[BioC] Antw: Re: SNP Analysis
Benilton Carvalho
bcarvalh at jhsph.edu
Mon Oct 26 12:56:22 CET 2009
To genotype 100 samples, you'd need somethng like 3GB RAM and the
package is available for all platforms.
b
On Oct 26, 2009, at 8:46 AM, Peter Ganske wrote:
> Thanks for all the replies. I will check out some ideas.
> What systems you used for the CRLMM- Analysis? It would be helpfull,
> if you can tell me something about the memory i need for this
> thanks in advance
> Peter
>
>
>
> >>> Paul Leo <p.leo at uq.edu.au> 25.10.2009 23:48 >>>
> Very nice!
> Thanks for the heads up.
> Cheers
> Paul
>
>
> Date: Fri, 23 Oct 2009 12:10:36 -0200
>
> Regarding CNA, the 'crlmm' package does include a copy number tool:
>
> http://www.bepress.com/jhubiostat/paper197/
> http://www.bioconductor.org/packages/2.5/bioc/vignettes/crlmm/inst/doc/copynumber.pdf
>
> b
>
> On Oct 23, 2009, at 5:03 AM, Paul Leo wrote:
>
> > I have to say R/Bioconductor is my favourite too , but PLINK is just
> > great for GWAS for many reasons. You see PLINK , MACH (other
> > imputation
> > algorithms), HALOVIEW and EIGENSTRAT can all be mixed and matched to
> > some extent and provide a workhorse for QC, association and first
> stop
> > visualisation. I typically use the output of these into R/
> > Bioconductor.
> > That said there are some neat Bioconductor tools that you can use
> > along
> > side... I probably underutilise those myself , but I recommend
> highly
> > those "common" tools.
> >
> > Plink binary format for genotype data is very handy. MACH-MACH2DAT
> can
> > take covariates (at the end of the ped files). If is not clear what
> > you
> > are doing but if you have 100 cases, what are your controls ? are
> you
> > going to use a historical set ...WTCCC ?
> >
> > As for Birdseed and crlmm.To be honest I do not know if crlmm calls
> > genotype AND copy number variations (like birdsuite, Birdseed is
> apart
> > of that), would be neat if it did. If you have expression data then
> > genotypes + copy number data might be quite useful to you .....
> > depending
> > on what you are studying.
> >
> > 100 cases is very small but I have seen success with as few as 200
> for
> > some specific genetic diseases
> >
> > I would consider
> > 1) Affymetrics own SNP calling to get genotypes + PennCNV( or
> > other) to
> > get copy number variations, easy and most straight forward
> > OR
> > Buidsuite if you feel confident (never tried it, I use Illumina)
> cause
> > then you get copy number variations straight away.
> >
> > 2) your cases + historical controls do a mini GWAS
> > combine genotypes and QC with plink / eigenstrat for SAMPLES and
> SNPS
> > ie:
> >
> > SAMPLES check:(stratification, related
> > individuals,missingness,heterozygosity... look for outliers )
> >
> > SNPS:(MAF , genotyping rate, HWE all need to be filtered on)
> >
> > 3) do straight up affy micro array analysis and check against your
> > mini
> > GWAS
> >
> > 4) Really combine expression and genotypes; Try GGtools
> (Bioconductor)
> > on you genotypes + expression data
> > OR use plink or MACH2DAT with the expression data as covariates,
> > maybe.
> > Plink has an excellent manual check that out.
> >
> > Note GGtools will not require that you do the mini GWAS as you will
> > only need the cases genotypes I think see that package for details,
> > but
> > still do filtering before you begin.
> >
> > my 2c worth of ideas....
> >
> > Cheers
> > Paul
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> > -----Original Message-----
> > From: Peter Ganske <peterganske at mac.com>
> > To: Claus-Jürgen Scholz <scholz at klin-biochem.uni-wuerzburg.de>
> > Cc: bioconductor at stat.math.ethz.ch
> > Subject: Re: [BioC] SNP Analysis
> > Date: Thu, 22 Oct 2009 14:51:52 +0200
> >
> > Dear Claus- Jürgen,
> > thanks for the reply. In which way you would analyze the genotype
> > frequency wit PLINK?
> > And why you use this program instead of any bioconductor- package?
