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The Genome Factor: What the Social Genomics Revolution Reveals about Ourselves, Our History, and the Future
How genomics is revolutionizing the social sciences
For a century, social scientists have avoided genetics like the plague. But the nature-nurture wars are over. In the past decade, a small but intrepid group of economists, political scientists, and sociologists have harnessed the genomics revolution to paint a more complete picture of human social life than ever before. The Genome Factor describes the latest astonishing discoveries being made at the scientific frontier where genomics and the social sciences intersect.
The Genome Factor reveals that there are real genetic differences by racial ancestry―but ones that don't conform to what we call black, white, or Latino. Genes explain a significant share of who gets ahead in society and who does not, but instead of giving rise to a genotocracy, genes often act as engines of mobility that counter social disadvantage. An increasing number of us are marrying partners with similar education levels as ourselves, but genetically speaking, humans are mixing it up more than ever before with respect to mating and reproduction. These are just a few of the many findings presented in this illuminating and entertaining book, which also tackles controversial topics such as genetically personalized education and the future of reproduction in a world where more and more of us are taking advantage of cheap genotyping services like 23andMe to find out what our genes may hold in store for ourselves and our children.
The Genome Factor shows how genomics is transforming the social sciences―and how social scientists are integrating both nature and nurture into a unified, comprehensive understanding of human behavior at both the individual and society-wide levels.
For a century, social scientists have avoided genetics like the plague. But the nature-nurture wars are over. In the past decade, a small but intrepid group of economists, political scientists, and sociologists have harnessed the genomics revolution to paint a more complete picture of human social life than ever before. The Genome Factor describes the latest astonishing discoveries being made at the scientific frontier where genomics and the social sciences intersect.
The Genome Factor reveals that there are real genetic differences by racial ancestry―but ones that don't conform to what we call black, white, or Latino. Genes explain a significant share of who gets ahead in society and who does not, but instead of giving rise to a genotocracy, genes often act as engines of mobility that counter social disadvantage. An increasing number of us are marrying partners with similar education levels as ourselves, but genetically speaking, humans are mixing it up more than ever before with respect to mating and reproduction. These are just a few of the many findings presented in this illuminating and entertaining book, which also tackles controversial topics such as genetically personalized education and the future of reproduction in a world where more and more of us are taking advantage of cheap genotyping services like 23andMe to find out what our genes may hold in store for ourselves and our children.
The Genome Factor shows how genomics is transforming the social sciences―and how social scientists are integrating both nature and nurture into a unified, comprehensive understanding of human behavior at both the individual and society-wide levels.
296 pages, Hardcover
Published January 24, 2017
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July 11, 2019
Conley's book is clearly articulated, with some excellent explanations of the science (especially in the appendices, which are worth the effort) and an elaboration of the emerging field of sociogenetics. Unfortunately - or fortunately - the clarity of the presentation just aided my growing sense that this discipline is presenting results as meaningful when they are not. Paradoxically, Conley is most persuasive when he is arguing against the use of sociogenetic analysis, and least when he is arguing for it.
Conley starts great, with an introduction explaining that who we are - our cognitive and personality traits if you like - and what happens to us - the way we experience the schooling system, our jobs, our health - are all a result of a complex interplay between our genetics and our environment. However, from here, it is mostly downhill, as instead of focusing on how that interaction works, Conley describes a field which primarily uses statistical techniques to correlate social outcomes to genetic profiles. Environmental factors are either ignored, or "controlled for" in ways which create imaginary realities, in which those measured are largely white and socially uniform, but conclusions are extrapolated to a world which is anything but.
Underpinning Conley's work is a set of unexamined assumptions about society, which I do not think hold up. This is manifested in a conflating of "success" with educational achievement, and intelligence with salary outcomes. This culminates in a bizarre sequence at the end of the book in which Conley (apocalyptically) imagines an upper-middle-class couple genetically engineering their children for maximum IQ because it will guarantee them a good job, even at the risk of blindness. The assumption that IQ is a better predictor of income than social privilege doesn't hold in our real world. Nor, frankly, does imagining any world in which IQ can be predicted from DNA alone.
So what happened to the dream, outlined at the beginning, of studying the interaction between genes and environment? The frustration is that apparently, sociogenomics doesn't look at interaction at all. Instead, it tries to separate the "environment" and "genetic" components, and estimate a numerical proportional contribution of each to the progress of a person's life. This actually seems to entrench the 'nature' ' nurture' divide, despite a body of research making it less and less tenable to see the world in those terms.
Conley reinforces this frequently through a joking "geneticists vs sociologists" meme which implies sociologists are barracking for social impacts, while geneticists want to disregard them. The times he does refer to interaction, he reinforces a genetic-determinist view, suggesting that someone may be genetically predetermined to choose particular environments.
At no point does Conley define the "environment". Understanding our relationship with bacteria within us, for example, challenges the simplistic idea of genetics vs society. Epigenetics does get a mention, but not in any depth. Recent advances in these fields are hugely significant, suggesting a myriad of subtle interactions shaping our bodies and brains. Cognitive plasticity, the way our brains change as a result of both physical changes and external events, also gets barely a discussion. Environment to Conley seems to be always determined by social policies and parenting techniques. At one point, he suggests that an equitable society could be one in which everything is fully heritable as if the environment is entirely controllable in predictable ways.
Instead, we are primarily in a world of mapping genes (or alleles) to outcomes, and extrapolating causality. Unsurprisingly, as Conley freely admits, it doesn't work very well most of the time. To summarise, before gene sequencing technologies, his sociogeneticist forebears came up with a proportional "heritability" percentage by comparing how similar outcomes between identical twins are to how similar outcomes between non-identical twins are. (I know, terrible sentence, Conley explains better).
Before I go any further, I just want to point out the biggest issue here, the assumption that this is a constant, across different social and natural environments. That is, in a relatively equal society, you would expect outcomes to be less affected by social inequality than in an unequal society. This could also apply to natural environments - if two districts have similar soil properties, the crop production may be more influenced by farmer. But if one district has uneven soil - some farms better than others - and the other is equal, the environment contribution will vary between them. So twin studies carried across differing social conditions are going to produce averages which may not represent the variety within them. Or in other words, aren't very meaningful. Where studies have only measured in constant conditions, those results can't meaningfully be extrapolated to other states.
