Fouladi Ramouna, Bessonov Kyrylo, Van Lishout François, Van Steen Kristel
Systems and Modeling Unit, Montefiore Institute, and Bioinformatics and Modeling, GIGA-R, University of Liège, Liège, Belgium.
Hum Hered. 2015;79(3-4):157-67. doi: 10.1159/000381286. Epub 2015 Jul 28.
Genome-wide association studies have revealed a vast amount of common loci associated to human complex diseases. Still, a large proportion of heritability remains unexplained. The extent to which rare genetic variants (RVs) are able to explain a relevant portion of the genetic heritability for complex traits leaves room for several debates and paves the way to the collection of RV databases and the development of novel analytic tools to analyze these. To date, several statistical methods have been proposed to uncover the association of RVs with complex diseases, but none of them is the clear winner in all possible scenarios of study design and assumed underlying disease model. The latter may involve differences in the distributions of effect sizes, proportions of causal variants, and ratios of protective to deleterious variants at distinct regions throughout the genome. Therefore, there is a need for robust scalable methods with acceptable overall performance in terms of power and type I error under various realistic scenarios. In this paper, we propose a novel RV association analysis strategy, which satisfies several of the desired properties that a RV analysis tool should exhibit.
全基因组关联研究已经揭示了大量与人类复杂疾病相关的常见基因座。然而，很大一部分遗传力仍无法解释。罕见遗传变异（RVs）能够解释复杂性状遗传力的相关部分的程度引发了诸多争论，并为RV数据库的收集以及分析这些数据的新型分析工具的开发铺平了道路。迄今为止，已经提出了几种统计方法来揭示RVs与复杂疾病的关联，但在所有可能的研究设计和假设的潜在疾病模型场景中，没有一种方法是绝对的优胜者。后者可能涉及效应大小的分布、因果变异的比例以及全基因组不同区域中保护性变异与有害性变异的比例差异。因此，需要在各种现实场景下，在功效和I型错误方面具有可接受的总体性能的强大且可扩展的方法。在本文中我们提出了一种新颖的RV关联分析策略，该策略满足了RV分析工具应具备的几个理想特性。
