# [R] How to represent the effect of one covariate on regression results?

Ana Marija @okov|c@@n@m@r|j@ @end|ng |rom gm@||@com
Wed Sep 16 03:11:07 CEST 2020

```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.

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 <dwinsemius using 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 <dwinsemius using 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.
> >>
> >>
> >>
> >>
> >> --
> >>
> >> 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
> >>>
> >>> ______________________________________________
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