Genome Wide Association (GWA) studies (GWAS) are hypothesis-free experiments aimed at identifying possible associations between subtle genetic variations and disease risk and/or disease state (see overview in Nature Review Drug Discovery 7, 221). Over the last few years, the number of GWAS has exploded thanks to the shrinking cost and improved performance of all-genome analysis tools. Despite their power to decipher the genetic susceptibility of many diseases, GWAS suffer from a major limitation: the sample size required to identify credible associations. Because GWAS are hypothesis-free, all possible genetic variations are tested for possible association with the phenotype of interest, requiring much more stringent thresholds for statistical significance: depending on the penetrance of the genetic variation the type I error threshold (aka alpha) is usually set between 10-7 and 10-5. Thus, sample sizes in GWAS tend to be well above a 1,000 cases and often exceed the 10,000 mark. When you are considering a relatively rare disease or condition, these numbers can become a limiting factor. Furthermore, these large sample sizes only permit testing of direct association hypothesis and not more complex hypotheses such as interactions between variants; the latter would require even larger sample size. So, if sample size constitutes an inherent limit in GWAS, how can this field progress beyond this barrier?
A recent paper published by Hicks and colleagues in Cancer Informatics (Cancer Inform 2011; 10: 285-204) offers a possible solution. Focusing on breast cancer, the authors combined GWAS information with gene expression data to determine the combined contribution of multiple genetic variants acting within genes and putative biological. In addition, thanks to this approach, the authors were able to identify novel genes and biological pathways that could not be identified using traditional GWAS.
Thierry
Sornasse for Integrated Biomarker Strategy
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