Chao Ning1, Dan Wang1, Lei Zhou1, Julong Wei2, Yuanxin Liu3, Huimin Kang1, Shengli Zhang1, Xiang Zhou2, Shizhong Xu4, Jian-Feng Liu1. 1. National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China. 2. Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA. 3. School of English, Beijing International Studies University, Beijing, China. 4. Department of Botany and Plant Science, University of California, Riverside, CA, USA.
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.
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.
Authors: Bernadette Wendel; Sergi Papiol; Till F M Andlauer; Jörg Zimmermann; Jens Wiltfang; Carsten Spitzer; Fanny Senner; Eva C Schulte; Max Schmauß; Sabrina K Schaupp; Jonathan Repple; Eva Reininghaus; Jens Reimer; Daniela Reich-Erkelenz; Nils Opel; Igor Nenadić; Susanne Meinert; Carsten Konrad; Farahnaz Klöhn-Saghatolislam; Tilo Kircher; Janos L Kalman; Georg Juckel; Andreas Jansen; Markus Jäger; Maria Heilbronner; Martin von Hagen; Katrin Gade; Christian Figge; Andreas J Fallgatter; Detlef E Dietrich; Udo Dannlowski; Ashley L Comes; Monika Budde; Bernhard T Baune; Volker Arolt; Ion-George Anghelescu; Heike Anderson-Schmidt; Kristina Adorjan; Peter Falkai; Thomas G Schulze; Heike Bickeböller; Urs Heilbronner Journal: Transl Psychiatry Date: 2021-07-10 Impact factor: 6.222