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Polygenic prediction: risk estimation for individuals with high familial risk applied in inflammatory bowel disease

Subject Area Epidemiology and Medical Biometry/Statistics
Human Genetics
Term since 2025
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 567050653
 
A positive family history is an important risk factor for many diseases with at least partly genetic etiology, and is often used to identify individuals at high risk. For these high-risk individuals, precise risk prediction has a large incentive on a personal basis and can result in intensified or earlier screening or specific prevention. For complex diseases, the genetic background is usually highly polygenic and the assessment of their genetic risk component has taken big steps forward with the recent development of polygenic scores (PGS). There is very limited evidence, however, on how effectively PGS perform in persons with high familial risk when additional family members with genetic and phenotypic data are included. Addressing this requires advanced statistical methods and a dataset of related individuals. We will investigate this topic and use inflammatory bowel disease (IBD) as an exemplary clinically important disease. IBD has two main subtypes, ulcerative colitis and Crohn’s disease and is a chronic, relapsing disorder of the gastrointestinal tract. It is an extremely relevant complex disease with increasing prevalence in Western Europe with a high familial component and for which family-based cohort data are readily available to us. We will investigate the predictive performance of a) classical PGS for IBD as a weighted summation of the dosage of risk alleles, b) mixed model-based PGS (MM-PGS) including relatedness between (affected and unaffected) family members, and c) MM-PGS which additionally take local genetic similarity from identity-by-descent sharing regions into account. We expect that this project will result in substantially improved risk prediction for the vulnerable group of individuals with high familial risk.
DFG Programme Research Grants
 
 

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