This essay was written by a user named “Well_Behaved” on Twitter, but he is no longer on that platform. Before leaving, he sent me this essay upon request.

Here is a similar argument in support of the (at least partial) physical nature and therefore the (at least partial) scientific measurability of human behavior: (1. Premise, If P then Q) If a phenomenon’s obtaining is to be fully caused or explained, then all the necessary conditions required for that phenomenon to obtain must be present. (2. Premise, If P then Q) If a partial cause or explanation of a phenomenon is a necessary contributor to a phenomenon’s obtaining, then that partial cause or explanation is a necessary condition for the obtaining of that phenomenon. (3. Conclusion, by Modus Tollens from 1. and 2.) Therefore, if a necessary contributor to a phenomenon’s obtaining is lacking, then the phenomenon does not obtain. (4. Premise) Genes necessarily contribute to the phenomenon of behavior obtaining in genetically based organisms. (5. Conclusion, from 3 and 4.) Therefore, genes necessarily at least partially cause or explain the phenomenon of behavior in genetically based organisms.

The Argument

(1)Believers must have the concept of belief 

(2)because in order to have beliefs one must recognize that they can be either true or false, (3)one cannot understand objective truths without knowing the nature of beliefs. 

(4)So in order to develop an understanding of objective truth, one must be able to talk with others about the world, 

(5)and so all believers must be language users.

(6)Other species lack language, 

(7)therefore they don’t have beliefs. 

(8)animals lack intentional states 

(9)therefore they lack belief and desire” 

Response

Response quoting and paraphrasing Searle from Animal Minds (1994): In order to have a thought there must be beliefs. But, as per (1), in order to have beliefs a creature must have the concept of belief. Why? Because, as per (2), in order to have belief one must be able to distinguish true from false beliefs. But, as per (3) and (4), this contrast, between the true and the false, “can only emerge in the context of interpretation” (of language)*. The notion of a true belief or a false belief depends, as per (4), on the notion of true and false utterances, and these notions, as per (3), (4)and (5), cannot exist without a shared language. So, only a creature who is the possessor and interpreter of a language can have thoughts. The basic idea in this argument seems to be that since, as per (2), (3), (4) and (5), truth is a metalinguistic semantic predicate and since, as per (2) and (3), the possession of beliefs requires the ability to make the distinction between true and false beliefs, it seems, as per (4) and (5), to follow immediately that the possession of beliefs requires metalinguistic semantic predicates, and that obviously requires a language. In summary: In order to tell the difference between true and false beliefs one must have a linguistically articulated concept of belief. As regards (3), (4) and (5), having an intentional state requires the capacity to discriminate conditions which satisfy from those that do not satisfy the intentional state. Indeed, I wish to generalize this point to all intentional states, and not just confine it to beliefs. In general, in order to have intentional states one must be able to tell the difference between satisfied and unsatisfied intentional states. But I see no reason at all to suppose that this necessarily requires a language, and even the most casual observation of animals suggests that they typically discriminate the satisfaction from the frustration of their intentional states, and they do this without a language. How does it work? Well the first and most important thing to notice is that beliefs and desires are embedded not only in a network of other beliefs and desires but more importantly in a network of perceptions and actions, and these are the biologically primary forms of intentionality. We talk as if perception and action were not forms of intentionality but of course they are; they are the biologically primary forms. Typically, for animals as well as humans, perception fixes belief, and belief together with desire determines courses of action. Consider real-life examples: Why is my dog barking up that tree? Because he believes that the cat is up the tree, and he wants to catch up to the cat. Why does he believe the cat is up the tree? Because he saw the cat run up the tree. Why does he now stop barking up the tree and start running toward the neighbor’s yard? Because he no longer believes that the cat is up the tree, but in the neighbor’s yard. And why did he correct his belief? Because he just saw (and no doubt smelled) the cat run into the neighbor’s yard; and Seeing and Smelling is Believing. The general point is that animals correct their beliefs all the time on the basis of their perceptions. In order to make these corrections they have to be able to distinguish the state of affairs in which their belief is satisfied from the state of affairs in which it is not satisfied. And what goes for beliefs also goes for desires. But why do we need to “postulate” beliefs and desires at all? Why not just grant the existence of perceptions and actions in such cases? The answer is that the behavior is unintelligible without the assumption of beliefs and desires; because the animal, e.g., barks up the tree even when he can no longer see or smell the cat, thus manifesting a belief that the cat is up the tree even when he cannot see or smell that the cat is up the tree. And similarly he behaves in ways that manifest a desire for food even when he is neither seeing, smelling, nor eating food. In such cases animals distinguish true from false beliefs, satisfied from unsatisfied desires, without having the concepts of truth, falsity, satisfaction, or even belief and desire. And why should that seem surprising to anyone? After all, in vision some animals distinguish between red-colored from green-colored objects without having the concepts vision, color, red, or green. I think many people suppose there must be something special about “true” and “false,” because they suppose them to be essentially semantic predicates in a metalanguage. Given our Tarskian upbringing, we tend to think that the use of “true” and “false” to characterize beliefs must somehow be derived from a more fundamental use to characterize linguistic entities. sentences, and statements, for example. And then it seems to us that if a creature could tell true from false beliefs it would first have to have an object language to give any grip to the original metalanguage distinction between truth and falsity, now being applied by extension to something nonlinguistic. But all of this is a mistake. “True” and “false,” are indeed metalinguistic predicates, but more fundamentally they are metaintentional predicates. They are used to assess success and failure of representations to achieve fit in the mind-to-world direction of fit, of which statements and sentences are a special case. It is no more mysterious that an animal, at least sometimes, can tell whether its belief is true or false, than that it can tell whether its desire is satisfied or frustrated. For neither beliefs nor desires does the animal require a language: rather what it requires is some device from recognizing whether the world is the way it seemed to be (belief) and whether the world is the way the animal wants it to be (desire). But an animal does not have to have a language in order to tell true from false beliefs, any more than it has to have a language to tell satisfied from unsatisfied desires. Consider the example of the dog chasing the cat, for an illustration. * D. Davidson, “Thought and Talk” in Truth and Interpretation (Oxford, 1984), 155-170.

Recently, I found this tweet that claims that there are no genes for schizophrenia, yet I have yet to find this user actually engaging in what the most recent research says on this subject (in 2020). No, I am not talking about twin studies and I am certainly not talking about GWAS either. I am referring to studies that actually elucidate the genetic architecture of schizophrenia, specifically in African Xhosa via exome sequencing. Exome sequencing is a process that I haven’t found much discussion on Twitter, nor do many people know or comment on what exome sequencing actually is. Gulsuner et al. found, through exome sequencing 2,092 Xhosa Africans, the genetic architecture of Schizophrenia in this population: 

“To characterize the genetic architecture of schizophrenia in the Xhosa population, we evaluated contributions from both rare alleles and common alleles. We first compared the numbers of cases versus controls carrying at least one private damaging variant in a gene that was intolerant to such mutations (table S3) (13). A “private variant” was defined as a variant that appeared in only one case or only one control among our participants and that was absent from other population databases (13). ‘Damaging variants’ were defined as nonsense mutations, frameshift mutations, and splice disrupting and missense mutations that were predicted to be damaging by multiple criteria (13)…Genes encoding synaptic proteins are well conserved, intolerant of mutations, and enriched for de novo mutations associated with neurodevelopmental disorders, including intellectual disability, autism, and schizophrenia (15). Among Xhosa cases, synaptic genes harboring private damaging mutations included glutamate (GRIA2), g-aminobutyric acid (GABRB1, GABRB2, GABRA5, and GABRD), dopamine (DRD2), and glycine (GLRA2) receptors; voltage-gated calcium channels (CACNA1A and CACNA1C); scaffold proteins (DLG1, DLG3, and DLGAP1); cell adhesion molecules (CNTNAP1, CTNNB1, CTNNA2, and CTNND2); and multiple postsynaptic density signaling proteins, kinases, and phosphatases (Fig. 4). Synaptic genes that are disrupted by private damaging mutations in multiple cases include CACNA1C, DLGAP1, and huntingtin (HTT) and its associated kinase kalirin (KALRN), each of which was mutant in three cases, and CNTNAP1, which was mutant in four cases.”

Ultimately, the claim that there are no genes for schizophrenia seems to be incorrect.

It has been assumed by some researchers that deep, and extremely segregated lineages are required for behavior differences in mammals such as humans. This is probably one of best examples of a false criteria: the idea that one needs to observe deep divides genetically to have any obvious differences in the brain, as per one example. Quite the contrary, we see the effects of genetic differences has on brain shape in humans overall in various studies, especially in the realm of physiology.

Population Differences in Brain Morphology and Microstructure among Chinese, Malay, and Indian Neonates

We studied a sample of 75 Chinese, 73 Malay, and 29 Indian healthy neonates taking part in a cohort study to examine potential differences in neonatal brain morphology and white matter microstructure as a function of ethnicity using both structural T2-weighted magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). We first examined the differences in global size and morphology of the brain among the three groups. We then constructed the T2-weighted MRI and DTI atlases and employed voxel-based analysis to investigate ethnic differences in morphological shape of the brain from the T2-weighted MRI, and white matter microstructure measured by fractional anisotropy derived from DTI. Compared with Malay neonates, the brains of Indian neonates’ tended to be more elongated in anterior and posterior axis relative to the superior-inferior axis of the brain even though the total brain volume was similar among the three groups. Although most anatomical regions of the brain were similar among Chinese, Malay, and Indian neonates, there were anatomical variations in the spinal-cerebellar and cortical-striatal-thalamic neural circuits among the three populations. The population-related brain regions highlighted in our study are key anatomical substrates associated with sensorimotor functions.

https://www.sciencedirect.com/science/article/abs/pii/S0925492716301147

Population differences in brain morphology: Need for population specific brain template

Brain templates provide a standard anatomical platform for population based morphometric assessments. Typically, standard brain templates for such assessments are created using Caucasian brains, which may not be ideal to analyze brains from other ethnicities. To effectively demonstrate this, we compared brain morphometric differences between T1 weighted structural MRI images of 27 healthy Indian and Caucasian subjects of similar age and same sex ratio. Furthermore, a population specific brain template was created from MRI images of healthy Indian subjects and compared with standard Montreal Neurological Institute (MNI-152) template. We also examined the accuracy of registration of by acquiring a different T1 weighted MRI data set and registering them to newly created Indian template and MNI-152 template. The statistical analysis indicates significant difference in global brain measures and regional brain structures of Indian and Caucasian subjects. Specifically, the global brain measurements of the Indian brain template were smaller than that of the MNI template. Also, Indian brain images were better realigned to the newly created template than to the MNI-152 template. The notable variations in Indian and Caucasian brains convey the need to build a population specific Indian brain template and atlas.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2964318/

Variability in Frontotemporal Brain Structure: The Importance of Recruitment of African Americans in Neuroscience Research

There are few studies specifically examining racial or ethnic differences and also few studies that test for race-related differences in context of other neuropsychiatric research, possibly due to the underrepresentation of ethnic minorities in clinical research. It is within this context that we conducted a secondary data analysis examining volumetric MRI data from healthy participants and compared the volumes of the amygdala, hippocampus, lateral ventricles, caudate nucleus, orbitofrontal cortex (OFC) and total cerebral volume between Caucasian and African-American participants. We discuss the importance of this finding in context of neuroimaging methodology, but also the need for improved recruitment of African Americans in clinical research and its broader implications for a better understanding of the neural basis of neuropsychiatric disorders.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2743876/

