Motivation: Deep generative models have the potential to overcome difficulties in sharing individual-level genomic data by producing synthetic genomes that preserve the genomic associations specific to a cohort while not violating the privacy of any individual cohort member. However, there is significant room for improvement in the fidelity and usability of existing synthetic genome approaches.
Results: We demonstrate that when combined with plentiful data and with population-specific selection criteria, deep generative models can produce synthetic genomes and cohorts that closely model the original populations. Our methods improve fidelity in the site-frequency spectra and linkage disequilibrium decay and yield synthetic genomes that can be substituted in downstream local ancestry inference analysis, recreating results with .91 to .94 accuracy.
Availability: The model described in this paper is freely available at github.com/rlaboulaye/clonehort .
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11230377 | PMC |
| http://dx.doi.org/10.1101/2024.06.25.600651 | DOI Listing |
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