The advent of large-scale biobank data has provided computational biologists with unprecedented opportunities to leverage individual-level deep phenotypic datasets to analyze genotype-phenotype associations on a phenome-wide scale.
This has enabled the implementation of computational approaches in genetic epidemiology, facilitating the detection of small combinatorial genetic effects and the development of robust genetic models for phenotype prediction.
Polygenic scores based on health-related phenotypes and associated biomarkers have shown significant potential to improve clinical risk stratification for complex multifactorial diseases, supporting advancements in personalized risk management and preventive medicine.
In parallel, genetic prediction models applied to multi-omics datasets have enabled the investigation of the genetic architecture underlying the regulation of molecular markers. This is particularly relevant as genome-wide associated loci for complex traits are often located in non-coding regions, where their effects on gene expression can provide the biological link between genetic risk susceptibility and the molecular endophenotypes potentially involved in disease development.
In this talk, it will be shown how advanced polygenic modeling approaches can be used to analyze and study the genetic architecture of multifactorial traits and predict the genetically driven components of complex phenotypes. With the growing availability of biobanks, these methods hold promise for bridging the gap between genomic research and its applications in genetic epidemiology, enabling more precise disease risk assessment and advancing personalized risk management.
Carlo Maj is a tenured Principal Investigator at the Center for Human Genetics at the University of Marburg, Germany, where he leads a research group specializing in genetic epidemiology. With a strong foundation in computational biology, he holds an MSc in Bioinformatics and a PhD in Computer Science from the University of Milano-Bicocca (Italy). Prior to joining Marburg, Dr. Maj was a junior group leader at the Institute for Genomic Statistics and Bioinformatics at the University of Bonn. Dr. Maj’s research focuses on uncovering the genetic architecture of complex multifactorial traits through the application of innovative computational and statistical methods. By analyzing large-scale multi-omics datasets and actively contributing to international consortia, his work has contributed to the identification of genetic risk factors associated with complex diseases and the development of polygenic models for disease risk stratification.
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