Brain Behavior Relationships amongst African Americans, Caucasians and Hispanics

There is increasing racial and ethnic diversity within the elderly population of the United States. While increased diversity offers unique opportunities to study novel influences on aging and dementia, some aspects of racial and ethnic research have been hampered by the lack of culturally and linguistically consistent testing protocols. Structural brain imaging is commonly used to study the biology of normal aging and cognitive impairment and may therefore serve to explore potential biological differences of cognitive impairment amongst racially and ethnically diverse individuals. To test this hypothesis we recruited a cohort of approximately 400 African American, Caucasian and Hispanic subjects with various degrees of cognitive ability. Each subject was carefully evaluated using standardized diagnostic protocols that included clinical review of brain MRI to arrive at a clinical diagnosis of normal cognition, mild cognitive impairment (MCI) or dementia. Each MRI was then independently quantified for measures of brain, WMH and hippocampal volumes by a technician blind to subject age, gender, ethnicity, race and diagnostic category. The appearance of infarction on MRI was also rated by examining neurologists. Regression analyses were used to assess associations with various MRI measures across clinical diagnostic categories in relation to racial and ethnic differences. Hispanic subjects were, on average, significantly younger and had less years of education than African Americans or Caucasians. Caucasians with dementia were significantly older than both African American and Hispanic dementia patients. Highly significant differences in MRI measures were associated with clinical diagnoses for the group as a whole after adjusting for the effects of age, gender, education, race and ethnicity. Subsequent independent analyses by racial and ethnic status revealed consistent relationships between diagnostic category and MRI measures. Clinical diagnoses were associated with consistent differences in brain structure amongst a group of racially and ethnically diverse individuals. We believe these results help to validate current diagnostic assessment of individuals across a broad range of racial, ethnic, linguistic and educational backgrounds. Moreover, interesting and potentially biologically relevant differences were found that might stimulate further research related to the understanding of dementia etiology within an increasingly racially and ethnically diverse population….Differences in associations between clinical diagnoses and brain morphology amongst the racial and ethnic groups, while subtle, may also offer new avenues for future research, particularly genetic research where MRI has recently been recognized as a suitable endophenotype (50–55) and major genetic causes of dementia may differ substantially by race (56).

https://www.nature.com/articles/s41598-018-23696-6

https://www.sciencedirect.com/science/article/abs/pii/S1876201817308298

Construction of population-specific Indian MRI brain template: Morphometric comparison with Chinese and Caucasian templates

Our results showed that there were significant differences in global brain features of Indian template in comparison with Chinese and MNI brain templates. The results of registration accuracy analysis revealed that fewer deformations are required when Indian brains are registered to Indian template as compared to Chinese and MNI templates.

https://www.biorxiv.org/content/10.1101/2020.05.08.077172v1.abstract

A series of five population-specific Indian brain templates and atlases spanning ages 6 to 60 years

…the Indian population (currently over 1.3 billion people) is spread across a wide range of geographies with diversity in linguistic-ethnic compositions as well as extensive genetic admixtures [Basu et al., 2016]. In this study, the final mean template for each cohort contained variability. However, this was relatively low compared to the mean dataset values, and the final mean template contained a large amount of clearly defined structure. Moreover, the fractional overlap of ROIs when generating the maximum probability map atlases showed a high degree of agreement across the group through most of the brain.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2862912/

The Construction of a Chinese MRI Brain Atlas: A Morphometric Comparison Study between Chinese and Caucasian Cohorts

Dissimilarities of genetics and environmental exposures between different populations lead to differences in brain structure and function. Areas of functional differences between Chinese and Caucasian groups have been identified by a rapidly growing body of imaging studies (Kuo et al., 2001; Kuo et al., 2003; Tan et al., 2001a; Tan et al., 2001b; Tan et al., 2003; Tan et al., 2000). Not all regions reported as having functional differences were seen to have anatomical difference. However, each area where anatomical differences were observed has been selectively implicated in Chinese language processing by one or more studies. Further, the areas detected as being anatomically different between the groups have shown robust and highly reproducible functional differences (Kochunov et al., 2003). To explore the anatomical differences between Chinese and Caucasian brains, we selected two comparable samples (35 subjects for each group) from the Chinese and Caucasian populations that are matched for gender and age. Although global brain shape and size can not provide detailed structural information throughout the human brain, these measures are important for comparing different brains. Analysis of these morphometric measurements indicated that the mean values of length, width, height and AC-PC line distance were significantly different (p<0.01) between the Chinese brain and Caucasian brain. Thus, if Caucasian-based brain atlases are employed as reference templates in Oriental neuroimaging studies, some bias, processing errors or localized differences may be observed that are driven by the intrinsic differences between these two cohorts, and not caused by the underlying process investigated in the studies.

Please note that I will be continually updating this piece, but publishing each part by itself.

For this article, I am just going to respond by each tweet, and then post the research that responds to the point. I am not going to give any introduction, I think each statement is self explanatory. This will be updated, section by section .

IQ certainly measures brain activity in relation to information and problem solving in a given situation, we know this because sensory discrimination is linked with IQ scores. Sensory discrimination is not a direct/pure product of culture, “The strong relationship between sensory discrimination and intelligence is what initially led Charles Spearman to formulate his theory on general intelligence that would go on to influence the field for decades to come. Spearman hypothesized that a central “Function” underlying differences in intelligence also plays a prominent role in sensory discrimination; “I take both sensory discrimination and the manifestations leading a teacher to impute general intelligence to be based on some deeper fundamental cause” (Deary, 1994b, p. 105).”

Sources

1.) Tsukahara, Jason S., and Randall W. Engle. “The locus coeruleus-norepinephrine system and fluid intelligence.” (2020).

Study:

https://pdfs.semanticscholar.org/58c5/c8a800968d7adbd479879a3b2bdee3c2cab9.pdf

This extensive series of essays will be a response to the treatise as written by Political Scientist Evan Charney. It was written in 2012, but it’s influence on anti-genetic polemics has been quite profuse. Therefore, a review of the claims and arguments made in the paper are necessary. Because we started from different sections, this comprehensive review will not be done in chronological order. This is going to have to be a long series, and we are determined to analyze all the sections when we have the chance. Future essays will establish what we mean by post-epigenetics in more detail, that is, the realization that many researchers are finally coming to: that genetic variation (which is innate to organisms) is partially determining the happenings called “epigenetics”, and that non-genetic factors aren’t the sole cause of DNA Methylation, Small RNAs, etc. (The evidence that we will discuss in turn would refute the anti-genetic ideology of “Neo-Lamarckism” that some left-wing conspiracy theory tracts now promote.) We don’t have a problem with the term epigenetics when it comes to identifying biological phenomena; colloquialisms can and will be used for certain things.

We will start by covering several quotes from Charney in regards to measuring animal heritability percentages. Among the most important aspects of heritability is the following: we firmly believe that heritability estimates are trait specific. In other words, we believe that some estimates are legitimate, while other estimates might not be at all. We believe in using extensive measures to test for all and any traits in this regard. In essence, the most credible and extensive methods are needed to validate or falsify heritability measurements. We believe this is the most charitable way to test a trait. If one looks at literature for DNA methylation, as per one example, one can see a clear picture: researchers can partition the genetic and non-genetic factors for some CpG sites.(13)(14)(15)(16)(17)(18)

Critique

Charney says: “Maternal stress during pregnancy–prenatal stress–has been associated in animal studies with abnormally high levels of fetal blood cortisol, which alters the development of neurons in the brain leading to many of the same morphological effects and behaviors that are observed in suboptimal prenatal maternal nutrition (Brown 2002;Brunton & Russell 2011; Lui et al. 2011; Mueller & Bale2008; Reyes-Castro et al. 2011), including lifelong com-promised neurodevelopment, enhanced stress reactivity,and increased fearful or anxious behavior.” 

Response: Most of these studies in question are studies of animal models. According to the literature, animal models might seem useful for studying human anxiety and depression at first glance, but many researchers have shown the problems with these conclusions. Readers are directed to such papers Garner et al, Sjoberg et al, and Maxosn et al. (5)(6)(7) Of interest for depression and anxiety, “When we find neurological elements that are the same in both the animal model and the human target group (that do not exist in controls), we should be careful to draw any conclusions based on this. Just like behavioural evidence, the links are suggestive and not necessarily conclusive. It is risky to assume that the physiological properties shared between humans and animals operate the same way. In drug research, over 90% of drugs that show effectiveness on animal models fail to work on humans, a problem called attrition.” (7) And, “There are numerous methods for study of anxiety related disorders in rodents. Many do not provide direct insight into the signs and symptoms of anxiety in humans; rather, they have been developed and refined to maximize sensitivity to standard pharmacological agents and thus are more accurately conceptualized as assays rather than models. Caution is needed when interpreting the data and making conclusions in the context of human anxiety disorders.”(9) This being the case the best models for humans would be human models in any for of research. Let us analyze the studies he uses because of this fact:

–Brunton & Russell 2011 discuss the effects of maternal stress on rodent anxiety phenotype. That anxiety would be less dependent on strains in relation to other behavior per se only is not controversial at all; massive reviews of inbred rodent strains show that, unlike other behavioral traits, anxiety is not as influenced by specific, strain genetic variation. Wahlsten et al found the following when comparing rodent behavioral traits:“For ethanol preference and loco motor activity, strain differences have been highly stable over a period of 40–50 years, and most strain correlations are in the range of r = 0.85–0.98, as high as or higher than for brain weight. For anxiety-related behavior…strain means often differ dramatically across laboratories or even when the same laboratory is moved to another site within a university… We find that several kinds of behavior are genetically quite stable.”(4) This being the case, anxiety has been proven to be influenced by genetic variation in several etiological rodent studies: “Review of the literature suggests that anxiety is a complex phenomenon, underlined not only by genetic or environmental factors but also by multiple interactions between genes and also between genetic and environmental factors.”(8)

-Brown 2002 is a human study that concludes that both environment and genes contribute to schizophrenia. We now have complex genetic architecture for this ailment. I am not sure why Charney cited this paper if he is trying to emphasize the non-genetic factors in this section. If the genes for schizophrenia aren’t present in the genome of a specific human, they won’t have schizophrenia, “Although schizophrenia is highly heritable, most cases are sporadic, and affected individuals have significantly fewer children, all of which are consistent with a critical role for de novo and recent ultrarare mutations”.( 11)

– Lui et al is a study that examined the effects of prenatal stress on rat offspring. The main with this study is that it there is no evidence that the researchers controlled for genetic variation; other studies point to the reality of genetic variation affecting different rat strains, “…the response to a particular neurotoxicant may vary between strains based on their particular genetic backgrounds and suggest that a multi-strain approach and multi-behavior approach may provide a more comprehensive understanding of differences in genetic-based vulnerability and resilience to developmental neurotoxicants…The result from the current study furthers our understanding of the complex interplay between sex, genetic variation and developmental window of exposure in influencing the neurobehavioral response to developmental Pb exposure.” (12) And of course this rule applies to other rodent species as well when it comes to general cognitive abilities, “…animals from different genetic background strains exhibited varying behavioural patterns when assessed for sociability/novelty, memory function, and negative behaviours like despair and stress calls. These results suggest that genetic variation among strains may be responsible—in part—for strain-specific behavioural phenotypes and potential predisposition to some neurological disorders.”(10)

-Mueller & Bale 2008 is another rodent study that tested prenatal stress on C57BL/6:129 mixed breed mice. This is one of the first rodent studies analyzed here that actually controlled for strain specific type, this is a great thing. However, it is important to recognize that stress in general is indeed strain specific. No seriously doubts that prenatal stress could/does affect emotionality, but that genetic variation still affects it as a phenotype (from population to population). (19)(20)

2.) Charney says: “Coe et al.(2003) evaluated the behavior of juvenile monkeys whose mothers were subjected to stress induction during preg-nancy as compared to controls. To induce stress, the pregnant female was acutely disturbed 5 days per week by being moved to a darkened test room and intermittently aroused with an acoustical startle protocol. At ages 2–3 years old,juvenile monkeys from undisturbed, normal pregnancies(control) were compared with offspring from mothers who were disturbed for 6 weeks during the 24-week preg-nancy, either early (days 50–92 postconception) or late(days 105–147) (these periods correspond to two distinct stages of cell growth and synaptogenesis in the fetal monkey cortex) (Bourgeois et al. 2000). Offspring of both early and late prenatally disturbed pregnancies engaged in  significantly lower levels of focused exploration(Fig. 3A), in line with prior research showing altered offspring emotionality after similar types of gestational”

