In general the response I think to the announcement of a polygenic-risk-score informed embryo selection has been right - one where the science is wrong, the clinical harm/benefit therefore also wrong, and one where ethical/societal considerations have to be folded in. However...
There are some people who say "but even if this is wrong now, it might not in the future" (true) and also "if genetics works, then this should work" often with some handwaving towards farm animal genetics/breeding/selection. In this twitter thread I'd like to tackle this.
(Context: I am a geneticist/genomicist. My two favourite organisms to study humans and Japanese rice paddy fish. I'm on the experiments/practical data science side, but have a pretty good understanding of the theory/stats side, partly because I've coded it myself/in my group)
So - the first point, saying no now doesn't mean no forever is true, but does show the need for good regulation, and there are at least 3 strands.
1. *can* one actually do something useful (science) 2. does the utility substantially outweigh harms (clinical) and 3. *should* we allow people to do this, if 1 and 2 are true (ethics and societal). 3 "trumps" 1 and 2, and 3 has to be decided by a societal process.
But lets go into 1 - science - can one actually do something useful now in theory? - if you have (say) 20 embryos, take one cell out, sequence each genome, and score not only for rare disease (which one does now for CFTR, other diseases) but also common via PRS?
One would then, as one does for pre-implantation genetic diagnosis, then implant a set of "good" embryos back into the mother (assumming she provided the eggs; the example that triggered this it was an egg donor with a surrogate mother - another layer of ethics).
First off, it's important to stress that the PRS for choosing between embryos is constrained by the parents (at least in the simple additive model from PRS); the parents already put bounds on what is feasible.
Second, PRSs are constructed to "predict" an average person presenting to a research/clinician, and in particular current PRSs accept they don't have the actual causal change in their model- it is linked to on average in the population. This is not the case in the embryo scenario
So we have an extra bit of uncertainity which is the linkage disequilibrium "trick" from the PRS chosen variants to the causal one, and if, for this parent, the causal variant is not there, no matter how many embryos one samples, the causal variant ... still wont be there.
Third we know the out-sample prediction (when we look at unknown people coming through the door, not how well the model thinks it is going to do) is worse than the model (no surprise) - its definitely not zero, but not worse.
The consequence of 1, 2 and 3 is that we can be confident that PRS for embryo selection is going to have far far more variance than PRS for out of sample in the population, itself worse than "variance explained".
In weighing up potential benefit vs potential harms, our potential benefit is clearly way down from our model readout.
This is *not* like farm animal breeding. In farm animal breeding the thing we are doing is selecting a parent (classically bull semen) on the basis of the often known and predicted score of their offspring for the trait.
Furthermore the ability to blend both genetics and observations of this genetics over multiple generations in the same environment means that we constrain the prediction problem on the parent alot.
But there's an even bigger issue - unintended consequences. This is best illustrated for me by the successful genomic selection on chicken layers (laying eggs) and broilers (for eating) which created great individual birds for both via genetics... but...
when these "Elite chickens" (this is the right term) became flocks of chickens, yields dropped. What happened? In this case, the selection for the these traits led to aggressive birds (I can't remember which one more) which was not being tracked.
Now, this is pretty extreme - multiple generations of selection on a narrowly focused trait - but it goes to the problem of these models which is that we don't know the universe of traits for a human that we care about.
There is a possibility that selecting for good attributes for one trait is going to have weird knock on impacts somewhere else.
Here rare disease is different as we know that the genetics is just bad and as the vast majority of the world doesn't have it, selecting the "correct" variant is selecting for something pretty much everyone has.
A valid question is could I *ever* see PRS for embryo selection happening sometime in the future? The closest I can imagine is the scenario you are doing PGD anyway (so the harms are accepted for rare disease genetic reasons) and a PRS with clear harms (eg, LDL cholesterol) >>
<< where the PRS was constructed from confirmed causal variants (no LD one-step-away with the embryo) and the PRS clearly did not have unintended consequences on a very very large number of traits. That latter one is a tough bar to get over; I can't imagine it this decade.
