Alison Motsinger-Reif, Ph.D.

Senior Investigator

Biostatistics & Computational Biology Branch

NIEHS

A332
David P Rall Building
111 TW ALEXANDER DR
Research Triangle Park, NC 27709

984-287-3705

alison.motsinger-reif@nih.gov

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 talented staff that is involved in biostatistical and computational methods development, software development, study design, and collaborative real data applications.

Biography

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. Lloyd D, House JS, Akhtari FS, Schmitt CP, Fargo DC, Scholl EH, Phillips J, Choksi S, Shah R, Hall JE, Motsinger-Reif AA. Questionnaire-based exposome-wide association studies for common diseases in the Personalized Environment and Genes Study. Exposome. 2024;4(1):osae002.
  4. Jiang T, Li Y, Motsinger-Reif AA. Knockoff boosted tree for model-free variable selection. Bioinformatics. 2021;37(7):976-983.
  5. Breeyear JH, Hellwege JN, Schroeder PH, House JS, Poisner HM, Mitchell SL, Charest B, Khakharia A, Basnet TB, Halladay CW, Reaven PD, Meigs JB, Rhee MK, Sun Y, Lynch MG, Bick AG, Wilson OD, Hung AM, Nealon CL, Iyengar SK, Rotroff DM, Buse JB, Leong A, Mercader JM, Sobrin L, Brantley MA Jr, Peachey NS, Motsinger-Reif AA, Wilson PW, Sun YV, VA Million Veteran Program, Giri A, Phillips LS, Edwards TL. Adaptive selection at G6PD and disparities in diabetes complications. Nat Med. 2024;30(9):2480-2488.

Related Scientific Focus Areas

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