Disentangling relationships between genotype and phenotype in complex genetic disorders, with particular application to liver and kidney disease
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AI plain-English summaryA 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.
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