Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg 79106, Germany.
Faculty of Mathematics and Physics, University of Freiburg, Freiburg 79104, Germany.
Bioinformatics. 2021 Sep 29;37(18):3061-3063. doi: 10.1093/bioinformatics/btab150.
When performing genome-wide association studies conventionally the additive genetic model is used to explore whether a single nucleotide polymorphism (SNP) is associated with a quantitative trait. But for variants, which do not follow an intermediate mode of inheritance (MOI), the recessive or the dominant genetic model can have more power to detect associations and furthermore the MOI is important for downstream analyses and clinical interpretation. When multiple MOIs are modelled the question arises, which describes the true underlying MOI best.
We developed an R-package allowing for the first time to determine study specific critical values when one of the three models is more informative than the other ones for a quantitative trait locus. The package allows for user-friendly simulations to determine these critical values with predefined minor allele frequencies and study sizes. For application scenarios with extensive multiple testing we integrated an interpolation functionality to determine critical values already based on a moderate number of random draws.
The R-package pgainsim is freely available for download on Github at https://github.com/genepi-freiburg/pgainsim.
Supplementary data are available at Bioinformatics online.
在进行全基因组关联研究时,传统上使用加性遗传模型来探索单核苷酸多态性 (SNP) 是否与数量性状相关。但是对于不符合中间遗传模式 (MOI) 的变体,隐性或显性遗传模型可以更有效地检测关联,此外,MOI 对于下游分析和临床解释很重要。当对多个 MOI 进行建模时,就会出现哪种模型最能描述真实潜在 MOI 的问题。
我们开发了一个 R 包,首次允许在定量性状基因座中,一个模型比其他模型更具信息量时,确定研究特定的临界值。该软件包允许用户友好地模拟,以在预设的次要等位基因频率和研究规模下确定这些临界值。对于具有广泛多重检验的应用场景,我们集成了内插功能,以便即使在随机抽取的数量适中的情况下也可以确定临界值。
R 包 pgainsim 可在 Github 上免费下载,网址为 https://github.com/genepi-freiburg/pgainsim。
补充数据可在生物信息学在线获得。