Genetics of Food Preferences: A First View from Silk Road Populations
Bibliographic record
Abstract
Food preferences are the main factor driving food intake and choice. There are good reasons to suspect some genetic influence on food acceptance, not least because genetic factors are implicated in a number of factors that are likely to be related to food choice. In addition, some food dislikes show themselves early in life, before there is any evidence for aversive experiences. Although taste has been widely studied in regards of pure tastes such as bitter or sweet perception, the relationship between taste-related genes and food preferences has seldom been explored. In this work we investigated relationship of 37 taste-related genes with food preferences. The study was carried out during a scientific expedition through Caucasus and Central Asia (Silk Road) analyzing more than 400 samples from 5 different countries. A food preference questionnaire was administered to each participant and a DNA sample was obtained. Other information, such as age, sex, life style and anthropometrical measures, were also collected. We found significant associations with variants of: (1) TAS1R2 [Correction added after initial online publication on 27 Aug 2012. TAS1R3 was changed to TAS1R2.] gene and liking of Vodka (P= 1.6 × 10(-3)), white wine (P= 4.0 × 10(-4)) and lamb meat (P= 1.6 × 10(-3)); (2) PCLB2 gene and preference for Hot Tea (P= 8.0 × 10(-4)); (3) TPRV1 gene and beet liking (P= 3.8 × 10(-5)); and (4) ITPR3 gene and liking of both lamb meat (5.8 × 10(-4)) and sheep cheese (8.9×10(-4)). These findings give a new insight on a better understanding, of genetic factors influencing food preferences which is critical to the development of effective dietary interventions, especially for people that may be genetically not predisposed for liking specific nutrients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".