Analysing the genetic architecture of complex traits using longitudinal data

There is great inter-individual variation in disease progression over the human lifecycle, which may be due to underlying genetic or epigenetic susceptibilities. The identification of these underlying components that influence complex disease progression through time may allow for increased precision in effective identification of risk, a greater understanding of disease aetiology, and potential new public health interventions.

The incorporation of longitudinal data into genetic epidemiological studies also has the potential to improve statistical power to identify genetic/epigenetic effects for common traits. For example, cross-sectional estimates of heritability for disease risk factors, such as blood pressure and lipids, have provided consistently lower estimates compared to those that examine longitudinal change or even summary measures of phenotypes across time.

Our research focuses on the challenges and benefits of analysing longitudinal data (e.g. scaling up analyses genome-wide, cost of longitudinal epi-typing, change in array-based technologies) and aims to identify novel ways of overcoming these obstacles using high-dimensional genetic and epigenetic data. This research involves collaborations with several international researchers through the Genetic Analysis Workshop, a long running collaborative effort among genetic epidemiologists to develop, evaluate and compare statistical genetic methods.

Selected publications

  • Nustad, H., M. Almeida, A. Canty, M. LeBlanc, C. Page and P. Melton (2018). “Epigenetics, heritability and longitudinal analysis.” BMC Genetics: in press.
  • Chiu, Y. F., A. E. Justice and P. E. Melton (2016). “Longitudinal analytical approaches to genetic data.” BMC Genet 17( Suppl 2): 4. [pubmed]
  • Melton, P. E., J. M. Peralta and L. Almasy (2016). “Constrained multivariate association with longitudinal phenotypes.” BMC Proc 10(Suppl 7): 329-332. [pubmed]
  • Wu, Z., Y. Hu and P. E. Melton (2014). “Longitudinal data analysis for genetic studies in the whole-genome sequencing era.” Genet Epidemiol 38(Suppl 1): S74-80. [pubmed]
  • Melton, P. E. and L. A. Almasy (2014). “Bivariate association analysis of longitudinal phenotypes in families.” BMC Proc 8(Suppl 1): S90-S90. [pubmed]


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