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A new, improved and generalizable approach for the analysis of biological data generated by -omic platforms

Part of: Epigenetics as the Mediator of the Gene/Environment Interactions in DOHAD

Published online by Cambridge University Press:  22 October 2014

A. B. Pleasants ,
G. C. Wake ,
P. R. Shorten ,
C. Z. W. Hassell-Sweatman ,
C. A. McLean ,
J. D. Holbrook ,
P. D. Gluckman  and
A. M. Sheppard
A. B. Pleasants
Affiliation:
Mathematical Biology Department, AgResearch, Hamilton, New Zealand Gravida National Centre for Growth and Development, Auckland, New Zealand
G. C. Wake
Affiliation:
Gravida National Centre for Growth and Development, Auckland, New Zealand Department of Mathematics and Statistics, Massey University, Albany, New Zealand
P. R. Shorten
Affiliation:
Mathematical Biology Department, AgResearch, Hamilton, New Zealand Gravida National Centre for Growth and Development, Auckland, New Zealand
C. Z. W. Hassell-Sweatman
Affiliation:
Liggins Institute, University of Auckland, Auckland, New Zealand
C. A. McLean
Affiliation:
Liggins Institute, University of Auckland, Auckland, New Zealand
J. D. Holbrook
Affiliation:
Singapore Institute for Clinical Sciences, National University of Singapore, Singapore
P. D. Gluckman
Affiliation:
Gravida National Centre for Growth and Development, Auckland, New Zealand Liggins Institute, University of Auckland, Auckland, New Zealand Singapore Institute for Clinical Sciences, National University of Singapore, Singapore
A. M. Sheppard*
Affiliation:
Gravida National Centre for Growth and Development, Auckland, New Zealand Liggins Institute, University of Auckland, Auckland, New Zealand
*
*Address for Correspondence: Dr A. M. Sheppard, Liggins Institute, The University of Auckland, Private Bag 92019, Victoria Street West, Auckland 1142, New Zealand. (E-mail [email protected])
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Abstract

The principles embodied by the Developmental Origins of Health and Disease (DOHaD) view of ‘life history’ trajectory are increasingly underpinned by biological data arising from molecular-based epigenomic and transcriptomic studies. Although a number of ‘omic’ platforms are now routinely and widely used in biology and medicine, data generation is frequently confounded by a frequency distribution in the measurement error (an inherent feature of the chemistry and physics of the measurement process), which adversely affect the accuracy of estimation and thus, the inference of relationships to other biological measures such as phenotype. Based on empirical derived data, we have previously derived a probability density function to capture such errors and thus improve the confidence of estimation and inference based on such data. Here we use published open source data sets to calculate parameter values relevant to the most widely used epigenomic and transcriptomic technologies Then by using our own data sets, we illustrate the benefits of this approach by specific application, to measurement of DNA methylation in this instance, in cases where levels of methylation at specific genomic sites represents either (1) a response variable or (2) an independent variable. Further, we extend this formulation to consideration of the ‘bivariate’ case, in which the co-dependency of methylation levels at two distinct genomic sites is tested for biological significance. These tools not only allow greater accuracy of measurement and improved confidence of functional inference, but in the case of epigenomic data at least, also reveal otherwise cryptic information.

Keywords

epigenomicsmethylationsystems biologytranscriptomics
Type
Original Article
Information
Journal of Developmental Origins of Health and Disease , Volume 6 , Issue 1 , February 2015 , pp. 17 - 26
Copyright
© Cambridge University Press and the International Society for Developmental Origins of Health and Disease 2014 

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