Do you think that bioinformatics is over-hyped as a field? What are the biggest issues preventing it to become relevant?

I should have been more specific. I was mostly thinking of sequence analysis like the PhD candidate in the OP wants to do. Bioinformatics in general covers a lot of ground which is directly clinical in nature, like image analysis.

Sequence analysis has had an important role to play in genome annotation and comparison, but the recent (extremely expensive) attempts to apply it to the genetics of human disease have been quite disappointing so far. The biggest technical issues are firstly that our tools for measuring genotypes are too imprecise and too expensive to support experiments which will nail down exactly which variants are contributing to a given disease and secondly that almost all related studies of human disease so far are epidemiological in nature, because you can't do experiments on actual humans (and I'm not arguing that you should be able to. :-) This means it is very difficult to establish a variant as causative, and hard to design reliably repeatable experiments.

The political issues arise out of health research funding being fundamentally motivated by hope and fear, leading to massive commitments to research strategies which are wishfully ignorant of the above technical issues. This is followed by a lot of wishful ignorance of the strategies' failures by the groups who implemented those commitments. Usually this ignorance comes from moving the goal posts post hoc. The current sunset of GWAS is a good example. (See Peter Visscher's article in the January issue of AJHG for example.)

The technical issues are being worked out. Better genotyping will be developed, and at least some disease biology can be modeled in animals or cultured tissue (cancer might be good for this.) But those issues have been ignored for too long, at tremendous cost, because the serious funding rewards such ignorance. For instance, essentially the same strategy as GWAS is now being pursued on a huge scale, just using a different genotyping technology. No one has run an initial small-scale experiment validating this new approach, it's just based on the hope that the extra information provided by the new technology will provide the ingredient that the studies to date have been missing.

Thanks for the excellent response. I can imagine how misguided over-generous funding can accelerate research to wrong direction. If I interpreted it correctly, one of the biggest weakness is that not enough effort is to put to verify findings of genome analysis in real biological setup. Is it because funding for genome sequencing and analysis has grown in over-proportion compared to more traditional clinical research or that the need for this is not realized in genome analysis circles or something else?

Btw. Can you elaborate on the following excerpt. Is there specific problems in genome analysis techniques that make it harder to nail down correct variants?

our tools for measuring genotypes are too imprecise and too expensive to support experiments which will nail down exactly which variants are contributing to a given disease