New subject: "dummy variable" in mri-glmfit

2 Dec 2009


      Hi Doug and Nick,
thanks for your quick answer. I think that the assumption of linearity can often be made in genetics and in large GWAS studies the coding is done as suggested by Nick. However, Nick's solution does not suit this particular case since one of the homozygous groups has less than 10 subjects which, in my opinion, might skew our results. Therefore we would like to lump homozygotes and the carriers together and have 2 groups (instead of 3).
Unfortunately I and Stefan Brauns do not understand your reply, Doug. We were suggesting to include the binary variable for genotype as a "variable" ( covariate) instead of a "class" (factor) in the FSGD. This would enable us to get around the problem of "small cell sizes".  Why do you say this is not possible with an FSGD?
We would just specify
Variables age SNP
and SNP would be 0 and 1 instead of 0, 1 and 2 (as suggested by Nick)
This is how the header would look like:
GroupDescriptorFile 1                 
Title    rs8216888_status             
MeasurementName thickness                 
Class SCZMALEMGH                 
Class SCZFEMALEMGH                 
Class HCMALEMGH                 
Class HCFEMALEMGH                 
Class SCZMALEIA                 
Class SCZFEMALEIA                 
Class HCMALEIA                 
Class HCFEMALEIA                 
Class SCZMALEUMN                 
Class SCZFEMALEUMN                 
Class HCMALEUMN                 
Class HCFEMALEUMN                 
Class SCZMALEUNM                 
Class SCZFEMALEUNM                 
Class HCMALEUNM                 
Class HCFEMALEUNM                 
Variables age SNP
In contrast, if we would include it as a class - we would have 32 instead of 16 classes and then "Variables age"
The question is if it violates some assumptions if we specify a "variables" (covariate) which is binary.
Many thanks, Stefan
Message: 6
Date: Wed, 02 Dec 2009 13:18:20 -0500
From: Douglas N Greve greve@nmr.mgh.harvard.edu
Subject: Re: [Freesurfer] "dummy variable" in mri-glmfit
To: Stefan Brauns stefan.brauns@googlemail.com
Cc: freesurfer freesurfer@nmr.mgh.harvard.edu
Message-ID: 4B16AF6C.9080103@nmr.mgh.harvard.edu
Content-Type: text/plain; charset=UTF-8; format=flowed
Do you mean having just another column in your design matrix with 0s and 
1s? You can do this, but not with an FSGD. You'll have to supply your 
own matrix. An easy way to do this would be to run mri_glmfit with and 
FSGD without the genotype. This will create a matrix Xg.dat in the 
output dir, then just modify that matrix and pass it to a new call to 
mri_glmfit
doug
Stefan Brauns wrote:
...
Hi there,
we would like to test the effect of a binary variable (genotype 
= carrier vs. homozygous) on cortical thicknes in mri-glmfit. Since we 
are also controlling for gender and aquisition site (4 sites) we 
already have 16 groups. In order to control for age as a covariate we 
need at least 2 subjects per group to be able to estimate an age slope.
If we include the aforementioned binary variable (genotype) as a 
factor (two different "groups"), we would have 32 groups and 
unfortunately not enough subjects per group.
Is it possible to include binary variables ("dummy variable" coded as 
0 and 1) such as genotype or gender as covariates (slope), in order to 
reduce the number of groups and examine the effect on thickness? In 
simple regression this would not affect the results - what would we 
expect here?
Many thanks,
Stefan