Approved Research
Discovery and replication of epistatic variants in complex diseases
Approved Research ID: 83957
Approval date: February 8th 2023
Lay summary
Complex diseases, the ones caused by the interaction of multiple genes, such as asthma, type 2 diabetes (T2D), or Alzheimer's have been broadly studied at the genomic level during the last decades. As a result, thousands of genomic variants have been found associated with these diseases, thus enhancing their detection, prevention, and treatment. However, there are still many genomic studies that can be done to complement and improve the genetic knowledge of complex diseases. In this direction, we have developed new methods to study the effect of the interaction between variants associated with disease, which is also named epistasis.
To study epistasis, we are currently 1) analysing large groups of individuals to find new genomic regions associated with complex diseases, including different types of variants which are usually excluded from the analysis, 2) analysing epistasis in complex diseases, to find groups of genomic variants that interact affecting the development of the disease, and 3) studying the contribution of these variants to disease subtypes.
We have applied our recently developed methods to a pilot dataset of T2D and non-diabetic individuals. The preliminary results obtained suggest that there is some relation between the variant interactions and the disease regulation. For this reason, to ensure the reproducibility of these preliminary results, and to test their applicability in other datasets, we are interested in replicating our results in the UK Biobank cohort. Furthermore, we will apply these methods to analyse subgroups of diseased individuals for the same complex disease, and extend these analyses to the study of other complex diseases.
Given the complexity of the project, its estimated duration is of 3 years. The outcomes of these analyses are expected to provide a better understanding of the pathophysiology of complex diseases, to result in high impact scientific publications, and to ultimately help diagnose and treat complex diseases in a personalised manner.