Question: Should Covariates be log transformed in a GWAS study
1
gravatar for mattmacknev
4.0 years ago by
mattmacknev10
United Kingdom
mattmacknev10 wrote:

Hi, I am analysing some quantitate traits on an Exome Chip SNP dataset in PLINK and RareMetalWorker.  I am Ln transforming my Quantative traits but I  am not sure whether I should do the same for my covariates.  For example,  If I am looking at regional fat distribution, age and % total-fatmass should be added as Covariates to adjust for overall fatness and increased fat with age.  The regional fat measure is Ln transformed, so should I therefore also Ln transform my %Total-fatmass covariate?, What about Age? 

 

 

ADD COMMENTlink modified 16 months ago by Kevin Blighe48k • written 4.0 years ago by mattmacknev10

Hi, I have the same question. Did you find out that? Could you explain the problem to me? Thx!

ADD REPLYlink written 24 months ago by jiangjing_ing0
1
gravatar for Kevin Blighe
16 months ago by
Kevin Blighe48k
Kevin Blighe48k wrote:

As I mention here: A: Log-tranformation and GWAS

You don't have to log everything. You just have to ensure that each variable has a distribution that is suitable to the assumptions of the statistical test that you are aiming to employ. Usually that means that they should be normally distributed. You do not have to log everything, though.

Kevin

ADD COMMENTlink modified 12 months ago • written 16 months ago by Kevin Blighe48k
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