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Re: st: Genome-wide associations in Stata


From   David Airey <david.airey@Vanderbilt.Edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Genome-wide associations in Stata
Date   Fri, 9 May 2008 11:32:13 -0500

.

I believe our Center for Human Genetics Research at the Vanderbilt School of Medicine piloted their GWAS (genome wide association study) efforts in Stata, but then ported to C++ and now that pipeline uses our parallel computing system (ACCRE). Statistical analyses in GWAS are very simple models, but the problem of housing and securing data for GWAS is not a desktop PC solution. Statistical analysis in GWAS lag the technology to gather the data, is one message.

There is no reason Stata cannot be used to analyze microarray data. I've done this and used Roger's smileplot in the process. I am at the moment starting a meta-analysis of microarray results, and Stata will work just fine for this. However, you will find no large cache of programs for microarray data analysis for Stata, like there is at Bioconductor.org for R. For this reason alone, you should consider knowing enough of R to make use of the packages on that site.

-Dave

On May 9, 2008, at 9:24 AM, tiago.pereira@incor.usp.br wrote:


Dear statalister,

Does anyone have experience in running genome-wide association analyses in
Stata?

Toby Andrew has provied useful tips for genome-wide linkage analysis, but
I would like to know in more detail your current experience in running
these sort of computer intensive stuff. I got frustrated with Stata
regarding its complete lack of programs to analyze microarray data. To put
it bluntly, Should I forget using Stata in these high-throughput
experiments from now on and rely on R only?
--
David C. Airey, Ph.D.
Pharmacology Research Assistant Professor
Center for Human Genetics Research Member
Vanderbilt University School of Medicine

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