GWAS of longitudinal trajectories at biobank scale

Seyoon Ko; Christopher A German; Aubrey Jensen; Judong Shen; Anran Wang; Devan V Mehrotra; Yan V Sun; Janet S Sinsheimer; Hua Zhou; Jin J Zhou

doi:10.1016/j.ajhg.2022.01.018

GWAS of longitudinal trajectories at biobank scale

Am J Hum Genet. 2022 Mar 3;109(3):433-445. doi: 10.1016/j.ajhg.2022.01.018. Epub 2022 Feb 22.

Authors

Affiliations

¹ Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90095, USA.
² Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90095, USA.
³ Biostatistics and Research Decision Sciences, Merck & Co., Inc., Kenilworth, NJ 07033, USA.
⁴ Department of Epidemiology, Emory University, Atlanta, GA 30322, USA.
⁵ Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA.
⁶ Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90095, USA. Electronic address: huazhou@ucla.edu.
⁷ Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ 85721, USA. Electronic address: jinjinzhou@ucla.edu.

Abstract

Biobanks linked to massive, longitudinal electronic health record (EHR) data make numerous new genetic research questions feasible. One among these is the study of biomarker trajectories. For example, high blood pressure measurements over visits strongly predict stroke onset, and consistently high fasting glucose and Hb1Ac levels define diabetes. Recent research reveals that not only the mean level of biomarker trajectories but also their fluctuations, or within-subject (WS) variability, are risk factors for many diseases. Glycemic variation, for instance, is recently considered an important clinical metric in diabetes management. It is crucial to identify the genetic factors that shift the mean or alter the WS variability of a biomarker trajectory. Compared to traditional cross-sectional studies, trajectory analysis utilizes more data points and captures a complete picture of the impact of time-varying factors, including medication history and lifestyle. Currently, there are no efficient tools for genome-wide association studies (GWASs) of biomarker trajectories at the biobank scale, even for just mean effects. We propose TrajGWAS, a linear mixed effect model-based method for testing genetic effects that shift the mean or alter the WS variability of a biomarker trajectory. It is scalable to biobank data with 100,000 to 1,000,000 individuals and many longitudinal measurements and robust to distributional assumptions. Simulation studies corroborate that TrajGWAS controls the type I error rate and is powerful. Analysis of eleven biomarkers measured longitudinally and extracted from UK Biobank primary care data for more than 150,000 participants with 1,800,000 observations reveals loci that significantly alter the mean or WS variability.

Keywords: biobank; biomarkers; disease progression; electronic medical records; longitudinal; trend; variations; within-subject variability.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Biological Specimen Banks*
Biomarkers
Cross-Sectional Studies
Electronic Health Records
Genome-Wide Association Study*
Humans
Longitudinal Studies

Substances

Biomarkers

Abstract

Publication types

MeSH terms

Substances

Grants and funding