<i><scp>MYORG</scp></i> is associated with recessive primary familial brain calcification
Bibliographic record
Abstract
Abstract Objective To investigate the genetic basis of the recessive form of primary familial brain calcification and study pathways linking a novel gene with known dominant genes that cause the disease. Methods Whole exome sequencing and Sanger‐based segregation analysis were used to identify possible disease causing mutations. Mutation pathogenicity was validated by structural protein modeling. Functional associations between the candidate gene, MYORG , and genes previously implicated in the disease were examined through phylogenetic profiling. Results We studied nine affected individuals from two unrelated families of Middle Eastern origin. The median age of symptom onset was 29.5 years (range 21–57 years) and dysarthria was the most common presenting symptom. We identified in the MYORG gene, a homozygous c.1233delC mutation in one family and c.1060_1062del GAC mutation in another. The first mutation results in protein truncation and the second in deletion of a highly conserved aspartic acid that is likely to disrupt binding of the protein with its substrate. Phylogenetic profiling analysis of the MYORG protein sequence suggests co‐evolution with a number of calcium channels as well as other proteins related to regulation of anion transmembrane transport (False Discovery Rate, FDR < 10 −8 ) and with PDCD 6 IP , a protein interacting with PDGFR β which is known to be involved in the disease. Interpretation MYORG mutations are linked to a recessive form of primary familial brain calcification. This association was recently described in patients of Chinese ancestry. We suggest the possibility that MYORG mutations lead to calcification in a PDGFR β ‐related pathway.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".