Estimation of Parent-Sib Correlations for Quantitative Traits Using the Linear Mixed Regression Model: Applications to Arterial Blood Pressures Data Collected From Nuclear Families
Why this work is in the frame
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Bibliographic record
Abstract
A fundamental question in quantitative genetics is whether observed variation in the phenotypic values of a particular trait is due to environmental or to biological factors. Proportion of variations attributed to genetic factors is known as heritability of the trait. Heritability is a concept that summarizes how much of the variation in a trait is due to variation in genetic factors. Often, this term is used in reference to the resemblance between parents and their offspring. In this context, high heritability implies a strong resemblance between parents and offspring with regard to a specific trait, while low heritability implies a low level of resemblance. While many applications measure the offspring resemblance to their parents using the mid-parental value of a quantitative trait of interest as an input parameter, others focus on estimating maternal and paternal heritability. In this paper we address the problem of estimating parental heritability using the nuclear family as a unit of analysis. We derive moment and maximum likelihood estimators of parental heritability, and test their equality using the likelihood ratio test, the delta method. We also use Fieller’s interval on the ratio of parental heritability to address the question of bioequivalence. The methods are illustrated on published arterial blood pressures data collected from nuclear families.
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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 | 0.000 | 0.001 |
| 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 it