Editors' ChoiceGenetics

It’s in Your Gene Pathways

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Science Translational Medicine  13 Jul 2011:
Vol. 3, Issue 91, pp. 91ec109
DOI: 10.1126/scitranslmed.3002850

No single gene is responsible for the inherited risk of developing many complex diseases, including cancer, osteoporosis, and cardiovascular disease. Instead, it is suspected that several genes working together within pathways lead to dysfunction in these systems. Genome-wide genotyping arrays include hundreds of thousands of single-nucleotide polymorphisms (SNPs) that represent the genetic variation in tens of thousands of genes. Thus, the sheer computational complexity of identifying disease genotype combinations has been a challenge. Braun and Buetow take advantage of the fact that many genes have known roles in pathways. Combining the pathway knowledge and data from human genome-wide association studies (GWAS), the authors developed a new analytical method that discriminates disease cases from healthy controls.

Braun and Buetow used the Pathway Interaction Database to select all genes represented on a genome-wide genotyping array in each pathway and then chose the SNP for each gene that had the strongest association with disease status. For each SNP, they calculated the minor allele frequency for cases versus controls. For each participant in the study, they determined how closely the distribution of SNPs in each pathway resembled the distribution for the remaining cases or controls. The authors used the method—which they termed Pathways of Distinction Analysis, or PoDA—to find pathways that were best able to discriminate cancer cases from controls for breast and liver cancer GWAS. For liver cancer, they found a strong tie to immune-related pathways; for breast cancer, it appeared that disease risk exists at the germline DNA level.

Although this method is limited by knowledge of which genes participate in which pathways, and follow-up studies to discover specific causal mutations will be more complex, Braun and Buetow’s initial results point to pathway-wide genomic differences that underlie disease susceptibility and provide insight into complex disease at a systems level.

R. Braun, K. Buetow, Pathways of distinction analysis: A new technique for multi-SNP analysis of GWAS data. PLoS Genetics. 7, e1002101 (2011). [Abstract]

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