Whole-Exome sequencing identifies novel<i>LEPR</i>mutations in individuals with severe early onset obesity
Bibliographic record
Abstract
OBJECTIVE: Obesity is a major public health problem that increases the risk for a broad spectrum of co-morbid conditions. Despite evidence for a strong genetic contribution to susceptibility to obesity, previous efforts to discover the relevant genes using positional cloning have failed to account for most of the apparent genetic risk variance. DESIGN AND METHODS: Deploying a strategy combining analysis of exome sequencing data in extremely obese members of four consanguineous families with segregation analysis, we screened for causal genetic variants. Filter-based analysis and homozygosity mapping were used to identify and prioritize putative functional variants. RESULTS: Two novel frameshift mutations in the leptin receptor in two of the families were identified. CONCLUSIONS: These results provide proof-of-principle that whole-exome sequencing of families segregating for extreme obesity can identify causal pathogenic mutations. The methods described here can be extended to additional families segregating for extreme obesity and should enable the identification of mutations in novel genes that predispose to obesity.
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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 | 0.000 | 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".