Thank you for writing such a thoughtful comment. I have to confess, I probably gave this post the wrong title. For the longest time I simply titled it “Genetic Engineering Part 3” as I wasn’t sure what to call it when I first started. I then accidentally left that title in when I first published it and hastily changed it to its current title even though that doesn’t quite fit either.
You’re correct, of course, that I did not comprehensively review all possible techniques for genetic engineering. Most notably among these is whole-genome synthesis, with which we could theoretically create an entire genome with any base pairs we wanted. In my research I estimated that synthesizing a whole human genome from scratch would cost about $200 million. So we still have a few orders of magnitude to go before whole genome sequencing becomes a viable method for creating superhumans.
I also have some serious concerns about other much more dangerous uses of whole-genome synthesis. If the technology becomes cheap enough and widely enough available it could become an incredibly dangerous weapon for engineering biological weapons. This is such a big worry that I think pursuing human genetic modification via genome synthesis might actually end up INCREASING the risk of human extinction rather than decreasing it.
Regarding the first, one may doubt GWAS on the grounds of reliability (false positives) and power (not enough variance accounted for)
If there were false positives in a GWAS then the model would have poor performance on the test set. Of course there ARE issues with GWAS predictive power when you try to generalize to other populations with a high ancestral distance from your training set. For example I remember reading about a GWAS for general cognitive ability that predicted about 10% of variance in Europeans, but only 2.5% for people of African descent. However that isn’t an issue of false positives. It’s an issue of different genes having different frequencies in each population. We could create a good predictor for people of African descent if we had data sets that included more people from those populations.
Regarding the second, one would like to know that this process isn’t creating e.g. some cumulative epigenetic artefact.
This is something I didn’t even think about when writing the paper, so thanks for bringing it up. I would think that the epigenome would be preserved throughout this process, but that assumption might be wrong.
Thank you for writing such a thoughtful comment. I have to confess, I probably gave this post the wrong title. For the longest time I simply titled it “Genetic Engineering Part 3” as I wasn’t sure what to call it when I first started. I then accidentally left that title in when I first published it and hastily changed it to its current title even though that doesn’t quite fit either.
You’re correct, of course, that I did not comprehensively review all possible techniques for genetic engineering. Most notably among these is whole-genome synthesis, with which we could theoretically create an entire genome with any base pairs we wanted. In my research I estimated that synthesizing a whole human genome from scratch would cost about $200 million. So we still have a few orders of magnitude to go before whole genome sequencing becomes a viable method for creating superhumans.
I also have some serious concerns about other much more dangerous uses of whole-genome synthesis. If the technology becomes cheap enough and widely enough available it could become an incredibly dangerous weapon for engineering biological weapons. This is such a big worry that I think pursuing human genetic modification via genome synthesis might actually end up INCREASING the risk of human extinction rather than decreasing it.
If there were false positives in a GWAS then the model would have poor performance on the test set. Of course there ARE issues with GWAS predictive power when you try to generalize to other populations with a high ancestral distance from your training set. For example I remember reading about a GWAS for general cognitive ability that predicted about 10% of variance in Europeans, but only 2.5% for people of African descent. However that isn’t an issue of false positives. It’s an issue of different genes having different frequencies in each population. We could create a good predictor for people of African descent if we had data sets that included more people from those populations.
This is something I didn’t even think about when writing the paper, so thanks for bringing it up. I would think that the epigenome would be preserved throughout this process, but that assumption might be wrong.