A novel method to identify significant DNA motifs in the human genome associated with Alzheimer’s disease
Bibliographic record
Abstract
Alzheimer’s disease (AD) is a complex disorder influenced by both environmental and genetic factors. Around 47 million people worldwide are living with dementia, most have AD. Genome wide association studies (GWAS) have identified 21 associated loci (Lambert et al, 2013). The proposed method is to compare the DNA sequences around the SNPs of interest (for example GWAS hits) (these regions will be referred to as Areas of Interest- AOI) with regions around matched SNPs in the rest if the genome (Areas Not of Interest- ANOI). We aim to identify motifs with significant differences in the frequency. Such motifs have the potential to help us understand the functional role of GWAS hits and identify risk variants in other studies. We are currently investigating AOI from the AD GWAS mentioned above. The most significant SNP at each locus (index SNPs) and all SNPs in high linkage disequilibrium (LD >0.8) are included. AOIs of 200bp around each SNP are defined. Index ANOI SNPs are matched to the AD index SNPs on the basis of allele frequency. We count of all possible DNA motifs (of a predetermined length) in the AOI and ANOI. Next the counts are grouped according to complementary strands matching and directional matching. Finally, statistical tests e.g. Fisher Exact test and Cochran Armitage trend test are performed. We will use this method to analyses other data such as expression quantitative trait loci data from the Genome-Tissue expression (GTex) project.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".