Hello, I'm writing in regards to your recent bioinformatics paper.
Improvements in SNP genotyping has been something that has been in urgent
need for some time. I remember these issues when we used a 412 SNP array for
Arabidopsis
<http://naturalsystems.uchicago.edu/naturalsystems/Nordborgetal.pdf>  back
in 2000.  Only 163 SNPs could be accurately typed and training was
essential.  At that stage I decided to move on to higher density arrays with
Elizabeth Winzeler and we moved to SFP
<http://naturalsystems.uchicago.edu/naturalsystems/Borevitz_etal_GR2003.pdf>
discovery and genotyping.  Now I'm focused on tiling arrays for SFP and copy
number variant discovery and genotyping, using whole genome labeling in
Arabidopsis.  The ultimate goal is whole genome association mapping. Our
next move has been resequencing of 20 Arabidopsis strains with Perlegen so
now there is no shortage of SNPs.  By the way as you know the resequencing
arrays really type, and call, SNP alleles using 4 probes, reference on each
strand, and alternative SNP allele on each strand.  As we scale up to do a
large population we plan to make a custom array and I prefer a hybrid
SNP/tiling array for discovery and typing of known alleles.  Without MM we
gain real estate to cover the genome (for new rare variants and copy number
variants) or accurately type known SNPs, there could be some ideal trade off
in design.  I have some HMM methods for copy number detection I can share,
it's nothing too fancy.

 

One nice thing about Arabidopsis is we have clonal lines so we can easily
make training data for all genotypes and have plenty of replicates..

 

1) any how my interest is how this quality distribution changes as we reduce
probes interrogating the same SNP.  Certainly the MM is not necessary (never
has been worth its space), Could 2 probes be enough, or 4 (each allele and
each strand central position). How about 8,12 etc.. I'm guessing that 4
probes is enough for most SNPs and certainly one can get more SNPs on an
array using just 4 (or smaller cheaper arrays). I'm coming from the
perspective on using a single probe and testing for differential
hybridization intensity across replicates. I was considering running this
analysis using data in your paper but figured you had already done.

 

2) What is the distribution of "easily called" vs poorly called SNPs using
your method on the 100k human arrays.  I suppose this is the Distribution of
Mannahobolis distances would tell me this but I didn't quite follow how the
classification ranked the callability or discriminability, with a single
cutoff on a minimum distance.  Perhaps the principal component vector(s)
show the distribution of SNP qualities. Is there a way to consider the
probability of each genotype rather than just taking the minimum D2g? What
if the test is nearly the same statistic for each genotype, wouldn't we want
to trust that one less..

 

3) By the way is it important to specifically model strand effects? I
noticed major differences in some cases. Is this common? From my point of
view if we know one strand works better we could drop the other on a custom
array.

 

I found the raw data on affy's site so I can play with it some, but I
thought you might have done this power (SNP number/ quality) analysis
already

 

4) finally the SNPs are often in LD with eachother anyhow and the haplotype
is really the unit to be called.  I'm working toward this with SFP genotypes
(papers in press at Genetics) but its going slowly.  Has anyone started to
work on this with regards to SNP calling?

 

Thanks again

Justin

-----
Justin Borevitz
Ecology and Evolution (CLSC 915E)
University of Chicago
1101 E. 57th St.
Chicago, IL 60637 USA
(773) 702-5948 office
(773) 834-4055 lab
borevitz at uchicago.edu
 <http://naturalvariation.org> http://naturalvariation.org
  

 


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