Wenbin Lu

Title: Copy Number Variation Identification and Association Study on Alzheimer’s Disease Whole Genome Sequencing Data

Funding agency: NIH

Role: co-I

Time period: 09/2021 – 08/2026

Goals: This study aims to identify copy number variations (CNVs) associated with Alzheimer’s Disease (AD) by analyzing whole genome sequencing data and to assess their contribution to AD risk using established and novel CNV association methods.

Jonathan Stallrich

Title: IMAGiNE: Dissecting Neuronal and Systemic Responses to Interacting Environmental Stressors

Funding agency: National Science Foundation (NSF)

Role: Co-PI

Time period: 08/15/2021 – 07/31/2026

Description: This project studies how animals respond to harmful environmental chemicals, especially mixtures that produce reactive oxygen species (ROS) which are molecules that damage cells. While most research looks at single chemicals, real-world exposures involve complex mixtures that can change or mask effects. Using the nematode C. elegans as a model organism, the research examines how stress responses vary over time and across different biological systems (such as neurons, gene activity, and enzyme production). It also investigates whether the nervous system plays a role in detecting and responding to these chemical stresses. A key goal is to understand how multiple stressors interact and how responses from different tissues combine to produce an overall organism response. To do this, the project uses advanced techniques like imaging, microfluidics, and data-driven modeling to measure responses at multiple levels.

Len Stefanski

Title: Fractional Ridge Regression.

Funding agency: National Science Foundation.

Role: PI.

Time period: September 2023 — August 2026.

Description: Ridge regression was introduced by Hoerl and Kennard (1970a,b) and twenty-six years later was followed by the introduction of the lasso Tibshirani (1996). The body of research ensuing from these seminal papers is staggering, and has contributed immensely to our understanding of shrinkage and selection methodology and to the practice of regression modeling in many areas of science. In some applications of regression modeling the goal is simply to achieve the best possible predictions of future response values. In other applications, interpretation is important as a way to guide understanding of the process under investigation. Ridge regression is very good at prediction, although is often eclipsed by the lasso in terms of both prediction and interpretation because the lasso also allows for selection. The method to be studied with this grant, fractional ridge regression, has the potential to improve both prediction (as measured by mean square error) and interpretability (as measured by the specificity of variable selection) relative to the lasso.

Jung-Ying Tzeng

Title: Novel Imprint Control Regions (ICRs) Responsive to Environmental Exposures

Funding agency: NIH

Role: co-I

Time period: 09/2021-06/2026

Goals: The major goals of this project are to detect methylation marks that are responsive to environmental cadmium exposure and predictive of metabolic outcomes.