Response: In relation to the various monkey studies cited by Charney, they are quite old and do not showcase other studies at the time when it comes to lab monkey behavior. Case and point, one study found that “…monkeys mutations in the gene encoding for the serotonin transporter result in increased anxiety in adults life when combined with a stressful environment during development…Such examples illustrate how specific mutations leading to abnormal brain development may increase vulnerability to environmental insults which may in turn lead to specific anxiety disorders…We propose that life-long anxiety behavior is determined by the interaction of early genetic and environmental factors which can be modified later in life by the induction of compensatory plastic changes after psychological or pharmaco-logical intervention.”  In other words, credible research indicates that genetic mutations probably facilitate the environmental vulnerability to prenatal effects as well in these model monkeys, (Gross et al, 2004) “…found significant heritability for behavioral inhibition, i.e. the duration of freezing in response to the NEC challenge, in this population of macaques. We also found a significant genetic effect on a related behavior, vigilance or orienting to the intruder. Threat‐induced freezing in rhesus monkeys and the analogous behavioral inhibition in humans are adaptive responses reflecting underlying anxiety, and in certain situations are protective (Kalin & Shelton 1989, 1998). Increased vigilance associated with freezing is adaptive because it facilitates ongoing evaluation of potential risk. However, extreme levels of behavioral inhibition and hypervigilance reflect excessive anxiety.” (J.Rogers, 2008) The study that Evan Charney cites, Coe et al, was even outdated at the time he was writing; Coe et al was published in 2003, while the study cited above (2008) pointed to genetic variation partially causing anxiety in rhesus monkeys. Charney doesn’t acknowledge these papers, nor does he attempt to address the primary methodologies. So older and newer research established that genetic variation did effect monkeys. Again, Cahrney didn’t analyze studies like Rogers et al, which found the following: “Williamson et al. (2003) also used variance components methods to examine the heritability of anxiety and fearfulness in a different pedigree of young rhesus monkeys. They found that the latency to leave the mother and explore a novel play room, latency to inspect a novel food item and duration of exploratory behavior while separated from their mother were all significantly heritable…Overall, the results of Williamson et al. (2003) and Fairbanks et al. (2004) are consistent with our conclusion that individual variation in specific elements of primate behavior related to anxiety and fear is influenced by genetic differences among animals.” ( Rogers, 2007).

Sources

1. Gross, Cornelius, and René Hen. “Genetic and environmental factors interact to influence anxiety.” Neurotoxicity research 6.6 (2004): 493-501.
2. Rogers, J., et al. “Genetic influences on behavioral inhibition and anxiety in juvenile rhesus macaques.” Genes, Brain and Behavior 7.4 (2008): 463-469.
3. Shi, Lei, et al. “Transgenic rhesus monkeys carrying the human MCPH1 gene copies show human-like neoteny of brain development.” National Science Review 6.3 (2019): 480-493.
4. Wahlsten, Douglas, et al. “Stability of inbred mouse strain differences in behavior and brain size between laboratories and across decades.” Proceedings of the national academy of sciences 103.44 (2006): 16364-16369.
5. Maxson, S. C. (2001). Animal models of human behaviour. e LS.
6. Sjoberg, Espen A. “Logical fallacies in animal model research.” Behavioral and Brain Functions 13.1 (2017): 3.
7. Garner, Joseph P. “The significance of meaning: why do over 90% of behavioral neuroscience results fail to translate to humans, and what can we do to fix it?.” ILAR journal 55.3 (2014): 438-456.
8. Clément, Yan, François Calatayud, and Catherine Belzung. “Genetic basis of anxiety-like behaviour: a critical review.” Brain research bulletin 57.1 (2002): 57-71.
9. Lezak, Kimberly R., Galen Missig, and William A. Carlezon Jr. “Behavioral methods to study anxiety in rodents.” Dialogues in clinical neuroscience 19.2 (2017): 181.
10. Sultana, Razia, Olalekan M. Ogundele, and Charles C. Lee. “Contrasting characteristic behaviours among common laboratory mouse strains.” Royal Society open science 6.6 (2019): 190574.
11. Gulsuner, S., et al. “Genetics of schizophrenia in the South African Xhosa.” Science 367.6477 (2020): 569-573.
12. Verma, Megha, and J. S. Schneider. “Strain specific effects of low level lead exposure on associative learning and memory in rats.” Neurotoxicology 62 (2017): 186-191.
13. Galanter, Joshua M., et al. “Differential methylation between ethnic sub-groups reflects the effect of genetic ancestry and environmental exposures.” Elife 6 (2017): e20532.
14. Kader, Farzeen, and Meenu Ghai. “DNA methylation-based variation between human populations.” Molecular genetics and genomics 292.1 (2017): 5-35.
15. van Dongen J, Nivard MG, Willemsen G, Hottenga JJ, Helmer Q, Dolan CV, Ehli EA, Davies GE, van Iterson M, Breeze CE, Beck S, BIOS Consortium, Suchiman HE, Jansen R, van Meurs JB, Heijmans BT, Slagboom PE, Boomsma DI (2016) Genetic and environmental influences interact with age and sex in shap-ing the human methylome. Nat Commun 7:11115
16. Quon, Gerald, et al. “Patterns of methylation heritability in a genome-wide analysis of four brain regions.” Nucleic acids research 41.4 (2013): 2095-2104.
17. Kader, Farzeen, Meenu Ghai, and Ademola O. Olaniran. “Characterization of DNA methylation-based markers for human body fluid identification in forensics: a critical review.” International journal of legal medicine (2019): 1-20.
18. Hannon, Eilis, et al. “Characterizing genetic and environmental influences on variable DNA methylation using monozygotic and dizygotic twins.” PLoS genetics 14.8 (2018).
19. Tsuchimine, Shoko, et al. “Comparison of physiological and behavioral responses to chronic restraint stress between C57BL/6J and BALB/c mice.” Biochemical and Biophysical Research Communications (2020).
20. Abbott, P. W., Gumusoglu, S. B., Bittle, J., Beversdorf, D. Q., & Stevens, H. E. (2018). Prenatal stress and genetic risk: How prenatal stress interacts with genetics to alter risk for psychiatric illness. Psychoneuroendocrinology, 90, 9-21.

Recently, a youtuber named Modern Heresy released a video entitled A Critique of Ken Richardson: Initial Impressions and Social Class. We are well aware of Richardson’s papers and we were going to make a response to some of the claims made in his work. Of course, Modern Heresy beat us, but we are not phased. Ken Richardson makes the following claim in his paper What IQ Tests Test about the tests themselves, “As a result, IQ tests are unusual instruments in several respects. Ordinary scientific measurement of concealed inner states, whether of inorganic, organic or psychological systems, is usually based on a widely agreed characterization or clear theoretical model of the internal processes involved in that state: in particular, how its parameters are reliably manifested in some external index that can be monitored, and against which an instrument can be calibrated. In this way we expect breathalyzers to be valid tests of blood alcohol levels; or white cell counts to be valid measures of pathogenic infection” (1)

Howard says: If I may respectfully say, the major problem with a statement like this is that Ken is comparing mental tests with breathalyzer tests; it is a false comparison fallacy because a breathalyzer doesn’t test the specific organ as mental tests do, nor does it test the function of the brain per se in the way mental tests purportedly do either. A breathalyzer tests blood alcohol levels within the whole human body upon consumption of an alcoholic beverage, “Your BAC level measures the amount of alcohol in your blood, therefore traveling through your body to every organ, including your brain. In its simplest form, calculating a person’s BAC level is based on how much alcohol went into what kind of body over a period of how much time.”(2) So they test the amount of the chemical in question within a person. This contrasts with mental tests in general, which in the very least, are intended to test the function of the human brain specifically (regardless if they are or aren’t successful at that very goal).(8)(9)(10) In essence, breathalyzers do not purport to test even remotely the function of a target organ necessarily as in the case of IQ tests, as the mere presence of a chemical in the human body cannot be equated to the function of the brain (although alcohol can affect the brain, but regardless of the fact that alcohol obviously affects the functioning of the brain). Everyone would readily understand this if we were talking about the majority of individuals with Down’s Syndrome; much of their IQ scores are obviously low and they prove that physiologically brains functions differently, “In contrast to normally developing children, there is a progressive IQ decline in DS beginning in the first year of life. In other words, the ratio of mental age (MA)…is not constant (Hodapp & Zigler,1990). By adulthood, IQ is usually in the moderately to severely retarded range (IQ25–55), with an upper limit on MA of approximately 7 to 8 years(Gibson, 1978)…”(8). The IQ tests demonstrate the mental-functional differences between people with Downs Syndrome versus those who do not.

Secondly: until the wide distribution of fuel-cell breathalyzers (which have a success rate well over 80%), it was questionable that breathalyzers in general were agreed to be accurate.(3)(4) I do not know where Ken lives, but this is why we have specialized defense attorneys in the USA for cases involving alcohol. A statement from an American lawyer is quite revealing on this point,

“In many DUI cases, breath analysis is used as a method to test for blood alcohol concentrations. Scientists have conducted studies regarding breath alcohol analysis and most have agreed with Dr. Michael Hiastala, Professor of Physiology, Biophysics and Medicine at the University of Washington, in concluding that breathalyzer accuracy is inherently unreliable.”

Furthermore, at the time Ken was writing in 2002, the physiological literature recorded that breathalyzers were replete with problems,“Thus, for example, a recent study determined that breath readings vary at least 15 percent from actual blood alcohol levels. (Simpson, Accuracy and Precision of Breath-Alcohol Measurements for a Random Subject in the Postabsorptive State, 33(2) Clinical Chemistry 261 (1987)). Furthermore, at least 23 percent of all individuals tested will have breath results in excess of true blood-alcohol levels. The author concluded that, “[g]iven the choice, it would seem that if a conclusion is to be made about the BAC of a random subject, especially when the conclusion can have serious consequences, it would be far preferable to make it on the basis of a direct [blood] measurement….”. (4) While an extensive review of such literature may be beyond the focus of this essay, readers are directed to consult any legal firm in their area on this issue or PubMed for the most recent research. Clearly, using the most credible sources of that era, Richardson was incorrect presuming there was vast agreement on the subject. Ultimately his claim about breathalyzers was not only a false comparison but technically incorrect.

As for white blood cell counts, this is also a false comparison; white blood cells are produced primarily in bone marrow and are used to fend off disease (12). White blood cell count is related to infection; therefore, the process of cognition is very different from the human bodies’ way of defending itself from disease. (13)

But we have to dig even deeper on his statements on IQ. Within the study, Ken says, “I shall argue that the basic source of variation in IQ test scores is not entirely (or even mainly) cognitive, and what is cognitive is not general or unitary. It arises from a nexus of sociocognitive-affective factors determining individuals’ relative preparedness for the demands of the IQ test. These factors include (a) the extent to which people of different social classes and cultures have acquired a specific form of intelligence (or forms of knowledge and reasoning); (b) related variation in ‘academic orientation’ and ‘self-efficacy beliefs’; and (c) related variation in test anxiety, self-confidence, and so on, which affect performance in testing situations irrespective of actual ability.”(1)

In essence, Ken believes that IQ tests simply test cultural knowledge and aren’t meaningful for testing an actual process or component of the brain overall. Hence the claim that IQ tests would not be construct valid. Personally, we are not satisfied with the typical arguments against this thesis, as they often rely on dismissing the importance of how researchers construct tests to understand cognition. In other words, in order to test the validity of IQ tests for any given population, I believe that other related variables of brain function ought to be considered (especially in relation to IQ). If IQ is just cultural knowledge and not largely involved in cognitive processes, there should really be no strongly linked effect to other mental processes. But is this actually the case? Two largely ignored studies have been produced to answer this question within this decade alone: Melnick et al 2013 (5) and Paraiso et al 2018 (which is a replication of Melnick et al’s findings). In the past, some have sought to defend IQ tests by simply showing the consistency of results for students across the 20th century. The need for exterior evidence that tests other variables in conjunction for IQ tests is demonstrable (precisely because in order to convince people, a wide range of evidence would suffice). So what do the above studies test?

