Genetic backgrounds and modifier genes of NTD mouse models: An opportunity for greater understanding of the multifactorial etiology of neural tube defects
Bibliographic record
Abstract
Neurulation, the early embryonic process of forming the presumptive brain and spinal cord, is highly complex and involves hundreds of genes in multiple genetic pathways. Mice have long served as a genetic model for studying human neurulation, and the resulting neural tube defects (NTDs) that arise when neurulation is disrupted. Because mice appear to show mostly single gene inheritance for NTDs and humans show multifactorial inheritance, mice sometimes have been characterized as a simpler model for the identification and study of NTD genes. But are they a simple model? When viewed on different genetic backgrounds, many genes show significant variation in the penetrance and expressivity of NTD phenotypes, suggesting the presence of modifier loci that interact with the target gene to affect the phenotypic expression. Looking at mutations on different genetic backgrounds provides us with an opportunity to explore these complex genetic interactions, which are likely to better emulate similar processes in human neurulation. Here, we review NTD genes known to show strain-specific phenotypic variation. We focus particularly on the gene Cecr2, which is studied using both a hypomorphic and a presumptive null mutation on two different backgrounds: one susceptible (BALB/c) and one resistant (FVB/N) to NTDs. This strain difference has led to a search for genetic modifiers within a region on murine chromosome 19. Understanding how genetic variants alter the phenotypic outcome in NTD mouse models will help to direct future studies in humans, particularly now that more genome wide sequencing approaches are being used. Birth Defects Research 109:140-152, 2017. © 2016 Wiley Periodicals, Inc.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".