Association with replication between estrogen-related receptor γ (<i>ESRRγ</i>) Polymorphisms and bone phenotypes in women of European ancestry
Bibliographic record
Abstract
Osteoporosis is a bone disease characterized by low bone mineral density (BMD), a highly heritable polygenic trait. Women are more prone than men to develop osteoporosis owing to a lower peak bone mass and accelerated bone loss at menopause. Lack of estrogen thus is a major risk factor for osteoporosis. In addition to having strong similarity to the estrogen receptor 1 (ESR1), the orphan nuclear estrogen-related receptor gamma (ESRRgamma) is widely expressed and shows overlap with ESR1 expression in tissues where estrogen has important physiologic functions. For these reasons, we have undertaken a study of ESRRgamma sequence variants in association with bone measurements [heel quantitative ultrasound (QUS) by measurements of broadband ultrasound attenuation (BUA), speed of sound (SOS), and stiffness index (SI) and dual-energy X-ray absorptiometry (DXA) at the femoral neck (FN) and lumbar spine (LS)]. A silent variant was found to be associated with multiple bone measurements (LS, BUA, SOS, and SI), the p values ranging from .006 to .04 in a sample of 5144 Quebec women. The region of this variant was analyzed using the HapMap database and the Gabriel method to define a block of 20 kb. Using the Tagger method, eight TagSNPs were identified and genotyped in a sample of 1335 women. Four of these SNPs capture the five major block haplotypes. One SNP (rs2818964) and one haplotype were significantly associated with multiple bone measures. All SNPs involved in the associations were analyzed in two other sample sets with significant results in the same direction. These results suggest involvement of ESRRgamma in the determination of bone density in women.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".