Jiacheng Miao jiacheng.miao at wisc.edu
Jiacheng is a fourthyear Ph.D. candidate in Biomedical Data Science at UWMadison, where he works with Prof. Qiongshi Lu. and Prof. Lauren Schmitz.
He used to intern at Regeneron Genetics Center. He got his B.S. in Statistics from Nanjing University.
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Google scholar


News
 Jan 4 2024: POPGWAS is preprinted at medRxiv! Try it to use machine learning to boost your GWAS power.
Research
My recent research has focused on
 Machine learning (ML)assisted inference
 Heterogeneous treatment effect, geneenvironment interactions, and genegene interactions
 Transfer learning and portability of crossancestry polygenic risk score
In general, I am interested in using rigorous statistics and interpretable machine learning to answer scientific questions, especially in human genetics.
Below are my publications and preprints: ('*' denotes equal authorship, representative papers are highlighted).
Leadauthored.

[16]
Valid inference for machine learningassisted GWAS.
Miao J., Wu Y., Sun Z., Miao X., Lu T., Zhao J., Lu Q. (2024).
Submitted. (preprint available on medRxiv)
Preprint /
Software
We report the pervasive risks for false positive associations in conventional GWAS on outcomes predicted by machine learning (ML). We introduce POPGWAS, a novel statistical framework that reimagines GWAS on MLimputed outcomes.
POPGWAS provides valid and optimal statistical inference irrespective of the quality of imputation or variables and algorithms used for imputation. It also only requires GWAS summary statistics as input and is optimized for the characteristics of GWAS data.

[15]
Assumptionlean and dataadaptive postprediction inference.
Miao J*., Miao X.*, Wu Y., Zhao J., Lu Q. (2023).
Submitted. (preprint available on arXiv)
Preprint
/
Software
We introduce an assumptionlean and dataadaptive PostPrediction Inference (POPInf) procedure that allows valid and powerful inference based on MLpredicted outcomes.
Its "assumptionlean" property guarantees reliable statistical inference without assumptions on the MLprediction, for a wide range of statistical quantities.
Its "dataadaptive'" feature guarantees an efficiency gain over existing postprediction inference methods, regardless of the accuracy of MLprediction.

[14]
Statistical Methods for Geneenvironment Interaction Analysis.
Miao J., Wu Y., Lu Q. (2023).
WIREs Computational Statistics (Review)
Journal
We provide a comprehensive review of the evluation statistical methods for GxE interaction analysis from preGWAS era to the present data, featured by metaanalysis conducted by big genetics consortia, sharing of
summary association statistics, and statistical analysis only requiring summary data as input.

[13]
Reimagining GeneEnvironment 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 available on bioRxiv)
Preprint
/
Software
We present a unified theory and framework, called PIGEON, for modeling polygenic GxE effects for complex traits.
It allows us to define the estimands of interest and to establish the connection and distinction between different methods in GxE inference.
Motivated by our theory, we have also developed an innovative approach to estimate GxE interactions using genomewide summary data.
It is unbiased, computationally efficient, robust to sample overlap, heteroscedasticity, and geneenvironment correlation.

[12]
Quantifying portable genetic effects and improving crossancestry genetic prediction with GWAS summary statistics.
Miao J.*, Guo H.*, Song G., Zhao Z., Hou L.^{†}, Lu Q.^{†} (2023).
Nature Communications, 14, 832
Journal /
Preprint
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Software /
Poster
We introduce a crosspopulation genetic risk prediction framework, called XWing, that (1) quantifies crosspopulation local genetic correlation
(2) incorporates it as prior into a Bayesian framework which amplifies correlated SNP effects between populations
(3) uses summary statisticsbased ensemble learning to further improve prediction accuracy.

[11]
Identifying genetic loci associated with complex trait variability.
Miao J., Lu Q. (2022).
Handbook of Statistical Bioinformatics (2nd Edition). Springer. (Book Chapter)
Book Chapter
We provide a comprehensive review of the evluation statistical methods for identifying genetic loci associated with complex trait variability. It talks about the history of the development of the methods, the assumptions they make, and the pros and cons of each method.

[10]
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).
Proceedings of the National Academy of Sciences (PNAS), 119(39): e2212959119.
Journal /
Preprint /
Software /
Slides
 Winner of Distinguished Student Paper Award from the Section on Statistical Genomics and Genetics of the American Statistical Association (ASA) 2022.
 Winner of Reviews's Choice Award from American Society of Human Genetics Meeting (ASHG) Meeting 2021. (Top 10%)
We propose a unified statistical framework, called QUAIL, for estimating genetic effects on the variability of quantitative traits and for prioritizing genetic variants involved in interactions.
QUAIL constructs a quantileintegral phenotype that aggregates information from all quantile levels, makes no assumptions about the distribution of the phenotype, and accounts for confounding effects at both the trait level variability.

