O-103 A method for mapping sex-specific meiotic crossovers from PGT-A data elucidates the role of aberrant recombination in the origins of aneuploidy
Bibliographic record
Abstract
Abstract Study question How do the number and location of meiotic crossovers contribute to the formation of aneuploidies observed in preimplantation human embryos? Summary answer Normalized across chromosomes, trisomies possess 35% fewer crossovers on average compared to disomies, while the genomic distribution of crossovers is also substantially altered. What is known already Meiotic recombination is a crucial source of genetic diversity and is also critical for ensuring the accuracy of chromosome segregation. Understanding the landscape of meiotic recombination, its variation across individuals, and the processes by which it goes awry are long-standing goals in human genetics. Current approaches for inferring the landscape of recombination either rely on population genetic patterns of linkage disequilibrium—capturing a time-averaged view—or direct detection of crossovers in gametes or multi-generation pedigrees, limiting the scale and availability of relevant datasets. Moreover, most of these methods are designed for discovering recombination using data from normal, disomic chromosomes. Study design, size, duration We present a method for mapping sex-specific recombination landscapes from low-coverage (<0.1×) data from preimplantation genetic testing for aneuploidy (PGT-A) of embryos with arbitrary ploidy configurations. To overcome the sparsity of these data, our method exploits its inherent relatedness structure, knowledge of haplotypes from external population reference panels, as well as the frequent occurrence of chromosome loss in embryos, whereby the remaining chromosome is phased by default. Participants/materials, setting, methods We benchmarked our method by simulating crossovers between known haplotypes. Encouraged by the performance on simulated data we extended our study to retrospective analysis utilizing de-identified PGT-A data obtained between April 2021 and August 2022 at the CReATe Fertility Centre (Toronto, Canada). The data include 20,160 embryos (2,559 IVF patients) with an average depth of coverage of ∼0.05×, facilitating the mapping of crossovers at an average resolution of ∼150 kbp. Main results and the role of chance Our benchmarking results demonstrate high sensitivity and specificity across all ancestries at a coverage of 0.05x per homolog (AUC = 0.989), with AUC declining by 0.014 and 0.053 for when coverage is reduced to 0.025x and 0.013x, respectively. Extending our analysis to real PGT-A data, we observed that our inferred sex-specific landscapes of meiotic crossovers on disomic chromosomes were strongly correlated with published genetic maps from studies based on high-coverage sequencing of parent-offspring trios (r = 0.86 for female map; r = 0.53 for male map), broadly supporting the accuracy of our method. Notably, the total length of the female genetic map was reduced by 35% for trisomies compared to disomies, consistent with the hypothesized role of reduced crossovers and exchangeless chromosomes in the origins of female meiotic aneuploidy. In addition, the genomic distribution of crossovers is also altered in a chromosome-specific manner. Examples include a reduction in crossovers near the centromere of trisomies versus disomies of chromosome 16, as well as an enrichment of crossovers on the q-arm of trisomies versus disomies of chromosome 22. Together, our results provide a detailed corroboration of the hypothesis that aberrant meiotic recombination contributes to the origins of aneuploidies. Limitations, reasons for caution The accuracy of our method is influenced by genomic heterogeneity in depth of coverage, rates of heterozygosity, and mismatches between the ancestry of the reference panel and the tested sequence. Moreover, technical errors such as spurious alignment and genotyping could hinder analysis in repetitive genomic regions. Wider implications of the findings Together, our study helps clarify the dual function of meiotic recombination in generating genetic diversity while ensuring meiotic fidelity. Our method for patient-specific mapping of meiotic recombination phenotypes may offer clues about how dysregulation of this process contributes to infertility. Trial registration number NIH r R35GM13374
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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 | 0.002 | 0.001 |
| 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".