Genome-wide association studies (GWAS) using longitudinal phenotypes collected over time is appealing due to the improvement of power. However, computation burden has been a challenge because of the complex algorithms for modeling the longitudinal data. Approximation methods based on empirical Bayesian estimates (EBEs) from mixed-effects modeling have been developed to expedite the analysis. However, our analysis demonstrated that bias in both association test and estimation for the existing EBE-based methods remains an issue. We propose an incredibly fast and unbiased method (simultaneous correction for EBE, SCEBE) that can correct the bias in the naive EBE approach and provide unbiased P-values and estimates of effect size. Through application to Alzheimer’s Disease Neuroimaging Initiative data with 6 414 695 single nucleotide polymorphisms, we demonstrated that SCEBE can efficiently perform large-scale GWAS with longitudinal outcomes, providing nearly 10 000 times improvement of computational efficiency and shortening the computation time from months to minutes. The SCEBE package and the example datasets are available at https://github.com/Myuan2019/SCEBE.

中文翻译：

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SCEBE：一种高效且可扩展的算法，用于使用混合效应建模对纵向结果进行全基因组关联研究。

由于功效的提高，使用随时间收集的纵向表型的全基因组关联研究 (GWAS) 很有吸引力。然而，由于纵向数据建模的复杂算法，计算负担一直是一个挑战。已经开发了基于来自混合效应模型的经验贝叶斯估计 (EBE) 的近似方法以加快分析速度。然而，我们的分析表明，现有基于 EBE 的方法在关联测试和估计方面的偏差仍然是一个问题。我们提出了一种非常快速且无偏的方法（EBE、SCEBE 的同时校正），它可以校正朴素 EBE 方法中的偏差并提供无偏的P- 效应值和估计值。通过对具有 6 414 695 个单核苷酸多态性的阿尔茨海默病神经影像学倡议数据的应用，我们证明了 SCEBE 可以有效地执行具有纵向结果的大规模 GWAS，将计算效率提高近 10 000 倍，并将计算时间从几个月缩短到几分钟。SCEBE 包和示例数据集可从 https://github.com/Myuan2019/SCEBE 获得。