MedicalResearch.com Interview with: [caption id="attachment_74522" align="alignleft" width="222"]Dr Daniel Liang-Dar HwangBSc, MBiotech, MSc, PhD ARC DECRA Fellow Institute for Molecular Bioscience The University of Queensland Brisbane, Australia and Monell Chemical Senses Center Philadelphia, PA, USA Dr. Daniel Hwang[/caption] Dr. Daniel Liang-Dar Hwang BSc, MBiotech, MSc, PhD ARC DECRA Fellow Institute for Molecular Bioscience The University of Queensland Brisbane, Australia and Monell Chemical Senses Center Philadelphia, PA, USA   MedicalResearch.com: What is the background for this study? What are the main findings? Response: One of the biggest challenges in nutrition research is distinguishing causation from correlation. People who consume particular foods often differ in many other ways, such as income, education, physical activity, or overall health, making it difficult to determine whether a food itself influences disease risk. Mendelian randomization has emerged as a powerful tool for investigating causal relationships by using genetic variants as proxies for exposures. However, finding genetic variants that reliably reflect what people eat remains a major challenge. In this study, we developed a biologically informed framework for instrument selection using genetic variation in taste and smell receptor genes. Because taste and smell are major biological drivers of food choice, variants in these genes may provide biologically meaningful proxies for studying dietary exposures. We examined more than 1,200 genetic variants in taste and smell receptor genes and tested their associations with preferences for 140 foods and beverages in more than 160,000 participants. We identified 700 significant gene–food associations, many of which were also linked to actual food intake and replicated in an independent cohort. We then used these biologically informed variants in Mendelian randomization analyses to investigate potential causal relationships between diet and health, demonstrating how sensory genetics can be used to strengthen causal inference in nutrition research and identify foods that may influence disease risk.