Characterization of direct and/or indirect genetic associations for multiple traits in longitudinal studies of disease progression

• Published in: Genetics (Austin) Vol. 225; no. 1
• Main Authors: Brossard, Myriam, Paterson, Andrew D, Espin-Garcia, Osvaldo, Craiu, Radu V, Bull, Shelley B
• Format: Journal Article
• Language: English
• Published: US Oxford University Press 31.08.2023; Genetics Society of America
• Subjects: Bias; Blood pressure; Classification; Data analysis; Diabetes; Diabetes mellitus (insulin dependent); Disease control; Genetic effects; Hypothesis testing; Longitudinal studies; Modelling; Nephropathy; Nucleotides; Retinopathy; Risk factors; Single-nucleotide polymorphism; Survival; frailty model; joint models; longitudinal study; complex genetic architecture; quantitative trait trajectory; multiple-trait analysis; random measurement error; direct and/or indirect genetic association; pleiotropy; mixed model
• ISSN: 1943-2631; 0016-6731; 1943-2631
• DOI: 10.1093/genetics/iyad119

Abstract When quantitative longitudinal traits are risk factors for disease progression and subject to random biological variation, joint model analysis of time-to-event and longitudinal traits can effectively identify direct and/or indirect genetic association of single nucleotide polymorphisms (SNPs) with time-to-event. We present a joint model that integrates: (1) a multivariate linear mixed model describing trajectories of multiple longitudinal traits as a function of time, SNP effects, and subject-specific random effects and (2) a frailty Cox survival model that depends on SNPs, longitudinal trajectory effects, and subject-specific frailty accounting for dependence among multiple time-to-event traits. Motivated by complex genetic architecture of type 1 diabetes complications (T1DC) observed in the Diabetes Control and Complications Trial (DCCT), we implement a 2-stage approach to inference with bootstrap joint covariance estimation and develop a hypothesis testing procedure to classify direct and/or indirect SNP association with each time-to-event trait. By realistic simulation study, we show that joint modeling of 2 time-to-T1DC (retinopathy and nephropathy) and 2 longitudinal risk factors (HbA1c and systolic blood pressure) reduces estimation bias in genetic effects and improves classification accuracy of direct and/or indirect SNP associations, compared to methods that ignore within-subject risk factor variability and dependence among longitudinal and time-to-event traits. Through DCCT data analysis, we demonstrate feasibility for candidate SNP modeling and quantify effects of sample size and Winner's curse bias on classification for 2 SNPs identified as having indirect associations with time-to-T1DC traits. Joint analysis of multiple longitudinal and multiple time-to-event traits provides insight into complex traits architecture.