Trump 'fact checking' his autism claim
If the human genome had been intelligently designed by an omniscient, omnibenevolent, omnipotent supernatural deity, as creationists insist, it should be perfect and free from defects of any sort. In fact, it is difficult to see why there would be any variance in such an intelligently designed genome, let alone variance that causes genetic defects—unless those were intentionally included by the designer, who then cannot reasonably be described as omnibenevolent or omniscient.
If, however, the human genome is the product of hundreds of millions of years of gradual evolutionary processes — processes that prioritise survival and reproduction, with all the sub-optimal compromises that a utilitarian form of ‘design’ entails — then variance and defects are exactly what we would expect.
Creationists traditionally ignore questions about the origin of variance in a supposedly ‘perfect’ intelligently designed genome. The existence of genetic defects is usually explained away by resorting to Bible-literalist mythology about ‘The Fall’ — an abandonment of the Discovery Institute’s Wedge Strategy, which seeks to present creationism as real science rather than a fundamentalist religion dressed in a lab coat. News that autism may in fact be a by-product of the evolution of intelligence in humans will therefore be an even greater problem for creationists, who insist that our high intelligence sets us apart as the special creation of a perfect god.
Ironically, as well as possessing high intelligence, humans — unlike any other primates — also have autism and schizophrenia. It is this correlation that provides a clue to their shared evolutionary origins.
My book, The Body of Evidence: How the Human Body Refutes Intelligent Design, lists lots of examples of how the human body is the result of these sub-optimal evolutionary compromises with all the problems that has produced. This example is just another instance and more evidence of the lack of intelligence in the process.
Autism and Paracetamol in Pregnancy^ Myth and facts.The evidence that autism is a by-product of the evolution of intelligence is presented in an open-access paper in the Oxford University Press journal, Molecular Biology and Evolution, by Alexander L. Starr and Hunter B. Fraser of Stanford University, Stanford, CA, USA. It is also summarised in a press release from Oxford University Press on EurekAlert!
(In the USA, Paracetamol is marketed as Tylenol, Mapap or Panadol)
Myth - promoted by US president Donald Trump:
Paracetamol (acetaminophen) taken during pregnancy causes autism.
Fact:
- This claim is not supported by science.
- A few studies have shown weak correlations, but these are confounded by other factors such as maternal illness, genetics, and environment.
- The far stronger correlation is between rising recorded incidence of autism and improvements in diagnosis and awareness, especially after Asperger’s syndrome and related conditions were included within the autism spectrum.
- Public health authorities (FDA, NHS) continue to regard paracetamol as safe in pregnancy when used as directed.
- In reality, poorly managed pain and fever during pregnancy carries clearer risks for foetal development than paracetamol.
Conclusion:
Trump’s assertion is a dangerous textbook case of abusing weak statistics for political point-scoring and narcissistic self-promotion. By presenting correlation as causation, he misleads the public and undermines trust in science, while offering no evidence of his own. His cavalier disregard for truth and for the health and welfare of women and children has resulted in misinformation and policies that risk causing real harm while achieving nothing. On past form, he will not accept any scientific evidence that proves him wrong but will instead rally his MAGA supporters to abuse and threaten any scientists who contradict him — such is the nature of his narcissistic personality disorder.
How evolution explains autism rates in humans
A new paper in Molecular Biology and Evolution, published by Oxford University Press, finds that the relatively high rate of Autism-spectrum disorders in humans is likely due to how humans evolved in the past.
About one in 31 (3.2%) children in the United States has been identified with Autism Spectrum Disorder. Globally, the World Health Organization estimates that around one in 100 children have autism. From an evolutionary perspective, many scientists believe that autism and schizophrenia may be unique to humans. It is very rare to find behaviors associated with the disorders in non-human primates. In addition, behaviors associated with those disorders generally involve cognitive traits like speech production and comprehension that are either unique to or much more sophisticated in humans.
