NETWORK ANALYSIS COURSE: UCLA Summer 2015
A Practical Introduction to Online Complex Trait Analysis using GeneNetwork
Workshop and lectures on Thursday and Friday led by Robert W. Williams
1. Introduction to objectives
Teach you how to use GeneNetwork in your own research program and how to use it as a teaching
tool at any level.
Trait mapping, QTL maps, estimates of precision, estimates of power
Exercise: Mapping a dummy trait to see what we get: the problem of false discovery and the problem
of low power
Where to find HELP, Glossary, FAQ Etc.
How should you structure your own genetic analysis of Trait X, Y, Z?
2. Trait distributions and transformations (Chapter 11: Matters of Scale in Genetics and Analysis of
Quantitative Traits by Lynch and Walsh, 1998)
Indicator variables, dichotomous, thresholds and cut-offs (very common in human genetics)
Ordinal scale
Quantitative scales
Exercise: Look at brain weight data 12661
Dealing with outliers (Winsorising)
Exercise: Go to Phenotype data for BXDs and put data into Trait Collection
Heritability: Look at BXD Aged hippocampus data and review Normal Distribution Plot
3. Genotypes and the genome
Recombination event density in BXD and F2
Exercise: Look at a map with haplotype display
Recombination events and their density
Exercise: Look at correlations of one genotype to other genotypes
The cost of high recombination is loss of power
High power but low resolution
4. The genetic architecture of cohorts (mouse and human)
F2 intercross
Standard recombinant inbred strain (two parents)
8-way RI cross, such as Collaborative Cross
Heterogeneous stock or wild caught populations
Human cohorts
5. Mapping methods
Marker regression
Interval mapping on cM or on megabase scale
How much precision
6. Some fun stuff that is more complicated
Pair-scan analysis
Mapping a whole transcriptome
Advanced command sets
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Introduction to network analysis and other advanced topics
1. Aims
Teach you how to use GeneNetwork in your own research program and a look at more exotic modules
in GN.
Review the data resources and some of the algorithms currently implemented in GN
Comment on the software architecture (GN1, GN2, Arvados for new plug-in friendly workflows)
Where are we going with GN2?
1b. Review of data for network construction.
Review three complementary methods of evaluating covariation of genes/transcripts/proteins—
genetic, tissue, and literature.
2. Advanced mapping:
One locus with control (composite or with cofactors)
Two-locus epistatic models
Exercise: Brain weight or expression data sets for examples
Advanced topics in trait mapping, estimates of precision, estimates of power
Exercise: Empirical precision of monogenic QTLs
2a. Network analysis seeded by single genes, transcripts, proteins...
Review of mouse, rat, and human data sets
Exercise: Cdc20 and the cell cycle
Correlation to PC eigentrait. Sets of eigentraits
Mapping of large numbers of traits and eigentraits. QTL Cluster maps
2b. Partial correlation and using cofactors
Exercise: Controlling for linkage in teasing apart effects of linked eQTLs
2c. Network seeded by small families of genes/proteins. How to exploit the GeneWiki feature.
2d. Network QC analysis by GO and by literature correlation
3. Network analysis seeded by complex phenotypes
Exercise: Schizophrenia candidate genes using gene list or the GeneRIF function
4. Cis and trans eQTL network analysis
Exercise: Using advanced search methods. Building up small networks from strong cis eQTLs.
5. Phenome-wide scans and result interpretation
Exercise: Analysis of Comt and analysis of mitochondrial unfolded protein response
6. Exporting data
Exercise: Export all phenome data, export all covariates, export expression data