Independent and epistatic effects of variants in VPS10-d receptors on Alzheimer disease risk and processing of the amyloid precursor protein (APP)
Bibliographic record
Abstract
Genetic variants in the sortilin-related receptor (SORL1) and the sortilin-related vacuolar protein sorting 10 (VPS10) domain-containing receptor 1 (SORCS1) are associated with increased risk of Alzheimer's disease (AD), declining cognitive function and altered amyloid precursor protein (APP) processing. We explored whether other members of the (VPS10) domain-containing receptor protein family (the sortilin-related VPS10 domain-containing receptors 2 and 3 (SORCS2 and SORCS3) and sortilin (SORT1)) would have similar effects either independently or together. We conducted the analyses in a large Caucasian case control data set (n=11,840 cases, 10,931 controls) to determine the associations between single nucleotide polymorphisms (SNPs) in all the five homologous genes and AD risk. Evidence for interactions between SNPs in the five VPS10 domain receptor family genes was determined in epistatic statistical models. We also compared expression levels of SORCS2, SORCS3 and SORT1 in AD and control brains using microarray gene expression analyses and assessed the effects of these genes on γ-secretase processing of APP. Several SNPs in SORL1, SORCS1, SORCS2 and SORCS3 were associated with AD. In addition, four specific linkage disequilibrium blocks in SORCS1, SORCS2 and SORCS3 showed additive epistatic effects on the risk of AD (P≤0.0006). SORCS3, but not SORCS2 or SORT1, showed reduced expression in AD compared with control brains, but knockdown of all the three genes using short hairpin RNAs in HEK293 cells caused a significant threefold increase in APP processing (from P<0.001 to P<0.05). These findings indicate that in addition to SORL1 and SORCS1, variants in other members of the VPS10 domain receptor family (that is, SORCS1, SORCS2, SORCS3) are associated with AD risk and alter APP processing. More importantly, the results indicate that variants within these genes have epistatic effects on AD risk.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".