[TODO: tie into farmer/forager theory]
-Women are better at verbal ability and social cognition, whereas men are better at visuospatial skills. The sexes achieve similar levels of overall performance via somewhat different mental "toolkits." Murray devotes a section to a 2007 result of Johnson and Bouchard, who report that ["_g_ masks the dimensions on which [sex differences in mental abilities] lie"](/papers/johnson-bouchard-sex_differences_in_mental_abilities_g_masks_the_dimensions.pdf): overall levels of mental well-functioning lead to underestimates of the effect sizes of specific mental abilities, which you want to statistically correct for. This result in particular is _super gratifying_ to me personally, because [I independently had a similar idea](/2019/Sep/does-general-intelligence-deflate-standardized-effect-sizes-of-cognitive-sex-differences/)—it's _super validating_ as an amateur to find that the pros have been thinking along the same track!
+Women are better at verbal ability and social cognition, whereas men are better at visuospatial skills. The sexes achieve similar levels of overall performance via somewhat different mental "toolkits." Murray devotes a section to a 2007 result of Johnson and Bouchard, who report that ["_g_ masks the dimensions on which [sex differences in mental abilities] lie"](/papers/johnson-bouchard-sex_differences_in_mental_abilities_g_masks_the_dimensions.pdf): overall levels of mental well-functioning lead to underestimates of the effect sizes of specific mental abilities, which you want to statistically correct for. This result in particular is _super gratifying_ to me personally, because [I independently had a similar idea a few months back](/2019/Sep/does-general-intelligence-deflate-standardized-effect-sizes-of-cognitive-sex-differences/)—it's _super validating_ as an amateur to find that the pros have been thinking along the same track!
The second part of the book is about some ways in which people with different ancestries are different from each other! Obviously, there are no "distinct" "races" (that would be dumb), but it turns out (as found by endeavors such as [Li _et al._ 2008](/papers/li_et_al-worldwide_human_relationships_inferred.pdf)) that when you throw clustering and [dimensionality-reduction](https://en.wikipedia.org/wiki/Dimensionality_reduction) algorithms at SNP data (<em>s</em>ingle <em>n</em>ucleotide <em>p</em>olymorphisms, places in the genome where more than one allele has non-negligible frequency), you get groupings that are a pretty good match to classical or self-identified "races".
Population differences are important when working with genome-wide association studies, because a model "trained on" one population won't perform as well against the "test set" of a different population. Suppose you do a big study and find a bunch of SNPs that correlate with a trait, like schizophrenia or liking opera. The frequencies of those SNPs for two populations from the same continent (like Japanese and Chinese) will hugely correlate (Pearson's _r_ ≈ 0.97), but for more genetically-distant populations from different continents, the correlation will still be big but not huge (like _r_ ≈ 0.8 or whatever).
-What do these differences in SNP frequencies mean in practice?? We ... don't know yet. At least some population differences are fairly well-understood: I'd tell you about sickle-cell and lactase persistence, except [then I would have to scream](/2017/Dec/interlude-xi/). There are some cases where we see populations independently evolve different adaptations that solve the same problem: people living on the plateaus of Tibet and Peru have both adapted to high altitudes, but the Tibetans did it by breathing faster and the Peruvians did it with more hemoglobin!
+What do these differences in SNP frequencies mean in practice?? We ... don't know yet. At least some population differences are fairly well-understood: I'd tell you about sickle-cell and lactase persistence, except [then I would have to scream](/2017/Dec/interlude-xi/). There are some cases where we see populations independently evolve different adaptations that solve the same problem: [people living on the plateaus of both Tibet and Peru have both adapted to high altitudes](https://www.pnas.org/content/104/suppl_1/8655.long), but the Tibetans did it by breathing faster and the Peruvians did it with more hemoglobin!
Sorry, "the Tibetans did it with ..." is sloppy phrasing on my part; what I actually mean is that the Tibetans who weren't genetically predisposed to breathe faster were more likely to die without leaving children behind. That's how evolution works!
-[TODO: link, Cynthia M. Beall, "Two Routes to Functional Adaptation", double-check what "resting ventilation" means]
+The third part of the book is about genetic influences on class structure! Untangling the true causes of human variation is a really hard technical philosophy problem, but behavioral geneticists have at least gotten started with their simple _ACE_ model. It works like this: first, assume (that is, "pretend") that the genetic variation for a trait is _additive_ (if you have the appropriate SNP, you get more of the trait), rather than exhibiting _epistasis_ (where the effects of different loci interfere with each other) or Mendelian _dominance_ (where the presence of just one copy of an allele (of two) determines the phenotype, and it doesn't matter whether you heterozygously have a different allele as your second version of that gene). Then we pretend that we can partition the variance in phenotypes as the sum of the "additive" genetic variance _A_, plus the environmental variance "common" within a family, plus "everything else" (including measurement "error" and the not-shared-within-families "environment") _E_. Briefly (albeit at the risk of being _cliché_): nature, nurture, and _noise_.
-The third part of the book is about genetic influences on class structure! Untangling the true causes of human variation is a really hard technical philosophy problem, but behavioral geneticists have at least gotten started with their simple _ACE_ model. It works like this: first, assume that genetic variation for a trait is _additive_ (if you have the appropriate SNP, you get more of the trait), rather than exhibiting epistasis () or Mendelian dominance ().
+Then we can estimate the sizes of the _A_, _C_, and _E_ components by studying fraternal and identical twins. (If you hear people talking about "twin studies", this is what they mean—_not_ case studies of identical twins raised apart, which _are_ really cool but don't happen very often.) Both kinds of twins have the same family environment _C_ at the same time (parents, socioeconomic status, schools, _&c._), but identical twins are twice as genetically related to each other as fraternal twins, so the extent to which the identical twins are more similar is going to pretty much be because of their genes. "Pretty much" in the sense that while there are ways in which the assumptions of the model aren't quite true (assortative mating makes fraternal twins more similar in the ways their parents were _already_ similar before mating, identical twins might get treated more similarly by "the environment" on account of their appearance), the _quantitative_ effect of these deviations are probably pretty small.
-[p. 212-4: A + C + E model and comparing identical and fraternal twins (different from twins raised apart)]
-[ACE model assumes no assortative mating, which leads to an underestimate of A: because it makes fraternal twins resemble each other for non-environmental reasons]
-[equal environments assumption could be violated]
+Anyway, it turns out that the effect of the shared environment _C_ is way smaller than most people intuitively expect—next to zero for personality and adult intelligence. The environment matters—just not the part of the environment shared by sibling in the same family. Just not the part of the environment we know how to control. Thus, a lot of economic and class stratification actually ends up being along genetic lines: the nepotism of family wealth can buy opportunities and second chances, but it doesn't actually live your life for you.
-[shared environment is zero for personality]
+It's important not to overinterpret the heritability results; there are a bunch of standard caveats that go here that everyone's treatment of the topic needs to include. Heritability is about the _variance_ in phenotypes that can be predicted by _variance_ in genes. This is _not_ the same concept as "controlled by genes." To see this, notice that the trait "number of heads" has a heritability of zero because the variance is zero: all living people have exactly one head. (Uh, I'm counting Siamese twins as two people.) Heritability estimates are also necessarily bound to a particular population in a particular place and time, which can face constraints shaped solely by the environment. If you plant half of a batch of seeds in the shade and half in the sun, the variance in the heights of the resulting plants will be associated with variance in genetics _within_ each group, but the difference _between_ the groups is solely determined by the sunniness of their environments. Likewise, in a Society with a cruel caste system under which children with red hair are denied internet access, part of the heritability of intellectual achievement is going to come from alleles that code for red hair. Even though (_ex hypothesi_) redheads have the same inherent intellectual potential as everyone else, the heritability computation can't see into worlds that are not our own, which might have vastly different gene–environment correlations.
-[standard examples:red haired children, plants with sunshine]
+Old-timey geneticists used to think that they would find small number of "genes for" something, but it turns out that we live in an omnigenetic, pleiotropic world where lots and lots of SNPs each exert a tiny effect on potentially lots and lots of things. I feel like this probably _shouldn't_ have been surprising (genes code for proteins, variation in what proteins get made is going to affect high-level behaviors, but high-level behaviors involve _lots_ of proteins in a complicated unpredictable way), but I guess it was.
-[parental SES also tracks parental genes]
+Murray's penultimate chapter summarizes the state of a debate between a "Robert Plomin school" and an "Eric Turkheimer school" on the impact and import of polygenic scores, where we tally up all the SNPs someone has that are associated with a trait of interest.
-[not determinism for individuals, but shapes class structure]
+The starry-eyed view epitomized by Plomin says that polygenic scores are _super great_ and everyone _and her dog_ should be excited about them: they're causal in only one direction (the trait can't cause the score) and they let us assess risks in individuals before they happen. Clinical psychology will enter a new era of "positive genomics", where we understand how to work with the underlying dimensions along which people vary (including positively), rather than focusing on treated "diagnoses" that people allegedly "have".
