As the MIT Technology Review article mentions, irresponsible, unsupported, overheated reports on "significant" interpretations of genomic data are already influencing policy and fueling racist...
Advocates of sociogenomics envision a prospect that not everyone will find entirely benevolent: health “report cards,” based on your genome and handed out at birth, that predict your risk of various diseases and propensity for different behaviors. In the new social sciences, sociologists will examine the genetic component of educational attainment and wealth, while economists will envision genetic “risk scores” for spending, saving, and investment behavior.
Without strong regulation, these scores could be used in school and job applications and in calculating health insurance premiums. Your genome is the ultimate preexisting condition.
Such a world could be exciting or scary (or both). But sociogenomicists generally focus on the sunny side. And anyway, they say with a shrug, there’s nothing we can do about it. “The genie is out of the bottle,” writes the educational psychologist Robert Plomin, “and cannot be stuffed back in again.”
As the MIT Technology Review article mentions, irresponsible, unsupported, overheated reports on "significant" interpretations of genomic data are already influencing policy and fueling racist propaganda.
I can't really say enough about the ever-green bad science of taking a pile of data, running linear regression analysis to discover correlations, then reporting those correlations as if they're proven hypotheses. In fact, there are whole books, very nicely written by Ben Goldacre, about how badly this process affords genuine understanding of complex causation, and how easily the results can be cherry-picked to favor whatever conclusion one's biases may suggest.
As the MIT Technology Review article mentions, irresponsible, unsupported, overheated reports on "significant" interpretations of genomic data are already influencing policy and fueling racist propaganda.
I can't really say enough about the ever-green bad science of taking a pile of data, running linear regression analysis to discover correlations, then reporting those correlations as if they're proven hypotheses. In fact, there are whole books, very nicely written by Ben Goldacre, about how badly this process affords genuine understanding of complex causation, and how easily the results can be cherry-picked to favor whatever conclusion one's biases may suggest.