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Record W1569031808 · doi:10.1111/ahg.12125

Leveling the Playing Field in Homozygosity Mapping Using Map Distances

2015· article· en· W1569031808 on OpenAlexaff
Yi Li, H. Busra Cagirici, Sukanya Horpaopan, Jürg Ott, Atsuko Imai, Jacek Majewski, Mark Lathrop

Bibliographic record

VenueAnnals of Human Genetics · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Mapping and Diversity in Plants and Animals
Canadian institutionsMcGill University and Génome Québec Innovation Centre
FundersNational Natural Science Foundation of China
KeywordsRuns of HomozygosityDisease gene identificationField (mathematics)BiologyGeneticsMathematicsMutationGenotype

Abstract

fetched live from OpenAlex

Summary Studies of linkage disequilibrium (LD) and its variation in the genome are of central importance for understanding evolutionary history, population structure, and selective sweeps. Extreme forms of the latter may result in runs of homozygosity (ROH). In human gene mapping, long ROHs are the basis for homozygosity mapping (HM) with length measured in terms of Mb (10 6 base pairs physical distance). LD varies greatly over the human genome so that long ROHs tend to occur preferentially in regions of high LD and ROHs of the same length in different regions are not strictly comparable. Thus, in human gene mapping, LD appears as a confounder that needs to be taken into account in the interpretation of ROHs. The effect of varying LD can be mitigated by working on a scale of centimorgans (cM, genetic distance) instead of Mb. We demonstrate this effect for HapMap 3 data on chromosome 19 and show examples with different ROH lengths depending on whether physical or genetic lengths are used. These results suggest that HM should preferably be done on genetic rather than physical distances.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.184
GPT teacher head0.341
Teacher spread0.157 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2015
Admission routes1
Has abstractyes

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