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

TitleA genetic stochastic process model for genome-wide joint analysis of biomarker dynamics and disease susceptibility with longitudinal data.
Publication TypeJournal Article
Year of Publication2017
AuthorsHe, L, Zhbannikov, I, Arbeev, KG, Yashin, AI, Kulminski, AM
JournalGenet Epidemiol
Volume41
Issue7
Pagination620-635
Date Published2017 11
ISSN1098-2272
KeywordsBiomarkers, Body Mass Index, Computational Biology, Diabetes Mellitus, Type 2, Female, Gene-Environment Interaction, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Models, Genetic, Models, Statistical, Polymorphism, Single Nucleotide, Risk, Stochastic Processes
Abstract

Unraveling the underlying biological mechanisms or pathways behind the effects of genetic variations on complex diseases remains one of the major challenges in the post-GWAS (where GWAS is genome-wide association study) era. To further explore the relationship between genetic variations, 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 GWAS. 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 = 5.77 × 10 ). We also found a SNP rs1551133 located on 2q14.2 that reversed the effect of BMI on T2D (P = 6.70 × 10 ). In conclusion, the proposed GSPM provides a promising and useful tool 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.

DOI10.1002/gepi.22058
Alternate JournalGenet Epidemiol
PubMed ID28636232
PubMed Central IDPMC5643257
Grant ListHHSN268201100012C / HL / NHLBI NIH HHS / United States
HHSN268201100009I / HL / NHLBI NIH HHS / United States
HHSN268201100010C / HL / NHLBI NIH HHS / United States
HHSN268201100008C / HL / NHLBI NIH HHS / United States
HHSN268201100005G / HL / NHLBI NIH HHS / United States
HHSN268201100008I / HL / NHLBI NIH HHS / United States
HHSN268201100007C / HL / NHLBI NIH HHS / United States
HHSN268201100011I / HL / NHLBI NIH HHS / United States
HHSN268201100011C / HL / NHLBI NIH HHS / United States
U01 HG004402 / HG / NHGRI NIH HHS / United States
P30 AG034424 / AG / NIA NIH HHS / United States
HHSN268201100006C / HL / NHLBI NIH HHS / United States
HHSN268201100005I / HL / NHLBI NIH HHS / United States
P01 AG043352 / AG / NIA NIH HHS / United States
HHSN268201100009C / HL / NHLBI NIH HHS / United States
HHSN268201100005C / HL / NHLBI NIH HHS / United States
HHSN268201100007I / HL / NHLBI NIH HHS / United States
U01 HG009088 / HG / NHGRI NIH HHS / United States
R01 AG047310 / AG / NIA NIH HHS / United States