I can only cross my fingers that we have enough time before AGI for this to become an actual concern.

There’s so much variance in the human gene pool that people on the left tail won’t gain much more than those on the right tail.

I haven’t done the simulations to figure out how large the reduction would be, but there’s like 1700 IQ points worth of additive variance in the human gene pool. It would take a truly stupid amount of selection to make a dent in that.

Interesting. I took Lumina around December and have noticed no change, though I don't think I've actually been hungover during that time period. I definitely didn't notice any difference in the AMOUNT needed to produce a hangover; I drunk a substantial amount and didn't get hangovers any more easily.

To the extent that transposon sequences affect traits and risks but can't be measured we should expect that be reflected in "missing heritability".

You may very well be right that highly repetitive sequences like transposons do affect traits, but that's accounted for.

Thanks a lot for the comment. I'll amend the post with some of this information in the next week. If your numbers are correct (and I have no current reason to doubt them, that substantially increases my estimate of the effectiveness of whole genome embryo sequencing.

I've been meaning to write a whole post about the different screening companies but a combination of little time due to starting a new company and a lack of clear data have preventing me from doing so thus far. With this information I might reconsider.

One more thing I'd like to ask at some point is whether you're going to publish the AUCs of all the predictors in your panel within some reference population. That would be extremely helpful for patients trying to compare Orchid vs Genomic Prediction or any other company.

I am just now learning the origin of the quokka meme. The first and only time I ever saw the reference was with no explanation when someone posted this meme on Twitter

Who do you recommend asking to be a reader?

That's a difficult question. I always tell readers that the number one thing I'm interested in is where they got bored and stopped reading. I ask them to be brutally honest and not feel like they need to keep reading to flatter my ego or because they are afraid of being harsh on me.

If they aren't interested in the topic in the first place it's harder. You need to be able to at least find an audience that is interested in sitting down to read it. Can you like join a hobbyist club for this stuff, or find a subreddit for it?

Here's a kind of galaxy-brained idea that might just work for finding your crowd:

• Go onto reddit and find the subreddit community closest to the thing you're interested in/writing about
• Go to https://subredditstats.com and enter the name of that subreddit to see which communities it has the most overlap with.
• Go to meetup.com and see if you can find a local group dedicated to one of those related topics (or better yet, the topic itself)
• Go to the meetup, pitch your thing, and see if people are into it. Maybe just TALK about what you've written first and if people seem interested offer to send them what you've written.

If you decide to actually give the above a shot, tell me how it goes. I'd be very interested to hear whether my idea works.

https://www.nature.com/articles/s41598-020-69927-7

This is one of the better papers I know of examining sibling validation. To quote from the article:

Sibling comparisons are a powerful method with which to validate genomic prediction in humans. Siblings (i.e., children who share the same mother and father) have typically experienced similar environments while growing up: family social status, exposure to toxins, diet, climate, etc. all tend to be similar3,4. Furthermore, siblings are concordant for ancestry and display negligible differences in population structure.

1. If environmental conditions in a specific region, such as, e.g., Northern England, affect disease risk, the predictor trained on UK data might assign nonzero effect sizes to SNPs associated with ancestries found in that region—i.e., the predictor learns to use population structure correlated to environmental conditions. These specific SNPs are correlated to disease risk for environmental reasons, but might not have any connection to genetic mechanisms related to the disease. They likely have little power to differentiate between siblings, who experienced similar family conditions and have have identical ancestry.
2. It is also possible that some SNP variants affect nurture (the way that parents raise their children). These SNPs could affect the child phenotype via an environmental mechanism under parental control, not a biochemical pathway within the child. This is sometimes referred to as a genetic nurture effect9,10,11,12,13. Note, siblings raised together would both be affected by parental genetic nurture variants, so these effects are weakened in family designs.

Sibling comparisons reduce the impact of factors such as those described above. We expect some reduction in power when predictors trained in a population of non-sibling individuals are tested among sibs. Sibling validation likely yields a better estimate of truly causal genetic effects. A more complicated measure of familial relatedness might lead to even better results14, but we restrict our analyses here to siblings.

There's more in the paper if you care to take a look.

A relevant tweet from Nate Silver on the methodology used to conduct the survey:

This is not a scientific way to do a survey. The biggest issue is that it involved personalized outreach based on a totally arbitrary set of criteria. That's a huge no-no. It also, by design, had very few biosafety or biosecurity experts.

The tweet has some screenshots of relevant parts of the paper