With the advent of gene sequencing, sociogenomics moved to look first at particular genes or alleles, comparing them to outcomes. This produced very, very small heritability estimates. Nothing like the estimates twin studies had accustomed sociologists to. There are many reasons for this, as Conley freely admits. The most likely relates to the complexity of how genes translate into people. Even for most physical traits, hundreds of genes will contribute to a phenotype (e.g. hair colour). When it comes to cognitive traits - well, we don't even understand how our physical brains produce the result in most cases. It is likely to be thousands of genes contributing to something as simple as memory, for example, making a gene-by-gene approach improbable to provide much that is meaningful. But Conley and his colleagues are not trying to correlate to memory. They are going for things like school attendance and IQ tests. Which relate to a wide range of cognitive traits, each of which probably has thousands of genes. And, of course, we have social impacts and variations (IQ tests vary by government structures, for very basic starters). Every time Conley referred to college education as a "phenotype", I winced. Phenotype is used by biologists to represent a trait resulting from a genotype. School, which is a social outcome dependent on having access and a billion other things, is stretching the definition.
These studies are most successful where populations are relatively genetically homogenous, so differences on the genome are clearly delineated, and socially homogenous, so environmental factors are reduced. It should be unsurprising that Scandinavia, which keeps meticulous records of adoptees, is a significant source of the more successful studies, which Conley describes as a "godsend". However, these studies tell us very little about anything outside of Scandinavia, for precisely those reasons. This, unfortunately, does not stop these studies being cited broadly as universal human outcomes. Conley to his credit does not do this (much), and the clarity of his explanation made me better understand why it is so problematic.
In any case, to move beyond the difficulties of specific studies, genome-wide studies are becoming more common. These crunch vast amounts of data, looking for correlations across the entire genomes of multiple individuals. The size of the datasets makes finding patterns easier, but the methodology is rife with risk. If you go looking for any correlation in a large data set, you will find random relationships. It is for precisely this reason that scientific funding bodies increasingly regulate against studies which don't have a clear hypothesis to test. It is not impossible to use these techniques responsibly, but academic structures, which reward positive novel findings over negative, provide strong incentives to take shortcuts. Conley describes the safeguards in place, but risk remains.
Even with these techniques, the results provide lower heritability percentages than twin studies had predicted.
It isn't that this kind of research is unique to this field. Schizophrenia, for example, is a mental illness diagnosed based on symptoms whose physical causes are not well understood, and almost certainly have a complex web of genetic contributors. Statistical correlation studies are used to try to develop a better understanding of this disease, despite all the same difficulties and problems. The risks here, however, may be better balanced against potential gains. Schizophrenia causes enormous suffering, and identifying possible genetic correlations may help identify the neurological processes associated with it. This is a more interactive process than a crude guess about cause and effect.
These studies have another problem, however, which is that with very large datasets, it becomes difficult to control for known social factors adequately. Conley discusses this in terms of the Chopsticks problem - an actual study which purported to have found a gene correlation to chopsticks use before realising it correlated with south-east populations much more likely to be taught to use chopsticks as children.
Most of the arguments I've put here, Conley covers himself in some form in his chapters on race and global inequality.
Here Conley starts raising for the first time that we do know a lot about the impact of some environmental measures on generating inequality. In the lead up to the first quote above, Conley talks about the impossibility of finding an area to study African-American school outcomes where known factors like poor school funding, health disparities and racism do not obliterate any other differences.
Similarly, Conley deconstructs the various attempts to find a genetic basis for global inequality, pointing and efficiently and effectively to the number of possible interpretations for the data, and the impossibility of narrowing it down. For example,
Conley's deconstruction of racist interpretations is welcome. It is particularly necessary given the resurgence of racist science circles based around arguments for hypothetical genetic differences in intelligence between population groups. It hovers over the entire field, given race is a substantial predictor of exactly the factors Conley equates with success - educational attainment, IQ score and absence of incarceration. One of the biggest dangers in heading into genome-wide studies is that correlations caused by clearly understood social factors - easily summarised as 'racism' (personal and systemic) - become used to justify and perpetuate inequality, rather than dismantle it.
In other areas, however, Conley fails to discuss the specifics of social factors at all. While he pays lip service to their importance, this tends to trivialise or dismiss them. For example, this quote took my breath away a little "Someone who is highly cognitively endowed and able to earn lots of money could choose a stay-at-home spouse who contributes other abilities to the household, such as empathy and care-taking ability." There is a set of assumptions about both how high salaries work, and the reasons that caregivers choose to take on the role, which are not discussed here at all. The impact that having children has on the careers of women, their pay, and their subsequent options for domestic divisions of work, are extensively studied. But to Conley, it seems, smart men earn money, and empathetic women are driven to domesticity.
Conley doesn't discuss the disproportionate exposure to violence that some communities have, and how trauma effects precisely the outcomes he is measuring. Similarly, in discussing spousal choice, Conley fails to identify one of the most significant driving factors - the social stratification between college students and others. It would be laughable if it weren't so serious.
And it is a bit serious. Conley starts to speculate about what this research - which by his own account has yet to produce any persuasive evidence of anything - might mean for policy. What if we start streaming kids by genetics, based on teeny tiny effect sizes? Conley may argue to exclude race from these calculations, but it is hard to see that happening in practice. What if dubious genetic associations are used in recruitment? Even at the more medical end, what if we determine medical treatment based on a genetic association, which is still a generalisation which statistically will be wrong a proportion of the time?
In theory, this shouldn't happen because the science doesn't justify it. In practice, however, it is likely for precisely the reason that sociogenomics is attracting much more Foundation and philanthropic funding than more proven research approaches to the impact of inequality. That is that power, who has it and who wants to retain it, is not threatened by science which can identify people's failures in their genes, while it is by research which indicates that we need to redistribute what we have.
To be clear here, I am not a reader who believes genetics have no impact on our cognition or motivations. In fact, I am fascinated by the complexity that makes us *us*, that produces such an amazing array of people, no two of whom are that much alike. I want this science to be better, to tell us things. But the more I engage with it, the more convinced I become that it is going nowhere useful. Nothing here is very meaningful, and it seems that the researchers themselves want a simpler reality than the one we live in. More dangerously, this research seems to be coming at the expense of examining well-defined social analysis, and it is doing so as we see a resurgence in white supremacy, including among governments looking for ways to justify closed borders and ongoing inequality. It scares me far more than a hypothetical future IQ/blindness trade-off.
Conley starts great, with an introduction explaining that who we are - our cognitive and personality traits if you like - and what happens to us - the way we experience the schooling system, our jobs, our health - are all a result of a complex interplay between our genetics and our environment. However, from here, it is mostly downhill, as instead of focusing on how that interaction works, Conley describes a field which primarily uses statistical techniques to correlate social outcomes to genetic profiles. Environmental factors are either ignored, or "controlled for" in ways which create imaginary realities, in which those measured are largely white and socially uniform, but conclusions are extrapolated to a world which is anything but.
Underpinning Conley's work is a set of unexamined assumptions about society, which I do not think hold up. This is manifested in a conflating of "success" with educational achievement, and intelligence with salary outcomes. This culminates in a bizarre sequence at the end of the book in which Conley (apocalyptically) imagines an upper-middle-class couple genetically engineering their children for maximum IQ because it will guarantee them a good job, even at the risk of blindness. The assumption that IQ is a better predictor of income than social privilege doesn't hold in our real world. Nor, frankly, does imagining any world in which IQ can be predicted from DNA alone.
So what happened to the dream, outlined at the beginning, of studying the interaction between genes and environment? The frustration is that apparently, sociogenomics doesn't look at interaction at all. Instead, it tries to separate the "environment" and "genetic" components, and estimate a numerical proportional contribution of each to the progress of a person's life. This actually seems to entrench the 'nature' ' nurture' divide, despite a body of research making it less and less tenable to see the world in those terms.
Conley reinforces this frequently through a joking "geneticists vs sociologists" meme which implies sociologists are barracking for social impacts, while geneticists want to disregard them. The times he does refer to interaction, he reinforces a genetic-determinist view, suggesting that someone may be genetically predetermined to choose particular environments.
At no point does Conley define the "environment". Understanding our relationship with bacteria within us, for example, challenges the simplistic idea of genetics vs society. Epigenetics does get a mention, but not in any depth. Recent advances in these fields are hugely significant, suggesting a myriad of subtle interactions shaping our bodies and brains. Cognitive plasticity, the way our brains change as a result of both physical changes and external events, also gets barely a discussion. Environment to Conley seems to be always determined by social policies and parenting techniques. At one point, he suggests that an equitable society could be one in which everything is fully heritable as if the environment is entirely controllable in predictable ways.
Instead, we are primarily in a world of mapping genes (or alleles) to outcomes, and extrapolating causality. Unsurprisingly, as Conley freely admits, it doesn't work very well most of the time. To summarise, before gene sequencing technologies, his sociogeneticist forebears came up with a proportional "heritability" percentage by comparing how similar outcomes between identical twins are to how similar outcomes between non-identical twins are. (I know, terrible sentence, Conley explains better).
Before I go any further, I just want to point out the biggest issue here, the assumption that this is a constant, across different social and natural environments. That is, in a relatively equal society, you would expect outcomes to be less affected by social inequality than in an unequal society. This could also apply to natural environments - if two districts have similar soil properties, the crop production may be more influenced by farmer. But if one district has uneven soil - some farms better than others - and the other is equal, the environment contribution will vary between them. So twin studies carried across differing social conditions are going to produce averages which may not represent the variety within them. Or in other words, aren't very meaningful. Where studies have only measured in constant conditions, those results can't meaningfully be extrapolated to other states.
With the advent of gene sequencing, sociogenomics moved to look first at particular genes or alleles, comparing them to outcomes. This produced very, very small heritability estimates. Nothing like the estimates twin studies had accustomed sociologists to. There are many reasons for this, as Conley freely admits. The most likely relates to the complexity of how genes translate into people. Even for most physical traits, hundreds of genes will contribute to a phenotype (e.g. hair colour). When it comes to cognitive traits - well, we don't even understand how our physical brains produce the result in most cases. It is likely to be thousands of genes contributing to something as simple as memory, for example, making a gene-by-gene approach improbable to provide much that is meaningful. But Conley and his colleagues are not trying to correlate to memory. They are going for things like school attendance and IQ tests. Which relate to a wide range of cognitive traits, each of which probably has thousands of genes. And, of course, we have social impacts and variations (IQ tests vary by government structures, for very basic starters). Every time Conley referred to college education as a "phenotype", I winced. Phenotype is used by biologists to represent a trait resulting from a genotype. School, which is a social outcome dependent on having access and a billion other things, is stretching the definition.
These studies are most successful where populations are relatively genetically homogenous, so differences on the genome are clearly delineated, and socially homogenous, so environmental factors are reduced. It should be unsurprising that Scandinavia, which keeps meticulous records of adoptees, is a significant source of the more successful studies, which Conley describes as a "godsend". However, these studies tell us very little about anything outside of Scandinavia, for precisely those reasons. This, unfortunately, does not stop these studies being cited broadly as universal human outcomes. Conley to his credit does not do this (much), and the clarity of his explanation made me better understand why it is so problematic.
In any case, to move beyond the difficulties of specific studies, genome-wide studies are becoming more common. These crunch vast amounts of data, looking for correlations across the entire genomes of multiple individuals. The size of the datasets makes finding patterns easier, but the methodology is rife with risk. If you go looking for any correlation in a large data set, you will find random relationships. It is for precisely this reason that scientific funding bodies increasingly regulate against studies which don't have a clear hypothesis to test. It is not impossible to use these techniques responsibly, but academic structures, which reward positive novel findings over negative, provide strong incentives to take shortcuts. Conley describes the safeguards in place, but risk remains.
Even with these techniques, the results provide lower heritability percentages than twin studies had predicted.
It isn't that this kind of research is unique to this field. Schizophrenia, for example, is a mental illness diagnosed based on symptoms whose physical causes are not well understood, and almost certainly have a complex web of genetic contributors. Statistical correlation studies are used to try to develop a better understanding of this disease, despite all the same difficulties and problems. The risks here, however, may be better balanced against potential gains. Schizophrenia causes enormous suffering, and identifying possible genetic correlations may help identify the neurological processes associated with it. This is a more interactive process than a crude guess about cause and effect.
These studies have another problem, however, which is that with very large datasets, it becomes difficult to control for known social factors adequately. Conley discusses this in terms of the Chopsticks problem - an actual study which purported to have found a gene correlation to chopsticks use before realising it correlated with south-east populations much more likely to be taught to use chopsticks as children.
Most of the arguments I've put here, Conley covers himself in some form in his chapters on race and global inequality.
"But our sibling difference methods—or any other approach so far developed—can not separate the more context-independent (i.e., nonsocially mediated) biological effects from genetic effects that interact with the social system, such as when lighter skin is rewarded. That is, it could be that cognitive differences are genetically based, but the mechanism linking genes to IQ acts through social pathways (i.e., response to skin tone) rather than biological ones (i.e., brain structure). ... The near impossibility of a definitive, scientific approach as outlined above stands in stark contrast to the loose claims of pundits or scholars who assert that there is a genetic explanation for the black-white test score gap. In walking through the logic of genetic methods, we believe this discussion provides a cautionary tale for how scientists should proceed (or not) with investigations that combine questions of race, genetics, and socioeconomic or cognitive outcomes. With the outpouring of genetic data we are witnessing in society today, there will no doubt be further ventures in this direction. Clear efforts are therefore needed to sharpen the scientific questions that can be answered and also to guard against repeating past instances of pseudoscientific racism relying on ideologically motivated inferences from inadequate evidence."
Here Conley starts raising for the first time that we do know a lot about the impact of some environmental measures on generating inequality. In the lead up to the first quote above, Conley talks about the impossibility of finding an area to study African-American school outcomes where known factors like poor school funding, health disparities and racism do not obliterate any other differences.
Similarly, Conley deconstructs the various attempts to find a genetic basis for global inequality, pointing and efficiently and effectively to the number of possible interpretations for the data, and the impossibility of narrowing it down. For example,
"Even if we assume that the associations between genetic diversity and economic development show a cause-and-effect relationship, the interpretation of the finding is far from obvious. Although the authors have their preferred interpretation, it is not possible to discard many alternative interpretations. For example, that genetic diversity is correlated with country-level racial and ethnic composition. The potential for these correlations means it is difficult to know whether the links between genetic diversity and country success are tied to Ashraf and Galor’s theoretical ideas or are capturing other processes—such as histories of colonization, war, natural resource exploitation, and so on—that are tied up with race and racism.
Conley's deconstruction of racist interpretations is welcome. It is particularly necessary given the resurgence of racist science circles based around arguments for hypothetical genetic differences in intelligence between population groups. It hovers over the entire field, given race is a substantial predictor of exactly the factors Conley equates with success - educational attainment, IQ score and absence of incarceration. One of the biggest dangers in heading into genome-wide studies is that correlations caused by clearly understood social factors - easily summarised as 'racism' (personal and systemic) - become used to justify and perpetuate inequality, rather than dismantle it.
In other areas, however, Conley fails to discuss the specifics of social factors at all. While he pays lip service to their importance, this tends to trivialise or dismiss them. For example, this quote took my breath away a little "Someone who is highly cognitively endowed and able to earn lots of money could choose a stay-at-home spouse who contributes other abilities to the household, such as empathy and care-taking ability." There is a set of assumptions about both how high salaries work, and the reasons that caregivers choose to take on the role, which are not discussed here at all. The impact that having children has on the careers of women, their pay, and their subsequent options for domestic divisions of work, are extensively studied. But to Conley, it seems, smart men earn money, and empathetic women are driven to domesticity.
Conley doesn't discuss the disproportionate exposure to violence that some communities have, and how trauma effects precisely the outcomes he is measuring. Similarly, in discussing spousal choice, Conley fails to identify one of the most significant driving factors - the social stratification between college students and others. It would be laughable if it weren't so serious.
And it is a bit serious. Conley starts to speculate about what this research - which by his own account has yet to produce any persuasive evidence of anything - might mean for policy. What if we start streaming kids by genetics, based on teeny tiny effect sizes? Conley may argue to exclude race from these calculations, but it is hard to see that happening in practice. What if dubious genetic associations are used in recruitment? Even at the more medical end, what if we determine medical treatment based on a genetic association, which is still a generalisation which statistically will be wrong a proportion of the time?
In theory, this shouldn't happen because the science doesn't justify it. In practice, however, it is likely for precisely the reason that sociogenomics is attracting much more Foundation and philanthropic funding than more proven research approaches to the impact of inequality. That is that power, who has it and who wants to retain it, is not threatened by science which can identify people's failures in their genes, while it is by research which indicates that we need to redistribute what we have.
To be clear here, I am not a reader who believes genetics have no impact on our cognition or motivations. In fact, I am fascinated by the complexity that makes us *us*, that produces such an amazing array of people, no two of whom are that much alike. I want this science to be better, to tell us things. But the more I engage with it, the more convinced I become that it is going nowhere useful. Nothing here is very meaningful, and it seems that the researchers themselves want a simpler reality than the one we live in. More dangerously, this research seems to be coming at the expense of examining well-defined social analysis, and it is doing so as we see a resurgence in white supremacy, including among governments looking for ways to justify closed borders and ongoing inequality. It scares me far more than a hypothetical future IQ/blindness trade-off.
March 24, 2024
Even though I originally studied Genetics (a wee while ago tbh) I found a lot of this book hard to process. It doesn't help that the thesis of the tome isn't actually that clear - it introduces itself as a book zeroing in on important genetic information - however I would describe this book as more a critique on the LIMITs of genetic information and understanding.
And just to be clear these were good critiques - confronting offensive works such as The Bell Curve, explaining the problems with genetic determinism, and suggesting future directions of the science. However the book falls short of proving its own thesis!
It's also quite short, at least than 200 pages (although with a very healthy series of appendices) I wasn't actually unhappy when the book ended because it was just that detail heavy! But overall I learnt a lot and thought it was a good piece of information overall.
And just to be clear these were good critiques - confronting offensive works such as The Bell Curve, explaining the problems with genetic determinism, and suggesting future directions of the science. However the book falls short of proving its own thesis!
It's also quite short, at least than 200 pages (although with a very healthy series of appendices) I wasn't actually unhappy when the book ended because it was just that detail heavy! But overall I learnt a lot and thought it was a good piece of information overall.
December 15, 2018
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.
February 7, 2017
Some of this book was quite interesting. However, there were several parts that went over my head and that I did not understand well at all. I read some sections several times, and I still don't understand them that well. Some of the analogies really didn't work for me.
March 11, 2017
Surpassed my expectations. Careful, detailed, and exceptionally clear descriptions of genomics and a candid and enlightening discussion about the interplay with social science. Fascinating. Will buy this book to read it again.
May 27, 2025
Book Review
The Genome Factor
4/5 stars
"Moderately interesting mental masturbation; it doesn't really help."
*****
This is a university label book and it is about as difficult as you would expect.
The prose is good enough, but the background is just so technical that only a few will be able to take much out of it.
