Alison Motsinger-Reif, Ph.D.

Senior Investigator

Biostatistics & Computational Biology Branch


David P Rall Building
111 Tw Alexander Dr
Research Triangle Park, NC 27709


Research Topics

My lab members and I develop and extend methods that detect gene-gene and gene-environment interactions. These methods include Multifactor Dimensionality Reduction and Grammatical Evolution Neural Networks. We also work on methods for dose-response curve modeling using evolutionary algorithms and methods for variable selection and dimensionality reduction in genome-wide association studies. We work on performing association mapping to detect genes that are associated with differential response to pharmaceutical agent exposure. We study clinical trials and in cell line models of response. We also collaborate with several investigators to understand complex human diseases, compare disease etiology across species, and perform gene mapping for a range of common, complex diseases.

As chief of the NIEHS Biostatistics & Computational Biology Branch, I oversee a dynamic and diverse staff that is involved in biostatistical and computational methods development, software development, study design, and collaborative real data applications.


Motsinger-Reif obtained her Ph.D. in Human Genetics and a MS in Applied Statistics from Vanderbilt University. She was faculty member at North Carolina State University from 2007 to 2018. In December 2018, she joined NIEHS as chief of the Biostatistics & Computational Biology Branch.

Selected Publications

  1. Akhtari FS, Lloyd D, Burkholder A, Tong X, House JS, Lee EY, Buse J, Schurman SH, Fargo DC, Schmitt CP, Hall J, Motsinger-Reif AA. Questionnaire-Based Polyexposure Assessment Outperforms Polygenic Scores for Classification of Type 2 Diabetes in a Multiancestry Cohort. Diabetes Care. 2023;46(5):929-937.
  2. Lee EY, Akhtari F, House JS, Simpson RJ Jr, Schmitt CP, Fargo DC, Schurman SH, Hall JE, Motsinger-Reif AA. Questionnaire-based exposome-wide association studies (ExWAS) reveal expected and novel risk factors associated with cardiovascular outcomes in the Personalized Environment and Genes Study. Environ Res. 2022;212(Pt D):113463.
  3. Marvel SW, House JS, Wheeler M, Song K, Zhou YH, Wright FA, Chiu WA, Rusyn I, Motsinger-Reif A, Reif DM. The COVID-19 Pandemic Vulnerability Index (PVI) Dashboard: Monitoring County-Level Vulnerability Using Visualization, Statistical Modeling, and Machine Learning. Environ Health Perspect. 2021;129(1):17701.
  4. Jiang T, Li Y, Motsinger-Reif AA. Knockoff boosted tree for model-free variable selection. Bioinformatics. 2021;37(7):976-983.
  5. Akhtari FS, Green AJ, Small GW, Havener TM, House JS, Roell KR, Reif DM, McLeod HL, Wiltshire T, Motsinger-Reif AA. High-throughput screening and genome-wide analyses of 44 anticancer drugs in the 1000 Genomes cell lines reveals an association of the NQO1 gene with the response of multiple anticancer drugs. PLoS Genet. 2021;17(8):e1009732.

Related Scientific Focus Areas

This page was last updated on Tuesday, August 27, 2019