Jason Furman's Reviews > The Genome Factor: What the Social Genomics Revolution Reveals about Ourselves, Our History, and the Future
The Genome Factor: What the Social Genomics Revolution Reveals about Ourselves, Our History, and the Future
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This was exactly the book I was looking for: a rigorous guide to the cutting edge of the emerging genetic-based social science that explains the methodologies scientists are using and also their limitations. The book is both hopeful about the future of research but also appropriately critical of the limitations of the study so far. Could not recommend it more highly—and I would not skip any of the footnotes or appendices which contain a lot of important insights, elaborations, and background. Reading the book left me excited for the future of the field but also frustrated about our ability to overcome the many inevitable limitations that they are so careful in expositing.
Dalton Conley and Jason Fletcher are both sociologists (and Dalton is also got a Ph.D. in genetics). The main point of their book is that genetic analysis is giving social scientists a powerful new tool to better understand the causes of differential educational attainment, incomes, poverty and other social phenomenon. But that we are still having a hard time linking particular genes to social outcomes let alone understanding the biological pathways and there is a huge amount of complex intersection between different genes and genes and the environment. Much of the book is about the social outcome of educational attainment, both because it is important but also because it is easier to study because is often included in the genetic data.
Conley and Fletcher start out by going through the research that establishes the high degree of heredity in many traits, including physical ones like height (80%) but also social ones like educational attainment (40%). They explain the models used to assess heredity, like comparing identical twins and fraternal twins or other well measured genetic distances. They explain in detail the methodology of the ACE model, the assumptions underlying it, why they thought it might be wrong, and how they ended up confirming it in their own research.
They then go on from the overall measures of the hereditary component of different traits to linking these to actual genes. Their take on the attempts to link behavior to single genes is that it was also spurious data mining, caused by the fact that while medical outcomes are well defined social outcomes have numerous measures and you can always find one correlated with the genes. The single gene efforts have given way to genome wide association studies (GWAS) that look at millions of genes and correlate them with outcomes at very high levels of significance to avoid data mining. Taken together, GWAS can produce a “polygenic score” that predicts a particular outcome, like educational attainment. GWAS, however, has three shortcomings: (1) you need to be careful to avoid the “chopsticks problem” (i.e., mistakenly inferring there is a gene for chopsticks when really is just a correlation with East Asian genes); (2) it gives up most any hope of a biological pathway because so many genes; and (3) it suffers from the “missing heredibility” problem of not explaining as much hereditability as know is there from twin studies. This last problem, they suggest, is caused by not analyzing the full genome for cost reasons.
They then go through a series of topics. One is genetic sorting where they examine the arguments in The Bell Curve about the increasing salience of genes and increased marriage based on them and thus locked in genetic stratification. Contrary to this the evidence they produce shows: (1) some traits are more genetically determined now than in the past (like height or weight, because most people have access to enough food so environment matters less) but others are less genetically determined now than in the past (like education, because of compulsory schooling laws); (2) people do mate based on phenotypes but much less on genotypes; and (3) no strong correlation between polygenic score for education and number of children.
They then have a chapter on race where they make the (familiar) point that there is much more genetic distance between most any two African tribes than between Europeans and Asians—which undermines many genetic concepts of race. This is because of the bottleneck of only ~1,000 people leaving Africa for Europe and Asia. They rebut the standard Stephen Jay Gould arguments against important racial differences (e.g., small genetic differences can matter a lot and evolution can happen quickly), but they establish that much of genetic variation is due to random drift not natural selection and that there is no links, and not really much research that could find a link, between race-based genetics and important social outcomes.
The chapter on the genetics of economic growth and war is a good literature review on the non-genetic studies in these areas but thin on the actual genetic studies, really just one for each topic. And finally the book concludes with a discussion of future “designer babies” either through embryo selection or gene editing, raising many concerns including that often “bad” traits are associated with “good” ones and have a benefit for the ecosystem as a whole so we will be taking risks.
Overall, I really appreciated that the book was research-based, did not just list discoveries but explained their methodology, and also that it was critical and skeptical throughout—but used that as an argument for more research not less.
Dalton Conley and Jason Fletcher are both sociologists (and Dalton is also got a Ph.D. in genetics). The main point of their book is that genetic analysis is giving social scientists a powerful new tool to better understand the causes of differential educational attainment, incomes, poverty and other social phenomenon. But that we are still having a hard time linking particular genes to social outcomes let alone understanding the biological pathways and there is a huge amount of complex intersection between different genes and genes and the environment. Much of the book is about the social outcome of educational attainment, both because it is important but also because it is easier to study because is often included in the genetic data.
Conley and Fletcher start out by going through the research that establishes the high degree of heredity in many traits, including physical ones like height (80%) but also social ones like educational attainment (40%). They explain the models used to assess heredity, like comparing identical twins and fraternal twins or other well measured genetic distances. They explain in detail the methodology of the ACE model, the assumptions underlying it, why they thought it might be wrong, and how they ended up confirming it in their own research.
They then go on from the overall measures of the hereditary component of different traits to linking these to actual genes. Their take on the attempts to link behavior to single genes is that it was also spurious data mining, caused by the fact that while medical outcomes are well defined social outcomes have numerous measures and you can always find one correlated with the genes. The single gene efforts have given way to genome wide association studies (GWAS) that look at millions of genes and correlate them with outcomes at very high levels of significance to avoid data mining. Taken together, GWAS can produce a “polygenic score” that predicts a particular outcome, like educational attainment. GWAS, however, has three shortcomings: (1) you need to be careful to avoid the “chopsticks problem” (i.e., mistakenly inferring there is a gene for chopsticks when really is just a correlation with East Asian genes); (2) it gives up most any hope of a biological pathway because so many genes; and (3) it suffers from the “missing heredibility” problem of not explaining as much hereditability as know is there from twin studies. This last problem, they suggest, is caused by not analyzing the full genome for cost reasons.
They then go through a series of topics. One is genetic sorting where they examine the arguments in The Bell Curve about the increasing salience of genes and increased marriage based on them and thus locked in genetic stratification. Contrary to this the evidence they produce shows: (1) some traits are more genetically determined now than in the past (like height or weight, because most people have access to enough food so environment matters less) but others are less genetically determined now than in the past (like education, because of compulsory schooling laws); (2) people do mate based on phenotypes but much less on genotypes; and (3) no strong correlation between polygenic score for education and number of children.
They then have a chapter on race where they make the (familiar) point that there is much more genetic distance between most any two African tribes than between Europeans and Asians—which undermines many genetic concepts of race. This is because of the bottleneck of only ~1,000 people leaving Africa for Europe and Asia. They rebut the standard Stephen Jay Gould arguments against important racial differences (e.g., small genetic differences can matter a lot and evolution can happen quickly), but they establish that much of genetic variation is due to random drift not natural selection and that there is no links, and not really much research that could find a link, between race-based genetics and important social outcomes.
The chapter on the genetics of economic growth and war is a good literature review on the non-genetic studies in these areas but thin on the actual genetic studies, really just one for each topic. And finally the book concludes with a discussion of future “designer babies” either through embryo selection or gene editing, raising many concerns including that often “bad” traits are associated with “good” ones and have a benefit for the ecosystem as a whole so we will be taking risks.
Overall, I really appreciated that the book was research-based, did not just list discoveries but explained their methodology, and also that it was critical and skeptical throughout—but used that as an argument for more research not less.
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Reading Progress
November 16, 2018
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Started Reading
November 16, 2018
– Shelved
December 3, 2018
–
Finished Reading