-[useful outside interventions are hard]
+The curmudgeonly view epitomized by Turkheimer says that science is about understanding the _causal structure_ of phenomena, and that polygenic scores don't fucking tell us anything. [Divorce is heritable _in the same way_ that intelligence is heritable](http://www.geneticshumanagency.org/gha/the-ubiquity-problem-for-group-differences-in-behavior/), not because there are divorce genes in any meaningful biological sense, but because of a "universal, nonspecific genetic pull on everything".
-[last section looks ahead]
+Notably, Plomin and Turkheimer aren't actually disagreeing here: it's a difference in emphasis rather than facts. Polygenic scores _don't_ explain mechanisms—but might they end up being useful, and used, anyway? Murray's vision of social science is content to make predictions and "explain variance" while remaining ignorant of ultimate causality. Meanwhile, my cursory understanding (while kicking myself for [_still_](/2018/Dec/untitled-metablogging-26-december-2018/#daphne-koller-and-the-methods) not having put in the hours to get farther into [_Daphne Koller and the Methods of Rationality_](https://mitpress.mit.edu/books/probabilistic-graphical-models)) was that you need to understand causality in order to predict what interventions will have what effects—maybe our feeble state of knowledge is _why_ we don't know how to find large-effect environmental interventions.
-[geneticists used to think that they would find small number of "genes for" something, but it turns out that lots of SNPs (omnigenetic) affect lots of things (pleiotropy)]
-
-[vertical pleiotropy: LDL affects heart attack; vs. horizontal]
-
-["tag" SNP: https://www.gwern.net/Embryo-selection#limits-to-iterated-selection-the-paradox-of-polygenicity ]
-
-[Plomin school vs. Turkheimer school debate on the usefulness of polygenic scores]
-
-[Plomin says, causal in one direction (trait can't cause score), predict from brith, predict individuals. Can assess risk before it happens, clinical psychology will move towards dimensions rather than diagnoses, "positive genomics"—looking at the right tail in contrast to clinical psychology's focus on disorders]
-
-[Eric Turkheimer: polygenic scores don't tell you anything about causality, and science is about causes: heritability without mechanism. Divorce is heritable _in the same way_ that IQ is. http://www.geneticshumanagency.org/gha/the-ubiquity-problem-for-group-differences-in-behavior/ a "universal, nonspecific genetic pull on everything"]
-
-[Murray says social science is about explaining variance, not causality]
-
-There are also some appendicies at the back of the book! Appendix 1 (reproduced from one of Murray's earlier books) explains some basic statistics concepts. Appendix 2 ("Sexual Dimorphism in Humans") goes over the prevalence of intersex conditions and gays, and then—so much for this post broadening the [topic scope of this blog](/tag/two-type-taxonomy/)—transgender typology! Murray presents the Blanchard–Bailey–Lawrence–Littman view as fact, which I think is basically _correct_, but a more comprehensive treatment (which I concede may be too much too hope for from a mere Appendix) would have at least _mentioned_ alternative views ([Serano](http://www.juliaserano.com/)? [Veale](https://www.waikato.ac.nz/fass/about/staff/jveale)?), if only to explain _why_ they're worth dismissing. (Contrast to the eight pages in the main text explaining why "But, but, epigenetics!" is worth dismissing.)
-
-Then Appendix 3 ("Sex Differences in Brain Volumes and Variance") has tables of brain-size data, and an explanation of the greater-male-variance hypothesis. Cool!
+There are also some appendicies at the back of the book! Appendix 1 (reproduced from one of Murray's earlier books) explains some basic statistics concepts. Appendix 2 ("Sexual Dimorphism in Humans") goes over the prevalence of intersex conditions and gays, and then—so much for this post broadening the [topic scope of this blog](/tag/two-type-taxonomy/)—transgender typology! Murray presents the Blanchard–Bailey–Lawrence–Littman view as fact, which I think is basically _correct_, but a more comprehensive treatment (which I concede may be too much too hope for from a mere Appendix) would have at least _mentioned_ alternative views ([Serano](https://rationalwiki.org/wiki/Intrinsic_Inclinations_Model)? [Veale](http://unremediatedgender.space/papers/veale-lomax-clarke-identity_defense_model.pdf)?), if only to explain _why_ they're worth dismissing. (Contrast to the eight pages in the main text explaining why "But, but, epigenetics!" is worth dismissing.) Then Appendix 3 ("Sex Differences in Brain Volumes and Variance") has tables of brain-size data, and an explanation of the greater-male-variance hypothesis. Cool!
-----
projects / Ultimately_Untrue_Thought.git / blobdiff