1. Melnick et al: A strong interactive link between sensory discriminations and intelligence

Firstly, Melnick et al sought to test if there was variability between people with different IQ scores when comparing visual processing. It has long been known that people with higher IQ scores can process sensory information at much greater rate than those who have lower scores.(5) Ultimately, “High IQ individuals, although quick at perceiving small moving objects, exhibit disproportionately large impairments in perceiving motion as stimulus size increases.”(5) The implication here is that, physiologically, the brains of higher IQ people have advantages and disadvantages just like lower IQ test takers, “We conjecture that the ability to suppress irrelevant and rapidly process relevant information fundamentally constrains both sensory discrimination and intelligence, providing an information-processing basis for the observed link.”(5) Overall, the empirical literature shows this link to be exceptionally strong, and *not* simply the result of cultural knowledge, “…the ability to suppress information is a fundamental neural process that applies not only to perception but also to cognition in general…individual differences in spatial suppression of motion signals strongly predict individual variations in IQ scores.”(11) These facts do not suggest that there isn’t a differences between people between middling IQ scores. Because it was demonstrated that people with Down Syndrome have large cognitive defects and a lower IQ, it’s far better to have a higher IQ in this regard in comparison. But this extends also to people who do not have Downs Syndrome either who have lower IQ; even those people with extreme high IQ, such as Autistic people, have a better chance of success then people with Downs Syndrome, “This hypothesis is supported by convergent evidence showing that autism and high IQ share a diverse set of convergent correlates, including large brain size, fast brain growth, increased sensory and visual-spatial abilities, enhanced synaptic functions, increased attention focus, high socioeconomic status, more deliberative decision-making, profession and occupational interests in engineering and physical sciences, and high levels of positive assortative mating.”(14) In terms of of increased sensory abilities, impaired motion perception is the exception to these general trends. (5)(14)

I invite all readers to watch the sample battery of the tests utilized in the study:

The methods were as follows: On each trial, a moving stimulus was presented and a subject simply identified perceived motion direction (leftward vs. rightward). Feedback was provided. Unlike conventional measures of inspection time [2, 4], discriminations of stimulus motion are not confounded by visual persistence, precluding the need for a backward mask. The dependent variable was log₁₀(stimulus duration) [22, 27-29, 48]. For each condition, eight duration thresholds (82% correct) were estimated by QUEST staircases [49]. The first two were measured on a separate day and used as practice. The highest and lowest results were excluded and the remaining thresholds were averaged. This yielded very high split-half reliabilities. The average (over three sizes) split-half reliability was 0.978 and 0.985 for Study 1 and 2, respectively. The QUEST procedure is designed to ‘work’ in the log space [49], directly estimating log₁₀(threshold). Thus, data presented here are not log transformed, but rather they were natively estimated in the log space…Subjects (n = 30 from Study 2) performed a 3-back match-to-sample task. Stimulus bank consisted of 100 face images [50] and 100 most frequently used nouns taken from the Academic Word List [51]. Face images were scaled to 3.8° x 5.2°, while words were 3.8° wide. Each stimulus was presented for 1s. Subjects’ task was to respond if the presented stimulus (either a face or a word) matched the stimulus presented 3 trials behind (target trials, occurred on ~25% of total trials). Lure trials were defined as trials associated with either a 2-back, 4-back or 5-back match (~25% of trials). These are high interference trials that require suppression of lures [16]. Trials without a preceding match were non-target trials (~50% of trials). Feedback was provided. Standard deviations of between-subjects results for different trial types were relatively constant, ranging between 11.3% and 15.2%. For this data set, the correlation between SI and IQ scores was 0.65.(5, Supplemental Experimental Procedures)

Discussions: “Our findings endorse Sir Francis Galton and Charles Spearman’s original hypotheses [1] and reveal a close empirical link between sensory discriminations and intelligence. Since the early days of intelligence research, psychologists have hypothesized that sensory discriminations and intelligence are constrained by common underlying mechanisms [1, 2]. The proposed sensory correlates held promise of providing non-verbal, culture-fair measures of intelligence within a biologically constrained theoretical framework. Indeed, it is well established that IQ scores correlate with measures of inspection time: high IQ subjects require shorter stimulus exposure times to make simple perceptual judgments [1–5]. Similar results were found using reaction time measures [1–4]. These findings are intuitive—rapid information processing is important for both sensory discriminations and intelligence. However, the reported links were modest, with uncorrected correlations typically between 0.2 and 0.4 [2–4]. Still, there are indications that the underlying relationship between sensory discriminations and IQ is likely stronger than suggested by bivariate correlations. Structural equation modeling has revealed remarkably strong links (0.68 < r < 0.92) between two latent traits: general intelligence and general sensory discrimination [37, 38]. Moreover, basic sensory processing has been shown to account for intelligence variations in old age, suggesting that a common cause might underlie both cognitive and sensory declines in senescence [39].

While our results are the first report linking sensory suppression and intelligence, the central importance of inhibition in cognitive processing is well established [12–19, 42]. Working memory performance is predicted not by neural enhancement of task-relevant information, but rather by individual differences in neural suppression of distracters [12, 13]. The ability to ignore highly distracting items in working memory predicts individual differences in intelligence [18] and can account for differences in prefrontal cortex activity between low and high IQ individuals [16, 17]. Our results, while consistent with this framework, differ in important ways by implicating a very different form of neural suppression. Our subjects were not asked to ignore distracting stimuli or inhibit a prepotent response. Instead, our approach involves a low-level motion discrimination task that likely involves inhibitory center-surround receptive field mechanisms in cortical area MT [22–27]. We also considered the possibility that attentional differences might underlie our results. If high IQ individuals were somehow less attentive to large moving stimuli, such attentional effects, even if unconscious, would implicate top-down processes. We, however, find that explanation highly unlikely. Our subjects’ only task was to discriminate motion direction of a single stimulus presented in isolation. This absence of competing stimuli along with brief stimulus durations (~100ms) precludes most top-down attentional effects [9]. In Study 2, stimulus sizes were randomly interleaved, ruling out differences in sustained attention. Additionally, when stimuli were presented at low contrast, we found a positive trend between IQ and performance with large moving stimuli (Figure 4C)—a finding inconsistent with top-down attentional biases against large stimuli. Finally, as outlined above, the behavioral results reported here are believed to reflect neural center-surround suppression [22, 25, 27]. These suppressive mechanisms are found both in awake [24, 25] and anesthetized animals [23], further ruling out a possible role of attention.

Overall, our results highlight the fundamental importance of suppression in neural processing. Suppressive mechanisms play critical roles in low-level sensory processing, where they enable our perceptual systems to efficiently process an enormous amount of incoming sensory information [8, 10, 11]. Suppression plays an analogous role in intelligent cognition [15–17, 20, 21], contributing to overall neural efficiency [43]. While above we outlined an information-processing framework for the link between perceptual suppression and intelligence, we can only speculate about underlying neural mechanisms. Based on prior work [12–19, 42], we posit that the efficacy of neural suppression could provide a mechanistic explanation of our results. However, neural suppression is not a unitary mechanism but includes a broad range of inhibitory processes. Many such processes are only weakly related with one another and only some strongly predict IQ scores [44, 45], namely measures of attentional and working memory control over distracting information [16–18]. To determine whether SI is related to these higher-level suppressive processes, we measured working memory performance using a 3-back task that incorporates highly distracting lure targets (Supplemental Experimental Procedures). Consistent with earlier results [16–18], we found that subjects’ performance on distracting lure trials was correlated with IQ scores (r = 0.55, P = 0.001), while target and non-target trial performance did not (r < 0.25, P > 0.19). Notably, SI was correlated with lure trial performance (r = 0.43, P = 0.016), but not with performance on other types of trials (r < 0.23, P > 0.22). These results link the efficacy of low-level perceptual suppression with a measure of top-down suppression that has been linked with IQ. Of course, bottom-up visual and top-down working memory suppression involve very different neural mechanisms. However, we speculate that different biological instantiations of suppression might, at least in part, depend on similar underlying computations. One possible candidate is normalization, a divisive neural computation that may underlie operations in a wide range of brain systems, ranging from perceptual suppression to decision making [8].” (5)

In conclusion, we report a strong link between low-level sensory discriminations and intelligence that is based on a simple visual task that involves reasonably well-understood neural mechanisms of motion processing, spatial suppression and evidence accumulation [22, 24, 25, 27, 46]. As such, SI provides a tractable paradigm for investigating sensory correlates of intelligence.”(5)

2. Testing the link between visual suppression and intelligence.

Procedure

“In order to measure the participant’s intelligence, we administered the Reynolds Intellectual Assessment Scales^™ test (Spanish version) (RIAS test, [66, 67]). We measured general, verbal, and non-verbal intelligence (RIAS_general, RIAS_verbal, and RIAS_non-verbal). These three IQ values are highly correlated with WAIS-III (RIAS_general, r = 0.77; RIAS_verbal, r = 0.63; RIAS_non-verbal, r = 0.58; p<0.01; [68]). Administering this test takes about 40 min. We also administered the screening version for general intelligence, the Reynolds Intellectual Screening Test^™ (RIST), that takes about 20 min (highly correlated with WAIS-III, r = 0.75, p<0.01; [68]).

In the motion experiment (motion discrimination task), the participants were instructed to fixate on a small cross presented on the center of the screen. Once the cross disappeared, a drifting Gabor patch appeared on the screen moving leftwards or rightwards randomly. The participant’s task was to indicate the direction of motion (left or right) by pressing a button. After the participant’s response, a new trial was initiated. The duration of the presentation (for details, see the Stimuli section) was controlled by a Bayesian adaptive staircase [69]. The particular characteristics of the staircase can be seen in Serrano-Pedraza et al. ([15] see the Procedure section). Duration thresholds, defined as the minimum presentation time of the drifting stimuli needed to discriminate the correct direction of motion, corresponded to twice the standard deviation of the temporal Gaussian envelope. Duration thresholds were defined as a stimulus presentation duration such that performance in a motion direction discrimination task was 82% correct responses. Each staircase stopped after 40 trials, where the mean of the final probability distribution corresponds to the value of the duration threshold ([70]). The staircases in each session were interleaved randomly for the small and large window sizes. In total, 12 duration thresholds were estimated: six thresholds per spatial window size (0.7 and 6 deg).

In the spatial experiment (contrast detection task) the participants were also instructed to fixate on a cross presented on the center of the screen; this time, the cross was a rotating one. The fixation cross was visible during the stimulus presentation in order to drive the participant’s attention to the center of the screen. The participant’s task was to identify the position where the target was presented. In order to measure the contrast detection threshold of the target, we used a spatial 4AFC task where the target randomly appeared in one out of four possible positions (see Fig 1C and 1D). The contrast of the target in each trial was controlled by a Bayesian adaptive staircase (see details in [48]). The contrast threshold was defined as the minimum contrast needed in order to detect the target with a performance of 62% correct responses. Each staircase stopped after 30 trials, and the mean of the final probability distribution was assumed as the value of the contrast threshold. The staircases were interleaved for the three conditions: parallel surround, orthogonal surround, and no surround. Three contrast thresholds were measured per condition.”

