New analysis sheds light on how to uncover causal variants for immune-mediated diseases

Mapping regulatory trait loci can help uncover which genetic variants are most likely to be the cause of immune-mediated diseases.

Email newsletter

News and blog updates

Sign up

New genetic analysis has reanalysed existing data to try and pinpoint the genetic variants that cause 12 different immune-mediated diseases, such as inflammatory bowel disease, multiple sclerosis, and rheumatoid arthritis.

This new research, from the Wellcome Sanger Institute and collaborators, shows that fine-mapping using regulatory quantitative trait loci (QTLs) is more accurate at predicting disease causal variants than using genome-wide association studies (GWAS) alone.

The study, published on 14 March 2022 in Nature Genetics, found that adding these regulatory data produces biologically relevant predictions of the causal variants. This information could be used to help develop new drugs for immune-mediated diseases by targeting the molecular mechanisms impacted.

Also, the researchers found that particular classes of variants, mainly INDELs1 are underrepresented in genome-wide association studies, and that the data produced are much more accurate when the whole genome sequences are available.

Immune-mediated diseases are chronic health conditions that affect up to 9.4 per cent of the world population2. While genome-wide association studies, known as GWAS, have discovered thousands of genetic variants that are associated with these diseases, they do not show which one is the cause and the molecular mechanism through which these variants lead to disease risk.

Regulatory trait loci, known as QTLs, are specific regions of DNA that are associated with certain regulatory traits, such as gene expression. It is possible to show which genetic variants are related to which gene in a certain tissue using QTL mapping. If these QTLs are co-localised to a genomic region shown to be associated with the disease, identified by GWAS, there is a higher probability that they are the causal variants and gives information on their molecular mechanism.

This new research, from the Wellcome Sanger Institute and collaborators, reprocessed the data collected from previous projects that focused on three types of immune cells: monocytes, neutrophils, and T cells.

From the data, they identified 340 genetic loci that could be linked to 12 different immune-mediated diseases. They fine-mapped these loci, and identified the genetic variants that were most likely to be the cause of disease.

To increase the resolution of their study, they harnessed information derived from multiple layers of regulatory data, and marked the molecular switches that regulate the activity of human genes. When they used these data, they were able to refine the location of causal variants by more than three times.

By combining these genetic data, the team identified functionally important variants and suggest these for further biological study. As an example, the researchers suggest possible causal variants in the ITGA4 gene for inflammatory bowel disease.

In addition to this, they revealed that specific classes of causal variants, particularly a type known as INDELs, are systematically under-represented in current studies, widening the spectrum of genetic variants to consider.

QTL mapping requires a much smaller number of study individuals compared to GWAS studies – hundreds as opposed to tens of thousands – meaning they could be more economically viable in some cases of research and still give important biological information about the cause of disease.

“Identifying the genetic variants and how they cause a certain disease can be a very complex task. Our research shows that by including multiple layers of functional genetic information, you can help increase the probability of finding potential causal genetic variants, putative effector genes, and molecular mechanisms underpinning disease associations. In addition to this, the amount of data needed for these layers of regulatory information is much smaller than traditional association studies, making them more accessible to researchers.”

Dr Kousik Kundu, first author from the Wellcome Sanger Institute and University of Cambridge

“Our research shows that using regulatory trait loci data can refine the number of possible causal variants down to a much smaller number. This can save time as it gives you much more functionally and biologically relevant theories to test, as well as more information on the mechanisms behind these variants.”

Dr Klaudia Walter, an author from the Wellcome Sanger Institute

“Understanding more about the genetic variants that cause immune-mediated diseases is necessary if we are to develop drugs that can help treat these at a molecular level. This research shows that using regulatory trait loci to identify causal variants gives a large amount of important information on genetic mechanisms and gene expression, advancing the search for new treatments.”

Professor Nicole Soranzo, senior author from the Wellcome Sanger Institute

More information

The research used data collected from BLUEPRINT, GTEx and eQTLGen projects.

  1. INDELs is a term used to describe insertion, deletion, or insertion and deletion of nucleotides in genomic DNA.
  2. Cooper, G. S., Bynum, M. L. K. & Somers, E. C. (2009) Recent insights in the epidemiology of autoimmune diseases: Improved prevalence estimates and understanding of clustering of diseases. J. Autoimmun. 33, 197-207.


K. Kundu, M Tardaguila, A. L. Mann, et al. (2022) Genetic associations at regulatory phenotypes improve finemapping of causal variants for twelve immune-mediated diseases. Nature Genetics. DOI: 10.1038/s41588-022-01025-y


This research was funded by National Institute for Health Research Cardiovascular Theme, the EU FP7 High Impact Project BLUEPRINT and the Canadian Institutes of Health Research. The full funding acknowledgements can be found in the publication.