Potential genetic modifiers of disease risk and age at onset in patients with frontotemporal lobar degeneration and GRN mutations: a genome-wide association study
Bibliographic record
Abstract
BACKGROUND: Loss-of-function mutations in GRN cause frontotemporal lobar degeneration (FTLD). Patients with GRN mutations present with a uniform subtype of TAR DNA-binding protein 43 (TDP-43) pathology at autopsy (FTLD-TDP type A); however, age at onset and clinical presentation are variable, even within families. We aimed to identify potential genetic modifiers of disease onset and disease risk in GRN mutation carriers. METHODS: ) were genotyped in a replication cohort of patients and controls, followed by a meta-analysis. The effect of genome-wide significant variants at the GFRA2 locus on expression of GFRA2 was assessed using mRNA expression studies in cerebellar tissue samples from the Mayo Clinic brain bank. The effect of the GFRA2 locus on progranulin concentrations was studied using previously generated ELISA-based expression data. Co-immunoprecipitation experiments in HEK293T cells were done to test for a direct interaction between GFRA2 and progranulin. FINDINGS: ). Expression analyses showed that the risk-associated allele at rs36196656 decreased GFRA2 mRNA concentrations in cerebellar tissue (p=0·04). No effect of rs36196656 on plasma and CSF progranulin concentrations was detected by ELISA; however, co-immunoprecipitation experiments in HEK293T cells did suggest a direct binding of progranulin and GFRA2. INTERPRETATION: TMEM106B-related and GFRA2-related pathways might be future targets for treatments for FTLD, but the biological interaction between progranulin and these potential disease modifiers requires further study. TMEM106B and GFRA2 might also provide opportunities to select and stratify patients for future clinical trials and, when more is known about their potential effects, to inform genetic counselling, especially for asymptomatic individuals. FUNDING: National Institute on Aging, National Institute of Neurological Disorders and Stroke, Canadian Institutes of Health Research, Italian Ministry of Health, UK National Institute for Health Research, National Health and Medical Research Council of Australia, and the French National Research Agency.
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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".