In a WebMD article, the results from a large genetic-factor-only study gleefully reports that the newest, highest-ever estimate of the percent liability of autism risk that can be attributed to “genetics” is 80%, leaving the remaining 20% to environmental factors.
The article also claims that this new, highest estimate is reported by the study authors to be “…roughly in line with those from prior, smaller studies on the issue, further bolstering their validity“.
Consistent Results From Invalid Methodology Does not Make Those Results “Valid”. It Makes Them “Consistent”.
The “roughly in line with” is an appeal to consistency. But the Liability Threshold Models differ from other approaches methodologically. Previous studies, one of which was conducted by the same group of researchers, had estimates that ranged from 0 to 99% heritability. The average, until this group started using liability-threshold models, was around 40% attribution to genetics. Their studies increased the average, but it still hovered around 50% liability. Only the liability threshold models, used by this group, show results around 80% liability. So their method is consistent with itself. No surprise there. But that’s nowhere near “roughly in line” with all prior studies.
One of those studies is discussed in the article “Non-genetic factors play surprisingly large role in determining autism, says study by group“.
Why Autism is Not “Genetic”
The article skips over the fact that the newest, latest study, like the prior studies, fails to actually measure the contribution of a single environmental factor. While the article rails against “anti-vaxxers”, the study ignores the vaccination status of those involved in the study. The mantra of so many studies never showing association has to be tempered with a mature, responsible and realistic interpretation in the context of how those studies were conducted: restricted to one vaccine (MMR), and then there is this:
Assumptions Without Measurement Lead to Assumptions as Conclusions
Their entire methodology is based on familial correlations. In the current study under consideration, no exposure levels to pesticides, medical exposures in utero, smoking history, nothing environmental was measured. And yet somehow the study authors pretend they can estimate the % liability from environmental factors. How do they pretend to achieve such a feat?
The first problem is that they have not measured any interaction between genetics and environmental factors. There is, in fact, established knowledge of special risk of autism that involves combined risk of specific genes and specific environmental factors. Check out, for example, Bowers and Erickson (2014):
Their Liability Threshold Model Approach is Both Under- and Mis-Specified
You really have to understand population genetics a bit to get this next part, so I apologize to the lay public, but please take what understanding you can from this:
Their model (generically represented) is
ASD risk = “Genetics” + e
where e = measurement error, leaving whatever variation appears to be unexplained to Environment. That’s unusual because the usual interpretation of such unexplained variation is “Error” and “Unknown Variation”. In technical terms, their model is underspecified. Environmental variation is not “Error” in a genetic model, it’s “Environmental Variation”.
If they HAD measured environmental factors, say, vaccination exposure, their model form would be
ASD risk = “Genetics” + “Environment” + e
but this model would still be underspecified.
The more fully specified model would be
ASD risk = “Genetics” + “Environment“+ “(Genetics x Environment)” + e
And if the interaction term “(Genetics + Environment)” is more highly significant than “Genetics” or “Environment“, a reasonable interpretation would be that we cannot interpret genetics in a vacuum, that the significance of many ADK risk alleles must be modified by environmental factors. If during model selection, G or E is significant, but then in the full model G x E is significant, we attribute liability to both G and E working together.
Instead of this standard approach to studying genetic and environmental contribution to phenotypic variation (ASD phenotype), they do something very odd.
In the Supplementary Material, they report that they made assumptions about environmental factors. Non-specified “Shared Environmental” effects are ASSUMED to be 1.0 for siblings and 0 for cousins. Families quite often stop vaccinating after an older sibling experiences seizures. The study authors also EQUATE “Non-Shared Environmental Factors” with “residual errors”, which is patently absurd. That’s “e“, which is unspecified variation (error), not designated environmental factors.
If I had conducted an analysis of environmental factors and their contribution to ASD, and used their methodology, I would be able to attribute any unexplained variation to “Genetics” after allowing “Environmental Factors” to consume most of the variation. I might arbitrarily add in some assumptions, such as assuming that risk from dominant alleles were 1.0 (which they are not, if the impact of those alleles are modified by environmental factors) and all recessive risk alleles contributed zero risk, which would be, as described, arbitary. Their conclusions draw directly from their assumptions.
Evidence? What Evidence?
The WebMD article cites the entire team of researchers as saying “the current study results provide the strongest evidence to our knowledge to date that the majority of risk for autism spectrum disorders is from genetic factors,” [‘said a team led by Sven Sandin, an epidemiological researcher at the Karolinska Institute in Stockholm, Sweden’]“ – as quoted by WebMD.
Evidence? What evidence? If you assume no contribution of environment, measure no environment, and conclude no contribution, there is no evidence.
There are over 850 genes that have been determined to contribute to ASD risk – and not one of them explains >1% of ASD risk individually. Most of these are Common Variants – meaning they are ancient – as in, they pre-date both the ASD epidemic (and yes, there is an epidemic) and vaccination. Here’s a figure from my book, which reviews all of the genetic and environmental studies published to mid-2016:
This explains why ASD pedigrees look like humanity dipping its toes into a toxic soup:
The study also does not explain why >20% of children with ASD have higher copy number variation – de novo genetic variation – compared to the rest of the population, nor why people with ASD – and their mothers – have anti-brain protein antibodies – nor why people with ASD have strange misfolded proteins, lifelong microglial activation, why studies of replacing the microbiome show a reduction in the severity of autism traits by 50%… a feat for a diagnosis that is allegedly 80% “genetic”… and so on, and so on.
Then There is Phenomimicry
The study ignores the fact that environmental factors can impact genes, proteins and biological pathways in a manner that is identical to the effects of genetic variation. This is called Phenomimicry – a term so cool I wish I had invented it. Examples of Phenomimicry are known in science relevant to ASD.
“Guess What? Being Human is Heritable”
It’s worth pointing out that thousands of human “traits” are heritable, and that includes traits that contribute to sociality, language ability, intellect, and even perhap tendency toward repetitive motion. That means that genetic studies must subtract the heritability of these traits in the non-ASD population from the estimate of heritability in their contribution to ASD.
The WebMD article, and the research report itself, lauds the study for involving over 2 million people from five countries. This is not impressive because the study falls into the category of “Science-Like Activities“.
No More YAHUGS
It is highly unethical – and socially irresponsible – for “Genes-Only” studies to be conducted that claim to rule out environmental factors. All “Yet Another Highly Unethical Genes-Only Study”s – YAHUGS – should be replaced with fully and correctly specified models. That means measuring and studying both vaccination patterns and genetics.
Allison Park, PA
Note: A layman’s example will help. Let’s say you want to understand thumb injuries among carpenters,and you specify a model
Risk of Injury = Hammer Size
You SHOULD also include Length of Nail, i.e.,
Risk of Injury = Hammer Size + Length of Nail
but it is socially unacceptable to conduct science on the Length of Nail. So you leave it out. You then model
Risk of Injury = Hammer Size + e
and incorrectly attribute variation in the “Length of Nails” to “e“.
You SHOULD specify
Risk of Injury = Hammer Size + Length of Nail + (Hammer Size x Length of Nail) + e
But that pesky social pressure to ignore Length of Nail goes a long way.
So you don’t know “(Hammer Size x Length of Nail)“because you do not know Length of Nail.
So you attribute everything to “Hammer Size”, totally ignorant of any direct or interactive effect of the “Length of Nail“and “Hammer Size“.
So you conclude “Hammer Size explains more than Length of Nails” when you should publish
“We Do Not Know the Effect of Length of Nails in Isolation nor with Interaction with Hammer Size”.
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