Author ORCID Identifier

Date of Award


Document Type

Thesis (Ph.D.)

Department or Program

Quantitative Biomedical Sciences

First Advisor

Jiang Gui

Second Advisor

Michael N. Passarelli


In response to the increasing efforts in disease prevention and treatment, this thesis applies statistical methods to investigate environmental and genetic risk factors associated with two diseases: bladder cancer and amyotrophic lateral sclerosis (ALS). For bladder cancer, we investigated the association between toenail metal mixture and bladder cancer risk, along with gene expression levels associated with bladder cancer risk. For ALS, our investigation involves identifying genetic variants and gene expression levels associated with ALS risk and exploring gene-smoking interactions linked to ALS risk.

In chapter two, we developed an adaptive-mixture-categorization (AMC)-based g-computation method combining g-computation with optimized exposure categorization. We applied our method to the New Hampshire Bladder Cancer Study to evaluate the association between the mixture of toenail metals and the risk of non-muscle-invasive bladder cancer (NMIBC). This study identified that medium-level concentration of toenail zinc increases NMIBC risk compared to low-level toenail zinc.

In chapter three, PrediXcan, a method for transcriptome-wide association studies (TWAS), was applied to bladder cancer case-control data from the database of Genotypes and Phenotypes (dbGaP). We identified predicted expression levels of four genes, SLC39A3, ZNF737, FAM53A, and PPP1R2, which are associated with bladder cancer risk, with a false-discovery rate (FDR) adjusted P-value smaller than 0.05.

In chapter four, we conducted both genome-wide association study (GWAS) and TWAS on ALS cases and controls from Northern New England and Ohio. We identified several genetic variants and predicted gene expression levels associated with ALS risk. We also applied covariate-adjusted logistic regression to detect gene-smoking interactions related to ALS risk.

These findings uncovered potential genetic and environmental risk factors involved in the development of ALS and bladder cancer, which offers insights that may prompt future investigations into the prevention and treatment of these diseases.

Original Citation

Li, S., Karagas, M. R., Jackson, B. P., Passarelli, M. N., & Gui, J. (2022). Adaptive-mixture-categorization (AMC)-based g-computation and its application to trace element mixtures and bladder cancer risk. Scientific Reports, 12(1), 17841.

Available for download on Sunday, March 29, 2026