Sequencing methods, functional characterization, prevalence, and penetrance of rare coding mutations in panels of monogenic obesity genes from the leptin‐melanocortin pathway: A systematic review
Bibliographic record
Abstract
The recent development of next-generation sequencing (NGS) technologies has led to an increase of mutation screening reports of monogenic obesity genes in diverse experimental designs. However, no study to date has summarized their findings. Two reviewers independently conducted a systematic review of MEDLINE, Embase, and Web of Science Core Collection databases from inception to September 2022 to identify monogenic non-syndromic obesity gene screening studies. Of 1051 identified references, 31 were eligible after title and abstract screening and 28 after full-text reading and risk of bias and quality assessment. Most studies (82%) used NGS methods. The number of genes screened varied from 2 to 12 genes from the leptin-melanocortin pathway. While all the included studies used in silico tools to assess the functional status of mutations, only 2 performed in vitro tests. The prevalence of carriers of pathogenic/likely pathogenic monogenic mutations is 13.24% on average (heterozygous: 12.31%; homozygous/heterozygous composite: 0.93%). As no study reported the penetrance of pathogenic mutations on obesity, we estimated that homozygous carriers exhibited a complete penetrance (100%) and heterozygous carriers a variable penetrance (3-100%). The review provides an exhaustive description of sequencing methods, functional characterization, prevalence, and penetrance of rare coding mutations in monogenic non-syndromic obesity genes.
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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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".