Fig 4 shows the scatter plots for the motion suppression index (MSI) as a function of the IQ values for general intelligence (Fig 4A), verbal intelligence (Fig 4B), and non-verbal intelligence (Fig 4C). These results show a significant correlation between general intelligence (RIAS(IQ)) and MSI (r = 0.43, p = 0.002, 95% CI = [0.17, 0.64], N = 47). This replicates the findings from Melnick et al. (2013) (r = 0.71, p<0.001, N = 53), although our correlation is smaller. The screening IQ test (RIST, results not shown in the Fig 4) shows a significant positive correlation too (r = 0.38, p = 0.008, 95% CI = [0.11, 0.60], N = 47). There is also a significant correlation between Verbal(IQ) and MSI (r = 0.37, p = 0.012, 95% CI = [0.09, 0.59], N = 47) and Non-Verbal(IQ) and MSI (r = 0.34, p = 0.019, 95% CI = [0.06, 0.57], N = 47). This result is consistent with those from the study performed by Melnick et al. (2013), who found a strong correlation between Verbal Comprehension and MSI (r = 0.69, p<0.001, N = 53) too. After applying the Bonferroni correction for multiple comparisons, only the correlation Non-Verbal(IQ) vs. MSI turns out not to be significant p = 0.076 (p = 0.019 x 4). However, when the number of planned correlations is small, no correction is advised [71].

Discussion

“The main objective of this study was to test the link between visual surround suppression and intelligence. Previous results did find a strong link between motion surround suppression and IQ [1] and between contrast surround suppression and IQ [5]. Here we wanted to test this link using a similar motion suppression and contrast suppression task, but this time measuring contrast detection thresholds. Our results from the psychophysical experiments show the classic findings. For the motion discrimination task, we have found that at high contrasts, duration thresholds were higher for the large stimulus compared to the small one. On the other hand, for the contrast detection task, detection thresholds where higher when the target and the surround had the same orientation compared to targets and surrounds with orthogonal orientation or targets without a surround. The strength of the suppression in both tasks was quantified by a motion and a contrast suppression index. We have correlated both suppression indices and have found the correlation to be non-significant (r = 0.10, p = 0.47), thus replicating the main finding of Yazdani et al. (2015, r = -0.19, p = 0.24). These results suggest that motion and contrast surround suppression reflect the activation of independent cortical mechanisms. Although these two measurements are uncorrelated, still we could expect that both measurements would highly correlate with a third variable (IQ). For example, Cook et al. [5] showed that first and second-order surround suppression strength do not correlate, but both of them do correlate with GABA concentration.

Our results show that only the motion surround suppression index (MSI) correlates with IQ, in particular, we have found significant positive correlations between MSI and general (r = 0.43), verbal (r = 0.37), and non-verbal (r = 0.34) intelligence.

These results replicate previous findings, even though the correlations are weaker than in the original study (general intelligence: r = 0.71) [1]. The biggest difference between both studies is the intelligence test used. Melnick et al. [1] administered a short version of WAIS-III [72] in the first experiment and the full-length WAIS-IV [73] in the second one. In both experiments, they obtained similar results. Conversely, we have used the RIAS test and its screening version, the RIST. Both tests are highly correlated with WAIS-III, so probably the IQ test used is not responsible for the differences in the correlation values between ours and Melnick et al’s [1] study. The straightforward explanation for these differences could be due to the different range of IQs used in both studies. The average IQ in Melnick et al’s. [1] study is 112.92 (N = 53) and the one in our study is 106.2 (N = 47). When re-analysing the data of Melnick et al. [1], if we eliminate those participants with IQs higher than 120, then the average IQ becomes 106.4 (N = 36), a value similar to our results. Then, the correlation between IQ and MSI becomes r = 0.41, p = 0.013 (N = 36), which is similar to the one we have found. This means that the strong correlation found by Melnick et al. [1] is probably driven by participants with very high IQs. Thus, this replication shows that although it is known that people with high IQ are faster when it comes to processing visual information [74–76], the correlation between MSI and IQ cannot be explained solely by the speed of visual processing. Although our results show a significant positive correlation between the duration thresholds for small and large stimuli (r = 0.51), when these are correlated with IQ, we find opposite correlations. This is, a positive correlation for the large stimulus and a negative correlation for the small stimulus. Therefore, participants with a high IQ show higher MSI values because they tend to perform better in the case of the small stimulus (lower duration thresholds), and motion discrimination is impaired for the large stimulus (higher duration thresholds).

Although our results replicate Melnick et al’s results (with a weaker correlation), it is important to note that, in a recent study, Troche et al. [4] couldn’t replicate them. They found significant negative correlations between the duration thresholds for all the sizes they tested and the g factor. Therefore, the correlation between the suppression index (MSI) and the g factor (IQ) was practically 0 (r = -0.01, p = 0.84, N = 177). These authors also used a different intelligence test which was a short form of the Berlin Intelligence Structure test [77]. Previous studies have shown that different IQ tests are highly correlated [78], so we can assume that the differences between the IQ test used by Melnick et al. [1], Troche et al. [4] and our study are not related to the differences obtained in the results. The study of Troche et al. [4] presents a very high statistical power given by the large sample of participants they used (N = 177), thus it might be possible that because of the smaller sample used in our study (N = 47) and Melnick et al., [1] (N = 53), we are giving rise to false positive results. However, the results of Troche et al. [4] present incongruences when compared to previous psychophysical findings. For example, the average MSI of Troche et al. [4] study is much smaller (MSI = 0.22 ± 0.16, N = 177) than the one from Melnick et al., [1] (MSI = 0.32 ± 0.15, N = 53), Yazdani et al. [8] (MSI = 0.40 ± 0.22, N = 36), Read et al. [39] (MSI = 0.31 ± 0.16, N = 56), and the present study (MSI = 0.37 ± 0.14, N = 47). This becomes more surprising when we compare the age of the participants between the studies. In Troche el at’s. [4], the mean age was 21.1 ± 2.7 years, in Melnick et al. [1] 33.14 ± 13.36 years, in Yazdani et al’s [8] 42.3 years, and in our study it was 20.74 ± 2.44 years. There is multiple psychophysical evidence indicating that motion surround suppression (MSI) decreases with age [8, 22, 24, 25]. Therefore, one would expect that the study of Troche et al. [4] should show a much higher MSI than Melnick et al. [1]. This is even more surprising if we take into account that Troche et al. [4] used higher contrasts (95%) than Melnick et al. [1] (42%). Higher contrasts rise the MSI because they facilitate the discrimination of small stimuli (lowering the duration thresholds) and increase the strength of the surround suppression for large stimuli (increasing the duration thresholds) [9].

However, the biggest difference between the results from Troche et al., [4] and previous studies lies in the average of the duration thresholds for the smallest size tested (1.8 deg) (82 ± 28 msec, mean ± SD). Using lower contrast (42%), Melnick et al. [1] found an average of 39.13 ± 17.5 msec, and in our study, using a similar contrast (92%) to that of Troche et al. [4], we found an average of 38.2 ± 8.37 msec.

All these differences in the psychophysical results could be explained by the different equipment used in the study of Troche et al. [4]. All previous studies used CRTs or DLP projectors (Depth-Q 360, Cambridge Research Systems, UK) [1, 8, 9, 39]. Conversely, in Troche el al. [4] study they used an LCD display. We don’t know, however, whether the particular characteristics of LCD displays might be responsible for these differences. Troche et al. [4] suggested that this weaker suppression could be related to an attenuation of the onset transient provided that surround suppression is weaker for weaker transients [79]. Future studies should compare CRT monitors to LCD displays in order to find out the effect of the onset transients on motion discrimination.

Finally, our results from the spatial experiment showed no correlation between contrast surround suppression (CSI) and general (r = -0.09, p = 0.52, N = 46), verbal (r = 0.015, p = 0.919, N = 46), and non-verbal intelligence (r = -0.23, p = 0.13, N = 46). These results do not show the strong correlation between suppression index and visuospatial IQ (r = 0.87, p = 0.0021, N = 9) found by Cook et al. [5]. Although both studies measure contrast surround suppression, there are experimental differences that may explain this discrepancy. One of the differences lies in the IQ test used; unlike us, Cook et al. [5] administered the Weschler Abbreviated Scale of Intelligence (WASI, [80]), but as we stated before, different IQ test are usually highly correlated [78]. Another difference is the small number of participants used (N = 9) by Cook et al. [5]. Although this small number of participants could have led to a false positive result, the authors provide a robust measurement from the psychophysical task (they average results across different eccentricities), and also those measurements are highly correlated with GABA. It is therefore unlikely that Cook et al. [5] finding were accidental. On the other hand, one significant methodological difference between both studies is that in our experiment we performed a contrast detection task and Cook et al. [5] performed a contrast matching task. Previous psychophysical results have shown a similar behavior of the contrast surround suppression mechanism for contrast detection and contrast matching tasks. For example, for both kinds of tasks, contrast surround suppression is stronger in the periphery and weaker in the fovea [6, 45]; suppression is stronger for parallel surrounds than for orthogonal surrounds [6, 7, 45, 57, 61]; contrast surround suppression is spatial-frequency tuned [6, 7, 56, 57], and contrast surround suppression increases when increasing the contrast of the surround [6, 61]. All these similar characteristics in contrast detection and contrast matching suggest a similar mechanism underlying contrast surround suppression. However, there are also differences. For example, Xing & Heeger, [45], using a contrast matching task, found that the orientation and the spatial frequency of the surround does not have a strong impact on surround suppression in the periphery, whereas other studies that used a contrast detection task found that surround suppression in the periphery was orientation tuned (full bandwidth at half function about 30 deg) [6], and spatial-frequency tuned (between 1 and 3 octaves) [6, 7]. In Xing & Heeger, [45] and Petrov et al., [6] the authors suggest that center-surround interactions may have different functional roles in the fovea and the periphery. Recently, using a contrast matching task, McKendrick et al., [81] have found that surround suppression in the fovea, is larger in older adults, whereas Nguyen & McKendrick [82] have found the opposite in the periphery (6 degrees eccentricity). On the other hand, surround suppression in the parafovea (4–5 degrees eccentricity) (measured using contrast thresholds) remains constant between the ages of 20 and 70 years [8, 48]. These results suggest that contrast surround suppression in the fovea and in the periphery, as well as contrast surround suppression measured with thresholds or perceived contrast likely reveal independent neuronal mechanisms [83]. In our contrast suppression experiment we measured detection thresholds in the periphery (5 deg eccentricity) whereas in Cook et al. [5] they averaged their suppression indices across four eccentricities (0, 3, 6, and 9 deg). Consequently this makes our results difficult to be compared with Cook et al’s. Therefore, it could be suggested that the activation of different contrast suppression mechanisms explains the absence of a correlation in our study, but not in Cook et al’s study. However, the evidence about two different contrast suppression mechanisms for contrast thresholds and contrast matching is less compelling than the similar surround suppression properties revealed by both measurements.”(6)

Sources

1. Richardson, K. (2002). What IQ Tests Test. Theory & Psychology, 12(3), 283–314.
2. https://www.csbsju.edu/chp/health-promotion/alcohol-guide/understanding-blood-alcohol-content-(bac) Understanding Blood Alcohol Content (BAC):
3. https://edguyerlawyer.com/dui-breathalyzers/dui-breathalyzer-accuracy/
4. Sorbello, Jacob G et al. “Fuel-cell breathalyser use for field research on alcohol intoxication: an independent psychometric evaluation.” PeerJ vol. 6 e4418. 14 Mar. 2018, doi:10.7717/peerj.4418
5. Melnick MD, Harrison BR, Park S, Bennetto L, Tadin D. A strong interactive link between sensory discriminations and intelligence. Curr Biol. 2013;23(11):1013–1017. doi:10.1016/j.cub.2013.04.053
6. Arranz-Paraíso, Sandra, and Ignacio Serrano-Pedraza. “Testing the link between visual suppression and intelligence.” PloS one 13.7 (2018): e0200151.
7. Sheppard LD, Vernon PA. Intelligence and speed of information-processing: A review of 50 years of research. Pers. Indiv. Differ. 2008;44:535–551.
8. Pennington, B. F., Moon, J., Edgin, J., Stedron, J., & Nadel, L. (2003). The neuropsychology of Down syndrome: evidence for hippocampal dysfunction. Child development, 74(1), 75-93.
9. Lott, I. T., & Dierssen, M. (2010). Cognitive deficits and associated neurological complications in individuals with Down’s syndrome. The Lancet Neurology, 9(6), 623-633.
10. Lanfranchi, S., Jerman, O., Dal Pont, E., Alberti, A., & Vianello, R. (2010). Executive function in adolescents with Down syndrome. Journal of intellectual disability research, 54(4), 308-319.
11. Tadin, Duje. “Suppressive mechanisms in visual motion processing: From perception to intelligence.” Vision research 115 (2015): 58-70.
12. Kumar V, et al. (2010). Robbins and Cotran pathologic basis of disease (8th ed.). Philadelphia, PA: Saunders/Elsevier. ISBN 1416031219.
13. Saladin K (2012). Anatomy and Physiology: the Unit of Form and Function (6 ed.). New York: McGraw Hill. ISBN 978-0-07-337825-1.
14. Crespi, Bernard J. “Autism as a disorder of high intelligence.” Frontiers in neuroscience 10 (2016): 300.

