Genomic features specific to the human lineage are associated with neurological diseases and intelligence
Bibliographic record
Abstract
While encephalisation has provided advantages to human evolution, it may have also predisposed us to neurological diseases as common neurodegenerative disorders such as Alzheimer’s and Parkinson’s disease do not occur naturally in aged non-human primates. Thus, human lineage-specific genomic features may provide insights into brain-related diseases. We leveraged high-depth whole genome sequencing data to generate a novel annotation that identifies genomic regions specific to humans and not conserved within non-human primates (termed constrained, non-conserved regions; CNCRs). We proposed that these regions have been subject to human-specific purifying selection and are enriched for brain-specific elements, relevant to human-specific disease. We found that CNCRs are depleted from protein-coding genes but enriched within the non-coding genome. Per-SNP heritability of a range of brain-relevant phenotypes are enriched within CNCRs including intelligence, Parkinson’s disease and schizophrenia. We found that genes implicated in neurological diseases have high CNCR density, in par- ticular: APOE, highlighting an unannotated intron-3 retention event. Using human brain RNA-sequencing data, we showed this human-specific intron-3-retaining transcript to be more abundant in Alzheimer’s disease with more severe tau and amyloid pathological burden. Thus, we demonstrate the importance of human-lineage-specific genomic sequences in neurological disease and make this information available in a public platform online. zhongbo chen@ucl.ac.uk 18
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".