Next-Generation Mapping of Genetic Mutations Using Bulk Population Sequencing
Bibliographic record
Abstract
Next-generation sequencing platforms have made it possible to very rapidly map genetic mutations in Arabidopsis using whole-genome resequencing against pooled members of an F2 mapping population. In the case of recessive mutations, all individuals expressing the phenotype will be homozygous for the mutant genome at the locus responsible for the phenotype, while all other loci segregate roughly equally for both parental lines due to recombination. Importantly, genomic regions flanking the recessive mutation will be in linkage disequilibrium and therefore also be homozygous due to genetic hitchhiking. This information can be exploited to quickly and effectively identify the causal mutation. To this end, sequence data generated from members of the pooled population exhibiting the mutant phenotype are first aligned to the reference genome. Polymorphisms between the mutant and mapping line are then identified and used to determine the homozygous, nonrecombinant region harboring the mutation. Polymorphisms in the identified region are filtered to provide a short list of markers potentially responsible for the phenotype of interest, which is followed by validation at the bench. Although the focus of recent studies has been on the mapping of point mutations exhibiting recessive phenotypes, the techniques employed can be extended to incorporate more complicated scenarios such as dominant mutations and those caused by insertions or deletions in genomic sequence. This chapter describes detailed procedures for performing next-generation mapping against an Arabidopsis mutant and discusses how different mutations might be approached.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".