Gene mapping: More Monty Python than Monty Python

Here’s a link to a famous Monty Python sketch on gene mapping.  We ask you to decide whether this is funnier than the daily blast of GWAS reports and their proclaimed transformative findings: which is more Monty than the full Monty.

Why we keep spending money on papers that keep showing how MontyPythonish genomewide association with complex traits is, is itself a valid question.  To say, with a straight face, that we now know of hundreds, much less of thousands, of genomewide sites that affect some trait–in some particular sample of humans, with much or most of the estimated heritability yet unaccounted for, without saying that enough is enough, is almost in itself a comedy routine.

We have absolutely no reason–or, at least, no need–to criticize anything about individual mapping papers.  Surely there are false findings, mis-used statistical tests, and so on, but that is part of the normal life in science, because we don’t know everything and have to make assumptions, etc.  Some of the findings will be ephemeral, sample-specific, and so on.  That doesn’t make them wrong.  Instead, the critique should be aimed at authors who present such work with a straight face as if it is (1) important, (2) novel in any really novel way, and (3) not saying that the paper shows why, by now with so many qualitatively similar results, we should stop public funding of this sort of work.  We should move on to more cogent science that reflects, but doesn’t just repeat, the discovery of genomic causal (or, at least, associational) complexity.

The bottom line
What these studies show, and there is no reason to challenge the results per se, is that complex traits are not to be explained by simple, much less additive genetic models.  There is massive causal redundancy with similar traits due to dissimilar genotypes.  But this shouldn’t be a surprise.  Indeed, we can easily account for this in terms of evolutionary phenomena, both related to processes like gene duplication and the survival protection that alternative pathways provides.

Even if each GWAS ‘hit’ is correct and not some sort of artifact, it is unclear what the message is.  To us, who have no vested interest in continuing, open-ended GWAS efforts with ever-larger samples, the bottom line is that this is not the way to understand biological causation.

We reach that view on genomic considerations alone, without even considering the environmental and somatic mutation components of phenotype generation, though these are often obviously determinative (as secular trends in risk clearly show).  We reach this view without worrying about the likelihood that many or perhaps even most of these ‘hits’ are some sort of statistical, sampling, analytic or other artifact, or are so indirectly related to the measured trait, or so environment-dependent as to be virtually worthless in any practical sense.

What GWAS ignore
There are also three clear facts that are swept under the rug, or just ignored, in this sort of work.  One is somatic mutation, which are not detected in constitutive genomewide studies but could be very important (e.g., cancer).  The second is that DNA is inert and does something only in interaction with other molecules.  Many of those relate to environmental and lifestyle exposures, which candid investigators know are usually dreadfully inaccurately measured.  The third is that future mutations, not to mention future environments are unpredictable, even in principle.  Yet the repeatedly stressed objective of GWAS is ‘precision’ predictive medicine.  It sounds like a noble objective, but it’s not so noble given the known and knowable reasons these promises can’t be met.

So, if biological causation is complex, as these studies and diverse other sorts of direct and indirect evidence clearly show, then why can’t we pull the plug on these sorts of studies, and instead, invest in some other mode of thinking, some way to do focused studies where genetic causation is clear and real, rather than continuing to feed the welfare state of GWAS?

We’re held back by inertia, and the lack of better ideas, but another important if not defining constraint is that investigator careers depend on external funding and that leads to safe me-too proposals.  We should stop imitating Monty Python, and recognize that if the gene-causation question even makes sense, some new way of thinking about it is needed.