Publications

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Selected papers are below. Most of the papers described below is accompanied by open-source software and publicly released datasets. For a full list of papers, please see Google Scholar.

AI/ML for genetic and scientific discovery

AI/ML is widely used in many scientific disciplines. I am working to make the scientific conclusions from AI/ML more reliable. I have developed theory, methods, and software for machine-learning-assisted statistical inference. It makes reliable genetic and scientific discoveries from unreliable AI/ML predictions.

[Nature Genetics] Valid inference for machine learning-assisted genome-wide association studies.
Miao J., Wu Y., Sun Z., Miao X., Lu T., Zhao J., Lu Q. (2024).
[Journal] [PDF] [Preprint] [Software: POP-TOOLS] [Summary]
--Featured by [TheScientist Magazine], [UW-Madison Research], and [statsupai].
--Winner of the 2024 ICSA Student Paper Award

[NeurIPS] Task-agnostic machine-learning-assisted inference
Miao J. and Lu Q. (2024).
[Paper] [Preprint] [Software: PSPS]

[JMLR] Assumption-lean and data-adaptive post-prediction inference
Miao J.*, Miao X.*, Wu Y., Zhao J., Lu Q. (2023).
[Preprint] [Software: PSPA]

Gene-environment interactions and treatment effect heterogeneity

Genetic effects on complex traits may depend on contexts, such as age, sex, genetic background, or social settings. Similarly, treatment effects may depend on genetic variations. I am developing theory, methods, and software for detecting and interpreting genetic effects across diverse contexts, as well as understanding how genetic variations modify policy or treatment effects.

[Nature Human Behaviour] PIGEON: a statistical framework for estimating gene-environment interaction for polygenic traits
Miao J., Song G., Wu Y., Hu J., Wu Y., Basu S., Andrews J., Schaumberg K., Fletcher J., Schmitz L., Lu Q. (2022).
[Journal] [PDF] [Preprint] [Software: PIGEON] [Summary]
--Featured by [Center for Genomic Science Innovation, UW-Madison]

[PNAS] A quantile integral linear model to quantify genetic effects on phenotypic variability
Miao J., Lin Y., Wu Y., Zheng B., Schmitz L., Fletcher J., Lu Q. (2022).
[Journal] [Preprint] [Software: QUAIL] [Summary]
--Winner of the 2022 ASA Section on Statistics in Genomics and Genetics Student Paper Award

Transcriptome-level interpretation of gene-by-sex interactions for human complex traits
Miao J., Wu Y., Yang X., Schmitz L., Lu Q. (2024).
--Winner of the 2023 Charles J. Epstein Trainee Award for Excellence in Human Genetics Research Semifinalist

Genetic risk prediction

[Nature Communications] Quantifying portable genetic effects and improving cross-ancestry genetic prediction with GWAS summary statistics
Miao J.*, Guo H.*, Song G., Zhao Z., Hou L.†, Lu Q.† (2023).
[Journal] [Preprint] [Software: X-Wing] [Summary]
--Featured by [Department of Statistics and Data Science, Tsinghua university (Chinese)].