I want to applaud the author for loudly calling out the likelihood that their own data is bad and the reasons for that. You hardly ever see articles like this mention that, and if they do it's usually a quick aside.
I suspect this has something to do with the author's intentions. The author here has no reward attached to people believing her conclusions drawn from this data. Instead she may wish to just show people "look at this dumb cool thing I made" or she is using this to pitch her skills at potential recruiters, in which case honesty is a good policy to filter for good employers.
For scientists and commercial interests, the quality of the data could be fundamental to the point they are trying to make. So admitting their data sucks would basically ruin their whole argument, or at least make people more skeptical about the conclusions drawn. In science, the bad data eventually gets called out and everyone else is left wondering why the miracle panacea for discovering the genetic basis of complex disease still hasn't solved schizophrenia.
For scientists and commercial interests, the quality of the data could be fundamental to the point they are trying to make. So admitting their data sucks would basically ruin their whole argument, or at least make people more skeptical about the conclusions drawn. In science, the bad data eventually gets called out and everyone else is left wondering why the miracle panacea for discovering the genetic basis of complex disease still hasn't solved schizophrenia.