The Relative Importance of Genetics and Environment on Mammographic Density
Bibliographic record
Abstract
BACKGROUND: Although several environmental factors predict mammographic density, estimates of its heritability have been quite high. We investigated whether part of the presumed heritability might be attributed to differential sharing of modifiable risk factors in monozygotic (MZ) and dizygotic (DZ) twins. METHODS: We measured percent and absolute mammographic density using mammograms from 257 MZ and 296 DZ twin pairs. The correlation of intrapair mammographic density was compared according to zygosity across strata of modifiable risk factors. Portions of variance attributable to additive genetic factors, shared environment, and individual environment were calculated using a variance component methodology in the entire set, and within twin pairs stratified by environmental trait similarity. RESULTS: Both percent density and absolute mammographic density were more highly correlated between MZ twins than DZ twins, but the correlations varied across strata. Body mass index (BMI) and parity strongly predicted differences in mammographic density within MZ twin pairs. After adjusting for covariates, 53% of the total variance in percent density and 59% of that in absolute density seemed attributable to genetic effects, but these estimates varied greatly by stratum. For twins dissimilar on BMI (difference >2.5 kg/m(2)), the additive genetic component of absolute density was estimated at only 20% (+/-19%), and the common and individual environment at 21% (+/-14%) and 49%, respectively (P value for heterogeneity across BMI = 0.0001). CONCLUSION: Our results confirm that the genome is an important determinant of mammographic density but suggest that an unknown portion of the mammographic density effect attributed to the genome may be due to shared modifiable environmental factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".