Testing the thrifty gene hypothesis: the Gly482Ser variant in PPARGC1Ais associated with BMI in Tongans
Bibliographic record
Abstract
BACKGROUND: the thrifty gene hypothesis posits that, in populations that experienced periods of feast and famine, natural selection favoured individuals carrying thrifty alleles that promote the storage of fat and energy. Polynesians likely experienced long periods of cold stress and starvation during their settlement of the Pacific and today have high rates of obesity and type 2 diabetes (T2DM), possibly due to past positive selection for thrifty alleles. Alternatively, T2DM risk alleles may simply have drifted to high frequency in Polynesians. To identify thrifty alleles in Polynesians, we previously examined evidence of positive selection on T2DM-associated SNPs and identified a T2DM risk allele at unusually high frequency in Polynesians. We suggested that the risk allele of the Gly482Ser variant in the PPARGC1A gene was driven to high frequency in Polynesians by positive selection and therefore possibly represented a thrifty allele in the Pacific. METHODS: here we examine whether PPARGC1A is a thrifty gene in Pacific populations by testing for an association between Gly482Ser genotypes and BMI in two Pacific populations (Maori and Tongans) and by evaluating the frequency of the risk allele of the Gly482Ser variant in a sample of worldwide populations. RESULTS: we find that the Gly482Ser variant is associated with BMI in Tongans but not in Maori. In a sample of 58 populations worldwide, we also show that the 482Ser risk allele reaches its highest frequency in the Pacific. CONCLUSION: the association between Gly482Ser genotypes and BMI in Tongans together with the worldwide frequency distribution of the Gly482Ser risk allele suggests that PPARGC1A remains a candidate thrifty gene in Pacific populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".