Beyond Homozygosity Mapping: Family-Control analysis based on Hamming distance for prioritizing variants in exome sequencing
Bibliographic record
Abstract
A major challenge in current exome sequencing in autosomal recessive (AR) families is the lack of an effective method to prioritize single-nucleotide variants (SNVs). AR families are generally too small for linkage analysis, and length of homozygous regions is unreliable for identification of causative variants. Various common filtering steps usually result in a list of candidate variants that cannot be narrowed down further or ranked. To prioritize shortlisted SNVs we consider each homozygous candidate variant together with a set of SNVs flanking it. We compare the resulting array of genotypes between an affected family member and a number of control individuals and argue that, in a family, differences between family member and controls should be larger for a pathogenic variant and SNVs flanking it than for a random variant. We assess differences between arrays in two individuals by the Hamming distance and develop a suitable test statistic, which is expected to be large for a causative variant and flanking SNVs. We prioritize candidate variants based on this statistic and applied our approach to six patients with known pathogenic variants and found these to be in the top 2 to 10 percentiles of ranks.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".