“How much of the human genome can we “read” in a useable or predictive sense? Until recently, the capacity to predict fate from the human genome was limited by two fundamental constraints. First, most genes, as Richard Dawkins describes them, are not “blueprints” but “recipes.” They do not specify parts, but processes; they are formulaws for forms. If you change a blueprint, the final product is changed in a perfectly predictable manner: eliminate a widget specified in a the plan, and you get a machine with a missing widget. But the alteration of a recipe or formula does not change the product in a predictable manner: if you quadruple the amount of butter in a cake, the eventual effect is more complicated than just a quadruply buttered cake (try it; the whole thing collapses in an oily mess). By similar logic, you cannot examine most gene variants in isolation and decipher their influence on form and fate. That a mutation in the gene MECP2, whose normal function is to recognize chemical modifications to DNA, may cause a form of autism is far from self-evident (unless you understand how genes control neurodevelopmental processes that make a brain).The second constraint – possibly deeper in significance – is the intrinsically unpredictable nature of some genes. Most genes intersect with other triggers – environment, chance, behaviours, or even parental and prenatal exposures – to determine an organism’s form and function, and its consequent effects on its future. Most of these interactions, we have already discovered, are not systematic: they happen as a result of chance, and there is no method to predict or model them with certainty. These interactions place powerful limits on genetic determinism: the eventual effects of these gene-environment intersections can never be reliably presaged by the genetics alone. Indeed, recent attempts to use illnesses in one twin to predict future illnesses in the other have come up with only modest successes.
But there is no reason that the constraints on genetic diagnosis should be limited to diseases caused by mutations in single genes or chromosomes… A powerful enough computer should be able to hack the understanding of a recipe: if you input an alteration, one should be able to compute its effect on the product.
The genome will thus be read not in absolutes, but in likelihoods – like a report card that does not contain grades but probabilities, or a resume that does not list paste experiences but future propensities. It will become a manual of previvorship.”
Mukherjee is not an environmentalist, but he has illuminated the anthropological significance of the environment with more urgency than a generation of tree-huggers. A stable, healthy environment is a precondition for genetic engineering, and all other types of engineering, to make us fitter. Without that, science and technology will be wasted on a world that is slipping through our fingers.
There are two other interesting things about Mukherjee’s statement. First, how closely it mirrors Stephen Hawking’s resurrection of determinism under the guise of probability. If everyone had a statistically meaningful number of lives to live, then probabilistic determinism would totally work. In a lab with an unlimited supply of cloned mice, it really does work (for the researchers, not the mice). The second thing is the soft constraints entropy places on scientific investigation and how scientists chafe at the bit to break free. It’s true that there is no hard limit to genetic prediction, but there is an asymptotic boundary around what is possible in the translation from information to configuration. Much like the limit to how fast a person can run 100 meters, the absolute limit is unknowable. It may not be 9 seconds. It may not even be 8 seconds. But it is almost certainly not less than 7 seconds and absolutely not below 5 seconds. So also there will always be new diseases cured by genetic engineering or prevented by genetic testing, but human life will be changed far less by genetics than by private abuse, and institutional cruelty.
The genome project also carries with it the frailties of all mapping projects: that the information embedded in the map is subject to the entropy of the map material. It turns out that genes are subject to continuous mutation in individual bodies as well as the population at large.
Scientists Surprised to Find No Two Neurons Are Genetically Alike
The genetic makeup of any given brain cell differs from all others. That realization may provide clues to a range of psychiatric diseases
By Simon Makin on May 3, 2017, Scientific American Magazine
“Studies that preceded the consortium have confirmed mosaicism is commonplace. One report estimated there may be hundreds of changes in single letters of genetic code (single nucleotide variants, or SNVs) in each neuron in mouse brains. Another found over a thousand in human neurons. These findings suggest somatic mosaicism is the rule, not the exception, with every neuron potentially having a different genome than those to which it is connected. A primary cause of somatic mutations has to do with errors during the DNA replication that occurs when cells divide—neural progenitor cells undergo tens of billions of cell divisions during brain development, proliferating rapidly to produce the 80 billion neurons in a mature brain. The image of each cell carrying a carbon copy of the genetic material of all other cells is starting to fade—and for good reason. Genetic sequencing does not normally capture the somatic mutations in each cell. “You get a sort of average of the person’s genome, but that doesn’t take into account any brain-specific mutations that might be in that person,” says study lead author Michael McConnell of the University of Virginia.
A 2012 study found somatic mutations in the brains of children with hemimegalencephaly, a developmental disorder in which one hemisphere is enlarged, causing epilepsy and intellectual disability. The mutations were found in brain tissue, but not always in blood nor in cells from unaffected brain areas, and only in a fraction (around 8 to 35 percent) of cells from affected areas. Such studies, showing somatic mutations can cause specific populations of cells to proliferate, leading to cortical malformations, has researchers wondering whether somatic mutations may also play roles in more complex conditions.”