1.INTRODUCTION 

In recent times, a specific genetic study  has been used in a attempt to discredit race realism: Biological Races In Humans by Alan Templeton. Properly understood, Templeton’s Fallacy is this: his thesis is based on a false criteria that races must be a subspecies in order to be legitimate or meaningful. The historical and scientific observations of “man-like” groups was established regardless if the specifics of said racial theories were different: that races simply meant different populations was confirmed by people like Charles Darwin. Darwin uses the term race synchronously with the word population in his book The Descent of Man, and one can extend the definition based of off demonstrable genetic differences between sets of human populations. I fully advocate for the “race-as-a-population” definition that Darwin advocated well over a century ago. Often, genetic studies plot ethnic groups in the form of clusters and clines that measure Fst distance, but it is important to bear in mind that Fst measurement shouldn’t be the only way to measure genetic variation.  Populations often correlate with geography and linguistic categories. I will now attempt to analyze most of Templeton’s study as it is. This post is divided based on the sections that Templeton provides.

2. “The Biological Meaning of ‘Race’”

TEMPLETON WRITES: “For example, Lao et al. (2010) assessed the geographical ancestry of self-declared “whites” and “blacks” in the United States by the use of a panel of geographically informative genetic markers. It is well known that the frequencies of alleles vary over geographical space in humans. Although the differences in allele frequencies are generally very modest for any one gene, it is possible with modern DNA technology to infer the geographical ancestry of individuals by scoring large numbers of genes. Using such geographically informative markers, self-identified “whites” from the United States are primarily of European ancestry, whereas U.S. “blacks” are primarily of African ancestry, with little overlap in the amount of African ancestry between self-classified U.S. “whites” and “blacks.”(1)

This finding has been replicated many times. Bryc et al is quite arguably one of the most important genetic studies in the last ten years, as it confirms that racial labels tend correlate very well with genetic ancestry. For African Americans, 73.2% of their ancestry was African (varying regions, with the exception of Northern Africa). 24.0% of their ancestry was European. (4) The genetic evidence is resoundingly clear: the majority of African Americans have mostly Black ancestry despite have a large amount of European ancestry. In this way, we can say that African Americans are a population (race) with mostly African heritage, with European and Native American ancestry. So to the very least, Templeton is right here in that he quote a credible study.

Templeton then uses a study that analyzed the genetic admixture of Brazilians who identified as White, Black, Borwn, etc.(1)(5) Specifically, he uses Santos et al to prove that because there is massive admixture within these populations, that this makes racial labels invalid:

TEMPLETON WRITES: “In contrast, Santos et al. (2009) did a similar genetic assessment of Brazilians who self-identified themselves as “whites”, “browns”, and “blacks” and found extensive overlap in the amount of African ancestry among all these “races”. Indeed, many Brazilian “whites” have more African ancestry than some U.S. ‘blacks’. Obviously, the culturally defined racial categories of “white” and “black” do not have the same genetic meanings in the United States and Brazil.”

But what does the study actually say? The genetic results contained in that study prove that, on average, racial labels still correlate well with majoritarian ancestry. If one looks at Figure 1 within the study, the researches found that most “White” students had majority European ancestry, while a majority of “Blacks” from that area also have more African ancestry than other categories. (6) While the percentages aren’t the same necessarily, the same general phenomena is comparable to populations of the USA as I previously mentioned before from Bryc et al (2015). The most recent genetic analyses prove that if a person in Brazil has more African Ancestry there is higher,”…probability of self-declaring as black or brown increases according to the proportion of African ancestry and varies widely among cities. In Porto Alegre, where most of the population is white, with every 10% increase in the proportion of African ancestry, the odds of self-declaring as black increased 14 times (95%CI 6.08–32.81). In Salvador, where most of the population is black or brown, that increase was of 3.98 times (95%CI 2.96–5.35). The racial composition of the area of residence was also associated with the probability of self-declaring as black or brown.”(10) I simply cannot find evidence that supports the claim that “many” Brazilian whites have more African ancestry then some US blacks from Santos et al, as the study only mentions the different populations from Brazil and not America. The majority appear to have European ancestry as the largest percentage of their ancestry. Templeton’s statements are just not correct here as far as I am aware.

TEMPLETON says: “At the lowest level are demes, local breeding populations. Demes have no connotation of being a major subdivision or type within a species. In human population genetics, even small ethnic groups or tribes are frequently subdivided into multiple demes, whereas “race” always refers to a much larger grouping.”

Templeton cites no evidence for his claim that demes aren’t a “major” subdivision within  a species, and he doesn’t even define what “major” means in this context. Any subdivision would be major in terms of importance if it can be ascertained by legitimate evidence, through any proper method. And since this is ultimately about quantifying genetic variation, any credible evidence showing the said demes exist would be major. Templeton has gone to great lengths quantifying the genetic variation of human demes in his newer book entitled Human Population Genetics and Genomics. Clearly, demes are “major” in terms of importance if he is willing to go to great lengths in delineating variation for said categorization, regardless of his personal protests. That book deserves it’s own review, but regardless, suffice to say the original definition of deme was, “…any assemblage of taxonomically closely related individuals.”(4)  Bearing in mind that races are genetically distinct because of their ancestry, demes could be technically used to define races (although it isn’t my preference because of the original definition above doesn’t specify relation or admixture). Regardless, the existence of demes has certainly been contentious even among researchers who have denied race, “It may be worth mentioning some of the reasons that make it difficult to define human demes. Candidates could be ethnographic units (e.g., tribes) or geographically defined clusters of people (villages, towns, cities). They are all usually endogamous to some degree and may come closer to the definition of a deme, but there are always many possible, embarrassing choices. Many tribes have undergone extraordinary demographic expansions (e.g., in Nigeria) and are subdivided in complex ways. In tribal as in modern society, the choice of mates is largely dictated by geographic, socioeconomic, reli- races, but the continuity of the variation of gene frequencies and of mating distances at the geographic scale of demes is even more extreme than for races (Cavalli-Sforza 1958, 1963, 1986a, b)” (7) So overall, the insistence that demes must exist while race doesn’t isn’t justified. Lastly, Templeton gives no evidence for the idea that races are “larger” than demes. He cites none of the racial theorists before and after the 20th century. This is another example of a claim without any evidence.

TEMPLETON says, “The question of the existence of human “races” now becomes the question of the existence of human subspecies. This question can be addressed in an objective manner using universal criteria. The Endangered Species Act of the USA mandates the protection of endangered vertebrate subspecies (Pennock & Dimmick, 1997). Accordingly, conservation biologists have developed operational definitions of race or subspecies that are applicable to all vertebrates, and two have been used extensively in the non-human literature. These two biological definitions of subspecies or “race” will be applied to humans and to our nearest evolutionary relative, the chimpanzee, in order to avoid an anthropocentric, culture-specific definition of race.(1)

This statement can be refuted in several ways. Firstly, before Templeton had ever constructed this particular study, it was noted by Ernst Mayr that the term race was not always used synonymously with the term subspecies, “A race that is not formally designated as a subspecies is not recognized in the taxonomic hierarchy. However, the terms subspecies and geographic race are frequently used interchangeably by taxonomists working with mammals, birds, and insects. Other taxonomists apply the word race to local populations within subspecies.”(8) But as stated earlier, Charles Darwin readily categorized races as distinct populations based on phenotypic variety, and again, Darwin’s work predates Templeton. Specifically, Darwin made observations about Latin American populations, “We have now seen that a naturalist might feel himself fully justified in ranking the races of man as distinct species…On the other side of the question, if our supposed naturalist were to enquire whether the forms of man keep distinct like ordinary species, when mingled together in large numbers in the same country, he would immediately discover that this was by no means the case. In Brazil he would behold an immense mongrel population of Negroes and Portuguese…”(2) Furthermore, Darwin talks of the races as having real tangible differences and refers to these populations as races, “There is, however, no doubt that the various races, when carefully compared and measured, differ much from each other,–as in the texture of the hair, the relative proportions of all parts of the body, the capacity of the lungs, the form and capacity of the skull, and even in the convolutions of the brain. But it would be an endless task to specify the numerous points of difference. The races differ also in constitution, in acclimatisation and in liability to certain diseases. Their mental characteristics are likewise very distinct; chiefly as it would appear in their emotional, but partly in their intellectual faculties.”(2) For Darwin, the term “sub-species” was a rather vague term that designated a group of organisms below species. It was not employed in the same way that Templeton defines it through phylogenetic methods, “Some naturalists have lately employed the term “sub-species” to designate forms which possess many of the characteristics of true species, but which hardly deserve so high a rank. Now if we reflect on the weighty arguments, above given, for raising the races of man to the dignity of species, and the insuperable difficulties on the other side in defining them, the term “sub- species” might here be used with much propriety. But from long habit the term “race” will perhaps always be employed.”(2) In reading the Descent of Man chronologically, it is clear that Darwin used the term race as a first order rather than sub-species. Meaning that when it came to classifying groups of humans or acknowledging differences between them the term race was initial term to distinguish them. Since Charles Darwin did not have the most up-to-date genetic technologies at his disposal, it might be tempting to assume that a populationist-genetic theory of race wouldn’t hold by definition. However, the very general premise of races being differentiated groups/populations is confirmed by genetic studies as a whole, therefore Darwin’s initial, general thesis was proven true, especially with the advent of the ADMIXTURE software. (5)

TEMPLETON says: “If every genetically distinguishable population were elevated to the status of race, then most species would have hundreds to tens of thousands of races, thereby making race nothing more than a synonym for a deme or local population.”

This is not actually a problem at all, as per the prior statements above demonstrate. Again Templeton provides no evidence that there would be “thousands” of races but it wouldn’t be a problem even he if did.

