From classical mendelian randomization to causal networks for systematic integration of multi-omics

ABSTRACT: The number of studies with information at multiple biological levels of granularity, such as genomics, proteomics, and metabolomics, is increasing each year, and a biomedical questaion is how to systematically integrate these data to discover new biological mechanisms that have the potential to elucidate the processes of health and disease. Causal frameworks, such as Mendelian randomization (MR), provide a foundation to begin integrating data for new biological discoveries. Despite the growing number of MR applications in a wide variety of biomedical studies, there are few approaches for the systematic analysis of omic data. The large number and diverse types of molecular components involved in complex diseases interact through complex networks, and classical MR approaches targeting indivi

SUBMITTER: Yazdani A 

PROVIDER: S-EPMC9520987 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature