After the first couple of pages I was worried about what I had gotten myself into, but the prose mellows out and is easy enough for a layman to understand. The authors keep the technical jargon to a minimum and the graphs and charts make the data easier to digest. The book taught me a lot about the cutting edge of the genome revolution, how genotype relates to phenotype (and when it doesn’t), gene mapping and gene editing (CRISPR), how genetics might affect macroeconomic issues and larger social policy, and how genetics relates to issues of nature versus nurture. One of the basic points is that “instead of a single important genetic variant (or allele), there are often hundreds or thousands that contribute to variation in a given outcome.” After all, 93 percent of genes in the human genome are in some way connected.
One point that should have been obvious, but was not for me, is that siblings only share half of their genes on average, so that “some pairs of brothers and sisters can be more or less related than others thanks to two factors. The first factor is luck, since siblings each get half their deck of cards [genes] from each parent, but the extent to which the cards in those half-decks overlap with each other is a matter of chance. Some sibling pairs will actually be closer to half-siblings in terms of genetic similarity and others will be closer to the identical-twin end of the spectrum…. [The second factor is] if parents tend to be more genetically alike than they are similar to random other individuals in the population- that is, there is not random mating but rather assortative mating- then siblings (including fraternal twins) are more alike, on average, than the 50 percent we assume.”
As for broader social policy, the authors first tackle the idea of assortative mating- that we are breeding children who are increasingly genetically stratified. “The typical marriage in the United States is between people who are the genetic equivalent of second cousins.” However, in spousal selection it is still similar phenotypes and not genotypes that tend to attract pairings. “When we plot spousal correlations on outcomes for which we have decently predictive polygenic scores from major consortia (education, height, BMI, and depression)…. we find that, with one major exception (height), social sorting dwarfs genotypic sorting…. When we look at a phenotype that potential spouses can observe at the time of making a mate choice, such as education, we find a high correlation in phenotype.”
The authors stick with the accepted science in rejecting race as a biological classification. In fact, they point out that “the entire community of non-African (and non-African American) human beings collectively can display the same level of genetic similarity as the population of a single region of sub-Saharan Africa (namely the Rift Valley, where humans originated and which remains the deepest wellspring of human genetic diversity.)” That is because there was a huge bottleneck when migrants left Africa, meaning that all non-Africans descend from the same 2,000 individuals. So any pair of random Caucasian and Asian people will likely be much more genetically similar than two Kenyans from tribes just a few miles apart. The final part of the book looks at how genes and the environment interact. A gene that eases the digestion of milk would not necessarily have been selected for before the domestication of farm animals, but afterwards “a 10 percentage point increase in the prevalence of the beneficial [milk digestion] genetic variant in a population was associated with about a 15 percent increase in population density [which correlates with fitness].” Similarly, a gene that helps store fat from caloric intake would have been beneficial in hunter-gatherer societies, but would be a detriment in today’s sedentary culture.
One of the downsides of using polygenic scores, in general, is that because the samples collected are so large (from datasets that combine populations from all over the world), environmental and cultural differences get averaged away. The authors posit, “what if in social democratic countries, like Sweden or Norway, a certain allele that predisposed individuals to be more cooperative and less competitive led to significantly greater educational success, but in a more competitive, laissez-faire capitalist setting, like the United States or Australia, that very same allele had a negative effect on educational performance as a result of different cultural norms or expectations? In both societies this allele would be a predictor of education, but in pooled analysis, its effect would be zero because Scandinavia would cancel out the United States.”
In general, making policy informed by genetics is rife with ethical quandaries since one is dealing with averages and probabilities and not definitive facts for any particular individual. The authors conclude with cautionary words about what the ramifications might be as gene identification with phenotype becomes more advanced, as prenatal selection becomes common, as genetic information about individuals enters the public domain, and as gene editing tools for humans become a reality. Humans are at the very start of this genetic revolution, which in many ways might alter the very conception of how we think of humanity.
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