Circling back to original piece, and as I've said multiple times; I am positive about using PRSs in medicine in the right place, but this is *not* a good of PRS - for scientific reasons, for clinical reasons (I'm not an expert) and I'd also argue for ethical reasons.
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A reminder; in the UK this process would clearly fall under HFEA, and applications to do this would almost certainly be rejected on ethical / societal grounds, on clinical harm to benefit and underlying scientific validity
I’m very positive about the use of genomics in healthcare - many diverse uses and its growing - but I am firmly against this use on ethics, clinical (I’m not an expert) and science (I am an expert). Blogged on this in 2019 ewanbirney.com/2019/05/why-em…
I think the imperial weights thing in the UK is silly (deeply silly) but I do think there was more method in "12" units (and for that matter, 60). 12 is a nice number for division (halves, thirds, quarters, sixths) and then the next nice number for division is 60 (fifths).
Of course the pounds to stone (14!) and then madness of Guineas (I still don't really understand) doesn't fit this. On historical numerology, I was reminded of the arcane voting system for the Dodge in Venice that involves 11, 13s and 17s as supposed "hard to game" prime numbers
As well as the measuring unit changing depending on what you were measuring (this is another moment of deep madness) I think this use of effectively base 12 might be more about early medieaval maths and plenty of mental arithmetic.
After a great workshop in Paris (and hopefully, testing being ok, I will be returning next week) I've been thinking about my travel in the new normal, thinking about green (lowering carbon)
This has been informed by conversations with colleagues such as @embl's green officer, @BrenRouseHD, faculty colleagues such as @Alexbateman1, @PaulFlicek and Deltev Arendt (many thanks); these are currently my thoughts on this (insight from colleagues; missteps from me!)
First off pre-pandmic science travel was useful but often mad; flying for single meetings (sometimes in windowless rooms) with fast turn arounds. Not only was it carbon expensive but it was also bad for family life and just plain health.
As we enter yet another period of COVID uncertainity over outcomes (due mainly to human behaviour - what does "baseline/new normal" contacts look like in an European Autumn/Winter) a reminder about models. There are at least 3 different types; explanatory, forecast and scenario.
Explanatory - usually retrospective data to fit an understanding of the world (say infection->hospitalistion/not->death/discharge) for time series. Examples: excess deaths attributable to COVID, vaccine efficacy models and biological properties of variants.
Forecast - fit an up to date time series to understand outcomes in the near future, sometimes just to understand "now" (hence "nowcasting"). Examples: R rate and near time extrapolation; hospitalisation capacity near term management (often not public).
My annual reminder; if you propose doing large scale data gathering and analysis, just a *one sentence* power calculation, or "we have confidence this approach can provide robust results due to similar work of XXX in system YYY with similar sample sizes".
Why is this important in a grant? As a reviewer wont be able to fully check your power calculation (usually) but I do want to see that you are honest with *yourself* about whether the stats are going to work out. Too few samples, expected weak effects => it's never going to work
If your power calculation (which will always be pulling numbers out of the air for effect size etc; such is science) says its very unlikely you will find a credible result then... you need to reset your goals.
COVID thoughts from London, after a yesterday's day of slow cricket; TL;DR - the pandemic is reasonably predictable except for human behaviour on contacts; Europe will be likely navigating a complex winter; US (still) needs to vaccinate; The world needs more vaccines.
Context: I am an expert in genetics and computational biology. I know experts in viral genomics, infectious epidemiology, immunology and clinical trials. I have some COIs - I am a longestablished consultant to Oxford Nanopore and was on the Ox/Az vaccine trial.
Reminder: SARS-CoV-2 is a respiratory human virus where a subset of people infected get a horrible disease, COVID, often leading to death. Left unchecked many people would die and healthcare systems would have overflowed.