Rare coding mutations identified by sequencing of <scp>A</scp>lzheimer disease genome‐wide association studies loci
Bibliographic record
Abstract
OBJECTIVE: To detect rare coding variants underlying loci detected by genome-wide association studies (GWAS) of late onset Alzheimer disease (LOAD). METHODS: We conducted targeted sequencing of ABCA7, BIN1, CD2AP, CLU, CR1, EPHA1, MS4A4A/MS4A6A, and PICALM in 3 independent LOAD cohorts: 176 patients from 124 Caribbean Hispanics families, 120 patients and 33 unaffected individuals from the 129 National Institute on Aging LOAD Family Study; and 263 unrelated Canadian individuals of European ancestry (210 sporadic patients and 53 controls). Rare coding variants found in at least 2 data sets were genotyped in independent groups of ancestry-matched controls. Additionally, the Exome Aggregation Consortium was used as a reference data set for population-based allele frequencies. RESULTS: Overall we detected a statistically significant 3.1-fold enrichment of the nonsynonymous mutations in the Caucasian LOAD cases compared with controls (p = 0.002) and no difference in synonymous variants. A stop-gain mutation in ABCA7 (E1679X) and missense mutation in CD2AP (K633R) were highly significant in Caucasian LOAD cases, and mutations in EPHA1 (P460L) and BIN1 (K358R) were significant in Caribbean Hispanic families with LOAD. The EPHA1 variant segregated completely in an extended Caribbean Hispanic family and was also nominally significant in the Caucasians. Additionally, BIN1 (K358R) segregated in 2 of the 6 Caribbean Hispanic families where the mutations were discovered. INTERPRETATION: Targeted sequencing of confirmed GWAS loci revealed an excess burden of deleterious coding mutations in LOAD, with the greatest burden observed in ABCA7 and BIN1. Identifying coding variants in LOAD will facilitate the creation of tractable models for investigation of disease-related mechanisms and potential therapies.
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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 | 0.006 |
| 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".