Guanghao Qi

Guanghao Qi

Assistant Professor, University of Washington

Publications

  1. Heavner BD, Wheeler MM, …, Qi G,…, Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium, GREGoR Consortium Data Standards and Analysis Working Group, Berger SI, Chong J. Building an interoperable rare disease multi-omic resource: the GREGoR data model and dataset. bioRxiv (2026). https://doi.org/10.64898/2026.05.15.725546.

  2. Cui T, Qi G. DAESC+: High-performance, integrated software for single-cell allele-specific expression data. BMC Bioinformatics (2026). https://link.springer.com/article/10.1186/s12859-026-06426-y.

  3. Qi G, Lila E, Ji Z, Shojaie A, Battle A, Sun W. Transcriptome-wide association studies at cell state level using single-cell eQTL data. Cell Genomics (2026). https://www.cell.com/cell-genomics/fulltext/S2666-979X(25)00316-7.

  4. Yu, B, Liu, D, Qi, G, Huangfu, D, Hsu, L, Shojaie, A, & Sun, W. CausalGRN: deciphering causal gene regulatory networks from single-cell CRISPR screens. bioRxiv (2025). https://doi.org/10.64898/2025.12.30.692369.

  5. Cui EH, Yu HX, Qi G, Wong WK. Metaheuristics Meets Statistics: Cuckoo Search for Designs, Inference, and Data Geometry. Preprint at Research Square (2025). https://www.researchsquare.com/article/rs-7438415/v1.

  6. Qi G, Chhetri SB, Ray D, Dutta D, Battle A, Bhattacharjee S, Chatterjee N. Genome-wide large-scale multi-trait analysis characterizes global patterns of pleiotropy and unique trait-specific variants. Nature Communications (2024). https://doi.org/10.1038/s41467-024-51075-5.

  7. Qi G, Battle A. Computational methods for allele-specific expression in single cells. Trends in Genetics (2024). https://doi.org/10.1016/j.tig.2024.07.003.

  8. Jin J, Qi G, Yu Z, Chatterjee N. Mendelian randomization analysis using multiple biomarkers of an underlying common exposure. Biostatistics (2024). https://doi.org/10.1093/biostatistics/kxae006.

  9. Strober BJ, Karl K, Popp J, Qi G, Gordon MG, Perez R, Ye CJ, Battle A. SURGE: uncovering context-specific genetic-regulation of gene expression from single-cell RNA sequencing using latent-factor models. Genome Biology (2024). https://doi.org/10.1186/s13059-023-03152-z.

  10. Qi G, Strober BJ, Popp JM, Keener R, Ji H, Battle A. Single-cell allele-specific expression analysis reveals dynamic and cell-type-specific regulatory effects. Nature Communications (2023). https://doi.org/10.1038/s41467-023-42016-9.

  11. Elorbany R, Popp JM, Rhodes K, Strober BJ, Barr K, Qi G, Gilad YM, Battle A. Single-cell sequencing reveals lineage-specific dynamic genetic regulation of gene expression during human cardiomyocyte differentiation. PLoS Genetics (2022). https://doi.org/10.1371/journal.pgen.1009666.

  12. Qi G, Dutta D, Leroux A, Ray D, Crainiceanu C, Chatterjee N. Genome-wide association studies of 27 accelerometry-derived physical activity measurements identifies novel loci and genetic mechanisms. Genetic Epidemiology (2022). http://doi.org/10.1002/gepi.22441.

  13. Qi G, Chatterjee N. A comprehensive evaluation of methods for Mendelian randomization using realistic simulations of genome-wide association studies. International Journal of Epidemiology (2021). https://doi.org/10.1093/ije/dyaa262.

  14. Arvanitis M, Qi G, Bhatt DL, Post WS, Chatterjee N, Battle A, McEvoy JW. A linear and non-linear Mendelian randomization analysis of the association between diastolic blood pressure and cardiovascular events: the J curve revisited. Circulation (2020). https://doi.org/10.1161/CIRCULATIONAHA.120.049819.

  15. Yu Z, Coresh J, Qi G, …, Chatterjee N, Tin A. et al. A bidirectional Mendelian randomization study supports causal effects of kidney function on blood pressure. Kidney International (2020). https://doi.org/10.1016/j.kint.2020.04.044.

  16. Zhang H, Ahearn TU, Lecarpentier J, Barnes D, Beesley J, Qi G, …, Chatterjee N, and Garcia-Closas M. Genome-wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype-specific analyses. Nature Genetics (2020): 1-10. https://doi.org/10.1038/s41588-020-0609-2.

  17. Qi G, Chatterjee N. Mendelian randomization analysis using mixture models for robust and efficient estimation of causal effects. Nature Communications 10.1 (2019): 1941. https://doi.org/10.1038/s41467-019-09432-2.

  18. Qi G, Chatterjee N. Heritability informed power optimization (HIPO) leads to enhanced detection of genetic associations across multiple traits. PLoS Genetics 14, no. 10 (2018): e1007549. https://doi.org/10.1371/journal.pgen.1007549.

  19. Zhang Y, Qi G, Park JH, Chatterjee N. Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits. Nature Genetics 50, no. 9 (2018): 1318. https://doi.org/10.1038/s41588-018-0193-x.

  20. Geuter S, Qi G, Welsh RC, Wager TD, Lindquist MA. Effect size and power in fMRI group analysis. bioRxiv (2018): 295048. https://doi.org/10.1101/295048.