bioRxivGenomicsPreprint
Using sequence-to-function models to interpret archaic hominin introgression
Maddy ComerfordiD, Isabela AlvimiD, Timothy M. JohansoniD, Danat YermakovichiD, Christopher KinipiiD, Matthew LeavesleyiD, François‐Xavier RicautiD, Michael DannemanniD, Murray P. CoxiD, Rhys S. AllaniD, Nicolas BrucatoiD, Irene Gallego RomeroiD
Abstract
Understanding the functional impact of archaic hominin introgression remains challenging due to the poor representation of global introgression in publicly available genomics resources. Sequence-to-function models can predict the effects of any possible variant in the human genome and may fill this gap. Here, we used AlphaGenome to predict the effects of 144,139 introgressed SNPs segregating in present-day individuals of Papuan genetic ancestry. AlphaGenome's chromatin accessibility predictions recapitulate experimentally observed effects, but gene expression performs no better than chance. Predictions correlate more strongly with an independent reporter assay of single-variant activity than with the same variants' effects in live cells, indicating that AlphaGenome captures the regulatory potential of individual variants more reliably. Predictions carry tissue specificity, allowing us to predict specific tissues potentially impacted by introgressed haplotypes. We identify genes, including JAK1 and TAB2, that are associated with haplotypes that contain an excess of variants predicted by AlphaGenome to have large impacts on chromatin accessibility. Finally, we highlight the challenges and limitations associated with using sequence-to-function models for introgressed variant effect prediction, and show that while AlphaGenome's chromatin accessibility predictions can aid in prioritising candidate functional regions, expression predictions and the assignment of variants to target genes remain as open challenges.
- organism: human
- assay: whole-genome sequencing
- research focus: variant effect prediction
- research focus: gene regulatory network inference
- research focus: benchmarking
- approach: deep learning
- approach: statistical model
- Citations
- 0
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