We have no reason to question the technical accuracy of the papers, nor their relevance to biomedical and other genetics, but there are reasons to assert that this is nothing newly discovered, and that the story misses the really central point that should, I think, be undermining the expensive Big Data/GWAS approach to biological causation.
First, for some years now there have been reports of samples of individual humans (perhaps also of yeast, but I can't recall specifically) in which both copies of a gene appear to be inactivated. The criteria for saying so are generally indirect, based on nonsense, frameshift, or splice-site mutations in the protein code. That is, there are other aspects of coding regions that may be relevant to whether this is a truly thorough search to see that whatever is coded really is non-functional. The authors mention some of these. But, basically, costly as it is, this is science on the cheap because it clearly only addresses some aspects of gene functionality. It would obviously be almost impossible to show either that the gene was never expressed or never worked. For our purposes here, we need not question the finding itself. The fact that this is not a first discovery does raise the question why a journal like Nature is so desperate for Dramatic Finding stories, since this one really should be instead a report in one of many specialty human genetics journals.
Secondly, there are causes other than coding mutations for gene inactivation. They have to do with regulatory sequences, and inactivating mutations in that part of a gene's functional structure is much more difficult, if not impossible, to detect with any completeness. A gene's coding sequence itself may seem fine, but its regulatory sequences may simply not enable it to be expressed. Gene regulation depends on epigenetic DNA modification as well as multiple transcription factor binding sites, as well as the functional aspects of the many proteins required to activate a gene, and other aspects of the local DNA environment (such as RNA editing or RNA interference). The point here is that there are likely to be many other instances of people with complete or effectively complete double knockouts of genes.
Thirdly, the assertion that these double KOs have no effect depends on various assumptions. Mainly, it assumes that the sampled individuals will not, in the future, experience the otherwise-expected phenotypic effects of their defunct genes. Effects may depend on age, sex, and environmental effects rather than necessarily being a congenital yes/no functional effect.
Fourthly, there may be many coding mutations that make the protein non-functional, but these are ignored by this sort of study because they aren't clear knockout mutations, yet they are in whatever data are used for comparison of phenotypic outcomes. There are post-translational modification, RNA editing, RNA modification, and other aspects of a 'gene' that this is not picking up.
Fifthly, and by far most important, I think, is that this is the tip of the iceberg of redundancy in genetic functions. In that sense, the current paper is a kind of factoid that reflects what GWAS has been showing in great, if implicit, detail for a long time: there is great complexity and redundancy in biological functions. Individual mapped genes typically affect trait values or disease risks only slightly. Different combinations of variants at tens, hundreds, or even thousands of genome sites can yield essentially the same phenotype (and here we ignore the environment which makes things even more causally blurred).
Sixthly, other samples and certainly other populations, as well as individuals within the Pakistani data base, surely carry various aspects of redundant pathways, from plenty of them to none. Indeed, the inbreeding that was used in this study obviously affects the rest of the genome, and there's no particular way to know in what way, or more importantly, in which individuals. The authors found a number of basically trivial or no-effect results as it is, even after their hunt across the genome. Whether some individuals had an attributable effect of a particular double knockout is problematic at best. Every sample, even of the same population, and certainly of other populations, will have different background genotypes (homozygous or not), so this is largely a fishing expedition in a particular pond that cannot seriously be extrapolated to other samples.
Finally, this study cannot address the effect of somatic mutation on phenotypes and their risk of occurrence. Who knows how many local tissues have experienced double-knockout mutations and produced (or not produced) some disease or other phenotype outcome. Constitutive genome sequencing cannot detect this. Surely we should know this very inconvenient fact by now!
Given the well-documented and pervasive biological redundancy, it is not any sort of surprise that some genes can be non-functional and the individual phenotypically within a viable, normal range. Not only is this not a surprise, especially by now in the history of genetics, but its most important implication is that our Big Data genetic reductionistic experiment has been very successful! It has, or should have, shown us that we are not going to be getting our money's worth from that approach. It will yield some predictions in the sense of retrospective data fitting to case-control or other GWAS-like samples, and it will be trumpeted as a Big Success, but such findings, even if wholly correct, cannot yield reliable true predictions of future risk.
Does environment, by any chance, affect the studied traits? We have, in principle, no way to know what environmental exposures (or somatic mutations) will be like. The by now very well documented leaf-litter of rare and/or small-effect variants plagues GWAS for practical statistical reasons (and is why usually only a fraction of heritability is accounted for). Naturally, finding a single doubly inactivated gene may, but by no means need, yield reliable trait predictions.
By now, we know of many individual genes whose coded function is so proximate or central to some trait that mutations in such genes can have predictable effects. This is the case with many of the classical 'Mendelian' disorders and traits that we've known for decades. Molecular methods have admirably identified the gene and mutations in it whose effects are understandable in functional terms (for example, because the mutation destroys a key aspect of a coded protein's function). Examples are Huntington's disease, PKU, cystic fibrosis, and many others.
However, these are at best the exceptions that lured us to think that even more complex, often late-onset traits would be mappable so that we could parlay massive investment in computerized data sets into solid predictions and identify the 'druggable' genes-for that Big Pharma could target. This was predictably an illusion, as some of us were saying long ago and for the right reasons. Everyone should know better now, and this paper just reinforces the point, to the extent that one can assert that it's the political economic aspects of science funding, science careers, and hungry publications, and not the science itself, that leads to the persistence of drives to continue or expand the same methods anyway. Naturally (or should one say reflexively?), the authors advocate a huge Human Knockout Project to study every gene--today's reflex Big Data proposal.**
Instead, it's clearly time to recognize the relative futility of this, and change gears to more focused problems that might actually punch their weight in real genetic solutions!
** [NOTE added in a revision. We should have a wealth of data by now, from many different inbred mouse and other animal strains, and from specific knockout experiments in such animals, to know that the findings of the Pakistani family paper are to be expected. About 1/4 to 1/3 of knockout experiments in mice have no effect or not the same effect as in humans, or have no or different effect in other inbred mouse strains. How many times do we have to learn the same lesson? Indeed, with existing genomewide sequence databases from many species, one can search for 2KO'ed genes. We don't really need a new megaproject to have lots of comparable data.]
In the post, I mentioned the background inbreeding that must be in the same data (but not leading to detectable double KOs), and of course each person's inbreeding homozygosity will affect different genome regions. Thinking rather cursorily, this likely will have no systematic effect, but on average will reduce the level of heterozygosity and hence of potential functional redundancy, in the population. This, if correct, would mean that the level of redundancy that effectively protects against gene loss, is enough to be robust to loss of variation due to this genomewide inbreeding effect.
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