Biostatistics & Computational Biology Branch

Alison A. Motsinger-Reif, Ph.D.
Alison A. Motsinger-Reif, Ph.D.
Chief, Biostatistics & Computational Biology Branch and Principal Investigator
Tel 984-287-3705
[email protected]
Curriculum Vitae (788KB)
Google Scholar Profile
111 Tw Alexander Dr
David P Rall Building
Research Triangle Park, NC 27709

Research Summary

Alison Motsinger-Reif, Ph.D., is Chief of and a principal investigator in the Biostatistics and Computational Biology Branch. Overall, her group focuses on the development and application of modern statistical approaches for understanding the etiology of common, complex diseases. As the field of human genetics increasingly accepts a complex model of phenotypic development involving many genetic and environment factors, it is increasingly important to develop analytical strategies that incorporates this complexity. Data collected from different physiological compartments that represent biological flux across time and space, such as genetic, metabolomics, and environmental data, will need to be incorporated to gain a fuller understanding of the biological mechanism underlying complex phenotypes.

Her lab develops methods computational/statistical methods for such complex biological data. Her recent methods work includes the development of new approaches for boosted tree models while controlling false discovery rates, machine learning for predicting synergistic drug response, and the use of evolutionary algorithms for fitting nonlinear dose response modeling. While methods development is a key component of our research, real data applications are the driving factor. While her group works with a number of phenotypes, pharmacogenomic applications are a focus area.

More specifically, they use high throughput screens of anti-cancer drugs in lymphoblastoid cell lines to detect variants associated with differential response. Additionally, they leverage genome-wide genotyping of participants in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) to map a number of drug response and other clinically relevant traits. Finally, her group works with the Environmental Polymorphisms Registry (EPR). The EPR is an active North Carolina based cohort with exposure data from questionnaires and geospatial exposures, whole genome sequencing and electronic health record data. We are leveraging this powerful resource for exposome research, genetic mapping, and the discovery of gene-environment interactions. The lab also collaborates with several investigators to understand complex diseases and drug response with high throughput “omic” technologies. Example collaborations include projects in metabolomics, epigenomics, comparative medicine, and model organisms.

For a complete list of publications, please see:
Google Scholar Profile Alison Motsinger-Reif

Relevance to NIEHS Mission

The development and application of methods for gene-gene and gene-environment interactions readily addresses the NIEHS mission to better understand aspects of individual susceptibility. Our methods development and applications directly serve the data science and big data mission. Additionally, environmental exposure are major contributors to the disease that we study — including Type 2 Diabetes and cancer. The methods that we develop and apply contribute to advancing environmental health science research.

