Active Digestion, Kidneys & Other Organs Genetics & Molecular Biology

Disentangling relationships between genotype and phenotype in complex genetic disorders, with particular application to liver and kidney disease

In plain English

AI plain-English summary

A new statistical toolkit is being built to untangle how genetic variations actually cause liver and kidney disease, rather than just being correlated with it. The problem is that for complex diseases like primary biliary cholangitis (PBC) and chronic kidney disease, scientists can identify genetic variants linked to illness, but they often do not know which biological mechanisms—changes in gene expression, DNA methylation, or protein levels—actually drive the disease. Without that causal understanding, drug development remains a guessing game. This project will integrate multiple layers of molecular data (genotypes, gene expression, methylation, and proteomics) from existing clinical cohorts, including 100 PBC patients and 50 controls, to map the causal pathways from DNA variation to disease. If successful, the research could pinpoint specific proteins that alter disease risk, revealing new drug targets and opportunities to repurpose existing medicines. It could also predict which patients will respond to treatment based on baseline molecular profiles. This is primarily fundamental science—developing statistical methods to infer causation from complex data—but the practical payoff would be more rational, biology-driven drug discovery for common liver and kidney disorders.

View original technical description
The main goal of this research is to develop and apply advanced statistical methods to help elucidate the biological mechanisms and causal pathways underpinning the correlations seen between genotype and phenotype in complex genetic disorders, with specific emphasis on liver and kidney disease. This will allow us to better understand the biological processes leading to disease development, thus enabling the development of potential therapies and cures. Identifying genes and their protein products which alter disease risk will point to potential new drug targets and allow opportunities for re-purposing of existing drugs. We will use measurements of genetic factors and potential intermediate processes like gene expression, DNA methylation and protein levels, available through long-standing collaborations with clinical colleagues. A key goal of our research is to develop methods that integrate these different data types with one another and with similar data from external sources. We will expand the data available through UK-PBC (SNP-genotypes and gene-expression profiling in 100 pre-treatment cases plus 50 controls), by adding CpG-methylation and serum proteomics, enhancing our ability to identify causal pathways in disease development/progression.This will inform disease biology, subsequent treatment response, and the extent to which it can be predicted from baseline measures.

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Researchers

Heather Cordell (EPMC Awardee)

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Original classification

Investigator Award in Science

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