Univ Brest, Inserm, EFS, CHU Brest, UMR 1078, GGB, F-29200, Brest, France.
Inserm UMR-S1161, Génétique et Physiopathologie des Maladies Cérébro-vasculaires, Université Paris Diderot, Sorbonne Paris Cité, Paris, France.
Eur J Hum Genet. 2021 May;29(5):736-744. doi: 10.1038/s41431-020-00792-8. Epub 2021 Jan 14.
Rare genetic variants are expected to play an important role in disease and several statistical methods have been developed to test for disease association with rare variants, including variance-component tests. These tests however deal only with binary or continuous phenotypes and it is not possible to take advantage of a suspected heterogeneity between subgroups of patients. To address this issue, we extended the popular rare-variant association test SKAT to compare more than two groups of individuals. Simulations under different scenarios were performed that showed gain in power in presence of genetic heterogeneity and minor lack of power in absence of heterogeneity. An application on whole-exome sequencing data from patients with early- or late-onset moyamoya disease also illustrated the advantage of our SKAT extension. Genetic simulations and SKAT extension are implemented in the R package Ravages available on GitHub ( https://github.com/genostats/Ravages ).
稀有遗传变异预计在疾病中发挥重要作用，已经开发了几种统计方法来检测稀有变异与疾病的关联，包括方差分量检验。然而，这些检验仅处理二分类或连续表型，无法利用患者亚组之间的潜在异质性。为了解决这个问题，我们扩展了流行的稀有变异关联检验 SKAT，以比较两组以上的个体。在不同情况下进行了模拟，结果表明在存在遗传异质性的情况下提高了功效，而在不存在异质性的情况下稍微降低了功效。对早发性或晚发性烟雾病患者的全外显子组测序数据的应用也说明了我们的 SKAT 扩展的优势。遗传模拟和 SKAT 扩展在 GitHub 上的 R 包 Ravages 中实现（https://github.com/genostats/Ravages）。
