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

Publication ,  Journal Article
He, L; Zhbannikov, I; Arbeev, KG; Yashin, AI; Kulminski, AM
Published in: Genetic epidemiology
November 2017

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 × 10-7 ). We also found a SNP rs1551133 located on 2q14.2 that reversed the effect of BMI on T2D (P = 6.70 × 10-7 ). 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.

Duke Scholars

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Published In

Genetic epidemiology

DOI

EISSN

1098-2272

ISSN

0741-0395

Publication Date

November 2017

Volume

41

Issue

7

Start / End Page

620 / 635

Related Subject Headings

  • Stochastic Processes
  • Risk
  • Polymorphism, Single Nucleotide
  • Models, Statistical
  • Models, Genetic
  • Male
  • Humans
  • Genome-Wide Association Study
  • Genetic Predisposition to Disease
  • Gene-Environment Interaction
 

Citation

APA
Chicago
ICMJE
MLA
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He, L., Zhbannikov, I., Arbeev, K. G., Yashin, A. I., & Kulminski, A. M. (2017). A genetic stochastic process model for genome-wide joint analysis of biomarker dynamics and disease susceptibility with longitudinal data. Genetic Epidemiology, 41(7), 620–635. https://doi.org/10.1002/gepi.22058
He, Liang, Ilya Zhbannikov, Konstantin G. Arbeev, Anatoliy I. Yashin, and Alexander M. Kulminski. “A genetic stochastic process model for genome-wide joint analysis of biomarker dynamics and disease susceptibility with longitudinal data.Genetic Epidemiology 41, no. 7 (November 2017): 620–35. https://doi.org/10.1002/gepi.22058.
He L, Zhbannikov I, Arbeev KG, Yashin AI, Kulminski AM. A genetic stochastic process model for genome-wide joint analysis of biomarker dynamics and disease susceptibility with longitudinal data. Genetic epidemiology. 2017 Nov;41(7):620–35.
He, Liang, et al. “A genetic stochastic process model for genome-wide joint analysis of biomarker dynamics and disease susceptibility with longitudinal data.Genetic Epidemiology, vol. 41, no. 7, Nov. 2017, pp. 620–35. Epmc, doi:10.1002/gepi.22058.
He L, Zhbannikov I, Arbeev KG, Yashin AI, Kulminski AM. A genetic stochastic process model for genome-wide joint analysis of biomarker dynamics and disease susceptibility with longitudinal data. Genetic epidemiology. 2017 Nov;41(7):620–635.
Journal cover image

Published In

Genetic epidemiology

DOI

EISSN

1098-2272

ISSN

0741-0395

Publication Date

November 2017

Volume

41

Issue

7

Start / End Page

620 / 635

Related Subject Headings

  • Stochastic Processes
  • Risk
  • Polymorphism, Single Nucleotide
  • Models, Statistical
  • Models, Genetic
  • Male
  • Humans
  • Genome-Wide Association Study
  • Genetic Predisposition to Disease
  • Gene-Environment Interaction