2022 - Precision Genomics: Beating Big Data Bottlenecks
The Genetics & Genome Sciences Program
at Michigan State University
Presents a Mini-Symposium On
Precision Genomics: Beating Big Data Bottlenecks
Tuesday, May 10th, 2022
8:00 a.m. to 5:00 p.m.
Molecular Plant Sciences, Room 1200
1066 Bogue Street
Michigan State University
Luis Brito, Ph.D.
Assistant Professor of Quantitative Genetics and Genomics
Department of Animal Sciences
Title: Improving Livestock Welfare Through the Integration of Genomics and Complementary Data Sources
Dr. Luiz Brito is an Assistant Professor of Quantitative Genetics and Genomics in the Department of Animal Sciences at Purdue University. He also holds Adjunct Faculty positions at the University of Guelph (Canada) and at the University of Nebraska-Lincoln. Dr. Brito’s research program focuses on: 1) the integration of multiple data sources to reveal the genetic basis underlying the phenotypic variability in livestock behavior, welfare, and overall resilience; and, 2) the development of selection methods and approaches to enable efficient incorporation of these traits into livestock breeding programs, while maintaining enough populational genetic diversity. Furthermore, Dr. Brito’s is interested on the development of genomic approaches applied to breeding of minor (less researched) species (e.g., water buffaloes, alpacas, ducks, sheep, goats) and livestock populations in small-holder production systems.
Molly Jahn, Ph.D.
Defense Sciences Office
Title: Current and Future Role of Ubiquitous Data in Genomics
Molly Jahn’s research programs at University of Wisconsin-Madison and Cornell University have generated dozens of vegetable varieties grown commercially and for subsistence all over the world. She has published extensively on the molecular basis for disease resistance and quality traits and more recently has concentrated on a new line of work on risk in agriculture and food systems. Presently, she is on loan to the federal government serving as a Program Manager in the Defense Sciences Office at DARPA.
Arjun Krishnan, Ph.D.
Department of Computational, Mathematics, Science and Engineering
Department of Biochemistry and Molecular Biology
Michigan State University
Title: Training Machines and Humans to Beat Big Data Bottlenecks
The Krishnan Lab works in the areas of computational biology and biomedical data science. Our team develops data-driven computational approaches to: (1) Unravel mechanistic subtypes of complex traits/diseases, (2) Reveal age- and sex-specificity of physiology/disease, and (3) Translate data/knowledge between human and model systems. These approaches encompass methods and tools for harmonizing and integrating heterogeneous genomic and genetic data, reconstructing genome-scale networks for data and knowledge representation and transfer across experimental domains, natural language processing and text mining to unearth molecular insights and annotate omics data, building biology-informed machine learning models to capture patterns in omics data, and developing open software and interactive webservers. The approaches that we develop are highly general, thus applicable to a wide range of biological phenomena in both human and model organisms. Our research is primarily supported by the National Institutes of Health and the National Science Foundation. Please visit "The Krishnan Lab"
Abner Louissaint, M.D./Ph.D.
Medical Director of Hematology Laboratory, Massachusetts General Hospital
Associate Professor of Pathology, Harvard Medical school
Title: Translating Clinicopathologic Observations and Modelling of Lymphoma into Discovery of Novel Biology and More Precise Therapeutic Approaches
James Schnable, Ph.D.
Associate Professor & Charles O. Gardner Professor of Maize Quantitative Genetics
Department of Agronomy and Horticulture
University of Nebraska-Lincoln
Title: Predicting Phenotypes for an Unknown Future
Research in my group uses new and emerging technologies from statistics, engineering, and computer science to study and ultimately understand how the environment and genetic variation interaction to determine the phenotypes of maize and sorghum plants. Some members of my research group are working with experts in computer graphics to generate 3D reconstructions of individual corn and sorghum plants of different genotypes and model how they perform in different environments. Others are combining data from our own large scale field trials across the data of Nebraska with information mined from published studies and new statistical methods to identify genes controlling differences in phenotypic plasticity.
Addie Thompson, Ph.D.
Department of Plant, Soil and Microbial Sciences
Michigan State University
Title: Breaking Barriers and Building Bridges: Identifying and Addressing Bottlenecks in Interdisciplinary and Translational Plant Science Research
Dr. Addie Thompson is an Assistant Professor in the Department of Plant, Soil & Microbial Sciences at Michigan State. The Thompson lab works at the intersection of genetics, phenomics, and computational biology with a focus on field-grown maize and sorghum. Current members of the lab are working on modeling and prediction of 1) resistance to maize tar spot disease, 2) canopy architectural traits in maize and sorghum, and 3) nutrient levels under stress in maize, as well as creating and optimizing tools and resources for plant breeding. One of the overarching areas of interest is in improving predictions of variety performance across time, space, and environments through use of drone-based phenomics as well as genomic prediction and crop growth modeling.