Identifying useful predictors from genome-wide SNP association studies

使用具有多达一百万个标记的全基因组SNP基因分型技术进行疾病遗传标记关联研究正在变得司空见惯。 通常，这类研究的既定目标是开发一种“遗传预测因子”，以确定人群中哪些人患疾病的风险较高。 然而，这些研究的统计分析往往包括简单的测试之间的关联疾病和每一个标志物依次。 尽管任何单一的疾病相关标志物都有一定的预测效用，但由于几个原因，这种一次一个的关注过于简单化。 首先，如果一个特定的标记变异在人群中是罕见的，它将具有有限的预测能力，即使相关的风险或比值比是相当大的。 其次，当不同的基因或标记高度相关时，组合的预测能力不能通过单独检查每个的效果来计算。 第三，某些基因或标记的影响可能是通过与环境暴露或与其他基因的相互作用。 尽管统计检验的数量随之增加，但也需要考虑这种可能性。

Disease-genetic marker association studies, using genome-wide SNP genotyping technologies with up to a million markers, are becoming commonplace. Often, the stated goal of such studies is to develop a "genetic predictor" to identify those in the population who are at higher risk of developing disease. However, the statistical analyses of these studies have tended to comprise simple tests for association between disease and each marker in turn. Although any single disease-associated marker will have some predictive utility, this one-at-a-time focus is too simplistic for several reasons. Firstly, if a particular marker variant is rare in the population, it will have limited predictive capability, even if the associated risk or odds ratio is quite large. Secondly, when distinct genes or markers are highly correlated, the predictive capability of the combination cannot be calculated by examining the effects of each separately. Thirdly, the impact of some genes or markers may be through interactions with environmental exposures or interactions with other genes. Such possibilities also need to be considered, despite the concomitant increase in the number of statistical tests.