text: user4595: I have a qqplot of my whole genome sequencing data; A plot is for showing possibly significant driver genes. I tried to read about qqplot though but people only say about the skewedness while I want to know from these two genes which one are more likely to be driver
  
  
  user3848: This looks to be a pretty good Q-Q plot. My recollection of doing Q-Q plots is that a good Q-Q requires a "more or less" linear relationship for the model to be considered okay. Again this model looks good, its 1:1, albeit it does deviate a little (for the gene of interest). TP15 falls outside the 1:1.
  @Devon Ryan states TP15 has good biological credentials, which is clearly important. It maybe worth checking  MAP15 because this is "more or less" within your model (for my Q-Q plots definitely within the model :-) ). 
  In summary, its just my opinion - that on the basis of the Q-Q plot the driver is the not the expected candidate.
  
  user48: You can't know which is the driver and which is the carrier. At most you can say that a specific gene deviate more of the expected underlying hypothesis. See also other resources online.
  You also seem to ignore other genes that deviate more of your null hypothesis. I recommend to plot the histogram of the p-values to see if the distribution is uniform
  
  user77: Given that TP53 is the most significant and is already known to have driver mutations in cancer it would seem to be the logical choice.
  
original_text: 4595: <p>I have a qqplot of my whole genome sequencing data; A plot is for showing possibly significant driver genes. I tried to read about qqplot though but people only say about the skewedness while I want to know from these two genes which one are more likely to be driver</p>
  
  <p><a href="https://i.stack.imgur.com/7Otcg.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/7Otcg.png" alt="enter image description here"></a></p>
   3848: <p>This looks to be a pretty good Q-Q plot. My recollection of doing Q-Q plots is that a good Q-Q requires a "more or less" linear relationship for the model to be considered okay. Again this model looks good, its 1:1, albeit it does deviate a little (for the gene of interest). TP15 falls outside the 1:1.</p>
  
  <p>@Devon Ryan states TP15 has good biological credentials, which is clearly important. It maybe worth checking  MAP15 because this is "more or less" within your model (for my Q-Q plots definitely within the model :-) ). </p>
  
  <p>In summary, its just my opinion - that on the basis of the Q-Q plot the driver is the not the expected candidate.</p>
   48: <p>You can't know which is the driver and which is the carrier. At most you can say that a specific gene deviate more of the expected underlying hypothesis. See also <a href="https://stats.stackexchange.com/a/101290/105234">other</a> <a href="http://onlinestatbook.com/2/advanced_graphs/q-q_plots.html" rel="nofollow noreferrer">resources</a> <a href="https://support.sas.com/documentation/cdl/en/procstat/63104/HTML/default/viewer.htm#procstat_univariate_sect040.htm" rel="nofollow noreferrer">online</a>.</p>
  
  <p>You also seem to ignore other genes that deviate more of your null hypothesis. I recommend to plot the histogram of the p-values to see if the distribution is uniform</p>
   77: <p>Given that TP53 is the most significant and is already known to have driver mutations in cancer it would seem to be the logical choice.</p>
  