TEMPLETON says: “A race or subspecies requires a degree of genetic differentiation that is well above the level of genetic differences that exist among local populations. One commonly used threshold is that two populations with sharp boundaries are considered to be different races if 25% or more of the genetic variability that they collectively share is found as between population differences (Smith, et al., 1997). A common measure used to quantify the degree of differentiation is a statistic known as pairwise f_st. The pairwise f_st statistic in turn depends upon two measures of heterozygosity. The frequency with which two genes are different alleles given that they have been randomly drawn from the two populations pooled together is designated by H_t, the expected heterozygosity of the total population.” (1)

I have already demonstrated that races need not be subspecies in the way Templeton is describing. In regards to Smith et al 1997, the authors do not use the term “race” in their paper at all; the term species and subspecies is used. Why Templeton claimed that race was used as such is unknown, but clearly the burden is on him to answer why he implied race was disseminated in that article. Furthermore, other authors have noted that the 75% rule here is woefully vague, “The cited authors do not discuss magnitudes of genetic differentiation but rather the ambiguous 75% rule of thumb… Regarding the cladistic perspective…the general conventions regarding intraspecific classifications are unclear.” (9)

Sources

1. Templeton, Alan R. “Biological races in humans.” Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 44.3 (2013): 262-271.APA
2. Darwin, Charles. The Descent of Man: And Selection in Relation to Sex. London: J. Murray, 1871. Print.
3. Bryc, Katarzyna, et al. “The genetic ancestry of african americans, latinos, and european Americans across the United States.” The American Journal of Human Genetics96.1 (2015): 37-53.APA
4. GILMOUR, J. S. L., & GREGOR, J. W. (1939). Demes: A Suggested New Terminology. Nature, 144(3642), 333–333. doi:10.1038/144333a0 
5. Xing, Jinchuan, et al. “Toward a more uniform sampling of human genetic diversity: a survey of worldwide populations by high-density genotyping.” Genomics 96.4 (2010): 199-210.
6. Santos, Ricardo Ventura, et al. “Color, race, and genomic ancestry in Brazil: dialogues between anthropology and genetics.” Current anthropology 50.6 (2009): 787-819.
7. FULLERTON, MALIA. “The History and Geography of Human Genes. Abridged Paperback Edition. By L. Luca Cavalli-Sforza, Paolo Menozzi and Alberto Piazza. Princeton University Press, Princeton, New Jersey, 1996. Pp. 413.£ 25.00. ISBN 0 691 02905 9.” Annals of Human Genetics 61.5 (1997): 463-467.
8. Mayr, Ernst. “Principles of systematic zoology.” Principles of systematic zoology. (1969).
9. Fuerst, J. (2015). The Nature of Race: the Genealogy of the Concept and the Biological Construct’s Contemporaneous Utility.
10. Chor, Dóra, et al. “Context-dependence of race self-classification: Results from a highly mixed and unequal middle-income country.” PloS one 14.5 (2019): e0216653.

From Information Nation

In Population Genetics debates, many people have cited Long and Kittles et al as a means of refuting the notion that Fst distance is a valid way of measuring genetic differences between human populations.(1) Often times, this is done to discredit race realism from a populationist stance. The genetic tests of Long and Kittles revealed an Fst measurement of 0.11 for different human populations. The two researchers joined chimpanzee samples along with the human ones. The result? The Fst difference for the two species grew to .18, which is interpreted as being as very low. A multitude of reasons exist for why they received these results. Because of the genetic markers used, the highest value would be exactly 1. This is the case regardless if you are comparing humans and other primates. The fact of the matter is that one cannot solely use Fst alone as a measurement of genetic differentiation among humans.(4) For example, Bird et al conducted a study that compared various measurements of genetic differentiation: FST, F 0 ST, ΦST, Φ 0 ST, and Dest. This was done with 80 simulated populations. What did they find?

” FST is the best choice for datasets consisting of neutral unlinked single nucleotide polymorphism (SNP) datasets involving two alleles per locus…Overall, there is no single metric that best captures population genetic differentiation, and we recommend that researchers report both a fixation index (FST or ΦST) and an index of genetic differentiation (F 0 ST or Dest) for their datasets because they represent different properties of population partitioning. When indices of fixation and genetic differentiation are in agreement, one can be sure of the conclusion. When the two methods yield differing results, the pattern and direction of discord can be diagnostic of a particular phenomenon, and we provide a range of simulations across parameter space to illustrate both points.” (2)

Fst still a valid way of measuring genetic variation, there must be qualifiers when doing so however, “The statistical methodology for estimating F****-statistics is now well established. With the availability of methods to estimate locus- and population-specific effects on** FST, geneticists now have a set of tools for identifying genomic regions or populations with unusual evolutionary histories. Through further extensions of this approach, it is even possible to determine the relationship between the recent evolutionary history of populations and environmental or demographic variables”(3)**

So overall, a populationist theory of race isn’t refuted. The fact that different populations differ genetically on various fronts proves this uniformly. (5)

Sources

1. Long, Jeffrey C., and Rick A. Kittles. “Human genetic diversity and the nonexistence of biological races.” Human biology81.5/6 (2009): 777-799.
2. Bird, Christopher E., et al. “Detecting and measuring genetic differentiation.” Phylogeography and population genetics in Crustacea 19.3 (2011): l-55.
3. Holsinger, Kent E., and Bruce S. Weir. “Genetics in geographically structured populations: defining, estimating and interpreting F ST.” Nature Reviews Genetics 10.9 (2009): 639.
4. Fuerst, John. The Nature of Race. Open Behavioral Genetics , June 20, 2015. PDF.
5. Xing, Jinchuan, et al. “Toward a more uniform sampling of human genetic diversity: a survey of worldwide populations by high-density genotyping.” Genomics 96.4 (2010): 199-210.

Original article can be found on Information Nation (Author unknown)

informationnation.home.blog/2019/05/16/study-review-epigenetic-supersimilarity-of-monozygotic-twin-pairs/

In the early part of 2018, a new twin study surfaced that explores the epigenetic profiles of monozygotic twins. That study is called “Epigenetic supersimilarity of monozygotic twin pairs”.(6) I have wanted to read this study since it was released but I just have not had the time. Some people are using this study in an attempt to show that monozygotic twins aren’t genetically as similar as initially thought. Or that their similarities are a result of their environmental similarities only. My overall impression is that the researcher’s first statements in their Results section and their conclusion is not the highest quality. This is because of the sources used. Basically, the study doesn’t highlight more recent experiments that determined the heritability of DNA methylation variation that is attributable to genetic causes, “Regardless of potential genetic influences, our data indicate that interindividual epigenetic variation at ESS probes occurs systemically and is stable over time.”(6) Additionally, it seems that they haven’t seen the most recent research: genetic variation actually has a significant influence on DNA methylation profiles, but I am not aware of a single researcher who claims that genetic variation is sole cause of it, “Twin and family studies do not claim that all epigenetic variation can be accounted for by genetic variation.”(22) How did the researchers conduct their experiment? They did the following: “Reasoning that monozygotic twins offer a human analog of inbred mice, we explored a publicly available genome-scale CpG methylation data set for monozygotic (MZ) and dizygotic (DZ) twins based on the widely utilized Illumina Infinium Human-Methylation 450 (HM450) array.”(6)

As of now, I can’t fault the researchers for their actual methods in the experiment. It seems to be well done, although I will be willing to give a more extensive critique should I find any methodological problems. The principle problem with this study is, again, it’s conclusion and lack of recent research cited that I think is absolutely critical to be aware of (especially for a subject like this). The most important thing I want anyone reading this to understand is that, even if the influence of genetic variation is less than that of the environment for epigenetic phenomena overall, any amount of fixed genetic influence on these traits would have wide implications on a species. The truth of the matter is that we really don’t know the mechanisms for many of these epigenetic happenings, “Thus, while data strongly suggest the existence of transgenerational inheritance of epigenetic information, the principles that guide non-DNA based information transmission across generations remain largely unknown. Moreover, it remains to be determined for how long (or for how many generations) particular epigenetic effects can persist.” (26) As I will show, there is evidence that strongly suggests that DNA methylation variation is significantly influenced by genetic variation.

Results

The authors make the following claim, “Rather than being predominantly determined by genetics, interindividual variation in DNA methylation at MEs is determined, at least in part, stochastically (1) and influenced by the nutritional milieu of the preimplantation embryo (2, 3, 4)”. In that sentence alone, the authors references a much older study that looked at mammals from a general standpoint, specifically source 1 (Rakyan et al, 2002). They attempt to use this study to prove that DNA methylation is not primarily influenced by genetics, but Rakyan et al wasn’t an experiment on humans at all. It featured data that examined various epialleles for mice fur. Mice and humans aren’t the same species, therefore applying these findings readily to humans isn’t correct. Also, highly inbred mice might pass on deleterious alleles that make them more susceptible to certain types of ailments, therefore confounding epigenetic research even further. Furthermore, humans are only mentioned 4 times within the Raykan et al. As per their conclusion, here is what Rakyan et al say, “Clearly metastable epialleles have properties that are very different from classic alleles. But how common are these alleles, particularly in humans? It is not easy to address this question owing to our extreme genetic heterogeneity…In this regard, it has been observed that variation in the methylation status of autosomal allelic sites in human tissue can be transmitted through the germ line”(1) So there isn’t a specific conclusion given by Raykan et al in how these epialleles would function in humans per se. In my view, this was not a beneficial source to use for Von Baak’s claim. Two of the studies linked show the contribution of external factors (nutrition) on DNA methylation in the womb, but those studies utilize mice as opposed to humans in their experiments. However, we also know that there is demonstrable genetic differences between mice and humans, insomuch that there are large differences in gene expression, physiology, reproductive strategies, etc. As a result, it is not logical at all to make broad sweeping assumptions about human epigenetic phenomena via experiments on mice, “To date, the characterization of epigenetics in human development has been almost exclusively limited to DNA methylation profiling; these data reinforce that the global dynamics are conserved between mouse and human. However…it is becoming apparent that the mechanisms regulating these dynamics are distinct.”(15) And, “In comparison to mice, there appear to be similar dynamics in both gametes and the early embryo, and yet the proteins modulating these dynamics are often divergent in timing or function. Thus, future investigations of epigenetic patterns in human development may not only reveal further novel regulatory mechanisms, but also differences in the extent of epigenetic information transmitted from gametes to embryos.” (15) What is clear is that the utility of mice-epigenetic studies is becoming severely limited for deducing effects in humans. Humans and mice may be mammals, but the wide differences present themselves and we need to be pushing for epigenetic studies that compare human populations more often.

I will now highlight several differences that can be found overall, even in human/mice embryos:

1. Madisoon et all found that gene expression in human and mouse embryos for 70 genes was different across the board, “We found small differences in the maternally expressed and downregulated genes between human and mouse. In contrast, we found a set of genes that were upregulated in humans but not in mouse after zygotic genome activation. Sixteen out of 25 the genes in human “Up-down” and “Up” clusters had this difference in expression. Fifteen mouse orthologs shared the expression profile with maternally expressed genes and were downregulated in the course of preimplantation development, but were upregulated in humans. This difference in gene expression between human and mouse early embryos might account for part of the different preimplantation time in humans compared to mouse or for the differences in splicing.”(11) And, “Our findings highlight many significant differences in gene expression patterns during mouse and human preimplantation development. We also describe four cancer-testis antigen families that are also highly expressed in human embryos: PRAME, SSX, GAGE and MAGEA.” (11)

2. Certain genes for heart development are expressed far differently for humans and mice, “MYH6 and MYH7, the genes encoding alpha and beta myosin heavy chains, are differentially expressed in mice and humans. Myh6 encodes a predominant form of myosin heavy chain in adult mice heart and Myh7 is expressed in embryonic mice heart, whereas opposite expression pattern of these two genes is found in humans.” (12)

3. Human oocytes have a larger DNA methylation levels in comparison to mice, which specifically is 54% in humans and 40% in mice. (13)(14)

