Faculty Profile
Jean Morrison, PhD
- John G Searle Assistant Professor, Biostatistics
Jean Morrison joined the Department of Biostatistics at University of Michigan in
2020 after completing a postdoc in the Department of Human Genetics at the University
of Chicago and a PhD in Biostatistics at the University of Washington. Jean's research
is primarily motivated by problems in statistical genetics and genomics, with current
focuses on analysis of high dimensional or structured phenotypes and causal inference.
They also have interests in empirical and variational Bayes and genomic applications
of deep learning.
- PhD, Biostatisitcs, University of Washington, 2016
- BA, Mathematics, University of Chicago, 2009
Research Interests:
Genetic associations with high dimensional phenotypes: Several of my past and current research projects are motivated by an interest in "high dimensional phenotypes" or large collections of related traits that are measured in the same individuals. These might be traits derived from brain imaging, molecular traits like proteomics, or collections or related clinical traits. Some of my current work centers on estimating biologically informative low rank representations of genetic associations with high dimensional phenotypes. I have also worked on traits that have the special feature of being associated with a particular spatial position (for example position along the genome). In this case, we can share information across neighboring traits to identify regions of association.
Research Projects:
Mendelian randomization: Mendelian randomization (MR) is a variation of instrumental variable analysis that uses genetic variants as instruments.Unfortunately, genetic variants often violate several of the key assumptions required to apply traditional instrumental variable analysis approaches.There are several ways to make MR more robust to these violations and I created one of them (see https://jean997.github.io/cause/).This method uses an empirical Bayes mixture model to model departures from the traditional MR assumptions. I have continued interested inapplications and improvements of MR from both software development and methodological perspectives.
See my personal website for more details on individual projects!
Genetic associations with high dimensional phenotypes: Several of my past and current research projects are motivated by an interest in "high dimensional phenotypes" or large collections of related traits that are measured in the same individuals. These might be traits derived from brain imaging, molecular traits like proteomics, or collections or related clinical traits. Some of my current work centers on estimating biologically informative low rank representations of genetic associations with high dimensional phenotypes. I have also worked on traits that have the special feature of being associated with a particular spatial position (for example position along the genome). In this case, we can share information across neighboring traits to identify regions of association.
Research Projects:
Mendelian randomization: Mendelian randomization (MR) is a variation of instrumental variable analysis that uses genetic variants as instruments.Unfortunately, genetic variants often violate several of the key assumptions required to apply traditional instrumental variable analysis approaches.There are several ways to make MR more robust to these violations and I created one of them (see https://jean997.github.io/cause/).This method uses an empirical Bayes mixture model to model departures from the traditional MR assumptions. I have continued interested inapplications and improvements of MR from both software development and methodological perspectives.
See my personal website for more details on individual projects!
Morrison, J., Knoblauch, N., Marcus, J. H., Stephens, M., He, X., (2020). “Mendelian
randomization accounting for correlated and uncorrelated pleiotropic effects using
genome-wide summary statistics”. Nature Genetics 52, pp. 740–747
Morrison, J., Simon, N., (2018). “Rank Conditional Coverage and Confidence Intervalsin High Dimensional Problems”. Journal of Computational and Graphical Statistics 27.3, pp. 648–656.
Morrison, J., Witten, D., Simon, N., (2016). “Simultaneous detection and estimation of trait associations with genomic phenotypes”. Biostatistics 18.1, pp. 147–164.
Morrison, J., Laurie, C. C., Marazita, M. L., Sanders, A. E., Offenbacher, S., Salazar, C. R., (Dec. 2015). “Genome-wide association study of dental caries in the Hispanic Communities Health Study/Study of Latinos (HCHS/SOL).” Human Molecular Genetics 25.4, pp. 807–816.
Morrison, J. (Sept. 2013). “Characterization and correction of error in genome-wide ibd estimation for samples with population structure”. Genetic Epidemiology 37.6,pp. 635–641.
Below, J. E., Gamazon, E. R., Morrison, J. V., Konkashbaev, A., Pluzhnikov, A., McKeigue, P. M., (Aug. 2011). “Genome-wide association and meta-analysis in populations from Starr County, Texas, and Mexico City identify type 2 diabetes susceptibility loci and enrichment for expression quantitative trait loci in top signals”. Diabetologia 54.8, pp. 2047–2055.
Morrison, J., Simon, N., (2018). “Rank Conditional Coverage and Confidence Intervalsin High Dimensional Problems”. Journal of Computational and Graphical Statistics 27.3, pp. 648–656.
Morrison, J., Witten, D., Simon, N., (2016). “Simultaneous detection and estimation of trait associations with genomic phenotypes”. Biostatistics 18.1, pp. 147–164.
Morrison, J., Laurie, C. C., Marazita, M. L., Sanders, A. E., Offenbacher, S., Salazar, C. R., (Dec. 2015). “Genome-wide association study of dental caries in the Hispanic Communities Health Study/Study of Latinos (HCHS/SOL).” Human Molecular Genetics 25.4, pp. 807–816.
Morrison, J. (Sept. 2013). “Characterization and correction of error in genome-wide ibd estimation for samples with population structure”. Genetic Epidemiology 37.6,pp. 635–641.
Below, J. E., Gamazon, E. R., Morrison, J. V., Konkashbaev, A., Pluzhnikov, A., McKeigue, P. M., (Aug. 2011). “Genome-wide association and meta-analysis in populations from Starr County, Texas, and Mexico City identify type 2 diabetes susceptibility loci and enrichment for expression quantitative trait loci in top signals”. Diabetologia 54.8, pp. 2047–2055.
Email: jvmorr@umich.edu
For media inquiries: sph.media@umich.edu
For media inquiries: sph.media@umich.edu
Areas of Expertise: Biostatistics