With the development of single cell RNA-sequencing, it became possible to define specific cell types across the brain. As investigators published more large-scale datasets, it became clear that the mammalian brain contains a staggering array of neuronal cell types. In addition, large-scale sequencing studies have identified extensive genetic changes in the brain unique to Homo sapiens—genomic elements that did not change much in mammalian evolution in general but evolved rapidly in humans.
While previous investigations found that some cell types have remained more consistent throughout evolution than others, the factors driving these differences in evolutionary rate remain unknown. Researchers here investigated recently published cross-species single-nucleus RNA sequencing datasets from three distinct regions of the mammalian brain. They found that the most abundant type of outer-layer brain neurons, L2/3 IT neurons, evolved exceptionally quickly in the human lineage compared to other apes. Surprisingly, this accelerated evolution was accompanied by dramatic changes in autism-associated genes, which was likely driven by natural selection specific to the human lineage. The researchers here explain that although the results strongly suggest natural selection for Autism Spectrum Disorder-associated genes, the reason why this conferred fitness benefits to human ancestors is unclear.
Answering this is difficult because we do not know what human-specific features of cognition, brain anatomy, and neuronal wiring gave human ancestors a fitness advantage, but the investigators here speculate that many of these genes are associated with developmental delay, so their evolution could have contributed to the slower postnatal brain development in humans compared to chimpanzees. Furthermore, the capacity for speech production and comprehension unique to humans is often affected by autism and schizophrenia.
It’s possible that the rapid evolution of autism-linked genes conferred a fitness advantage by slowing postnatal brain development or increasing the capacity for language; the lengthier brain development time in early childhood was beneficial to human evolution because it led to more complex thinking.
Our results suggest that some of the same genetic changes that make the human brain unique also made humans more neurodiverse.
Alexander L. Starr, co-author.
Department of Biology
Stanford University
Stanford, CA, USA.
Publication:
A General Principle of Neuronal Evolution Reveals a Human-Accelerated Neuron Type Potentially Underlying the High Prevalence of Autism in Humans
Alexander L Starr & Hunter B Fraser
AbstractIntroduction
The remarkable ability of a single genome sequence to encode a diverse collection of distinct cell types, including the thousands of cell types found in the mammalian brain, is a key characteristic of multicellular life. While it has been observed that some cell types are far more evolutionarily conserved than others, the factors driving these differences in the evolutionary rate remain unknown. Here, we hypothesized that highly abundant neuronal cell types may be under greater selective constraint than rarer neuronal types, leading to variation in their rates of evolution. To test this, we leveraged recently published cross-species single-nucleus RNA-sequencing datasets from three distinct regions of the mammalian neocortex. We found a strikingly consistent relationship where more abundant neuronal subtypes show greater gene expression conservation between species, which replicated across three independent datasets covering >106 neurons from six species. Based on this principle, we discovered that the most abundant type of neocortical neurons—layer 2/3 intratelencephalic excitatory neurons—has evolved exceptionally quickly in the human lineage compared to other apes. Surprisingly, this accelerated evolution was accompanied by the dramatic down-regulation of autism-associated genes, which was likely driven by polygenic positive selection specific to the human lineage. In summary, we introduce a general principle governing neuronal evolution and suggest that the exceptionally high prevalence of autism in humans may be a direct result of natural selection for lower expression of a suite of genes that conferred a fitness benefit to our ancestors while also rendering an abundant class of neurons more sensitive to perturbation.
With the advent of single-cell RNA sequencing (scRNA-seq), it became possible to systematically delineate molecularly defined cell types across the brain (Zeisel et al. 2015; Tasic et al. 2016). As more large-scale datasets were published, it quickly became clear that the mammalian brain contains a staggering array of neuronal cell types, with recent whole-brain studies identifying nearly as many neuronal types as there are protein-coding genes in the genome (Zeisel et al. 2015 ; Tasic et al. 2016; Yao et al. 2023). In addition, cross-species atlases in the neocortex revealed that most cortical neuronal types are highly conserved in primates and rodents, with very few neocortical neuronal types being specific to primates and none being entirely specific to humans (Hodge et al. 2019; Krienen et al. 2020; Bakken et al. 2021a , 2021.1b; Ma et al. 2022; Jorstad et al. 2023.1). This suggests that divergence involving homologous cell types—such as their patterns of gene expression, relative proportions, and connectivity—may play a central role in establishing uniquely human cognition.
