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Association studies

The best of the rest

Nature Reviews Genetics volume 8, page 907 (2007)Cite this article

The approach is borrowed from the analysis of microarrays, for which pathway-based methods are used routinely. Rather than scoring each gene expression change individually, here the data are searched for groups of genes that, when considered jointly, have significant differential expression compared with other sets of related genes. This approach has the advantage of detecting variants that individually make only a small contribution, and would therefore be missed from the conventional strategy of taking only the most significant hits. Extending this principle to GWAS means no longer settling for just the top-scoring 20–50 SNPs but instead considering groups of interacting genes.

Applying the analysis algorithm to GWAS required some technical adjustments to account for the differences between microarray and SNP data sets. For example, whereas on microarray chips one gene is represented by only a handful of transcripts, in GWAS each gene is represented by a variable number of randomly distributed SNPs, of which only one or a few are associated with the trait of interest. Given that each gene is associated with more than one SNP, how do you choose the most representative one? And how do you correct for the fact that longer genes have more SNPs than shorter ones? Although there is no straightforward answer, a useful strategy involves using the P-value of the most significant SNP to represent each gene. A more robust approach to condensing P-values for correlated SNPs to a single value to represent a gene should increase the power of pathway-based approaches.

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ORIGINAL RESEARCH PAPER

  1. Wang, K. et al. Pathway-based approaches for analysis of genomewide association studies. Am. J. Hum. Genet. 26 October 2007 (doi: 10.1086/522374)

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  1. Tanita Casci
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Casci, T. The best of the rest. Nat Rev Genet 8, 907 (2007). https://doi.org/10.1038/nrg2280

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