Published in medRxiv, 2025
We proposed an efficient eQTL software for large-scale single-cell eQTL mapping using count-based models
Recommended citation: 1. Zhang, Z. E., Kim, A., Suboc, N., Mancuso, N. & Gazal, S. (2025). Efficient count-based models improve power and robustness for large-scale single-cell eQTL mapping. medRxiv.
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Published in American Journal of Human Genetics, 2024
We characterized genes differentially expressed across ancestries (ancDE genes) at the cell-type level by leveraging single-cell RNA-sequencing data from multiple ancestries.
Recommended citation: Wang, J., Zhang, Z., Lu, Z., Mancuso, N., and Gazal, S. (2024). Genes with differential expression across ancestries are enriched in ancestry-specific disease effects likely due to gene-by-environment interactions. Am. J. Hum. Genet.
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Published in medRxiv (preprint), 2024
We proposed methods to leverage cell-type-specific CREs to fine-map causal cell types for a trait and for its candidate causal variants.
Recommended citation: Kim, A., Zhang, Z., Legros, C., Lu, Z., de Smith, A., Moore, J.E., Mancuso, N., and Gazal, S. (2024). Inferring causal cell types of human diseases and risk variants from candidate regulatory elements. medRxiv.
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Published in American Journal of Human Genetics, 2023
We proposed a scalable factor analysis model to identify and characterize pleiotropic components using biobank GWAS summary data.
Recommended citation: Zhang, Z., Jung, J., Kim, A., Suboc, N., Gazal, S., and Mancuso, N. (2023). A scalable approach to characterize pleiotropy across thousands of human diseases and complex traits using GWAS summary statistics. Am. J. Hum. Genet. 110, 1863–1874.
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