I'm willing to give an ear to anybody with intelligent arguments, but both authors seem to be primarily sociologists, and, well ... Sociology is just one of "those" disciplines. (It's one of those Humanities that we find entertaining gobbledygook like "ethnic honor" [p.89]/"structural racism.")
The authors are (or at least claim to be) facing the reality of increasing availability of genetic testing, and they are saying that they want to get ahead of conversations in which they feel that the data could be "misused."
(Suspiciously, they spend a lot of time talking about a controversial book that was written several decades back: "The Bell Curve," by Murray and Herrnstein--which was actually written LONG before the availability of the molecular genetic data. Also, the authors do not seem to address any of Murray's later books--the ones that made upgrades on the data in TBC.)
I perceive two reasoning errors that are consistent throughout this book:
Error 1. Denial that "That which is can be." If you follow this author's line of reasoning, you would say because Bernoulli's principle is not fully understood then there are/can be no such things as planes. Or, if the mechanism of action of a pharmaceutic is not understood, then the drug doesn't exist.
If you live in the real world and have some type of tool, the question is how well it measures what it is supposed to. (And this was an argument made by Murray and Hernstein: maybe we don't understand the structure of intelligence, but how much is it worth to try to understand it? Especially if it can predict outcomes.)
In this case, a lot of people have noticed persistent African / African descended underachievement, and that is just a fact. (James Watson was forced to retire after pointing out the obvious.)
a. For example, they have lower academic achievement in the United States and on the African continent; you will not look to them to find large numbers of competent mathematicians or physicists.)
b. If you see a flash mob/pilfering/looting in the United States or a government on the African continent (or the United States) degenerating into a racketeering operation, how much more do you need to know other than what your eyes tell you? (And your eyes will not tell you that that flash mob/Kwame Kilpatrick/Ray Nagin is Chinese.)
You can't talk these such things away *just* because you cannot make every single genetic link in the data/logical chain.
Error 2. Separation of the "what?" from the "why?"
So, if you are looking at avoiding underperforming school districts in some place, and these districts are heavily black, then what difference does it make to you that it is *abstractly possible* that these districts could perform as well as everywhere else? (For example: if you look at the bottom 20 school districts out of the 610 in Michigan, ALL of them are >95% black.)
Or, if you know that there will be an influx of blacks as a result of a district closing next door... Do you have to wait for the school to collapse and quality before you head for the hills? Or do you head it off at the pass? (This exact situation happened in Romulus, Michigan after the Inkster School district closed down because of black administrative and competence.)
The author does some interesting speculation in Chapter 7 about how some genes could have been suitable for a different environment (such as if people had a warrior gene, then it would have been good for taking risks during hunting ... But it turns into a gene that will land you in jail in a modern society), or the gene *could have* been selected for at a species level.
If it is true, and you live in Detroit and you get your throat cut or your head blown off during a carjacking by LaDarius Tyrone Washington, do you really feel better if you could explain that this might have been a result of his "warrior gene" that was useful a thousand years ago on the African savannah?
*******
Other things that this author does not address quite to my satisfaction:
1. (Cochran and Harpending) Even if the genetic differences between populations of human beings are small, might that not be enough to explain different outcomes?
Different breeds of dog are useful for different things, even though there is a small genetic distance between them.
The authors here mentioned private alleles within African populations, but then won't go any further and speculate about what they may be able to mean in practice.
Or to rule out why private alleles could not be enough to explain obvious differences.
2. (Murray) It is not possible to exhaustively rigorously define race (nor almost anything else, to be honest), but do the definitions that we have work well enough that we can find trends? This was dealt with in the book "Human Diversity."
3. How strong are some of these relationships? There are almost no graphs (relative to the number of assertions made). And we all know that social science texts can make tons of assertions based on statistically-significant-but-not-practically- significant data.
*******
There is some interesting philosophical discussion about what will happen as sequencing becomes even cheaper.
Could you sequence newborns and predict the genetic difficulties that they will have before they have them?
Could dating sites add the genetic information to the profiles so that consumers will know more of what they are getting?
How do you do a cost benefit allocation to direct resources toward people that are more likely to need them? Or away from people that cannot use them?
And so on.
There's also an interesting final chapter that speculates about what a world could look like in which people are genetically engineered.
It's food for thought, but fortunately I won't be around when it comes.
All these speculations are interesting things, but we will just have to wait and see what happens when the technology gets here.
I can guarantee at least three things that were predicted by the authors will come true:
1. This technology/access will be driven by money. And people who have it (money) will be the first to take advantage of it.
2. Moratoria notwithstanding, there will be someone, somewhere that has no compunction about commercializing this technology. (People's Republic of China!)
3. Technology will be developed LONG before anybody has hashed out the ethical implications. (And that's if they even care to. PRC, again!)
Verdict:
This book might be worth a reread a couple of years down the road to go through and revisit some of the arguments.
It's way too much to pick up on a first pass.
Factoids:
(90) All humans not of direct African descent share 1,000 to 2000 ancestors from a genetic bottleneck about 100,000 years ago.
(95) Manipulation of just 4 genes can turn a mustard seed into a woody tree.
(96) African populations have the largest number of "private alleles" (these are alleles not shared with other human beings)
Vocabulary:
reprogenetics
gerontocracy
assortative mating
epistasis
genetic stratification
unobserved heterogeneity
single nucleotide polymorphism
copy number variants
murine
Manhattan Plot
hypodescent
hyperdescent
cladogram
Continental ancestry
"Ethnic honor" (???)
private allele
genetic mosaicism
The Genome Factor
4/5 stars
"Moderately interesting mental masturbation; it doesn't really help."
*****
This is a university label book and it is about as difficult as you would expect.
The prose is good enough, but the background is just so technical that only a few will be able to take much out of it.
I'm willing to give an ear to anybody with intelligent arguments, but both authors seem to be primarily sociologists, and, well ... Sociology is just one of "those" disciplines. (It's one of those Humanities that we find entertaining gobbledygook like "ethnic honor" [p.89]/"structural racism.")
The authors are (or at least claim to be) facing the reality of increasing availability of genetic testing, and they are saying that they want to get ahead of conversations in which they feel that the data could be "misused."
(Suspiciously, they spend a lot of time talking about a controversial book that was written several decades back: "The Bell Curve," by Murray and Herrnstein--which was actually written LONG before the availability of the molecular genetic data. Also, the authors do not seem to address any of Murray's later books--the ones that made upgrades on the data in TBC.)
