tstrait: a quantitative trait simulator for ancestral recombination graphs.

SUMMARY: Ancestral recombination graphs (ARGs) encode the ensemble of correlated genealogical trees arising from recombination in a compact and efficient structure and are of fundamental importance in population and statistical genetics. Recent breakthroughs have made it possible to simulate and infer ARGs at biobank scale, and there is now intense interest in using ARG-based methods across a broad range of applications, particularly in genome-wide association studies (GWAS). Sophisticated methods exist to simulate ARGs using population genetics models, but there is currently no software to simulate quantitative traits directly from these ARGs. To apply existing quantitative trait simulators users must export genotype data, losing important information about ancestral processes and producing prohibitively large files when applied to the biobank-scale datasets currently of interest in GWAS. We present tstrait, an open-source Python library to simulate quantitative traits on ARGs, and show how this user-friendly software can quickly simulate phenotypes for biobank-scale datasets on a laptop computer. AVAILABILITY AND IMPLEMENTATION: tstrait is available for download on the Python Package Index. Full documentation with examples and workflow templates is available on https://tskit.dev/tstrait/docs/, and the development version is maintained on GitHub (https://github.com/tskit-dev/tstrait).

tstrait: a quantitative trait simulator for ancestral recombination graphs.

Summary: Ancestral recombination graphs (ARGs) encode the ensemble of correlated genealogical trees arising from recombination in a compact and efficient structure, and are of fundamental importance in population and statistical genetics. Recent breakthroughs have made it possible to simulate and infer ARGs at biobank scale, and there is now intense interest in using ARG-based methods across a broad range of applications, particularly in genome-wide association studies (GWAS). Sophisticated methods exist to simulate ARGs using population genetics models, but there is currently no software to simulate quantitative traits directly from these ARGs. To apply existing quantitative trait simulators users must export genotype data, losing important information about ancestral processes and producing prohibitively large files when applied to the biobank-scale datasets currently of interest in GWAS. We present tstrait, an open-source Python library to simulate quantitative traits on ARGs, and show how this user-friendly software can quickly simulate phenotypes for biobank-scale datasets on a laptop computer. Availability and Implementation: tstrait is available for download on the Python Package Index. Full documentation with examples and workflow templates is available on https://tskit.dev/tstrait/docs/, and the development version is maintained on GitHub (https://github.com/tskit-dev/tstrait). Contact: daiki.tagami@hertford.ox.ac.uk.

Enhancer of trithorax/polycomb, Corto, regulates timing of hunchback gene relocation and competence in Drosophila neuroblasts.

BACKGROUND: Neural progenitors produce diverse cells in a stereotyped birth order, but can specify each cell type for only a limited duration. In the Drosophila embryo, neuroblasts (neural progenitors) specify multiple, distinct neurons by sequentially expressing a series of temporal identity transcription factors with each division. Hunchback (Hb), the first of the series, specifies early-born neuronal identity. Neuroblast competence to generate early-born neurons is terminated when the hb gene relocates to the neuroblast nuclear lamina, rendering it refractory to activation in descendent neurons. Mechanisms and trans-acting factors underlying this process are poorly understood. Here we identify Corto, an enhancer of Trithorax/Polycomb (ETP) protein, as a new regulator of neuroblast competence. METHODS: We used the GAL4/UAS system to drive persistent misexpression of Hb in neuroblast 7-1 (NB7-1), a model lineage for which the early competence window has been well characterized, to examine the role of Corto in neuroblast competence. We used immuno-DNA Fluorescence in situ hybridization (DNA FISH) in whole embryos to track the position of the hb gene locus specifically in neuroblasts across developmental time, comparing corto mutants to control embryos. Finally, we used immunostaining in whole embryos to examine Corto's role in repression of Hb and a known target gene, Abdominal B (Abd-B). RESULTS: We found that in corto mutants, the hb gene relocation to the neuroblast nuclear lamina is delayed and the early competence window is extended. The delay in gene relocation occurs after hb transcription is already terminated in the neuroblast and is not due to prolonged transcriptional activity. Further, we find that Corto genetically interacts with Posterior Sex Combs (Psc), a core subunit of polycomb group complex 1 (PRC1), to terminate early competence. Loss of Corto does not result in derepression of Hb or its Hox target, Abd-B, specifically in neuroblasts. CONCLUSIONS: These results show that in neuroblasts, Corto genetically interacts with PRC1 to regulate timing of nuclear architecture reorganization and support the model that distinct mechanisms of silencing are implemented in a step-wise fashion during development to regulate cell fate gene expression in neuronal progeny.