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

[David Winsemius]

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.
>>
>>
>> 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
>>>
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