Precise characterization of metabolic risk loci using combined long and short read NGS approach

• Applicants: Dr. Harald Grallert, Ph.D.; Professor Dr. Stephan Ossowski, Ph.D.; Professorin Dr. Annette Peters
• Subject Area: Epidemiology and Medical Biometry/Statistics
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 514060894

A key objective of human and medical genetics is to provide insights into disease etiology. Genome-wide association studies (GWASs) in large samples has become the workhorse of complex disease/trait genetics, and the number of discoveries has exploded. The major challenge nowadays is to extract the biological ‘‘gold’’ from the thousands of discoveries that have been made. For type 2 diabetes (T2D) currently, we know about 240 loci that influence T2D risk. While rare variants revealed no deeper insights from first short read sequencing approaches, structural variants, which are detectable by recently developed long read sequencing technologies, have not been studied very well so far. Thus, we aim to address this gap with a comprehensive combined genetic approach analyzing a T2D/prediabetes case control cohort of the KORA FF4 study, that are deeply cardio metabolically phenotyped with MRI data. We aim as well to take recently reported diabetes and prediabetes subtypes into account, within bounds of possibility. We will apply Illumina short read and Nanopore long read sequencing for a detailed fine mapping approach adding the layer of structural variants and well defined haplotypes to better understand the previously identified T2D risk loci. Our cohort is a unique resource to link genetic data to existing multi-omics data (genome wide methylation, RNAseq, metabolite and protein levels). This will provide a solid basis for refining the knowledge on known T2D risk loci. With this approach we aim to uncover another part of the puzzle of missing heritability and to better genetically characterize T2D risk loci for potential precision medicine approaches.

• DFG Programme: Research Grants