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Introduction to Quantitative Trait Loci (QTL) AnalysisGoals of this webinar (trait variance to QTL):
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Mapping Addiction and Behavioral Traits and Getting at Causal Gene Variants with GeneNetworkGoals of this webinar (QTL to gene variant):
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Data structure, disease risk, GXE, and causal modelingHuman disease is mainly due to complex interactions between genetic and environmental factors (GXE). We need to acquire the right "smart" data types—coherent and multiplicative data—required to make accurate predictions about risk and outcome for n = 1 individuals—a daunting task. We have developed large families of fully sequenced mice that mirror the genetic complexity of humans. We are using these Reference Populations to generate multiplicatively useful data and to build and test causal quantitative models of disease mechanisms with a special focus on diseases of aging, addiction, and neurological and psychiatric disease. Speaker Bio: Robert (Rob) W. Williams received a BA in neuroscience from UC Santa Cruz (1975) and a Ph.D. in system physiology at UC Davis with Leo M. Chalupa (1983). He did postdoctoral work in developmental neurobiology at Yale School of Medicine with Pasko Rakic where he developed novel stereological methods to estimate cell populations in brain. In 2013 Williams established the Department of Genetics, Genomics and Informatics at UTHSC. He holds the UT Oak Ridge National Laboratory Governor’s Chair in Computational Genomics. Williams is director of the Complex Trait Community (www.complextrait.org) and editor-in-chief of Frontiers in Neurogenomics. One of Williams’ more notable contributions is in the field of systems neurogenetics and experimental precision medicine. He and his research collaborators have built GeneNetwork (www.genenetwork.org), an online resource of data and analysis code that is used as a platform for experimental precision medicine. Presented by: |
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Introduction to Gene NetworkPlease note that this tutorial is based on GeneNetwork v1 GeneNetwork is a group of linked data sets and tools used to study complex networks of genes, molecules, and higher order gene function and phenotypes. GeneNetwork combines more than 25 years of legacy data generated by hundreds of scientists together with sequence data (SNPs) and massive transcriptome data sets (expression genetic or eQTL data sets). The quantitative trait locus (QTL) mapping module that is built into GN is optimized for fast on-line analysis of traits that are controlled by combinations of gene variants and environmental factors. GeneNetwork can be used to study humans, mice (BXD, AXB, LXS, etc.), rats (HXB), Drosophila, and plant species (barley and Arabidopsis). Most of these population data sets are linked with dense genetic maps (genotypes) that can be used to locate the genetic modifiers that cause differences in expression and phenotypes, including disease susceptibility. Users are welcome to enter their own private data directly into GeneNetwork to exploit the full range of analytic tools and to map modulators in a powerful environment. This combination of data and fast analytic functions enable users to study relations between sequence variants, molecular networks, and function. Presented by: |
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How to search in GeneNetworkPresented by Rob Williams University of Tennessee Health Science Center |
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GeneNetwork.org: genetic analysis for all neuroscientistsPresented by David G. Ashbrook Assistant Professor University of Tennessee Health Science Center |
| Title | Speaker | Video link |
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| Diallel Crosses, Artificial Intelligence, and Mouse Models of Alzheimer’s Disease | David G. Ashbrook Assistant Professor University of Tennessee Health Science Center |
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