Two decades before the generation of these cross-species cell type atlases, the first whole-genome sequences of eukaryotes were published, enabling genome-wide studies of evolution for the first time (Eyre-Walker 1999). One of the first questions to be addressed in the nascent field of evolutionary genomics was why some proteins are highly conserved throughout the tree of life, whereas, others evolve so quickly as to be almost unrecognizable as orthologs even over relatively short divergence times (Duret and Mouchiroud 2000; Hirsh and Fraser 2001; Pál et al. 2001.1; Fraser et al. 2002). A protein's expression level emerged as the strongest and most universal predictor of its evolutionary rate, with highly expressed proteins accumulating fewer protein-coding changes due to greater constraint (Pál et al. 2001.1; Drummond et al. 2005, 2006; Drummond and Wilke 2008).
In contrast to tens of thousands of publications about the evolutionary rates of proteins (Yang 2007), the evolutionary rates of cell types, another key building block of multicellular life, have received relatively little attention (Arendt et al. 2016.1). Just as different proteins make up every cell, different cell types make up every multicellular organism. Furthermore, just as protein evolutionary rates are measured by the total rate of change of their amino acids, the evolutionary rates of cell types—which are typically defined by their patterns of gene expression—can be measured by divergence in genome-wide gene expression (Hodge et al. 2019; Krienen et al. 2020; Bakken et al. 2021a , 2021.1b; Ma et al. 2022; Jorstad et al. 2023.1). For example, it is well-established that the gene expression in neurons is more conserved between humans and mice than the gene expression in glial cell types, such as astrocytes, oligodendrocytes, and microglia (Pembroke et al. 2021.2). Previous analogies between genes and neural cell types have been fruitful for understanding the evolution of novel cell types (Tosches et al. 2018; Hodge et al. 2019; Peng et al. 2019.1; Kebschull et al. 2020.1; Luo 2021.3), providing an encouraging precedent for our analogy.
One area that has been explored more thoroughly is the association of specific cell types with human diseases and disorders (Jagadeesh et al. 2022.1). For example, integration of gene-trait associations with cell type-specific expression profiles has revealed that microglia likely play a central role in Alzheimer's disease (Jansen et al. 2019.2; Wightman et al. 2021.4). Similar analyses have also revealed that layer 2/3 intratelencephalic excitatory (L2/3 IT neurons)—which enable communication between neocortical areas (Galakhova et al. 2022.2) and are thought to be important for uniquely human cognitive abilities (Berg et al. 2021.5; Galakhova et al. 2022.2)—likely play a particularly important role in autism spectrum disorder (ASD) and schizophrenia (SCZ) (Parikshak et al. 2013; Kanton et al. 2019.3; Velmeshev et al. 2019.4; Batiuk et al. 2022.3; Trubetskoy et al. 2022.4; Pintacuda et al. 2023.2; Dear et al. 2024; Wamsley et al. 2024.1), together with deep layer IT neurons (Trubetskoy et al. 2022.4; Ruzicka et al. 2024.2; Sullivan et al. 2024.3). For example, a recent large-scale single-cell RNA-sequencing study found that L2/3 IT neurons and Somatostatin+ (SST+) inhibitory neurons were the most affected in people with ASD (Wamsley et al. 2024.1). In another study, proteins that interact with ASD-linked proteins were strongly and specifically enriched for differential expression between ASD cases and controls in L2/3 IT neurons (Pintacuda et al. 2023.2). Collectively, these and other results point to a key role for L2/3 IT neurons in ASD, though other populations of neurons likely also play important roles. ASD and SCZ are neurodevelopmental disorders with different but overlapping characteristics, including major effects on social behavior (Dodell-Feder et al. 2015.1; Jutla et al. 2022.5; Sato et al. 2023.3). Interestingly, individuals with ASD are more likely to be diagnosed with SCZ than individuals without an ASD diagnosis (Lugo Marín et al. 2018.1; Lai et al. 2019.5; Zheng et al. 2021.6; Jutla et al. 2022.5). Furthermore, there is a strong overlap in the genes that have been implicated in both disorders (Jutla et al. 2022.5; Trubetskoy et al. 2022.4).
