.1 to 0.4 across the 50 SNPs, and case labels were assigned such that a combintion of homozygous minor alleles at SNPs 1 and 2 or 3 (i.e., ) conferred a three-fold relative risk, mimicking an epistatic interaction between SNPs 1 and 2 and SNPs 1 and 3 (Figure 1a). Alone, none of the 50 SNPs showed any association with case status, nor was any SNP significantly out of HWE in either cases or controls. However, the “shared pattern” in SNPs 1–3 is such that a 12 SNP pathway comprising SNPs 1–12 yields greater in cases than in controls as can been seen in Figure 1b, while a random 12 SNP pathway selected from the 50 SNPs (that includes SNP 3, but neither SNP 1 or 2) shows no difference in values as seen in Figure 1c.

Bottom Line: Importantly, PoDA improves on existing single-SNP and SNP-set enrichment analyses, in that it does not require the SNPs in a pathway to exhibit independent main effects.The results obtained strongly suggest that there exist pathway-wide genomic differences that contribute to disease susceptibility.PoDA thus provides an analytical tool that is complementary to existing techniques and has the power to enrich our understanding of disease genomics at the systems-level.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Population Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America.

ABSTRACT
Genome-wide association studies (GWAS) have become increasingly common due to advances in technology and have permitted the identification of differences in single nucleotide polymorphism (SNP) alleles that are associated with diseases. However, while typical GWAS analysis techniques treat markers individually, complex diseases (cancers, diabetes, and Alzheimers, amongst others) are unlikely to have a single causative gene. Thus, there is a pressing need for multi-SNP analysis methods that can reveal system-level differences in cases and controls. Here, we present a novel multi-SNP GWAS analysis method called Pathways of Distinction Analysis (PoDA). The method uses GWAS data and known pathway-gene and gene-SNP associations to identify pathways that permit, ideally, the distinction of cases from controls. The technique is based upon the hypothesis that, if a pathway is related to disease risk, cases will appear more similar to other cases than to controls (or vice versa) for the SNPs associated with that pathway. By systematically applying the method to all pathways of potential interest, we can identify those for which the hypothesis holds true, i.e., pathways containing SNPs for which the samples exhibit greater within-class similarity than across classes. Importantly, PoDA improves on existing single-SNP and SNP-set enrichment analyses, in that it does not require the SNPs in a pathway to exhibit independent main effects. This permits PoDA to reveal pathways in which epistatic interactions drive risk. In this paper, we detail the PoDA method and apply it to two GWAS: one of breast cancer and the other of liver cancer. The results obtained strongly suggest that there exist pathway-wide genomic differences that contribute to disease susceptibility. PoDA thus provides an analytical tool that is complementary to existing techniques and has the power to enrich our understanding of disease genomics at the systems-level.

Show MeSH
Related in: MedlinePlus