During the past couple of years I have been asked these questions by several department chairs and other senior statisticians interested in hiring or promoting faculty working in genomics. The main difficulty stems from the fact that we (statisticians working in genomics) publish in journals outside the mainstream statistical journals. This can be a problem during evaluation because a quick-and-dirty approach to evaluating an academic statistician is to count papers in the Annals of Statistics, JASA, JRSS and Biometrics. The evaluators feel safe counting these papers because they trust the fellow-statistician editors of these journals. However, statisticians working in genomics tend to publish in journals like Nature Genetics, Genome Research, PNAS, Nature Methods, Nucleic Acids Research, Genome Biology, and Bioinformatics. In general, these journals do not recruit statistical referees and a considerable number of papers with questionable statistics do get published in them. **However, **when the paper’s main topic is a statistical method or if it heavily relies on statistical methods, statistical referees are used. So, if the statistician is the corresponding or last author and it’s a stats paper, it is OK to assume the statistics are fine and you should go ahead and be impressed by the impact factor of the journal… it’s not east getting statistics papers in these journals.

But we really should not be counting papers blindly. Instead we should be reading at least some of them. But here again the evaluators get stuck as we tend to publish papers with application/technology specific jargon and show-off by presenting results that are of interest to our potential users (biologists) and not necessarily to our fellow statisticians. Here all I can recommend is that you seek help. There are now a handful of us that are full professors and most of us are more than willing to help out with, for example, promotion letters.

So why don’t we publish in statistical journals? The fear of getting scooped due to the slow turnaround of stats journals is only one reason. New technologies that quickly became widely used (microarrays in 2000 and nextgen sequencing today) created a need for data analysis methods among large groups of biologists. Journals with large readerships and high impact factors, typically not interested in straight statistical methodology work, suddenly became amenable to publishing our papers, especially if they solved a data analytic problem faced by many biologists. The possibility of publishing in widely read journals is certainly seductive.

While in several other fields, data analysis methodology development is restricted to the statistics discipline, in genomics we compete with other quantitative scientists capable of developing useful solutions: computer scientists, physicists, and engineers were also seduced by the possibility of gaining notoriety with publications in high impact journals. Thus, in genomics, the competition for funding, citation and publication in the top scientific journals is fierce.

Then there is funding. Note that while most biostatistics methodology NIH proposals go to the Biostatistical Methods and Research Design (BMRD) study section, many of the genomics related grants get sent to other sections such as the Genomics Computational Biology and Technology (GCAT) and Biodata Management and Anlayis (BDMA) study sections. BDMA and GCAT are much more impressed by Nature Genetics and Genome Research than JASA and Biometrics. They also look for citations and software downloads.

To be considered successful by our peers in genomics, those who referee our papers and review our grant applications, our statistical methods need to be delivered as software and garner a user base. Publications in statistical journals, especially those not appearing in PubMed, are not rewarded. This lack of incentive combined with how time consuming it is to produce and maintain usable software, has led many statisticians working in genomics to focus solely on the development of practical methods rather than generalizable mathematical theory. As a result, statisticians working in genomics do not publish much in the traditional statistical journals. You should not hold this against them, especially if they are developers and maintainers of widely used software.