GettingGeneticsDone.com

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Network analysis in systems biology workshop

Avi Ma'ayan, Ph.D., Assistant Professor in the Department of Pharmacology and Systems Therapeutics of Mount Sinai School of Medicine, will present "Network Analysis in Systems Biology", at 10:00 am on Wednesday, April 22 in Room 206 PRB.

Following the lecture, Dr. Ma’ayan will hold a series of one-hour interactive workshops on "Computational Methods for Analyzing Lists of Genes/Proteins and Building Networks in Systems Biology," which will highlight the software tools and databases he has helped to develop in the Systems Biology Center of New York. These computational tools will focus on modeling cellular pathways and using large-scale data in these models.

The workshop is being offered over 10 different time slots Wednesday and Thursday this week. See this link for more details about the schedule and information about registration.

Five articles on the success/failure of GWAS

Here are four interesting and provocative articles in New England Journal of Medicine:

David Goldstein: Common Genetic Variation and Human Traits

Joel Hirschhorn: Genomewide Association Studies — Illuminating Biologic Pathways

Peter Kraft and David Hunter: Genetic Risk Prediction — Are We There Yet?

John Hardy & Andrew Singleton: Genomewide Association Studies and Human Disease

And after all their hype over the last year or so, a rather pessimistic article in the NYT.

Share your thoughts in the comments!

Linux Command Du Jour: time

I briefly mentioned "time" in the previously posted Linux command line cheat sheet, but I can't overstate its utility especially for ACCRE/Vampire users. The time command does exactly what it sounds like: it times exactly how long it takes to run anything at the command line. And it couldn't be easier to use. Just type the word "time" preceding what you would normally type in at the command line.

For example, instead of:

> ./sMDR run.cfg

Type this in instead:

> time ./sMDR run.cfg

The MDR analysis (or whatever other command) runs just as before, but now there will be a couple lines displayed telling you how long the command took to execute. (Depending on whether you're doing this on the local cheeses or on Vampire the output will look slightly different, but you want to look at the "real" time). If you don't have anything to run right now, fire up a terminal window and just try "time ls". Obviously it won't take long to run, but you can see how the time command works.

Where this becomes very useful is if you are preparing to run analyses on Vampire, where you must estimate the time it takes to execute a command or analysis. You can time smaller versions of a large analysis for a better estimate of how long something will take. For more information, type "man time" at the command line.

I can has power calculations?

What's your power to detect a recessive effect with an odds ratio of 1.2 for a disease with 4.2% prevalence using 1200 cases and 2900 controls? What if the allele is rare? Is it worth it, in terms of power gain, to genotype 1000 more individuals? How small of an effect can you detect with 80% power using the data you have? These questions and others can be answered by power and sample size calculations. While any respectable statistical computing software can do these, it's often far simpler and faster to use "canned" software for power analysis. While there are tons of these on the web, here are a few that I or others around here commonly use.

CaTS Power: This software from the Abecasis lab at Michigan couldn't be simpler to use. Nice GUI with clicky-boxes and sliders, made specifically for power calculations in genetic association studies using case-control study design. Has some nice extra features related to multi-stage designs, or designing penetrance tables for simulation.

PS Power: Made here at Vanderbilt by Bill Dupont in Biostatistics. Not as pretty as CaTS, but much more flexible, allowing for studies with dichotomous, continuous, or survival response measures, as well as matched designs.

G*Power: Probably the most flexible of the three mentioned here, allowing for more specific questions to be addressed, but at the cost of a slightly steeper learning curve.

Epistasis Blog

If you're interested in gene-gene and gene-environment interaction (and who wouldn't be?), then you should check out the Epistasis Blog. Our friend and colleague Jason Moore at Dartmouth Medical School has maintained compgen.blogspot.com since 2005 writing about epistasis, computational genetics, and related topics. I've personally stumbled upon several interesting papers mentioned here that I may have otherwise missed.

Also, be sure to check out other posts in GGD's Noteworthy Blogs series!

Jason Moore's Epistasis Blog (compgen.blogspot.com)

What's the Right Analysis, Part II

Will recently posted a set of flowcharts made by Marylyn, Jason, and Tricia, to help choose which statistical analysis to use for nearly any situation. UCLA has a very similar chart with links to Stata code, examples, and annotated output for every method they mention. Also, check out their Stata help homepage and Stata learning modules. They're excellent resources for learning how to use Stata for data analysis!

What statistical analysis should I use? (UCLA Stat Computing)

Gene Prospector

This abstract in BMC Bioinformatics was presented in our Computational Genetics Journal Club a few weeks back: "Gene Prospector: An evidence gateway for evaluating potential susceptibility genes and interacting risk factors for human diseases."

As described by the authors at CDC, Gene Prospector is a bioinformatics tool designed to sort, rank, and display information about genes in relation to human diseases, risk factors and other phenotypes.

While there seem to be several tools like this out there, this one was truly pleasant to use. A quick search for "Alzheimer" turned up some very familiar results. Genes are ranked by the evidence strength that were calculated based on the volume of different types of published literature in human genome epidemiology. The results of a query provide tons of helpful links, including links to information about each candidate gene found, exportable lists of journal articles where the results of association studies, GWASs, and meta-analyses were published, information about SNPs in these genes, and more.

Overall this seems like a great place to start when you want to compare your results to others, or when you just want more information about particular genotype-phenotype associations.

Gene Prospector