Genome wide association studies (GWAS) have been successful at identifying regions of the genome associated with disease (1). However, improved understanding of the biological pathways underlying these associations is needed to aid development of disease therapies and to identify the causal variants and genes for disease (2). To this end, transcriptome-wide association study (TWAS) has been proposed and implemented in the PrediXcan (3), MetaXcan (4) and FUSION (5) methods. This methodology uses genotype data to predict gene expression values, then tests association of these predicted expression values with phenotypes to identify potentially causal genes whose expression may be involved in the phenotype of interest. This has helped to improve knowledge of the role of gene expression in a range of diseases (6-8).

DNA methylation at CpG sites across the genome is known to be important in disease, and epigenome-wide association studies (EWAS) of CpG methylation have become a common tool for identifying CpG probes related to disease (9). Using a methodology similar to TWAS, we propose to investigate prediction of genome-wide CpG methylation status using genotype and methylation data from ALSPAC. Models that can predict methylation status from genotype data will be built using genotype and methylation data collected as part of the ARIES project. These prediction models will then be applied to the remaining samples in ALSPAC to impute methylation values. These imputed methylation values will then be tested for association with a range of phenotypes to identify potentially causal methylation probes that may be involved in disease.

Detection of an association between two phenotypes does not prove causality. Causal inference and causal modelling methods can be used to identify the true causal biological mechanisms underlying genetic associations, and to determine the causal effects of biological intermediates such as DNA methylation and gene expression on phenotypes of interest. Of particular interest are structural equation modelling (SEM) and Bayesian networks, which have been successfully used to identify the roles of intermediates (including DNA methylation and gene expression) in a number of traits (10-12). These methods will be applied to genomic loci of interest to identify causal pathways for disease. These causal pathways represent strong targets for development of potential disease therapies.

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