Bio: Dr. Long received her Ph.D. in animal science (2011) and a master’s degree in statistics (2009), both from the University of Wisconsin-Madison. Her thesis work centered around high-dimensional genetic marker data used for predicting genetic values of individuals. She published 15 peer-reviewed journal articles during her Ph.D. study, by investigating and developing a variety of statistical modeling methods to enhance the accuracy of genome-enabled prediction of quantitative traits. She then pursued a three-year postdoctoral training at Duke University School of Medicine, where she continued working on statistical methodology, with a focus on identifying causal variants using both sequencing data and array data. Before joining ICER in the summer of 2017, she was a statistical geneticist at the University of North Carolina-Chapel Hill School of Pharmacy and worked on Drug-Induced Liver Injury Network, an NIDDK-funded large-scale genetic analysis consortium. Currently at ICER, Dr. Long commits 30% of her time in collaborating with MSU researchers, in addition to routine helpdesk tasks and training activities toward general HPCC users.

Research and technical skills

• Familiar with mainstream next-generation sequencing analysis software/pipelines
• Proficient with R, shell script, high performance computing cluster
• Knowledgeable in Python, Perl, C++

Selected publications

1. Dai Z, Long N and Huang W (2020) Influence of genetic interactions on polygenic prediction. G3, 10(1):109-115
2. Cirulli ET, Nicoletti P, Abramson K, Andrade RJ, Bjornsson ES, Chalasani NP, Fontana RJ, Hallberg P, Li Y-J, Lucena M, Long N, Molokhia M, Nelson MR, Odin JA, Pirmohamed M, Rafnar T, Serrano J, Stefansson K, Stolz A, Daly AK, Aithal GP and Watkins PB (2019) A Missense Variant in PTPN22 is a Risk Factor for Drug-induced Liver Injury. Gastroenterology, 156(6):1707-1716
3. Long N, Dickson SP, Maia JM, Kim HS, Zhu Q and Allen AS (2013) Leveraging prior information to detect causal variants via multi-variant regression. PLoS Computational Biology, 9(6):e1003093.
4. Long N, Gianola D, Rosa GJM, Weigel KA, Kranis A and González-Recio O (2010) Radial basis function regression methods for predicting quantitative traits using SNP markers. Genetics Research, 92(3):209-225. (Top five most-read papers in 2010)