Hi David,
thanks for the useful insight I did of course wrote to plink user
group but no answer there. I guess they are more concerned about how
to run commands with plink as oppose to interpret results.
What I can tell about my cohort is that about 80% of cases had Type 2
diabetes while about
On 9/15/20 8:57 AM, Ana Marija wrote:
Hi Abby and David,
Thanks for the useful tips! I will check those.
I completed the regression analysis in plink (as R would be very slow
for my sample size) but as I mentioned I need to determine the
influence of a specific covariate in my results and
> My question is how do I present/plot the effect of covariate "TD" in
> the example it has "P" equal to 3.32228e-12 for all IDs in the
> resulting file so that I show how much effect covariate "TD" has on
> the analysis. Should I run another regression without covariate "TD"
I'll take a second
Hi Abby and David,
Thanks for the useful tips! I will check those.
I completed the regression analysis in plink (as R would be very slow
for my sample size) but as I mentioned I need to determine the
influence of a specific covariate in my results and Plink is of no
help there.
I did Pearson
There is a user-group for PLINK, easily found by looking at the page you
cited. This is not the correct place to submit such questions.
https://groups.google.com/g/plink2-users?pli=1
--
David.
On 9/14/20 6:29 AM, Ana Marija wrote:
Hello,
I was running association analysis using --glm
I'm wondering if you want one of these:
(1) Plots of "Main Effects".
(2) "Partial Residual Plots".
Search for them, and you should be able to tell if they're what you want.
But a word of warning:
Many people (including many senior statisticians) misinterpret this
kind of information.
Because,
Hello,
I was running association analysis using --glm genotypic from:
https://www.cog-genomics.org/plink/2.0/assoc with these covariates:
sex,age,PC1,PC2,PC3,PC4,PC5,PC6,PC7,PC8,PC9,PC10,TD,array,HBA1C. The
result looks like this:
#CHROMPOSIDREFALTA1TESTOBS_CT
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