Whilst TWAS, SpliceWAS and PWAS provide a mechanistic framework for the associative evidence between genes and disease, there is also significant confounding by co-regulation derived correlation. TWAS associations can also be confounded by linkage disequilibrium which can bias SNP effect estimates for both expression weights and disease associations (Wainberg et al., 2019). Performing TWAS is also limited to some extent by the sample size of GReX data from different tissues and that some genes are not expressed and are therefore ‘missing’ from a relevant gene set. Incompleteness of gene expression data diminishes the effectiveness of finemapping, null models within the credible set could be better linked to potentially causal genes which would allow for the identification of other causal genes. Our understanding of the genetic architecture of AN is also far from complete, both in terms of common variants and effects mediated through rare or structural variants. Future larger-scale AN GWAS planned will help to consolidate the strength and replicability of these findings. Previous work in power analysis for TWAS approaches have suggested that both expression and trait heritability/sample size
