scholarly journals Genome-Wide Association Studies for Bivariate Sparse Longitudinal Data

2011 ◽  
Vol 72 (2) ◽  
pp. 110-120 ◽  
Author(s):  
Kiranmoy Das ◽  
Jiahan Li ◽  
Guifang Fu ◽  
Zhong Wang ◽  
Rongling Wu
Keyword(s):  
Longitudinal Data ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals Efficient multivariate analysis algorithms for longitudinal genome-wide association studies

2019 ◽  
Vol 35 (23) ◽  
pp. 4879-4885 ◽  
Author(s):  
Chao Ning ◽  
Dan Wang ◽  
Lei Zhou ◽  
Julong Wei ◽  
Yuanxin Liu ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Software Package ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Computational Speed

Abstract Motivation Current dynamic phenotyping system introduces time as an extra dimension to genome-wide association studies (GWAS), which helps to explore the mechanism of dynamical genetic control for complex longitudinal traits. However, existing methods for longitudinal GWAS either ignore the covariance among observations of different time points or encounter computational efficiency issues. Results We herein developed efficient genome-wide multivariate association algorithms for longitudinal data. In contrast to existing univariate linear mixed model analyses, the proposed method has improved statistic power for association detection and computational speed. In addition, the new method can analyze unbalanced longitudinal data with thousands of individuals and more than ten thousand records within a few hours. The corresponding time for balanced longitudinal data is just a few minutes. Availability and implementation A software package to implement the efficient algorithm named GMA (https://github.com/chaoning/GMA) is available freely for interested users in relevant fields. Supplementary information Supplementary data are available at Bioinformatics online.

Fast linear mixed model computations for genome-wide association studies with longitudinal data

2012 ◽  
Vol 32 (1) ◽  
pp. 165-180 ◽  
Author(s):  
Karolina Sikorska ◽  
Fernando Rivadeneira ◽  
Patrick J.F. Groenen ◽  
Albert Hofman ◽  
André G. Uitterlinden ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals Development of a fast and versatile method for genome‐wide association studies of longitudinal data

2020 ◽  
Vol 16 (S3) ◽  
Author(s):  
Luca Kleineidam ◽  
Victor Andrade ◽  
Michael Wagner ◽  
Jean‐Charles Lambert ◽  
Agustín Ruiz ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals Efficient Multivariate Analysis Algorithms for Longitudinal Genome-wide Association Studies

2018 ◽  
Author(s):  
Chao Ning ◽  
Dan Wang ◽  
Lei Zhou ◽  
Julong Wei ◽  
Yuanxin Liu ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Software Package ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
New Method ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Computational Speed

AbstractMotivationCurrent dynamic phenotyping system introduces time as an extra dimension to genome-wide association studies (GWAS), which helps to explore the mechanism of dynamical genetic control for complex longitudinal traits. However, existing methods for longitudinal GWAS either ignore the covariance among observations of different time points or encounter computational efficiency issues.ResultsWe herein developed efficient genome-wide multivariate association algorithms (GMA) for longitudinal data. In contrast to existing univariate linear mixed model analyses, the proposed new method has improved statistic power for association detection and computational speed. In addition, the new method can analyze unbalanced longitudinal data with thousands of individuals and more than ten thousand records within a few hours. The corresponding time for balanced longitudinal data is just a few minutes.Availability and ImplementationWe wrote a software package to implement the efficient algorithm named GMA (https://github.com/chaoning/GMA), which is available freely for interested users in relevant fields.

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