new version of the collaborative Post-GWAS article published

There are some recent news about the initiative of the collaborative article on Post-GWAS analysis launched last December[1]. It seems that a new version of the manuscript has been published on Nature Precedings (link), a few weeks earlier this month.

Well, in the end, with the exception of one figure, they did not include almost anything from what has been contributed in the wiki (I still have to check carefully). They thank the contributers in the acknowledgment section, leaving a link to the wiki page, but saying that these have not been included for reasons of space.

From a certain point of view, I understand why they didn’t include the feedback from the wiki. I recognize that the last days of the initiative have been confused. Since it was not clear how to proceed and how to edit the document, most people ended up editing the document during the last day before the submission deadline. So many changes have created so much confusion that the final version was very difficult to read.

On the other side, I think that, while the idea of collaborative editing was very good (in fact I copied it for the ‘Ten Simple Rules’ article), the authors of the original manuscript have lost an opportunity. They were never very active in following the initiative and answering questions from contributers. For the most of the time, contributers were left in a uncertain state where we didn’t know whether the changes we were proposing were useful and if the authors agreed with them. There it has been the potential for some very useful discussions, but, in short, the authors of the original manuscript did not answer :-(. It is frustrating to propose something and to not receive an answer.

From the scientific point of view, I am still convinced that the manuscript lacks a section dedicated to bioinformatics tools. We are no longer in the era where experimental and computational approaches can be considered as separate. Bioinformatics tools can save time and simplify experiments, and it is important to stress that each result obtained from a study must be integrated with the rest of the scientific knowledge in the databases. Moreover, they should at least have cited computational approaches like SNP prioritization (which aims at finding the exact SNPs associated with a disease from the result of a GWAS analysis) and the candidate gene approach.

What can we learn from this experience is, for me, that each collaboratively edited document (I hope there it will be others in the future) needs a mailing list or a channel of communication, where new and old authors can discuss the points to add.


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