Research

My research develops statistical methods for complex biomedical and genomic data, with a focus on quantile regression, high-dimensional signal detection, colocalization, and uncertainty quantification.

I am particularly interested in understanding genetic effects that vary across the distribution of a trait and connecting genetic associations with biological mechanisms.

Publications

* denotes equal contribution.

  1. Context-dependent genetic regulation of gene expression in pigs
    Wang F, Wang C, Teng J, Fang L, Ionita-Laza I.
    Genetics, 2026.
    [Paper]
  2. Computationally efficient whole-genome quantile regression at biobank scale
    Wang F, Wang C, Wang T, Masala M, Fiorillo E, Devoto M, Cucca F, Ionita-Laza I.
    Proceedings of the National Academy of Sciences, 2025, 122(50), e2513007122.
    [Paper]
  3. Computationally Efficient Whole-Genome Signal Region Detection for Quantitative and Binary Traits
    Wang F*, Zhang W*, Yao F.
    Annals of Applied Statistics, 2025.
    [Paper]
  4. Fast Signal Region Detection with Application to Whole Genome Association Studies
    Zhang W*, Wang F*, Yao F.
    Journal of the American Statistical Association, 2025.
    [Paper]
  5. Local genetic correlation via knockoffs reduces confounding due to cross-trait assortative mating
    Ma S, Wang F, Border R, Buxbaum J, Zaitlen N, Ionita-Laza I.
    The American Journal of Human Genetics, 2024, 111(12), 2839–2848.
    [Paper]
  6. Mouse and human studies support DSTYK loss of function as a low-penetrance and variable expressivity risk factor for congenital urinary tract anomalies
    Martino J, et al. (including Wang F).
    Genetics in Medicine, 2023.
    [Paper]
  7. A flexible summary statistics-based colocalization method with application to the mucin cystic fibrosis lung disease modifier locus
    Wang F, Panjwani N, Wang C, Sun L, Strug LJ.
    The American Journal of Human Genetics, 2022, 109(2), 253–269.
    [Paper]
  8. LocusFocus: Web-based colocalization for the annotation and functional follow-up of GWAS
    Panjwani N, Wang F, et al.
    PLOS Computational Biology, 2020, 16(10), e1008336.
    [Paper]
  9. Genetic association and transcriptome integration identify contributing genes and tissues at cystic fibrosis modifier loci
    Gong J*, Wang F*, Xiao B*, et al.
    PLOS Genetics, 2019, 15(2), e1008007.
    [Paper]
  10. Regularized partially functional quantile regression
    Yao F, Sue-Chee S, Wang F.
    Journal of Multivariate Analysis, 2017, 156, 39–56.
    [Paper]

Preprints

  1. Individualized Uncertainty Quantification in Polygenic Risk Scores Using Conformalized Quantile Regression
    Wang C, Wang F, Bogdan M, Masala M, Fiorillo E, Devoto M, Cucca F, Belsky D, Ionita-Laza I.
    bioRxiv, 2025, Preprint.
    [Paper]

Software