I perceive two reasoning errors that are consistent throughout this book:
Error 1. Denial that "That which is can be." If you follow this author's line of reasoning, you would say because Bernoulli's principle is not fully understood then there are/can be no such things as planes. Or, if the mechanism of action of a pharmaceutic is not understood, then the drug doesn't exist.
If you live in the real world and have some type of tool, the question is how well it measures what it is supposed to. (And this was an argument made by Murray and Hernstein: maybe we don't understand the structure of intelligence, but how much is it worth to try to understand it? Especially if it can predict outcomes.)
In this case, a lot of people have noticed persistent African / African descended underachievement, and that is just a fact. (James Watson was forced to retire after pointing out the obvious.)
a. For example, they have lower academic achievement in the United States and on the African continent; you will not look to them to find large numbers of competent mathematicians or physicists.)
b. If you see a flash mob/pilfering/looting in the United States or a government on the African continent (or the United States) degenerating into a racketeering operation, how much more do you need to know other than what your eyes tell you? (And your eyes will not tell you that that flash mob/Kwame Kilpatrick/Ray Nagin is Chinese.)
You can't talk these such things away *just* because you cannot make every single genetic link in the data/logical chain.
Error 2. Separation of the "what?" from the "why?"
So, if you are looking at avoiding underperforming school districts in some place, and these districts are heavily black, then what difference does it make to you that it is *abstractly possible* that these districts could perform as well as everywhere else? (For example: if you look at the bottom 20 school districts out of the 610 in Michigan, ALL of them are >95% black.)
Or, if you know that there will be an influx of blacks as a result of a district closing next door... Do you have to wait for the school to collapse and quality before you head for the hills? Or do you head it off at the pass? (This exact situation happened in Romulus, Michigan after the Inkster School district closed down because of black administrative and competence.)
The author does some interesting speculation in Chapter 7 about how some genes could have been suitable for a different environment (such as if people had a warrior gene, then it would have been good for taking risks during hunting ... But it turns into a gene that will land you in jail in a modern society), or the gene *could have* been selected for at a species level.
If it is true, and you live in Detroit and you get your throat cut or your head blown off during a carjacking by LaDarius Tyrone Washington, do you really feel better if you could explain that this might have been a result of his "warrior gene" that was useful a thousand years ago on the African savannah?
*******
Other things that this author does not address quite to my satisfaction:
1. (Cochran and Harpending) Even if the genetic differences between populations of human beings are small, might that not be enough to explain different outcomes?
Different breeds of dog are useful for different things, even though there is a small genetic distance between them.
The authors here mentioned private alleles within African populations, but then won't go any further and speculate about what they may be able to mean in practice.
Or to rule out why private alleles could not be enough to explain obvious differences.
2. (Murray) It is not possible to exhaustively rigorously define race (nor almost anything else, to be honest), but do the definitions that we have work well enough that we can find trends? This was dealt with in the book "Human Diversity."
3. How strong are some of these relationships? There are almost no graphs (relative to the number of assertions made). And we all know that social science texts can make tons of assertions based on statistically-significant-but-not-practically- significant data.
*******
There is some interesting philosophical discussion about what will happen as sequencing becomes even cheaper.
Could you sequence newborns and predict the genetic difficulties that they will have before they have them?
Could dating sites add the genetic information to the profiles so that consumers will know more of what they are getting?
How do you do a cost benefit allocation to direct resources toward people that are more likely to need them? Or away from people that cannot use them?
And so on.
There's also an interesting final chapter that speculates about what a world could look like in which people are genetically engineered.
It's food for thought, but fortunately I won't be around when it comes.
All these speculations are interesting things, but we will just have to wait and see what happens when the technology gets here.
I can guarantee at least three things that were predicted by the authors will come true:
1. This technology/access will be driven by money. And people who have it (money) will be the first to take advantage of it.
2. Moratoria notwithstanding, there will be someone, somewhere that has no compunction about commercializing this technology. (People's Republic of China!)
3. Technology will be developed LONG before anybody has hashed out the ethical implications. (And that's if they even care to. PRC, again!)
Verdict:
This book might be worth a reread a couple of years down the road to go through and revisit some of the arguments.
It's way too much to pick up on a first pass.
Factoids:
(90) All humans not of direct African descent share 1,000 to 2000 ancestors from a genetic bottleneck about 100,000 years ago.
(95) Manipulation of just 4 genes can turn a mustard seed into a woody tree.
(96) African populations have the largest number of "private alleles" (these are alleles not shared with other human beings)
Vocabulary:
reprogenetics
gerontocracy
assortative mating
epistasis
genetic stratification
unobserved heterogeneity
single nucleotide polymorphism
copy number variants
murine
Manhattan Plot
hypodescent
hyperdescent
cladogram
Continental ancestry
"Ethnic honor" (???)
private allele
genetic mosaicism
February 6, 2017
Si tuviese que resumirlo de alguna manera diría que es remarcablemente poco estúpido. Dada la calidad media de lo que se escribe sobre los efectos de las diferencias genéticas en la sociedad, con la miopía exageradora o negacionista captando más atención de la que merece, este sorprende por su evaluación honesta del campo y buen resumen de todos los conceptos y vías de evidencia sobre el tema. Al parecer los autores eran científicos sociales que querían refutar a los estudios de gemelos y genómica por ingenuos y sobre-estimar la heredabilidad, se encontraron con que sus resultados no refutaban los métodos si no que los reforzaban o mostraban sesgo bastante pequeño que podía ir en cualquier dirección y entonces van Y ACEPTAN LOS RESULTADOS Y CAMBIAN SU INVESTIGACIÓN DE AHÍ EN ADELANTE para tener en cuenta la genética. ¿Quién puñetas hace eso? Bichos raros.
Mis críticas serían mayormente nitpicking de las pocas partes en las que creo que sus referencias no son exhaustivas o no están al día (ej. citan la crítica a los GCTA y no la respuesta de Visscher defendiendo la técnica) con una excepción: en su capítulo de economía se toman a Ashraf & Galor en serio. No es que considere que en principio no puede haber influencia de las diferencias genéticas funcionales en cómo les va a unas naciones respecto a otras, igual que influye la historia, la geografía y los recursos locales, pero es que ese par hablaban de RELEVANCIA DE LA DIVERSIDAD GENÉTICA A PELO EN LA RIQUEZA DE LAS NACIONES. NEUTRAL. QUE NO TIENE NI POR QUÉ SER FUNCIONAL. Un completo disparate que no sé cómo se les ha podido pasar con lo bien que hablan de genómica, polygenic scores, los problemas para establecer las interacciones genes-ambiente y tendencias temporales en condiciones, etc.
Se lleva una estrella menos por el capítulo de economía. Incluso fuera de este, dudo que deje completamente satisfechos a lectores anticapitalistas al no cuestionar el sistema actual y tomarse cosas como la meritocracia medio en serio por bien que conozcan el origen del término. Pero aún así y a pesar de la puntuación final está muy muy recomendado a cualquiera interesado como la mejor introducción en tan poco espacio y al día para el tema de momento, evitando fallos muy frecuentes.
Mis críticas serían mayormente nitpicking de las pocas partes en las que creo que sus referencias no son exhaustivas o no están al día (ej. citan la crítica a los GCTA y no la respuesta de Visscher defendiendo la técnica) con una excepción: en su capítulo de economía se toman a Ashraf & Galor en serio. No es que considere que en principio no puede haber influencia de las diferencias genéticas funcionales en cómo les va a unas naciones respecto a otras, igual que influye la historia, la geografía y los recursos locales, pero es que ese par hablaban de RELEVANCIA DE LA DIVERSIDAD GENÉTICA A PELO EN LA RIQUEZA DE LAS NACIONES. NEUTRAL. QUE NO TIENE NI POR QUÉ SER FUNCIONAL. Un completo disparate que no sé cómo se les ha podido pasar con lo bien que hablan de genómica, polygenic scores, los problemas para establecer las interacciones genes-ambiente y tendencias temporales en condiciones, etc.
Se lleva una estrella menos por el capítulo de economía. Incluso fuera de este, dudo que deje completamente satisfechos a lectores anticapitalistas al no cuestionar el sistema actual y tomarse cosas como la meritocracia medio en serio por bien que conozcan el origen del término. Pero aún así y a pesar de la puntuación final está muy muy recomendado a cualquiera interesado como la mejor introducción en tan poco espacio y al día para el tema de momento, evitando fallos muy frecuentes.
July 7, 2019
Seems to be written by leaders in the fields of sociology and genomics which means they have the political capital to address, head on, really thorny and taboo issues like genetics, race, and social programs. They dont shy from these issues but their main point is that social sciences should be more open to looking at genomics data and the genomics studies that have been done do not have enough data to make firm conclusions one way or another in the nature/nurture debate. They take on some of the arguments from The Bell Curve but the few arguments they tackle directly they mostly argue and defeat straw men they create.
They talk about the initial candidate single gene studies that created a lot of excitement but ended up being all false positives given the way the data was run. Then they turned to Genome Wide Association Studies (GWAS) which is a good approach but needs more data to be effective.
One interesting insight was that there is much larger genetic variation between African genomes because genetic drift was happening for much longer in Africa than for the humans who left Africa 40,000 years ago and populated the rest of the world, who were basically just one branch off of many branches that were developing. So there is more of a difference between two neighboring tribes in many African countries than between Europeans and Asians.
The main thing I learned was that it is super complicated to separate culture (nurture) from genetics (nature). There seems to always be a way to suggest why the findings arent what they really show and the controls aren't adequate, no matter how carefully the experiment is planned.
But the cost of whole genome sequencing is dropping and the datasets are getting larger and larger, so the polygenic scores are getting more and more powerful at accounting for variation seen in everything. Now it might be 3% of the variation expected, but just in the last few years its jumped to 6%, and should get all the way there with more data.
Lots of good notes and great bibliography. Large amount of appendices, which I didn't find all that useful.
They talk about the initial candidate single gene studies that created a lot of excitement but ended up being all false positives given the way the data was run. Then they turned to Genome Wide Association Studies (GWAS) which is a good approach but needs more data to be effective.
One interesting insight was that there is much larger genetic variation between African genomes because genetic drift was happening for much longer in Africa than for the humans who left Africa 40,000 years ago and populated the rest of the world, who were basically just one branch off of many branches that were developing. So there is more of a difference between two neighboring tribes in many African countries than between Europeans and Asians.
The main thing I learned was that it is super complicated to separate culture (nurture) from genetics (nature). There seems to always be a way to suggest why the findings arent what they really show and the controls aren't adequate, no matter how carefully the experiment is planned.
But the cost of whole genome sequencing is dropping and the datasets are getting larger and larger, so the polygenic scores are getting more and more powerful at accounting for variation seen in everything. Now it might be 3% of the variation expected, but just in the last few years its jumped to 6%, and should get all the way there with more data.
Lots of good notes and great bibliography. Large amount of appendices, which I didn't find all that useful.
August 1, 2018
Besides the human genome development that could fundamentally change the human world and evolution process, it's like we are opening Pandora's box to look into a human's DNA to know their background, genetic diseases.
All this would contribute the society to evolve in a sense more unequal. Let alone new technology : CRISPR to edit our genomes. Bad habits of our ancestors could passing down to the offspring such as smoking, obesity, chronic diseases. People will also choose similar genes as their spouses.
Same genes in different environment or education could produce very different outcomes.
https://www.youtube.com/watch?v=5dBwM...
CRISPR is a family of DNA sequences in bacteria and archaea.[1] The sequences contain snippets of DNA from viruses that have attacked the prokaryote. These snippets are used by the prokaryote to detect and destroy DNA from similar viruses during subsequent attacks. These sequences play a key role in a prokaryotic defense system,[1] and form the basis of a technology known as CRISPR/Cas9 that effectively and specifically changes genes within organisms.[2]
CRISPR is an abbreviation of Clustered Regularly Interspaced Short Palindromic Repeats.[3] The name was minted at a time when the origin and use of the interspacing subsequences were not known. At that time the CRISPRs were described as segments of prokaryotic DNA containing short, repetitive base sequences. In a palindromic repeat, the sequence of nucleotides is the same in both directions. Each repetition is followed by short segments of spacer DNA from previous exposures to foreign DNA (e.g., a virus or plasmid).[4][5] Small clusters of cas (CRISPR-associated system) genes are located next to CRISPR sequences.) (from Wikipedia)
**CRISPR（IPA：/ˈkrɪspər/；DJ：/ˈkrispə/；KK：/ˈkrɪspɚ/）是存在于细菌中的一类基因组，该类基因组中含有曾攻击过该细菌的病毒的基因片段。细菌通过这些基因片段来侦测并抵御类似病毒的攻击，摧毁其DNA。这类基因组是细菌免疫系统的关键组成部分。通过这类基因组，人类可以准确有效地编辑有机体内的部分基因，也即CRISPR/Cas9基因编辑技术。
All this would contribute the society to evolve in a sense more unequal. Let alone new technology : CRISPR to edit our genomes. Bad habits of our ancestors could passing down to the offspring such as smoking, obesity, chronic diseases. People will also choose similar genes as their spouses.
Same genes in different environment or education could produce very different outcomes.
https://www.youtube.com/watch?v=5dBwM...
CRISPR is a family of DNA sequences in bacteria and archaea.[1] The sequences contain snippets of DNA from viruses that have attacked the prokaryote. These snippets are used by the prokaryote to detect and destroy DNA from similar viruses during subsequent attacks. These sequences play a key role in a prokaryotic defense system,[1] and form the basis of a technology known as CRISPR/Cas9 that effectively and specifically changes genes within organisms.[2]
CRISPR is an abbreviation of Clustered Regularly Interspaced Short Palindromic Repeats.[3] The name was minted at a time when the origin and use of the interspacing subsequences were not known. At that time the CRISPRs were described as segments of prokaryotic DNA containing short, repetitive base sequences. In a palindromic repeat, the sequence of nucleotides is the same in both directions. Each repetition is followed by short segments of spacer DNA from previous exposures to foreign DNA (e.g., a virus or plasmid).[4][5] Small clusters of cas (CRISPR-associated system) genes are located next to CRISPR sequences.) (from Wikipedia)
**CRISPR（IPA：/ˈkrɪspər/；DJ：/ˈkrispə/；KK：/ˈkrɪspɚ/）是存在于细菌中的一类基因组，该类基因组中含有曾攻击过该细菌的病毒的基因片段。细菌通过这些基因片段来侦测并抵御类似病毒的攻击，摧毁其DNA。这类基因组是细菌免疫系统的关键组成部分。通过这类基因组，人类可以准确有效地编辑有机体内的部分基因，也即CRISPR/Cas9基因编辑技术。
January 2, 2021
Can genes inform policy in the future? 🤔
🧬 The genome factor is written by Dalton Conley and Jason Fletcher both sociology professors. In this book, they break down the notion that a specific gene is responsible for IQ. In reality, the correlation between our genes and anything related to society can be quite complicated. This book is great for folks that are interested in the debate if genes should be used to inform policy for example. Some of the things they touch one are:
🧬 In one of the chapters, they debunk genetic superiority being the determining factor between race and class. They also pose an important question of WETHER and HOW to use genetic information for treatments, policies, etc.
🧬 They also covered things that might be on the horizon. Like including gene information in Tinder or eHarmony for better matches. For example, if you have one copy of the sickle cell you might not want to be matched with somebody that has that copy. I thought this was both a fascinating and horrible idea when thinking of the implications that can have.
🧬I wish they had covered more things that a currently happening like that CRISPR incident that was "suppose to cure HIV" but overall it was a solid book! If you like the GATTACA movie or genes in general I think you will like this book!
🌟If you want to give it a read, keep tuned because I will do my first giveaway early next week!🌟
Rating: ⭐ ⭐ ⭐ ⭐
Follow: @ScienceBey for giveaway nextweek (01/04/21)
🧬 The genome factor is written by Dalton Conley and Jason Fletcher both sociology professors. In this book, they break down the notion that a specific gene is responsible for IQ. In reality, the correlation between our genes and anything related to society can be quite complicated. This book is great for folks that are interested in the debate if genes should be used to inform policy for example. Some of the things they touch one are:
🧬 In one of the chapters, they debunk genetic superiority being the determining factor between race and class. They also pose an important question of WETHER and HOW to use genetic information for treatments, policies, etc.
🧬 They also covered things that might be on the horizon. Like including gene information in Tinder or eHarmony for better matches. For example, if you have one copy of the sickle cell you might not want to be matched with somebody that has that copy. I thought this was both a fascinating and horrible idea when thinking of the implications that can have.
🧬I wish they had covered more things that a currently happening like that CRISPR incident that was "suppose to cure HIV" but overall it was a solid book! If you like the GATTACA movie or genes in general I think you will like this book!
🌟If you want to give it a read, keep tuned because I will do my first giveaway early next week!🌟
Rating: ⭐ ⭐ ⭐ ⭐
Follow: @ScienceBey for giveaway nextweek (01/04/21)
June 18, 2024
Very happy I read this one. A few chapters went over my head (particularly the heritability ones), but I know that is because I am new to the social sciences. If you are like me, it will be dense!
The authors completely grabbed my attention with the topics of genetic sorting, race, the wealth of nations, and personalized policy. They did a fantastic job of debating all sides of the upcoming social genomics revolution - of which I am not equally intrigued (as a geneticist) and terrified (as a human being). I found the appendixes intriguing but sometimes overwhelming. The story-like epilogue tied the whole thing into one perfect bow. Overall, a great analysis of the old nature-nurture debate and a warning to a world on the cusp of legal gene editing.
The authors completely grabbed my attention with the topics of genetic sorting, race, the wealth of nations, and personalized policy. They did a fantastic job of debating all sides of the upcoming social genomics revolution - of which I am not equally intrigued (as a geneticist) and terrified (as a human being). I found the appendixes intriguing but sometimes overwhelming. The story-like epilogue tied the whole thing into one perfect bow. Overall, a great analysis of the old nature-nurture debate and a warning to a world on the cusp of legal gene editing.
June 18, 2020
Een overzicht van de stand van de wetenschap over de invloed van de genen op sociale uitkomsten.
Voor iemand die niet geregeld dit soort onderzoek leest geeft het enerzijds goed inzicht in alle haken en ogen die aan de interpretatie van dit soort onderzoek kleven. Anderzijds is het wat moeilijk leesbaar door het vele vakjargon.
Voor iemand die niet geregeld dit soort onderzoek leest geeft het enerzijds goed inzicht in alle haken en ogen die aan de interpretatie van dit soort onderzoek kleven. Anderzijds is het wat moeilijk leesbaar door het vele vakjargon.
December 5, 2021
Best book on the social genomics revolution I have read. Very, subtly argued ideas supported by carefully examined evidence.
May 16, 2017
A book that causes you to pause and think how genetics genetic structure and social customs and existence in the next century will play out. A good discussion of what genes are, hoe they work, our present state of progress in research and how this will affect us all socially and economically. The difficulty in separating environment from heredity is also discussed. Fascinating read, but it took a bit for me to digest.
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