Selected Publications

  1. Ma J, Motsinger-Reif A. 2021. Prediction of synergistic drug combinations using PCA-initialized deep learning. BioData Min. 14(1):46. doi: 10.1186/s13040-021-00278-3. [Abstract Ma J, Motsinger-Reif A. 2021. Prediction of synergistic drug combinations using PCA-initialized deep learning. BioData Min. 14(1):46. doi: 10.1186/s13040-021-00278-3.]
  2. Green AJ, Anchang B, Akhtari FS, Reif DM, Motsinger-Reif A. 2021. Extending the lymphoblastoid cell line model for drug combination pharmacogenomics. Pharmacogenomics. Jun. 22(9):543-551. doi:10.2217/pgs-2020-0160. Epub 2021 May 28. PMID: 34044623. [Abstract Green AJ, Anchang B, Akhtari FS, Reif DM, Motsinger-Reif A. 2021. Extending the lymphoblastoid cell line model for drug combination pharmacogenomics. Pharmacogenomics. Jun. 22(9):543-551. doi:10.2217/pgs-2020-0160. Epub 2021 May 28. PMID: 34044623.]
  3. Mariam A, Miller-Atkins G, Pantalone KM, Zimmerman RS, Barnard J, Kattan MW, Shah H, McLeod HL, Doria A, Wagner MJ, Buse JB, Motsinger-Reif AA, Rotroff DM. 2021. A Type 2 Diabetes Subtype Responsive to ACCORD Intensive Glycemia Treatment. Diabetes Care. 44(6):1410-1418. doi: 10.2337/dc20-2700. [Abstract Mariam A, Miller-Atkins G, Pantalone KM, Zimmerman RS, Barnard J, Kattan MW, Shah H, McLeod HL, Doria A, Wagner MJ, Buse JB, Motsinger-Reif AA, Rotroff DM. 2021. A Type 2 Diabetes Subtype Responsive to ACCORD Intensive Glycemia Treatment. Diabetes Care. 44(6):1410-1418. doi: 10.2337/dc20-2700.]
  4. Colonna CH, Henriquez AR, House JS, Motsinger-Reif AA, Alewel DI, Fisher A, Ren H, Snow SJ, Schladweiler MC, Miller DB, Miller CN, Kodavanti PRS, Kodavanti UP. 2021. The Role of Hepatic Vagal Tone in Ozone-Induced Metabolic Dysfunction in the Liver. Toxicol Sci. 181(2):229-245. doi: 10.1093/toxsci/kfab025. [Abstract Colonna CH, Henriquez AR, House JS, Motsinger-Reif AA, Alewel DI, Fisher A, Ren H, Snow SJ, Schladweiler MC, Miller DB, Miller CN, Kodavanti PRS, Kodavanti UP. 2021. The Role of Hepatic Vagal Tone in Ozone-Induced Metabolic Dysfunction in the Liver. Toxicol Sci. 181(2):229-245. doi: 10.1093/toxsci/kfab025.]
  5. McDonough CW, Warren HR, Jack JR, Motsinger-Reif AA, Armstrong ND, Bis JC, House JS, Singh S, El Rouby NM, Gong Y, Mychaleckyj JC, Rotroff DM, Benavente OR, Caulfield MJ, Doria A, Pepine CJ, Psaty BM, Glorioso V, Glorioso N, Hiltunen TP, Kontula KK, Arnett DK, Buse JB, Irvin MR, Johnson JA, Munroe PB, Wagner MJ, Cooper-DeHoff RM. 2021. Adverse Cardiovascular Outcomes and Antihypertensive Treatment: A Genome-Wide Interaction Meta-Analysis in the International Consortium for Antihypertensive Pharmacogenomics Studies. Clin Pharmacol Ther. 110(3):723-732. doi: 10.1002/cpt.2355. [Abstract McDonough CW, Warren HR, Jack JR, Motsinger-Reif AA, Armstrong ND, Bis JC, House JS, Singh S, El Rouby NM, Gong Y, Mychaleckyj JC, Rotroff DM, Benavente OR, Caulfield MJ, Doria A, Pepine CJ, Psaty BM, Glorioso V, Glorioso N, Hiltunen TP, Kontula KK, Arnett DK, Buse JB, Irvin MR, Johnson JA, Munroe PB, Wagner MJ, Cooper-DeHoff RM. 2021. Adverse Cardiovascular Outcomes and Antihypertensive Treatment: A Genome-Wide Interaction Meta-Analysis in the International Consortium for Antihypertensive Pharmacogenomics Studies. Clin Pharmacol Ther. 110(3):723-732. doi: 10.1002/cpt.2355.]
  6. Marvel SW, House JS, Wheeler M, Song K, Zhou YH, Wright FA, Chiu WA, Rusyn I, Motsinger-Reif A, Reif DM. 2021. The COVID-19 Pandemic Vulnerability Index (PVI) Dashboard: Monitoring county-level vulnerability using visualization, statistical modeling, and machine learning. Environ Health Perspect 129(1):17701. [Abstract Marvel SW, House JS, Wheeler M, Song K, Zhou YH, Wright FA, Chiu WA, Rusyn I, Motsinger-Reif A, Reif DM. 2021. The COVID-19 Pandemic Vulnerability Index (PVI) Dashboard: Monitoring county-level vulnerability using visualization, statistical modeling, and machine learning. Environ Health Perspect 129(1):17701.]