From an evolutionary perspective, it has been proposed that ASD and SCZ may be unique to humans (Crow 1997; Sikela and Searles Quick 2018.2; Zug and Uller 2022.6 ). This is primarily based on two main lines of reasoning. First, ASD- and SCZ-associated behaviors that could reasonably be observed in non-human primates (e.g. SCZ-associated psychosis) have been observed either infrequently or not at all in non-human primates (Crow 1997). However, ASD-like behavior has been observed in non-human primates (Yoshida et al. 2016.2) and the difficulties inherent to cross-species behavioral comparisons, combined with relatively low sample sizes, make it difficult to compare the prevalence of these behaviors in human and non-human primate populations. Second, core ASD- and SCZ-associated behavioral differences involve cognitive traits that are either unique to or greatly expanded in humans (e.g. speech production and comprehension or theory of mind) (Marrus et al. 2011; Mody and Belliveau 2013.1; Faughn et al. 2015.2; MacLean 2016.3; Chang et al. 2022.7). As a result, certain aspects of ASD and SCZ are inherently unique to humans.
While comparing interindividual behavioral differences across species remains challenging, recent molecular and connectomic evidence lend credence to the idea that the incidence of ASD and SCZ increased during human evolution. For example, large-scale sequencing studies in both ASD and SCZ cohorts have identified an excess of genetic variants in human-accelerated regions (HARs)—genomic elements that were largely conserved throughout mammalian evolution but evolved rapidly in the human lineage (Pollard et al. 2006.1; Doan et al. 2016.4; Shin et al. 2024.4). Furthermore, transcriptomic studies have identified a human-specific shift in the expression of some synaptic genes during development that is disrupted in ASD (Liu et al. 2016.5). In addition, connectomic studies have shown that human–chimpanzee divergence in brain connectivity overlaps strongly with differences between humans with and without SCZ (van den Heuvel et al. 2019.6). Overall, evidence suggests that ASD and SCZ may be particularly prevalent in humans, but the factors underlying this increased prevalence remain unknown. Positive selection—also known as adaptive evolution—of brain-related traits in the human lineage has been proposed to underlie this increase (Crow 1997; Burns 2004; Ploeger and Galis 2011.1; Sikela and Searles Quick 2018.2; Zug and Uller 2022.6). Although this idea is supported by the links between HARs (many of which are thought to have been positively selected [Pollard et al. 2006.1]) and ASD and SCZ, there is no direct evidence for positive selection on the expression of genes linked to ASD and SCZ.
Here, we set out to test whether the inverse relationship between the abundance and evolutionary rates—which has been well-established for proteins (Pál et al. 2001.1; Drummond et al. 2005, 2006; Drummond and Wilke 2008)—might also hold for cell types. We found a robust negative correlation between the cell type proportion and the evolutionary divergence in the neocortex, suggesting that this relationship holds at multiple levels of biological organization. Based on this, we identify unexpectedly rapid evolution of L2/3 IT neurons and strong evidence for polygenic positive selection for reduced expression of ASD-linked genes in the human lineage, suggesting that positive selection may have increased the prevalence of ASD in modern humans.
Fig. 1. More common neuronal cell types evolve more slowly than rare types. a) Rationale for the hypothesis that more common neuronal types might evolve more slowly than rarer types. A gene expression change in a common cell type has a large negative effect on fitness, whereas the same change in a rarer cell type has a smaller effect. Made with BioRender. b) Left: outline of our data analysis strategy. SnRNA-seq from the MTG of five species (14 subclasses of neurons) was used to estimate each cell type's proportion and pairwise divergence between species. Right: plot showing the correlation between the neuronal subclass proportion (log10 scale on the x-axis) and the subclass-specific divergence between the human and the marmoset in the MTG. A representative iteration from 100 independent down-samplings is shown. Spearman's ρ and P-value shown are the median across 100 independent down-samplings (see Materials and Methods for details). The line and shaded region are the line of best fit from a linear regression and 95% confidence interval, respectively. c) Same as b), but snRNA-seq from the DLPFC (17 subclasses of neurons) of four species was analyzed. d) Same as b), but snRNA-seq from M1 (12 subclasses of neurons) of three species was analyzed.
Fig. 2. More common neuronal cell types evolve more slowly than rare types within excitatory and inhibitory classes. a) Plot showing the correlation between the neuronal subclass proportion (log10 scale on the x-axis) and the subclass-specific divergence between the human and the marmoset for excitatory neurons in the MTG. A representative iteration from 100 independent down-samplings is shown. Spearman's ρ and P-value shown are the median across 100 independent down-samplings (see Materials and Methods for details). The line and shaded region are the line of best fit from a linear regression and 95% confidence interval, respectively. b) Same as in a) but for the DLPFC data. c) Same as in a) but for the M1 data. d) Same as in a) but for inhibitory neurons. e) Same as in b) but for inhibitory neurons. f) Same as in c), but for inhibitory neurons.
Fig. 3. More common neuronal cell types evolve more slowly than rarer types at the subtype level. a) Plot showing the correlation between the neuronal subtype proportion (log10 scale on the x-axis) and the subtype-specific divergence between the human and the marmoset in the MTG. A representative iteration from 100 independent down-samplings is shown. Spearman's ρ and P-value shown are the median across 100 independent down-samplings (see Materials and Methods for details). The line and shaded region are the line of best fit from a linear regression and 95% confidence interval, respectively. b) Same as in a) but for the DLPFC data. c) Same as in a) but for the M1 data. d) Same as in a) but for excitatory neurons. e) Same as in b) but for excitatory neurons. f) Same as in c), but for excitatory neurons. g) Same as in a) but for inhibitory neurons. h) Same as in b) but for inhibitory neurons. i) Same as in c) but for inhibitory neurons.
Fig. 4. More highly expressed, cell type-specific genes drive the negative correlation between the cell type proportion and the evolutionary divergence. a) Left: Plot showing the correlation between the neuronal subtype proportion (log10 scale on the x-axis) and the subtype-specific divergence for highly expressed genes between the human and the marmoset in the MTG. A representative iteration from 100 independent down-samplings is shown. Spearman's ρ and P-value shown are the median across 100 independent down-samplings (see Materials and Methods for details). The line and shaded region are the line of best fit from a linear regression and 95% confidence interval, respectively. Right: Same as the left, but for lowly expressed genes. b) Left: Same as in a) but for genes with more cell type-specific expression; Right: Same as left but for genes with less cell type-specific expression. c) Same as in b) but controlling for expression level (see Materials and Methods). d) Same as in a) but controlling for cell type-specificity of expression (see Materials and Methods).
Fig. 5. Accelerated evolution of L2/3 IT neurons in the human lineage. a) Plot showing the correlation between the neuronal subclass proportion (log10 scale on the x-axis) and the subclass-specific divergence on the chimpanzee branch in the MTG. Chimpanzee branch divergence was computed for each of 100 down-samplings, and the mean across those down-samplings is shown. The line and shaded region are the line of best fit from a linear regression and 95% confidence interval, respectively. The three rightmost points are L2-5 IT neurons. b) Same as in a) but for human branch divergence. The three rightmost points are L2-5 IT neurons. c) Bar plot showing the human branch divergence divided by the chimpanzee branch divergence for each subclass. d) Plot showing the correlation between the neuronal subclass proportion (log10 scale on the x-axis) and the subclass-specific interindividual variation across DLPFC samples from 25 human individuals. A representative iteration from 100 independent down-samplings is shown. Spearman's ρ and P-value shown are the median across 100 independent down-samplings (see Materials and Methods for details). The line and shaded region are the line of best fit from a linear regression and 95% confidence interval, respectively. e) Bar plot showing the human branch divergence divided by the within-human variability for each subclass. f) Conceptual model for accelerated evolution of L2/3 IT neurons in the human lineage. Made with BioRender.
Fig. 6. Positive selection for down-regulation of ASD-linked genes in the human lineage. a) Volcano plot showing the logs fold enrichment for down-regulation in humans (x-axis) and the −log10 binomial P-value (y-axis). SFARI high-confidence ASD-linked genes are the rightmost point, all other categories of genes are from the Human Phenotype Ontology. Data are from MTG L2/3 IT neurons. b) Bar plot showing the number of high-confidence ASD-linked genes that are up-regulated vs. down-regulated in human relative to chimpanzee in the MTG L6 IT Car3+ neurons. c) Plot showing the fold enrichment for down-regulation in the human MTG (x-axis) and the −log10 binomial FDR (y-axis). Both neuronal and glial subclasses are included. Only subclasses with at least 500 human vs. chimpanzee differentially expressed genes in each direction are shown. d) Bar plot showing the number of high-confidence ASD-linked genes that are up-regulated vs. down-regulated in human relative to chimp in MTG L2/3 IT neurons. e) Bar plot showing the number of differentially expressed ASD-linked genes with higher allele-specific expression from the human allele and higher expression from the chimpanzee allele in cortical organoids. ** indicates binomial P < 0.01. f) Bar plot showing the number of differentially expressed ASD-linked genes with higher allele-specific expression from the human allele and higher expression from the chimpanzee allele in day 100 cortical organoids for human-derived and chimpanzee-derived genes separately. ** indicates binomial P < 0.01. g) Plot showing the logs allele-specific expression ratios of differentially expressed, human-derived, ASD-linked genes in day 100 cortical organoids. Negative log2 fold-change indicates lower expression from the human allele. h) Left: Expression of DLG4 in MTG L2/3 IT neurons; Right: Predicted expression of DLG4 if one copy of the gene were non-functional. i) Conceptual model for how positive selection for down-regulation of ASD-linked genes led to a higher likelihood of ASD in humans compared to chimpanzees. Made with BioRender.
Alexander L Starr & Hunter B Fraser
A General Principle of Neuronal Evolution Reveals a Human-Accelerated Neuron Type Potentially Underlying the High Prevalence of Autism in Humans
Molecular Biology and Evolution (2025) 42(9), msaf189, https://doi.org/10.1093/molbev/msaf189
Copyright: © 2025 The authors/ Society for Molecular Biology and Evolution.
Published by Oxford University Press. Open access.
Reprinted under a Creative Commons Attribution 4.0 International license (CC BY 4.0)
If the human genome had truly been designed by an omniscient, omnipotent, and benevolent intelligence, it would be flawless. There would be no inherited disorders, no genetic vulnerabilities, and no random variations that could produce harmful outcomes. A perfect designer could not only anticipate every possibility but prevent every defect, leaving humanity uniformly healthy and robust.
What we actually see is the opposite. The genome is riddled with compromises and imperfections: mutations that predispose us to disease, structural quirks that leave us vulnerable, and an immense variation between individuals that reflects the blind trial-and-error of evolution. These features make sense in the light of natural selection, which produces workable solutions rather than perfect designs, but they are inexplicable under any claim of intelligent and benevolent design.
Autism provides a clear example of this evolutionary reality. Evidence suggests it is not the result of a “design flaw” by some imagined creator but a by-product of the unusually rapid evolution of high intelligence in humans compared with other primates. In the rush of evolutionary time, the development of our advanced cognitive capacities has carried unavoidable trade-offs, including an increased risk of conditions such as autism and schizophrenia. These are the fingerprints of a utilitarian, imperfect process — not of an omniscient designer.
Far from being proof of divine craftsmanship, the human genome bears the marks of its long, messy history. Our intelligence, along with its vulnerabilities, is the product of evolution’s compromises — evidence for natural processes and against the myth of a flawless creation.
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