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Article: SCEBE: an efficient and scalable algorithm for genome-wide association studies on longitudinal outcomes with mixed-effects modeling

TitleSCEBE: an efficient and scalable algorithm for genome-wide association studies on longitudinal outcomes with mixed-effects modeling
Authors
Keywordsgenome-wide association studies
longitudinal outcomes
mixed-effects model
empirical Bayesian estimates
shrinkage
Issue Date2020
PublisherOxford University Press. The Journal's web site is located at http://bib.oxfordjournals.org/
Citation
Briefings in Bioinformatics, 2020, Epub 2020-07-07 How to Cite?
AbstractGenome-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.
Persistent Identifierhttp://hdl.handle.net/10722/284115
ISSN
2019 Impact Factor: 8.99
2015 SCImago Journal Rankings: 4.086

 

DC FieldValueLanguage
dc.contributor.authorYuan, M-
dc.contributor.authorXu, XS-
dc.contributor.authorYang, Y-
dc.contributor.authorZhou, Y-
dc.contributor.authorLi, Y-
dc.contributor.authorXu, J-
dc.contributor.authorPinheiro, J-
dc.contributor.authorAlzheimer’s Disease Neuroimaging Initiative,-
dc.date.accessioned2020-07-20T05:56:13Z-
dc.date.available2020-07-20T05:56:13Z-
dc.date.issued2020-
dc.identifier.citationBriefings in Bioinformatics, 2020, Epub 2020-07-07-
dc.identifier.issn1467-5463-
dc.identifier.urihttp://hdl.handle.net/10722/284115-
dc.description.abstractGenome-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.-
dc.languageeng-
dc.publisherOxford University Press. The Journal's web site is located at http://bib.oxfordjournals.org/-
dc.relation.ispartofBriefings in Bioinformatics-
dc.subjectgenome-wide association studies-
dc.subjectlongitudinal outcomes-
dc.subjectmixed-effects model-
dc.subjectempirical Bayesian estimates-
dc.subjectshrinkage-
dc.titleSCEBE: an efficient and scalable algorithm for genome-wide association studies on longitudinal outcomes with mixed-effects modeling-
dc.typeArticle-
dc.identifier.emailXu, J: xujf@hku.hk-
dc.identifier.authorityXu, J=rp02086-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1093/bib/bbaa130-
dc.identifier.pmid32634825-
dc.identifier.hkuros311192-
dc.identifier.volumeEpub 2020-07-07-
dc.publisher.placeUnited Kingdom-

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