A Novel Statistical Method for Unmasking Sex‐Specific Genomics Signatures in Complex Traits
Bibliographic record
Abstract
Genotype-phenotype association studies have advanced our understanding of complex traits but often overlook sex-specific genetic signals. The growing awareness of sex-specific influences on human traits and diseases necessitates tailored statistical methodologies to dissect these genetic intricacies. Rare genetic variants play a significant role in disease development, often exhibiting stronger per-allele effects than common variants. In sex-dimorphic analysis, traits are viewed as having two sex-specific subsets rather than being uniformly defined. Existing methods for gene-based analysis of rare variants across multiple traits can identify shared genetic signals but cannot reveal the specific subsets from which significant signals originate. This means that when a significant signal is detected, it remains unclear whether it arises from the male samples, female samples, or both. To address this limitation, we propose SubsetRV, a new methodology capable of identifying genes associated with specific traits or diseases in males, females, or both. SubsetRV can also be applied to broader applications in multiple traits analysis. Simulation studies have demonstrated SubsetRV's reliability, and real data analysis on bipolar disorder and schizophrenia has revealed potential sex-specific genetic signals. SubsetRV offers a valuable tool for identifying sex-specific genetic candidates, aiding in understanding disease mechanisms. An R package for SubsetRV is available on GitHub. It can be accessed directly through this link: https://github.com/Mansouri-S/SubsetRV.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".