SCEBE: an efficient and scalable algorithm for genome-wide association studies on longitudinal outcomes with mixed-effects modeling. Issue 3 (7th July 2020)

• Record Type: Journal Article
• Authors: Yuan, Min; Xu, Xu Steven; Yang, Yaning; Zhou, Yinsheng; Li, Yi; Xu, Jinfeng; Pinheiro, Jose
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 3(2021)
• Publication Date: 2020-07-07
• Subjects: genome-wide association studies -- longitudinal outcomes -- mixed-effects model -- empirical Bayesian estimates -- shrinkage; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbaa130
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: 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 .