Response genetics in physiology- How to integrate data with advanced stem cell models

The data linking genes to biological phenotypes have expanded to the point where few scientists can logistically organize this knowledge into simple biological hypotheses. This often results in missing logistical overlaps within diseases that lack precise defining mechanisms separating any two diseases. Most importantly, the understanding of the events that give rise to human phenotypes and diseases can be discovered through extensive data integrations. Our team has developed a workflow that studies disease overlaps through systems biology that then addresses the temporal nature of relationships through biological mechanisms and advanced human stem cell culture assays. The extraction of genetic and physiological data within respiratory, cardiac, neural, and renal systems has shed light on publication bias. At the same time, tools such as quantitative trait loci (GWAS, PheWAS, eQTL, sQTL) and large-scale mouse knockouts (IMPC) can provide predictive genetics that, with colocalization analyses, can also elucidate mechanistic insights. In many of the top genetic markers, the potential for genetic by environmental interactions is also observed, where variants manifest disease phenotypes when an environmental condition is present. For example, variants in the nicotine receptor that are associated with smoking satiation and COPD/emphysema/cancer, variants in SHROOM3 that are responsive to podocyte environmental response for proteinuria risks, or variants in MUC5B primarily associated with pulmonary fibrosis in the presence of smoking. To further refine human genetics by environmental factors, we have begun exploring induced pluripotent stem cell models of respiratory, cardiac, neural, and renal systems with simple biological assays such as drug exposures, hypoxia, and nitric oxide response that can be applied to a diverse pool of human genetic backgrounds. Within physiology, we critically need new tools that highlight genetic by environmental response for disease risk modeling, and these proposed methods may shed light on numerous disease mechanisms if expanded upon. The work was funded by the American Heart Association grant 23SCISA1144887 and NIH Office of the Director R01AI171984. This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.