Copy Number Variant Duplications Associated with Essential Tremor
Bibliographic record
Abstract
Abstract Background Essential tremor (ET) is a complex neurological disorder with a strong genetic basis, yet there remains a disparity between the estimated heritability and the currently known genetic risk of ET. This missing heritability of ET has led to a lack of appropriate treatments and has exacerbated the misdiagnosis of patients. Methods To address the missing heritability of ET, we called copy number variants (CNVs) in a large cohort of ET patients (n=1,853) and unaffected controls (n=10,336). CNVs were called from single nucleotide polymorphism (SNP) microarray data using PennCNV and QuantiSNP and only rare CNVs (frequency < 1%) intersecting protein coding regions of the genome were retained for analysis. To investigate whether CNV occurrence was associated with ET, analyses were conducted on global burden, pathogenicity burden, gene set enrichment, and gene burden. Results Global duplication burden by number of CNVs (OR = 1.71 [1.30-2.23], p = 9.8×10 -5 ), CNV length (OR = 1.08 [1.02-1.15], p = 7.1×10 -3 ), and number of genes affected by CNVs (OR = 1.11 [1.05-1.18], p = 5.8×10 -4 ) were all significantly elevated in ET patients compared to controls. Duplications sized 100kbs-500kbs largely explained these associations (number of CNVs: OR = 2.14 [1.54-2.94], p = 3.95×10 -6 ; number of genes: OR = 1.15 [1.06-1.23], p = 3.8×10 -4 ). Across gene-sets, duplications affecting Mendeliome genes (OR = 2.01 [1.36-2.91], p = 3.4×10 -4 ), genes highly expressed in the brain (OR = 1.80 [1.27-2.46], p = 5.06×10 -4 ), and genes expressed in the cerebellum (OR = 1.16 [1.06-1.27], p = 7.31×10 -4 ) were significantly enriched for CNVs in ET patients compared to controls. Finally, gene-based burden testing indicated that duplications involving ZNF813 were significantly less frequent in ET patients than in controls (OR = 0.0411 [0.0026-0.66], p = 2.22×10 -5 ). No associations with deletion events were observed. Conclusions Our results point to rare copy number duplications mapping to protein coding regions of the genome as likely contributors to ET genetic risk. However, specific susceptibility genes could not be reliably identified, highlighting the need for further studies to clarify the genetic architecture of ET.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".