1600 words

Genetic reductionism refers to the belief that understanding our genes will have us understand everything from human behavior to disease. The behavioral genetic approach claims to be the best way to parse through social and biological causes of health, disease, and behavior. The aim of genetic reductionism is to reduce a complex biological system to the sum of its parts. While there was some value in doing so when our technology was in its infancy and we did learn a lot about what makes us “us”, the reductionist paradigm has outlived its usefulness.

If we want to understand a complex biological system then we shouldn’t use gene scores, heritability estimates, or gene sequencing. We should be attempting to understand how the whole biological system interacts with its surroundings—its environment.

Reductionists may claim that “gene knockout” studies can point us in the direction of genetic causation—“knockout” a gene and, if there are any changes, then we can say that that gene caused that trait. But is it so simple? Richardson (2000) puts it well:

All we know for sure is that rare changes, or mutations, in certain single genes can drastically disrupt intelligence, by virtue of the fact that they disrupt the whole system.

Noble (2011) writes:

Differences in DNA do not necessarily, or even usually, result in differences in phenotype. The great majority, 80%, of knockouts in yeast, for example, are normally ‘silent’ (Hillenmeyer et al. 2008). While there must be underlying effects in the protein networks, these are clearly buffered at the higher levels. The phenotypic effects therefore appear only when the organism is metabolically stressed, and even then they do not reveal the precise quantitative contributions for reasons I have explained elsewhere (Noble, 2011). The failure of knockouts to systematically and reliably reveal gene functions is one of the great (and expensive) disappointments of recent biology. Note, however, that the disappointment exists only in the gene-centred view. By contrast it is an exciting challenge from the systems perspective. This very effective ‘buffering’ of genetic change is itself an important systems property of cells and organisms.

Moreover, even when a difference in the phenotype does become manifest, it may not reveal the function(s) of the gene. In fact, it cannot do so, since all the functions shared between the original and the mutated gene are necessarily hidden from view. … Only a full physiological analysis of the roles of the protein it codes for in higher-level functions can reveal that. That will include identifying the real biological regulators as systems properties. Knockout experiments by themselves do not identify regulators (Davies, 2009).

All knocking-out or changing genes/alleles will do is show us that T is correlated with G, not that T is caused by G. Merely observing a correlation between a change in genes or knocking genes out will tell us nothing about biological causation. Reductionism will not have us understand the etiology of disease as the discipline of physiology is not reductionist at all—it is a holistic discipline.

Lewontin (2000: 12) writes in the introduction to The Ontogeny of Information: “But if I successfully perform knockout experiments on every gene that can be seen in such experiments to have an effect on, say, wing shape, have I even learned what causes the wing shape of one species or individual to differ from that of another? After all, two species of Drosophilia presumably have the same relevant set of loci.”

But the loss of a gene can be compensated by another gene—a phenomenon known as genetic compensation. In a complex bio-system, when one gene is knocked out, another similar gene may take the ‘role’ of the knocked-out gene. Noble (2006: 106-107) explains:

Suppose there are three biochemical pathways A, B, and C, by which a particular necessary molecule, such as a hormone, can be made in the body. And suppose the genes for A fail. What happens? The failure of the A genes will stimulate feedback. This feedback will affect what happens with the sets of genes for B and C. These alternate genes will be more extensively used. In the jargon, we have here a case of feedback regulation; the feedback up-regulates the expression levels of the two unaffected genes to compensate for the genes that got knocked out.

Clearly, in this case, we can compensate for two such failures and still be functional. Only if all three mechanisms fail does the system as a whole fail. The more parallel compensatory mechanisms an organism has, the more robust (fail-safe) will be its functionality.

The Neo-Darwinian Synthesis has trouble explaining such compensatory genetic mechanisms—but the systems view (Developmental Systems Theory, DST) does not. Even if a knockout affects the phenotype, we cannot say that that gene outright caused the phenotype, the system was screwed up, and so it responded in that way.

Genetic networks and their role in development became clear when geneticists began using genetic knockout techniques to disable genes which were known to be implicated in the development of characters but the phenotype remained unchanged—this, again, is an example of genetic compensation. Jablonka and Lamb (2005: 67) describe three reasons why the genome can compensate for the absence of a particular gene:

first, many genes have duplicate copies, so when both alleles of one copy are knocked out, the reserve copy compensates; second, genes that normally have other functions can take the place of a gene that has been knocked out; and third, the dynamic regulatory structure of the network is such that knocking out single components is not felt.

Using Waddington’s epigenetic landscape example, Jablonka and Lamb (2005: 68) go on to say that if you knocked a peg out, “processes that adjust the tension on the guy ropes from other pegs could leave the landscape essentially unchanged, and the character quite normal. … If knocking out a gene completely has no detectable effect, there is no reason why changing a nucleotide here and there should necessarily make a difference. The evolved network of interactions that underlies the development and maintenance of every character is able to accommodate or compensate for many genetic variations.”

Wagner and Wright (2007: 163) write:

“multiple alternative pathways . . . are the rule rather than the exception . . . such pathways can continue to function despite amino acid changes that may impair one intermediate regulator. Our results underscore the importance of systems biology approaches to understand functional and evolutionary constraints on genes and proteins.” (Quoted in Richardson, 2017: 132)

When it comes to disease, genes are said to be difference-makers—that is, the one gene difference/mutation is what is causing the disease phenotype. Genes, of course, interact with our lifestyles and they are implicated in the development of disease—as necessary, not sufficient, causes. GWA studies (genome-wide association studies) have been all the rage for the past ten or so years. And, to find diseases ‘associated’ with disease, GWA practioners take healthy people and diseased people, sequence their genomes and they then look for certain alleles that are more common in one group over the other. Alleles more common in the disease group are said to be ‘associated’ with the disease while alleles more common in the control group can be said to be protective of the disease (Kampourakis, 2017: 102). (This same process is how ‘intelligence‘ is GWASed.)

“Disease is a character difference” (Kampourakis, 2017: 132). So if disease is a character difference and differences in genes cannot explain the existence of different characters but can explain the variation in characters then the same must hold for disease.

“Gene for” talk is about the attribution of characters and diseases to DNA, even thoughit is not DNA that is directly responsible for them. … Therefore, if many genes produce or affect the production of the protein that in turn affects a character or disease, it makes no sense to identify one gene as the gene responsible “for” this character or disease. Single genes do not produce characters or disease …(Kampourakis, 2017: 134-135)

This all stems from the “blueprint metaphor”—the belief that the genome contains a blueprint for form and development. There are, however, no ‘genes for’ character or disease, therefore, genetic determinism is false.

Genes, in fact, are weakly associated with disease. A new study (Patron et al, 2019) analyzed 569 GWA studies, looking at 219 different diseases. David Scott (one of the co-authors) was interviewed by Reuters where he said:

“Despite these rare exceptions [genes accounting for half of the risk of acquiring Crohn’s, celiac and macular degeneration], it is becoming increasingly clear that the risks for getting most diseases arise from your metabolism, your environment, your lifestyle, or your exposure to various kinds of nutrients, chemicals, bacteria, or viruses,” Wishart said.

…

“Based on our results, more than 95% of diseases or disease risks (including Alzheimer’s disease, autism, asthma, juvenile diabetes, psoriasis, etc.) could NOT be predicted accurately from SNPs.”

It seems like this is, yet again, another failure of the reductionist paradigm. We need to understand how genes interact in the whole human biological system, not reducing our system to the sum of its parts (‘genes’). Programs like this are premised on reductionist assumptions; it seems intuitive to think that many diseases are ’caused by’ genes, as if genes are ‘in control’ of development. However, what is truly ‘in control’ of development is the physiological system—where genes are used only as resources, not causes. The reductionist (neo-Darwinist) paradigm cannot really explain genetic compensation after knocking out genes, but the systems view can. The amazing complexity of complex bio-systems allows them to buffer against developmental miscues and missing genes in order to complete the development of the organism.

Genes are not active causes, they are passive causes, resources—they, therefore, cannot cause disease and characters.