Gene-set analyses are used to assess whether there is any evidence of association with disease among a set of biologically related genes. Such an analysis typically treats all genes within the sets similarly, even though there is substantial, external, information concerning the likely importance of each gene within each set. For example, for traits that are under purifying selection, we would expect genes showing extensive genic constraint to be more likely to be trait associated than unconstrained genes. Here we improve gene-set analyses by incorporating such external information into a higher-criticism-based signal detection analysis. We show that when this external information is predictive of whether a gene is associated with disease, our approach can lead to a significant increase in power. Further, our approach is particularly powerful when the signal is sparse, that is when only a small number of genes within the set are associated with the trait. We illustrate our approach with a gene-set analysis of amyotrophic lateral sclerosis (ALS) and implicate a number of gene-sets containing SOD1 and NEK1 as well as showing enrichment of small p values for gene-sets containing known ALS genes. We implement our approach in the R package wHC.

中文翻译：

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在基于稀有变异基因集的分析中纳入外部信息以改善稀疏信号检测。

基因集分析用于评估一组生物学相关基因中是否存在与疾病相关的证据。即使存在大量有关每个基因在每个集合中的重要性的外部信息，这种分析也通常以相似的方式对待集合中的所有基因。例如，对于正在纯化选择中的性状，我们希望显示出广泛遗传限制的基因比不受限制的基因更有可能与性状相关。在这里，我们通过将此类外部信息纳入基于高批评度的信号检测分析中来改进基因集分析。我们表明，当这种外部信息可以预测基因是否与疾病相关时，我们的方法可以导致能力的显着提高。进一步，当信号稀疏时，即该集合中只有少数基因与该性状相关时，我们的方法特别强大。我们用肌萎缩性侧索硬化症（ALS）的基因集分析说明了我们的方法，并暗示了许多包含SOD1和NEK1的基因集，以及显示了包含已知ALS基因的基因集的小p值的富集。我们在WHC R软件包中实施我们的方法。