Nicole Soranzo, Tim Spector, Gabi Kastenmüller and colleagues report a large-scale analysis of genetic variants influencing human blood metabolite levels. They identify genome-wide significant associations at 145 loci, providing a framework for exploring relationships between genetic variation, metabolism and complex disease. Genome-wide association scans with high-throughput metabolic profiling provide unprecedented insights into how genetic variation influences metabolism and complex disease. Here we report the most comprehensive exploration of genetic loci influencing human metabolism thus far, comprising 7,824 adult individuals from 2 European population studies. We report genome-wide significant associations at 145 metabolic loci and their biochemical connectivity with more than 400 metabolites in human blood. We extensively characterize the resulting in vivo blueprint of metabolism in human blood by integrating it with information on gene expression, heritability and overlap with known loci for complex disorders, inborn errors of metabolism and pharmacological targets. We further developed a database and web-based resources for data mining and results visualization. Our findings provide new insights into the role of inherited variation in blood metabolic diversity and identify potential new opportunities for drug development and for understanding disease.

Genome-wide association studies (GWAS) with intermediate phenotypes, like changes in metabolite and protein levels, provide functional evidence to map disease associations and translate them into clinical applications. However, although hundreds of genetic variants have been associated with complex disorders, the underlying molecular pathways often remain elusive. Associations with intermediate traits are key in establishing functional links between GWAS-identified risk-variants and disease end points. Here we describe a GWAS using a highly multiplexed aptamer-based affinity proteomics platform. We quantify 539 associations between protein levels and gene variants (pQTLs) in a German cohort and replicate over half of them in an Arab and Asian cohort. Fifty-five of the replicated pQTLs are located in trans. Our associations overlap with 57 genetic risk loci for 42 unique disease end points. We integrate this information into a genome-proteome network and provide an interactive web-tool for interrogations. Our results provide a basis for novel approaches to pharmaceutical and diagnostic applications.

Detangling gene-disease connections: Many diseases are at least partially due to genetic causes that are not always understood or targetable with specific treatments. To provide insight into the biology of various human diseases as well as potential leads for therapeutic development, Pietzneret al. undertook detailed, genome-wide proteogenomic mapping. The authors analyzed thousands of connections between potential disease-associated mutations, specific proteins, and medical conditions, thereby providing a detailed map for use by future researchers. They also supplied some examples in which they applied their approach to medical contexts as varied as connective tissue disorders, gallstones, and COVID-19 infections, sometimes even identifying single genes that play roles in multiple clinical scenarios. —YN