Exploring the relationships between taste phenotypes, genotypes, ethnicity, gender and taste perception using Chi-square and regression tree analysis

Abstract

It is well known that perceived taste intensity varies greatly among individuals, and that several factors including taste phenotypes (PROP Taster Status (PTS), Sweet Liking Status (SLS), Thermal Taster Status (TTS)), ethnicity and gender, contribute to variation in taste responsiveness, although such factors are usually investigated in isolation. This study aimed to investigate the association between different taste pheno/genotypes, explore whether these taste phenotypes associated with ethnicity (Caucasian vs Asian) and gender, and determine the relative effects of the different factors on perceived taste intensity. As analysis of this type of data with ANOVA can be difficult due to confounding factors, interactions, and small sample sizes in subcategories, the use of regression tree analysis as an alternative approach was investigated. To that end, two-hundred and twenty-three volunteers were phenotyped for their PTS, SLS and TTS and genotyped for TAS2R38 –rs713598 and gustin –rs2274333. They also rated their perceived intensity of five basic taste and metallic solutions on a gLMS scale. No significant association between the three taste phenotypes were found indicating PTS, SLS and TTS are independent taste phenotypes. However, the results indicated that Asians were not only more likely to be PROP supertasters, but also more likely to be thermal tasters or Low Sweet Likers, compared to Caucasians. Gender was also significantly associated with SLS, where males were more likely to be High Sweet Likers. For perceived taste intensity, traditional ANOVA analysis proved to be challenging. The alternative approach, using regression trees, was shown to be an effective tool to provide a visualised framework to demonstrate the multiple interactions in this dataset. For example, ethnicity was the most influencing factor for perceived sour and metallic taste intensity, where Asians had heightened response compared to Caucasians. The regression tree analysis also highlighted that the PTS effect was dependent on ethnicity for sour taste, and PTS and TTS effect was dependent on ethnicity for metallic taste. This study is the first study to use regression tree analysis to explore variation in taste intensity ratings, and demonstrated it can be an effective tool to handle and interpret complex sensory datasets.