Collaborative & coauthored

[9]
Controlling for polygenic genetic confounding in epidemiologic association studies.
Zhao Z., Yang X., Miao J., Dorn S., Barcellos S., Fletcher J., Lu Q. (2024).
Submitted. (preprint available on bioRxiv)
Preprint

[8]
Pervasive biases in proxy GWAS based on parental history of Alzheimer's disease.
Wu Y.*, Sun Z.*, Zheng Q., Miao J., Dorn S., Mukherjee S., Fletcher J., Lu Q. (2023).
Submitted. (preprint available on bioRxiv)
Preprint

[7]
The impact of genomic variation on function (IGVF) consortium.
IGVF Consortium (2023)
Submitted. (preprint available on arXiv)
Preprint

[6]
Neurogenetic Mechanisms of Risk for ADHD: Examining Associations of FunctionallyAnnotated Polygenic Scores and Brain Volumes in a Population Cohort.
He, Q., Keding, T., Zhang, Q., Miao J., Herringa, R., Lu, Q., Travers, B., & Li, J. J. (2022).
Submitted. (preprint available on medRxiv)
Preprint

[5]
Optimizing and benchmarking polygenic risk scores with GWAS summary statistics.
Zhao, Z., Gruenloh, T., Wu, Y., Sun, Z., Miao J., Wu, Y., Song, J., & Lu, Q. (2022).
Submitted. (preprint available on bioRxiv)
Preprint

[4]
Neuropathologybased APOE genetic risk score better quantifies Alzheimer's risk.
Deming Y., Vasiljevic E., Morrow A., Miao J., Van Hulle C., Jonaitis E., Ma Y., Whitenack V., Kollmorgen G., Wild N., Suridjan I., Shaw L., Asthana S., Carlsson C., Johnson S., Zetterberg H., Blennow K., Bendlin B., Lu Q., Engelman C., the Alzheimer's Disease Neuroimaging Initiative (2023).
Alzheimer's & Dementia
Preprint

[2]
The socioeconomic gradient in epigenetic aging clocks: evidence from the Multiethnic Study of Atherosclerosis and the Health and Retirement Study.
Schmitz L., Zhao W., Ratliff S., Goodwin J., Miao J., Lu Q., Guo X., Taylor K., Ding J., Liu Y., Levine M., Smith J. (2021).
Epigenetics
Journal
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Preprint

Selected Awards
 Yixuan, an undergraduate researcher I mentored, won the 2023 ASHG/Charles J. Epstein Trainee Award for Excellence in Human Genetics Research  Semifinalist.
 Distinguished Student Paper Award from American Statistical Association (ASA) Section on Statistical Genomics and Genetics, 2022 [WiscBMI News]
 Reviews's Choice Award from American Society of Human Genetics Meeting (ASHG) (Top 10%), 2021

Software
 POPInf, a toolbox for statistical inference with variables predicted by machine learning.
 POPTOOLS, a comprehensive toolbox for genetic association analysis on outcomes predicted by machine learning.
 PIGEON for polygenic geneenvironment (GxE) interactions inference.
 XWing for improving genetic risk prediction in ancestrally diverse populations.
 QUAIL for estimating genetic effects on the variance of quantitative traits.

Mentoring
I find great fulfillment in mentoring students, witnessing their growth, and helping them achieve their goals. Below is a list of students I have had the privilege of mentoring:
 Gefei Song, Undergraduate Researcher at UWMadison '22, Winner of Hilldale Undergraduate/Faculty Research Award, now PhD in Biostatistics, University of California, Berkeley.
 Yixuan Wu, Undergraduate Researcher at UWMadison '24, Winner of Hilldale Undergraduate/Faculty Research Award,
Winner of 2023 ASHG/Charles J. Epstein Trainee Award for Excellence in Human Genetics Research  Semifinalist.

Notes
I employ EpsilonGreedy Algorithm for explorationexploitation tradeoff. Below are my notes on this exploration process:
 Causal Inference.
 An Ownerâ€™s Guide to the Human Genome.
 Patterns, Predictions, and Actions.

Quote
 "Everything should be made as simple as possible, but no simpler"  Albert Einstein

