Set-based tests for genetic association in longitudinal studies

Biometrics. 2015 Sep;71(3):606-15. doi: 10.1111/biom.12310. Epub 2015 Apr 8.

Abstract

Genetic association studies with longitudinal markers of chronic diseases (e.g., blood pressure, body mass index) provide a valuable opportunity to explore how genetic variants affect traits over time by utilizing the full trajectory of longitudinal outcomes. Since these traits are likely influenced by the joint effect of multiple variants in a gene, a joint analysis of these variants considering linkage disequilibrium (LD) may help to explain additional phenotypic variation. In this article, we propose a longitudinal genetic random field model (LGRF), to test the association between a phenotype measured repeatedly during the course of an observational study and a set of genetic variants. Generalized score type tests are developed, which we show are robust to misspecification of within-subject correlation, a feature that is desirable for longitudinal analysis. In addition, a joint test incorporating gene-time interaction is further proposed. Computational advancement is made for scalable implementation of the proposed methods in large-scale genome-wide association studies (GWAS). The proposed methods are evaluated through extensive simulation studies and illustrated using data from the Multi-Ethnic Study of Atherosclerosis (MESA). Our simulation results indicate substantial gain in power using LGRF when compared with two commonly used existing alternatives: (i) single marker tests using longitudinal outcome and (ii) existing gene-based tests using the average value of repeated measurements as the outcome.

Keywords: Generalized estimating equations; Generalized score test; Genetic association; Longitudinal study; Multi-marker test; Random field.

Publication types

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

MeSH terms

  • Atherosclerosis / epidemiology*
  • Atherosclerosis / genetics*
  • Computer Simulation
  • Data Interpretation, Statistical
  • Genetic Association Studies / methods*
  • Genetic Markers / genetics
  • Genetic Predisposition to Disease / epidemiology
  • Genetic Predisposition to Disease / genetics
  • Humans
  • Incidence
  • Longitudinal Studies*
  • Models, Statistical*
  • Polymorphism, Single Nucleotide / genetics*
  • Reproducibility of Results
  • Sensitivity and Specificity

Substances

  • Genetic Markers

Grants and funding