A reusable neural approach to recombination mapping for model and non-model species
- 1University of Pennsylvania
Abstract
Pedigree and crossing experiments can measure crossovers directly and provide the gold standard for recombination mapping, but their cost restricts fine-scale recombination mapping to only a few species. Patterns of linkage disequilibrium (LD) provide an alternative statistical approach for inferring variation in recombination along the genome. LD, however, is confounded by many evolutionary factors, such as demographic changes, life-history traits, and genomic structural variation. We present fastrho, a state-space neural-network estimator trained across a range of simulation-based priors. In simulated bottleneck and expansion scenarios, the fixed checkpoint recovered local map shape without target-specific retraining; comparisons with pyrho used lookup tables constructed under the simulation-generating history. We further evaluated generalizability across multiple species and, to account for additional confounders not represented in the initial training data, designed specialized models for inference in selfing plants, structured Arabis populations, and large- malaria-vector populations. A major biological application of the mosquito model was the construction of a five-arm recombination atlas spanning 13 Ag3 populations, providing a detailed view of recombination-rate variation across the dataset. Recombination maps inferred from Ag3 pedigrees provided independent, coarse-scale support for this atlas. Finally, analyses of resistance loci and redpoll bird supergenes demonstrate how selection and structural variation influence LD. Throughout our study, we use experimental maps for independent validation. Together, our results establish fastrho as a flexible framework for robust recombination mapping across diverse biological systems.
Related
- Cross-species molecular mapping of the photoreceptor sensory cilium and periciliary structures identifies conserved and species-specific architectural features
- Transfer Learning-Driven Convolutional Neural Networks for Classification of Plant Species from Leaf Vein Patterns
- Mapping the neural basis of the perception of “flow of time”: interval timing and mental time travel in the human brain
- Leveraging human-trained neural networks for cross-species chromatin regulation annotations
- Cross-species representation learning aligns mouse and human neural dynamics and tracks clinical drug efficacy
BibTeX
@misc{korfmann2026reusable,
title = {{A reusable neural approach to recombination mapping for model and non-model species}},
author = {K. Korfmann and N. Rahnamae and S. Mathieson},
howpublished = {bioRxiv},
year = {2026},
month = aug,
doi = {10.64898/2026.08.20.746066},
url = {https://www.biorxiv.org/content/10.64898/2026.08.20.746066v2},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - A reusable neural approach to recombination mapping for model and non-model species
AU - K. Korfmann
AU - N. Rahnamae
AU - S. Mathieson
PY - 2026
DA - 2026/08/20
PB - bioRxiv
DO - 10.64898/2026.08.20.746066
UR - https://www.biorxiv.org/content/10.64898/2026.08.20.746066v2
AB - Pedigree and crossing experiments can measure crossovers directly and provide the gold standard for recombination mapping, but their cost restricts fine-scale recombination mapping to only a few species. Patterns of linkage disequilibrium (LD) provide an alternative statistical approach for inferring variation in recombination along the genome. LD, however, is confounded by many evolutionary factors, such as demographic changes, life-history traits, and genomic structural variation. We present fastrho, a state-space neural-network estimator trained across a range of simulation-based priors. In simulated bottleneck and expansion scenarios, the fixed checkpoint recovered local map shape without target-specific retraining; comparisons with pyrho used lookup tables constructed under the simulation-generating history. We further evaluated generalizability across multiple species and, to account for additional confounders not represented in the initial training data, designed specialized models for inference in selfing plants, structured Arabis populations, and large- malaria-vector populations. A major biological application of the mosquito model was the construction of a five-arm recombination atlas spanning 13 Ag3 populations, providing a detailed view of recombination-rate variation across the dataset. Recombination maps inferred from Ag3 pedigrees provided independent, coarse-scale support for this atlas. Finally, analyses of resistance loci and redpoll bird supergenes demonstrate how selection and structural variation influence LD. Throughout our study, we use experimental maps for independent validation. Together, our results establish fastrho as a flexible framework for robust recombination mapping across diverse biological systems.
ER -