Transcriptome-wide association study
TWAS are valuable and flexible bioinformatics tools that looks at the associations between the expressions of genes and complex traits and diseases.
Transcriptomes include both mRNA, which functions as an intermediate to the central dogma; as well as noncoding RNAs that may play other roles in protein synthesis.
RNAs are present in a cell in varied concentrations, and play various roles outside of the central dogma and are able to be identified based on length and function.
[4] Transcriptome analysis is beneficial for obtaining information about all RNAs present and can provide valuable insight into the genetic mechanisms that are tissue specific.
However, many of these associations can be developed throughout an individual due to linkage disequilibrium and the large size of the genome.
Although GWAS provide valuable insight into identifying markers throughout the genome, a large portion of the SNPs are present in non-coding mRNA regions, and many have unknown functions that are difficult to determine through standard methods, as no product is manufactured by these regions of the genome.
Imputation is the process by which you can predict the expression levels of genes in other organisms through the variation that exists in their genome based on a reference panel.
PrediXcan[1] and FUSION[2] are both TWAS software that have been utilized in genetic studies to investigate the gene-trait associations.
PrediXcan is a well-developed TWAS software that has the ability to estimate genetically regulated expression and determine associations with the phenotype being investigated.
It uses a penalized regression model to give weight to levels of observed gene expression and cis-SNPs derived from the reference dataset.
FUSION is another TWAS software that utilizes a different statistical analysis to create the association tests.
[4] Additionally, as this method uses loci that were previously identified by GWAS analysis, there is a lower testing burden associated with a TWAS as less sites are analyzed.
TWAS cross tissue methods also have the possibility to identify potential causal genes for diseases and traits on a larger scale, however, single tissue methods have the ability to determine associations on a case specific basis.
This acts as a disadvantage for TWAS as trans-genetic component variants are any regulatory mechanisms that are outside of a 1 Megabase range of the gene, and even though they are a significant distance away from the gene of interest, many regulatory mechanisms have the potential to act long range and can still impact expression.
Even though a statistically significant association can be seen between the gene or loci of interest and the trait or disease, no causal relationship can be derived.
42 of these genes were found to have a statistically significant association with chromatin phenotypes, which is a regulatory mechanism that could further be investigated.
MAPK3 was one association that was observed to have a large impact on neurodevelopmental phenotypes, and was further prioritized as a candidate causal gene.
Data was collected from The Cancer Genome Atlas to establish genetic models as well as 229,000 women of European ancestry.
Results and findings that are published in the TWAS Atlas are able to be integrated and combined to aid future studies and the understanding of genetic regulation mechanisms.