> > All the best and thanks in advance
> > Peter
> >
> >
> >
> > Am 22.10.2009 um 13:13 schrieb Claus-Jürgen Scholz:
> >
> >>
> >> Dear Peter,
> >>
> >> indeed, Birdseed is a genotyping algorithm and I'd use it for
> >> genotype
> >> calling of SNP6.0 arrays (best suited for this platform). If you
> have
> >> the calls, export them into a table (export options and formats
> >> should
> >> be described in the Genotyping Console manual) and analyze the
> >> genotype
> >> frequency differences between responders and non-responders
> (valuable
> >> free software is e.g. PLINK). However, n=100 is a pretty small
> sample
> >> size for a GWAS...
> >>
> >> Bests,
> >> Claus-Jürgen
> >>
> >>
> >> Peter Ganske schrieb:
> >>> Dear Vincent,
> >>> thanks for the fast replay. Well, i thought, that the Genotyping
> >>> console used the Birdseed Algorithm and this algorithm is an
> >>> Genotyping Algorithm.
> >>>
> >>> Its hard to find paper or groups, who worked with this array and
> for
> >>> me ( i work as a student for an institue) is hard to find the
> right
> >>> workflow without help (nobody worked here with SNP arrays in the
> >>> past)
> >>>
> >>> So, i have 100 Arrays (100 CHP and 100 CEL files) of 100
> >>> patients. I
> >>> want to have a look at the SNPs of the patients. 50 are non-
> >>> responder
> >>> and 50 are responder. There should be a difference between the two
> >>> groups. Since yet, i looked for any papers for getting an
> "general"
> >>> workflow for sorting out most of the SNPs of the patients.
> >>>
> >>> So you think i have to try this package and create the genotyping
> >>> calls?
> >>> Whats about this workflow? So are my following thought right:
> >>>
> >>> - The package check every SNP for every Chips and put the result
> >>> in a
> >>> table
> >>> - i can combine the result of the SNPs with a selection of gene i
> >>> want....
> >>>
> >>> My boss talked about a top-list of 50 genes... Maybe this can help
> >>> me
> >>> out for the usage of CRLMM.. dont know
> >>>
> >>> Thanks a lot and sorry for the questions. First time for me to
> work
> >>> with SNP Arrays and the first time to work with Bioconductor/R
> >>> All the best from Germany
> >>> Peter
> >>> Am 21.10.2009 um 16:11 schrieb Vincent Carey:
> >>>
> >>>
> >>>> Briefly, you can perform genotype calling with a confidence
> measure
> >>>> using crlmm package, working from the CEL files. The crlmm
> >>>> package
> >>>> includes a vignette called crlmmDownstream.pdf that illustrates
> one
> >>>> approach to GWAS analysis based on 6.0, using snpMatrix package.
> >>>> To
> >>>> use crlmm you will also need a metadata package called
> >>>> genomewidesnp6crlmm.
> >>>>
> >>>> There are certainly other approaches possible. Our workflow
> >>>> documentation for this use case probably needs some enhancement.
> >>>>
> >>>> On Wed, Oct 21, 2009 at 9:42 AM, Peter Ganske <Peter.Ganske at hki-jena.de
> >>>>
> >>>>> wrote:
> >>>>>
> >>>>> Hello,
> >>>>> first time for me to work with SNP arrays. I got CEL- and CHP-
> >>>>> files
> >>>>> for my Analysis. The CEL are from Affymetrix Human-Wide Genome
> >>>>> SNP-
> >>>>> Array 6.0 and the CHP- files are dealed with the Birdseed-
> >>>>> Algorithm (part of the Genotyp Console from Affymetrix as well).
> >>>>> Is there anybody here, who worked with this arrays in the
> past? I
> >>>>> am looking for an (general) workflow for my study. I want to
> >>>>> analyse patients with Rheumatoid Arthritis with regard to SNPs
> and
> >>>>> the question "why there are respoonder and non-responder for the
> >>>>> therapy"?
> >>>>> I am looking for an workflow for the arrays. Is it better to
> work
> >>>>> with the CHP files or with the CEL- files?
> >>>>> Would me great, if anybody can help me out.
> >>>>> Thanks in advance
> >>>>> Peter
> >>>>>
> >>>>>
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> > --
> > Dr Paul Leo
> > Bioinformatician
> > Diamantina Institute for Cancer, Immunology and Metabolic Medicine
> > University of Queensland
> >
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> --
> Dr Paul Leo
> Bioinformatician
> Diamantina Institute for Cancer, Immunology and Metabolic Medicine
> University of Queensland
> --------------------------------------------------------------------------------------
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