Dear David,

Thank you very much for you help, I really appreciate it.


I am not using the read.snps.long() or any other import function, as the
data is already in snpMatrix, so I cannot specify it at the input step…
I am reading the data as a snpMatrix, so using load() after having called
the snpStats package as in the vignette: "Example of genome-wide
association testing":


require(snpStats)

data(for.exercise)


The objects loaded with this set are: 1) genotypes (in probability format
from imputed data); 2) SNP.support object, with information on  the SNPs
such as Allele 1, Allele 2, chr, position. Other information that I need
for the meta-analysis can be extracted from the 'col.summary' command in
snpStats: MAF, and I think RAF (risk allele frequency) can be considered as
the Effect allele frequency for the meta-analysis.


Then I am using snp.rhs.estimates and snp.rhs.tests for the associations.
The problem is that I don't know which allele is taken as the risk allele
in the association, is there a way to see this? Is it always the Allele 2
reported in the SNP.support file? Until I understand this, I won't be able
to harmonise the SNPs all to one reference, since I don't know if I need to
flip the betas when the effect allele is reversed for example…


I have noticed this because when comparing the frequency of the Allele 2
(taken as the risk allele) and the RAF which I thought was the frequency
associated with it, with the frequencies of the same allele found in the
1000 Genomes, I get concordance up to frequency= 0.5, then a shift in
direction happens and I get discordance up to 1 for the reference
frequency.


Thank you very much for any suggestions you may have,


Francesca



2014-05-27 2:07 GMT+01:00 David Duffy <David.Duffy@qimr.edu.au>:

> On Mon, 26 May 2014, francesca casalino wrote:
>
>  I am having this problem because I need to run a meta-analysis and to
>> align
>> all the variants between the different studies included in the
>> meta-analysis I need to know the effect allele used to get the beta (so
>> that I can flip the beta if the effect allele is flipped compared to all
>> other studies).
>>
>
> This depends on how the data has been sent to you.  Obviously, you should
> check the "A" allele frequency in the different datasets.  If they have
> used different genotyping assays and the strand of the SNP is ambiguous, eg
> G->C transversion, then this may be the only way to exclude problems. PLINK
> offers a tool to check for this using LD patterns.
>
> Cheers, David.
>
>
> | David Duffy (MBBS PhD)
> | email: David.Duffy@qimrberghofer.edu.au  ph: INT+61+7+3362-0217 fax:
> -0101
> | Genetic Epidemiology, QIMR Berghofer Institute of Medical Research
> | 300 Herston Rd, Brisbane, Queensland 4006, Australia  GPG 4D0B994A
>

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