Machine learning (ML)-assisted statistical inference
Machine learning is making predictions with uncertainty. How to make statistically rigorous scientific discovery with these imperfect predictions?
Valid inference for machine learning-assisted genome-wide association studies.Heterogeneous treatment effect, gene-environment interactions, and genetic modifier
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
Reimagining Gene-Environment Interaction Analysis for Human Complex Traits
Miao J., Song G., Wu Y., Hu J., Wu Y., Basu S., Andrews J., Schaumberg K., Fletcher J., Schmitz L., Lu Q. (2022).
Submitted
[Preprint]
[Software: PIGEON]
[Summary]
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).
--Winner of the 2022 ASA Section on Statistics in Genomics and Genetics Student Paper Award
PNAS
[Journal]
[Preprint]
[Software: QUAIL]
[Summary]
Generalizability and fairness of genetically-informed risk prediction across diverse contexts
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).
Nature Communications
[Journal]
[Preprint]
[Software: X-Wing]
[Summary]