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 8% had Type 1. (my TD covariate is reference for the type of diabetes) In the attach is the description of the data. Cheers, Ana On Tue, Sep 15, 2020 at 7:59 PM David Winsemius <dwinsem...@comcast.net> wrote: > > > 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 Plink is of no > > help there. > > > > I did Pearson correlation analysis for P values which I got in > > regression with and without my covariate of interest and I got this: > > > >> cor.test(tt$P_TD, tt$P_noTD, method = "pearson", conf.level = 0.95) > > Pearson's product-moment correlation > > > > data: tt$P_TD and tt$P_noTD > > t = 20.17, df = 283, p-value < 2.2e-16 > > alternative hypothesis: true correlation is not equal to 0 > > 95 percent confidence interval: > > 0.7156134 0.8117108 > > sample estimates: > > cor > > 0.7679493 > > > > I can see the p values are very correlated in those two instances. Can > > I conclude that my covariate then doesn't have a huge effect or what > > kind of conclusion I can draw from that? > > > I do not think it follows from the correlation of p-values that your > covariate "does not have a huge effect". P-values are not really data, > although they are random values. A simulation study of this would > require a much better description of the original dataset. Again, that > is something that the users of Plink are more likely to be able to > intuit than are we. I still do not see why this question is not being > addressed to the users of the software from which you are deriving your > "data". > > > -- > > David. > > > > > Thanks for all your help > > Ana > > > > > > > > On Tue, Sep 15, 2020 at 1:26 AM David Winsemius <dwinsem...@comcast.net> > > wrote: > >> 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 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: > >>> > >>> #CHROM POS ID REF ALT A1 TEST OBS_CT BETA > >>> SE Z_OR_F_STAT P ERRCODE > >>> 10 135434303 rs11101905 G A A ADD 11863 > >>> -0.110733 0.0986981 -1.12193 0.261891 . > >>> 10 135434303 rs11101905 G A A DOMDEV 11863 > >>> 0.079797 0.111004 0.718868 0.472222 . > >>> 10 135434303 rs11101905 G A A sex=Female > >>> 11863 -0.120404 0.0536069 -2.24605 0.0247006 . > >>> 10 135434303 rs11101905 G A A age 11863 > >>> 0.00524501 0.00391528 1.33963 0.180367 . > >>> 10 135434303 rs11101905 G A A PC1 11863 > >>> -0.0191779 0.0166868 -1.14928 0.25044 . > >>> 10 135434303 rs11101905 G A A PC2 11863 > >>> -0.0269939 0.0173086 -1.55957 0.118863 . > >>> 10 135434303 rs11101905 G A A PC3 11863 > >>> 0.0115207 0.0168076 0.685448 0.493061 . > >>> 10 135434303 rs11101905 G A A PC4 11863 > >>> 9.57832e-05 0.0124607 0.0076868 0.993867 . > >>> 10 135434303 rs11101905 G A A PC5 11863 > >>> -0.00191047 0.00543937 -0.35123 0.725416 . > >>> 10 135434303 rs11101905 G A A PC6 11863 > >>> -0.0103309 0.0159879 -0.646172 0.518168 . > >>> 10 135434303 rs11101905 G A A PC7 11863 > >>> 0.00790997 0.0144025 0.549207 0.582863 . > >>> 10 135434303 rs11101905 G A A PC8 11863 > >>> -0.00205639 0.0142709 -0.144096 0.885424 . > >>> 10 135434303 rs11101905 G A A PC9 11863 > >>> -0.00873771 0.0057239 -1.52653 0.126878 . > >>> 10 135434303 rs11101905 G A A PC10 11863 > >>> 0.0116197 0.0123826 0.938388 0.348045 . > >>> 10 135434303 rs11101905 G A A TD 11863 > >>> -0.670026 0.0962216 -6.96337 3.32228e-12 . > >>> 10 135434303 rs11101905 G A A array=Biobank > >>> 11863 0.160666 0.073631 2.18205 0.0291062 . > >>> 10 135434303 rs11101905 G A A HBA1C 11863 > >>> 0.0265933 0.00168758 15.7583 6.0236e-56 . > >>> 10 135434303 rs11101905 G A A GENO_2DF 11863 > >>> NA NA 0.726514 0.483613 . > >>> > >>> This results is shown just for one ID (rs11101905) there is about 2 > >>> million of those in the resulting file. > >>> > >>> 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" > >>> and than do scatter plot of P values with and without "TD" covariate > >>> or there is a better way to do this from the data I already have? > >>> > >>> Thanks > >>> Ana > >>> > >>> ______________________________________________ > >>> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > >>> https://stat.ethz.ch/mailman/listinfo/r-help > >>> PLEASE do read the posting guide > >>> http://www.R-project.org/posting-guide.html > >>> and provide commented, minimal, self-contained, reproducible code.
______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.