A genetic stochastic process model for genome‐wide joint analysis of biomarker dynamics and disease susceptibility with longitudinal data

Abstract

Unraveling the underlying biological mechanisms or pathways behind the identified effects of genetic variations on complex diseases remains one of the major challenges in the post-GWAS era. To further explore the relationship between genetic variation, biomarkers, and diseases for elucidating underlying pathological mechanism, a huge effort has been placed on examining pleiotropic and gene-environmental interaction effects. We propose a novel genetic stochastic process model (GSPM) that can be applied to GWAS and jointly investigate the genetic effects on longitudinally-measured biomarkers and risks of diseases. This model is characterized by more profound biological interpretation and takes into account the dynamics of biomarkers during follow-up when investigating the hazards of a disease. We illustrate the rationale and evaluate the performance of the proposed model through two genome-wide association studies. One is to detect single nucleotide polymorphisms (SNPs) having interaction effects on type 2 diabetes (T2D) with body mass index (BMI) and the other is to detect SNPs affecting the optimal BMI level for protecting from T2D. We identified multiple SNPs that showed interaction effects with BMI on T2D, including a novel SNP rs11757677 in the CDKAL1 gene (p=5.77e-07). We also found a SNP rs1551133 located on 2q14.2 that reversed the effect of BMI on T2D (p=6.70e-07). In conclusion, the proposed GSPM provides a promising and useful alternative in GWAS of longitudinal data for interrogating pleiotropic and interaction effects to gain more insights into the relationship between genes, quantitative biomarkers and risks of complex diseases.

Composite Measure of Physiological Dysregulation as a Predictor of Mortality: The Long Life Family Study

“…Applications of this model will also provide opportunities to take into account varying strength of association of biomarkers with mortality at different ages in construction of the composite measures. In addition, the SPM versions developed for analyses of genetic data (34,35) can be applied to find genetic factors associated with various hidden agingrelated mechanisms (e.g., decline in adaptive capacity and stress resistance, allostatic adaptation) which are not directly observed in the data but can be estimated by this model using longitudinal measurements of biomarkers and follow-up data on mortality or morbidity.…”

Methods for joint modelling of longitudinal omics data and time-to-event outcomes: Applications to lysophosphatidylcholines in connection to aging and mortality in the Long Life Family Study

“…Analyzing sex differences in LPCs (and phospholipids in general) in relation to aging in longitudinal cohort studies is of considerable interest and importance because of the paucity of such studies, and, in particular, considering inconsistencies regarding sex differences in LPC levels during aging observed in prior research [52]. Impacts of other factors on the observed relations between LPC trajectories and mortality can be explored using the tools in this paper including the genetic underpinnings of the relationships that can be evaluated using relevant tools [14,53].…”

Genetics of physiological dysregulation: findings from the long life family study using joint models

“…The "genetic" SPM [36][37][38] allows investigation of genetic determinants of such aging-related characteristics in applications to longitudinal observations of composite biomarkers (such as DM). In particular, recent developments in the SPM methodology [39] have considerably enhanced the computational speed (which was a critical barrier in implementing this approach to large-scale genetic analyses) and opened new avenues for applying this model to GWAS, with far reaching implications for significantly improving our understanding of the genetic underpinnings of complex aging-related traits.…”