A co-operative co-evolutionary genetic algorithm for haplotype pattern detection in case-control data
Bibliographic record
Abstract
Genomic variations such as Single Nucleotide Polymorphisms (SNP) and their underlying haplotype patterns in case-control cohorts are used to identify genes associated with diseases. Complex diseases involve multiple genes which may be distributed over the genome. A popular technique for detecting such markers and patterns is the sliding window technique using statistical models. However, the statistical techniques used are computationally expensive, and derived patterns are typically restricted both in length and to consist of contiguous markers. In this thesis, we have developed a cooperative coevolutionary genetic algorithm (CCGA) that can compute both contiguous and non-contiguous marker haplotype patterns from case-control haplotype data; moreover, this algorithm can tolerate missing/ambiguous positions in haplotype data arising during haplotype phasing from genotypes. -- We have tested our algorithm on three case-control cohorts (the Ankylosing Spondilitis (AS) inflammatory arthritis cohorts from Alberta (AL) and Newfoundland (NF) populations (genotyped for the IL1 gene cluster on chromosome 2) and the Japanese Schizophrenia cohort (genotyped for the Netrin Gl gene on chromosome 1). The results obtained using our CCGA are in strong accordance with previously published results. Specifically, (1) in the AL spondylitis cohort, we have found significant haplotype patterns (p < 0.0005 and haplotype risk ratio ≥ 1.5) that confer susceptibility of four genes (ILIA, IL1B, IL1F7 and IL1F10) with AS, three of which (ILIA, IL1B, IL1F10) were confirmed by two independent studies; and (2) in the Japanese schizophrenia cohort, 7 SNPs (rs4481881, rs4307594, rs3924253, rs4132604, rsl373336, rsi444042, and rs96501) and their haplotypes showed significant (p < 0.0005 and haplotype risk ratio ≥ 1.5) association with schizophrenia, the most significant of which (rs4307594, rs3924253, and rsl373336) were confirmed by two independent studies.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".