  7. Akhtari FS, Havener TM, Hertz DL, Ash J, Larson A, Carey LA, McLeod HL, Motsinger-Reif AA. 2021. Race and smoking status associated with paclitaxel drug response in patient-derived lymphoblastoid cell lines. Pharmacogenet Genomics 31(2):48-52. [Abstract Akhtari FS, Havener TM, Hertz DL, Ash J, Larson A, Carey LA, McLeod HL, Motsinger-Reif AA. 2021. Race and smoking status associated with paclitaxel drug response in patient-derived lymphoblastoid cell lines. Pharmacogenet Genomics 31(2):48-52.] 
  8. Craun K, Ekena J, Sacco J, Jiang T, Motsinger-Reif A, Trepanier LA. 2020. Genetic and environmental risk for lymphoma in boxer dogs. J Vet Intern Med 34(5):2068-2077. [Abstract Craun K, Ekena J, Sacco J, Jiang T, Motsinger-Reif A, Trepanier LA. 2020. Genetic and environmental risk for lymphoma in boxer dogs. J Vet Intern Med 34(5):2068-2077.] 
  9. Jiang T, Li Y, Motsinger-Reif AA. 2020. Knockoff boosted tree for model-free variable selection. Bioinformatics; doi: 10.1093/bioinformatics/btaa770 [Online 23 September 2020]. [Abstract Jiang T, Li Y, Motsinger-Reif AA. 2020. Knockoff boosted tree for model-free variable selection. Bioinformatics; doi: 10.1093/bioinformatics/btaa770 [Online 23 September 2020].]
  10. Ma J, Bair E, Motsinger-Reif A. 2020. Nonlinear Dose-Response Modeling of High-Throughput Screening Data Using an Evolutionary Algorithm. Dose Response; doi: 10.1177/1559325820926734 [Online 22 May 2020] [Abstract Ma J, Bair E, Motsinger-Reif A. 2020. Nonlinear Dose-Response Modeling of High-Throughput Screening Data Using an Evolutionary Algorithm. Dose Response; doi: 10.1177/1559325820926734 [Online 22 May 2020]]
  11. Wise CF, Hammel SC, Herkert N, Ma J, Motsinger-Reif A, Stapleton HM, Breen M. 2020. Comparative Exposure Assessment Using Silicone Passive Samplers Indicates That Domestic Dogs Are Sentinels To Support Human Health Research. Environ Sci Technol; 54(12):7409-7419. [Abstract Wise CF, Hammel SC, Herkert N, Ma J, Motsinger-Reif A, Stapleton HM, Breen M. 2020. Comparative Exposure Assessment Using Silicone Passive Samplers Indicates That Domestic Dogs Are Sentinels To Support Human Health Research. Environ Sci Technol; 54(12):7409-7419.]
  12. House JS, Motsinger-Reif AA. 2020. Fibrate Pharmacogenomics: Expanding Past the Genome. Pharmacogenomics 21(4):293-306. [Abstract House JS, Motsinger-Reif AA. 2020. Fibrate Pharmacogenomics: Expanding Past the Genome. Pharmacogenomics 21(4):293-306.]
  13. Roell KR, Havener TM, Reif DM, Jack J, McLeod M, Wiltshire T, Motsinger-Reif AA. 2019. Synergistic Chemotherapy Drug Response Is a Genetic Trait in Lymphoblastoid Cell Lines. Front Genet; doi: 10.3389/fgene.2019.00829 [Online 15 October 2019]. [Abstract Roell KR, Havener TM, Reif DM, Jack J, McLeod M, Wiltshire T, Motsinger-Reif AA. 2019. Synergistic Chemotherapy Drug Response Is a Genetic Trait in Lymphoblastoid Cell Lines. Front Genet; doi: 10.3389/fgene.2019.00829 [Online 15 October 2019].]
  14. Tang Y, Lenzini PA, Pop-Busui R, Ray PR, Campbell H, Perkins BA, Callaghan B, Wagner MJ, Motsinger-Reif AA, Buse JB, Price TJ, Mychaleckyj JC, Cresci S, Shah H, Doria A. 2019. A Genetic Locus on Chromosome 2q24 Predicting Peripheral Neuropathy Risk in Type 2 Diabetes: Results From the ACCORD and BARI 2D Studies. Diabetes 68(8):1649-1662. [Abstract Tang Y, Lenzini PA, Pop-Busui R, Ray PR, Campbell H, Perkins BA, Callaghan B, Wagner MJ, Motsinger-Reif AA, Buse JB, Price TJ, Mychaleckyj JC, Cresci S, Shah H, Doria A. 2019. A Genetic Locus on Chromosome 2q24 Predicting Peripheral Neuropathy Risk in Type 2 Diabetes: Results From the ACCORD and BARI 2D Studies. Diabetes 68(8):1649-1662.]