Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
2812084 | The American Journal of Human Genetics | 2007 | 6 Pages |
Abstract
Published genomewide association (GWA) studies typically analyze and report single-nucleotide polymorphisms (SNPs) and their neighboring genes with the strongest evidence of association (the “most-significant SNPs/genes” approach), while paying little attention to the rest. Borrowing ideas from microarray data analysis, we demonstrate that pathway-based approaches, which jointly consider multiple contributing factors in the same pathway, might complement the most-significant SNPs/genes approach and provide additional insights into interpretation of GWA data on complex diseases.
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Authors
Kai Wang, Mingyao Li, Maja Bucan,