Antonio Regalado is providing us with a recent series of the arguably best informed (compared to Guardian, Nature news etc.) popular science articles on genomics and its relevance to modern eugenics (embryo selection or genetic engineering) as well as the group differences causation question.

https://www.technologyreview.com/s/609204/eugenics-20-were-at-the-dawn-of-choosing-embryos-by-health-height-and-more/

https://www.technologyreview.com/s/610251/forecasts-of-genetic-fate-just-got-a-lot-more-accurate/

https://www.technologyreview.com/s/610339/dna-tests-for-iq-are-coming-but-it-might-not-be-smart-to-take-one/

As background for some of these, I had an email conversation with him.

Thus, no one can effectively compare races by polygenic IQ score. I checked with scientists who I felt were likely to be interested in such a comparison, like @KirkegaardEmil , and they also explained it was not possible. — Antonio Regalado (@antonioregalado) April 3, 2018

Since I spent so much time preparing answers for his questions, I asked him if it would be fine if I posted them here as well, to which he agreed. The reason I spent so much time answering his questions is that I am dissatisfied with most of the coverage of genomics about these matters in popular outlets, and even in the generalist science ones. Antonio requested that I paraphrase him instead of direct quotes.

Antonio:

Antonio asks about the recent Plomin piece in Nature and the use of polygenic scores to explore causes of group differences.

Emil:

Hi Antonio, I take it that you’re thinking of Plomin & von Stumm 2018 and not Plomin & Deary 2015, also in Nature. Of course, assessing the impact of a paper that came out only a month and a half ago is difficult. The altmetrics provides some guidance, and it seems to be a popular paper, mostly due to Twitter activity. It’s in the 96th centile for papers of its age, so relatively speaking, it’s getting a lot of attention. I don’t know how much of that is just due to it being in Nature. For people in the field, the review is probably mostly useful for the impact it has on outsiders’ view of the field. For insiders, it breaks little new ground but provides a useful reference/summary one can point to. As for PGSs being used to explain the IQ gaps for ancestry clusters, as far as I know, there has been not too much recent progress here, owing to interpretational issues with PGSs when used between ancestry groups. This is due to the LD decay problem and the nature of the associations found in GWASs. These are mostly understood to be tag variants, not causal variants, meaning that they are variants close on the genome to the causal variant but don’t do anything themselves. They happen to be statistically linked (in LD) with the causal variant. However, these LD patterns — which variants are statistically linked to others — depends strongly on random drift and founder effects, and thus is quite different between ancestry groups (and increases as their genetic distance increases). See Zanetti and Weale 2016, and my review from 2017. Basically, this line of research awaits either better statistical methods that account for LD decay somehow, more dense arrays that result in closer tagging of variants and thus less LD decay, or the use of ancestrally mixed discovery samples which should also reduce LD decay (see Traylor and Lewis 2016). The PGSs as they are calculated right now cannot be used to examine sex differences directly because GWASs exclude the sex chromosomes. The autosome genetic variation mixes every generation, so it is not useful here. Any sex differences that may exist are likely due to sex hormonal influences on normal pathways in the body and would be tricky to work out. I don’t see PGSs being of much use here in the near future. As for siblings and other within family variation, GWASs sometimes use these as validation samples. This sets the bar high because between sibling variation also suffers from some LD decay (due to recombination), and the design effectively controls for any cryptic ancestry that wasn’t controlled earlier (using the usual PCA ancestry approach). For an example, consider the recent GWAS on risk behavior/tolerance (Linnér et al 2018). I don’t recall any IQ GWASs using siblings for their predictive validation, but there’s been at least one study for education: Domingue et al 2015. Furthermore, in an independent sample, the same approach was used for both IQ and education, though strangely an effect was only detected for IQ (Willoughby and Lee 2017). Not sure about the interpretation there. I suggest waiting for a larger sample replication before putting much trust in this result.



Antonio:

Antonio asks 1) why GWASs omit sex chromosomes, 2) the impact of the Plomin and von Stumm review on research, 3) Plomin, his work and how he has avoided controversy, 4) the practical utility of polygenic scores (now to near future).

Emil:

Antonio:

Antonio asks about the prospects for increases in validity of the polygenic scores in the near future.

Emil: