The coordinated analysis of multi-omic data: “Synergomics”.

Rationale
Genomic and next generation sequencing technologies have exponentially increased the availability of genotype data whilst reducing the effective cost per information point respectively. Whilst accurate phenotyping at the molecular and whole body physiology level still remains relatively expensive, large-scale data collection approaches are now available and across multiple omic spectra including the metabolome, the transcriptome, the methylome and the wider phenome. Within the new MRC Integrative Epidemiology Unit at the University of Bristol, the Avon Longitudinal Study of Parents and Children represents a unique opportunity to jointly analyse multiple omic data sets, their characteristics and cross-talk. This source of data offer a series of analytical possibilities which may enhance the understanding of relationships between genetic perturbations, health related traits and biological pathways involved in systems meditating homeostasis, development and disease predisposition.

Aims & Objectives
1. To integrate data from multiple omic sources and interrogate their inter-relationship and association with a range of phenotypes or traits.

2. To address specific hypotheses relating to how certain exposures act upon these molecular phenotypes independently or in a co-ordinated manner.

Methods
The overall design of this work will involve the collection and preparation of data from multi-omic sources. These will be derived from the ALSPAC cohort and will comprise data on (i) whole genome sequence data on 1800 individuals (ii) genome-wide common variant array data on ~8000 mother child pairs (iii) extensive phenotypic data and (iv) extended phenotypic data from metabolomic, methylomic and transcriptomic data sources. All of these will require both quality control measures and diagnostic examination before application to further analyses.

One of the main technical aspects for the development of this work will be in the development of suitable approaches for the analysis of large-scale data sets from multiple sources and potentially multiple time points. Approaches will require the development of analytical capability for basic epidemiological methods and for the processing of omic data and analysis of relationships between these aspects.