4. In post implantation embyronic tissues, the number of imprinted tissues is different: 125-151 genes for mice and 50-90 imprinted genes in humans. (16)(17)(18)(19)(20)(21)Secondly, Van Baak et al cite an epigenetic study that examined the methylation of Gambian women during seasonal diets.(4) As far as I can tell, this study claimed that only one gene wasn’t influenced by genetic variation: “Pearson correlation of PBL DNA methylation at ZFYVE28 within 25 pairs of MZ twins.”(4). Early literature speculated that the DNA methylation changes over time could be attributed to age as well as genetic variation, “The familial clustering of methylation changes also raises the possibility that methylation changes also raises the possibility that methylation stability might be directly related to genetic variation, such as in genes controlling one-carbon metabolism or DNA methyltransferase activity.”(23) But even the most recent research in DNA methlyation patterns within families shows that there is certainly a significant genetic contribution. In 2011, Gertz et al found the following, “The vast majority of differential methylation between homologous chromosomes (>92%) occurs on a particular haplotype as opposed to being associated with the gender of the parent of origin, indicating that genotype affects DNA methylation of far more loci than does gametic imprinting. We found that 75% of genotype-dependent differential methylation events in the family are also seen in unrelated individuals and that overall genotype can explain 80% of the variation in DNA methylation...By following DNA methylation patterns through the family along with nearby SNPs, we found that allelic differences between chromosomes play a much larger role in determining DNA methylation than the parental origin of the chromosome, indicating that DNA sequence has a larger impact on DNA methylation than gametic imprinting. We also found that allelic differences in DNA methylation found in the family can also be observed in unrelated individuals.”(5) They conducted this study with the following method, “To discover differential methylation on homologous chromosomes, we used reduced representation bisulfite sequencing (RRBS), which can be used to measure the DNA methylation state in a subset of the genome in many samples. Because RRBS uses bisulfite treatment, it detects both 5-methylcytosine and 5-hydroxymethylcytosine. Thus, the allelic differences identified by the method can be differences in 5-methylcytosine or 5-hydroxymethylcytosine.”(5) Additionally, newer findings by the same researchers that conducted the Gambian study uphold the possibility of methylation with metastable epialleles being under genetic influence, “It remains the case, however, that methylation at MEs might be influenced by genotype. Our ME screen filtered out differences in methylation for CpGs within 60 base pairs (bp) of a genetic variant, but observed interindividual differences may be driven by more distant genetic variation.”(21)

To be clear, this does not mean that the environment doesn’t effect methylation profiles. External factors beyond the genome certainly DO affect methylation profiles. But anecdotally I can tell you that most people would rather focus on the environmental aspects of said methylation profiles. Ultimately, Von Baak admit that some DNA methylation was under partial genetic control, “Contrary to our expectation, MZ twin concordance in putative ME regions was between 2.5- and 16.5-fold higher than that of DZ twins (Fig. 1). This suggested that establishment of DNA methylation at these regions is under genetic control.”(6) One can look at other family studies that deal with the heritability of methylation. Among other things, it is a truism that age affects methylation. This phenomena is often called “epigenetic drift”. However, since genetic variation also contributes to DNA methylation, we see that this contributes to ageing process overall.(28)

Discussion

The following statement is made by Van Baak et al about their experiment:

“Remarkably, at seven of these, peripheral blood DNA methylation at baseline was significantly associated with later cancer (Fig. 7; Additional file 2: Table S13); three (SPATC1L, VTRNA2-1, and DUSP22) were significantly associated with multiple types of cancer. Elevated methylation in a cluster of six CpG sites at SPATC1L was associated with reduced risk of colorectal and prostate cancer (Fig. 7b, f), and elevated methylation in a cluster of 12 CpGs at VTRNA2-1 was associated with higher risk of lung cancer and mature B-cell neoplasm (Fig. 7d, e). Interestingly, elevated methylation in a cluster of eight CpG sites at DUSP22 was associated with increased risk of mature B-cell neoplasm (Fig. 6e) yet reduced risk of urothelial cell carcinoma”

So basically, they found that certain DNA methylation patterns were associated with a reduced risk of certain diseases, especially certain types of cancers. This is of course is not surprising considering a significant portion epigenetic literature is based on associations found in experiments.(25) In line what I have previously discussed, Van Baak et al’s experiment could very well be replicated in the future. However, what is NOT mentioned in the study is that prior research has shown, again, that genetic factors are associated with methylation rates as well. Galanter et al found that significant genetic association differences amongst Latino populations. Originally, the researcher’s hypothesis was to find, “…that environmental exposures may be responsible for the observed differences in methylation between ethnic groups…”.(8) This was done by testing, “…methylation found in the blood of 573 people from diverse Latino ethnic sub-groups.”(8) For this study, they used the Latino samples with other samples from other racial groups: 1000 European Americans from Utah (CEU) and Yoruba Africans (YRI)s, as well as 71 Native American (NAM) samples. The ADMIXTURE population software was used to delineate the population differentiation.(10) Lastly, a PCA (principal component analysis) was created using the EIGENSTRAT program. (9) Additionally, Galanter et al analyzed the CPG cites that were found to be genetically influenced. What does this mean? CPG sites are simply the portions of DNA where a cytosine nucleotide goes after a guanine nucleotide in the order of nucleobases. Recall that there is 5 different nucelobases for organisms on Earth: adenine, guanine, thymine, cytosine, and uracil. Therefore, cytosine and guanine is divided via a single phosphate group. The phosphate links any two nucleosides together in DNA. CPG methylation is indeed heritable; methylation is therefore linked to the expression of certain genes within an organism. However, gene expression varies widely across species. And this even holds for different human races as well. (24) Galanter et al attempted to show that some of their data was consistent with this hypothesis in the Supplemental File 1H. However, as their experiment progressed, they found that the link between the genetic ancestry and DNA methylation was apparent, “Genetic ancestry explained approximately 75% of the variation in methylation between the sub-groups. In addition, the methylation patterns at DNA locations known to be affected by environmental exposures – for example, by exposure to tobacco while in the womb – were disproportionately likely to be methylated differently in different sub-groups.”(8) And, “We conclude that differential methylation between ethnic groups is partially explained by the shared genetic ancestry but that environmental factors not captured by ancestry significantly contribute to variation in methylation.” (8)
Finally, other studies have shown the importance of genetic variation on DNA methylation. One of the most interesting examples I know of is the results of Guilani et al, Variability across Human Populations: A Focus on DNA Methylation Profiles of the KRTCAP3, MAD1L1 and BRSK2 Genes. This study showed that, despite living in the exact same city in Italy, a diverse sample of racial/ethnic groups had different rates of methylation for the following genes: KRTCAP3, MAD1L1, and BRSK2. The study tested, “…individuals belonging to different populations from Morocco, Nigeria, Philippines, China, and Italy…”.(27) And, “A total of 90 individuals of European, Asian, and African ancestry, but all living in the same place (Bologna, Italy), were typed and, in particular, we selected 17 individuals from China, 13 from Philippines, 16 from Morocco, and 14 from Nigeria to investigate methylation variability at a microgeographical level.”(27) This was done through a whole blood methylation comparison. The experiment was primarily done KRTCAP3 and MAD1L1 genes in the African samples were found to be hypermethylated. In addition, “Individuals of African origins presented lower values of DNA methylation than subjects of European and Asian ancestry also at the region located in the MAD1L1 gene, as well as for CpGs sites nearby cg16658412.” (28) Finally, the researchers concluded that the DNA methylation that affected these genes was primarily of a genetic origin, ” DNA methylation differences at the KRTCAP3, MAD1L1 and BRSK2 genes were observed in individuals living in Bologna (Italy), as well as in the study published by Heyn and colleagues that includes individuals living in America (Heyn et al. 2013). This indicates that ancestry is the principal factor that influences DNA methylation profiles at these loci. The different environments (American and Italian city) seems not to have a crucial role in shaping DNA methylation profiles of these regions. Although more data on gene expression and protein analysis are needed to draw further conclusions, these findings strongly suggest that the examined genomic regions have high probability to have exerted an important role in the recent evolutionary history of human populations.” (27)

Sources

1. Rakyan VK, Blewitt ME, Druker R, Preis JI, Whitelaw E. Metastable epialleles in mammals. Trends Genet. 2002;18:348–51.View ArticlePubMedGoogle Scholar
2. Waterland RA, Jirtle RL. Transposable elements: targets for early nutritional effects on epigenetic gene regulation. Mol Cell Biol. 2003;23:5293–300.View ArticlePubMedPubMed CentralGoogle Scholar
3. Waterland RA, Dolinoy DC, Lin JR, Smith CA, Shi X, Tahiliani KG. Maternal methyl supplements increase offspring DNA methylation at Axin fused. Genesis. 2006;44:401–6.View ArticlePubMedGoogle Scholar
4. Dominguez-Salas P, Moore SE, Baker MS, Bergen AW, Cox SE, Dyer RA, Fulford AJ, Guan Y, Laritsky E, Silver MJ, et al. Maternal nutrition at conception modulates DNA methylation of human metastable epialleles. Nat Commun. 2014;5:3746.View ArticlePubMedPubMed CentralGoogle Scholar
5. Gertz, Jason, et al. “Analysis of DNA methylation in a three-generation family reveals widespread genetic influence on epigenetic regulation.” PLoS genetics 7.8 (2011): e1002228.
6. Van Baak, Timothy E., et al. “Epigenetic supersimilarity of monozygotic twin pairs.” Genome biology 19.1 (2018): 2.
7. Carver, Ethan A., and Lisa Stubbs. “Zooming in on the human–mouse comparative map: genome conservation re-examined on a high-resolution scale.” Genome Research 7.12 (1997): 1123-1137.
8. Galanter, Joshua M., et al. “Differential methylation between ethnic sub-groups reflects the effect of genetic ancestry and environmental exposures.” Elife 6 (2017): e20532.
9. Patterson, Nick, Alkes L. Price, and David Reich. “Population structure and eigenanalysis.” PLoS genetics 2.12 (2006): e190.
10. Alexander, David H., John Novembre, and Kenneth Lange. “Fast model-based estimation of ancestry in unrelated individuals.” Genome research (2009).
11. Madissoon, Elo, et al. “Differences in gene expression between mouse and human for dynamically regulated genes in early embryo.” PLoS One 9.8 (2014): e102949.
12. Uosaki, Hideki, and Y. H. Taguchi. “Comparative gene expression analysis of mouse and human cardiac maturation.” Genomics, proteomics & bioinformatics 14.4 (2016): 207-215.
13. Kobayashi, Hisato, et al. “Contribution of intragenic DNA methylation in mouse gametic DNA methylomes to establish oocyte-specific heritable marks.” PLoS genetics 8.1 (2012): e1002440.
14. Okae, Hiroaki, et al. “Genome-wide analysis of DNA methylation dynamics during early human development.” PLoS genetics 10.12 (2014): e1004868.
15. Hanna, Courtney W., Hannah Demond, and Gavin Kelsey. “Epigenetic regulation in development: is the mouse a good model for the human?.” Human reproduction update (2018).
16. Crowley, James J., et al. “Analyses of allele-specific gene expression in highly divergent mouse crosses identifies pervasive allelic imbalance.” Nature genetics 47.4 (2015): 353.
17. Babak, Tomas, et al. “Genetic conflict reflected in tissue-specific maps of genomic imprinting in human and mouse.” Nature genetics 47.5 (2015): 544.
18. Sanchez-Delgado, Marta, et al. “Human oocyte-derived methylation differences persist in the placenta revealing widespread transient imprinting.” PLoS genetics 12.11 (2016): e1006427.
19. Santoni, Federico A., et al. “Detection of Imprinted Genes by Single-Cell Allele-Specific Gene Expression.” The American Journal of Human Genetics 100.3 (2017): 444-453.
20. Andergassen, Daniel, et al. “Mapping the mouse Allelome reveals tissue-specific regulation of allelic expression.” Elife 6 (2017): e25125.
21. Kessler, Noah J., et al. “Establishment of environmentally sensitive DNA methylation states in the very early human embryo.” Science advances 4.7 (2018): eaat2624.
22. Dickins, Thomas E., and Qazi Rahman. “The extended evolutionary synthesis and the role of soft inheritance in evolution.” Proc. R. Soc. B 279.1740 (
23. Bjornsson, Hans T., et al. “Intra-individual change over time in DNA methylation with familial clustering.” Jama 299.24 (2008): 2877-2883.
24. Powell, Isaac J., et al. “Genes associated with prostate cancer are differentially expressed in African American and European American men.” Cancer Epidemiology and Prevention Biomarkers (2013): cebp-1238.
25. Heard, Edith, and Robert A. Martienssen. “Transgenerational epigenetic inheritance: myths and mechanisms.” Cell 157.1 (2014): 95-109.
26. Houri-Zeevi, Leah, and Oded Rechavi. “A matter of time: small RNAs regulate the duration of epigenetic inheritance.” Trends in Genetics 33.1 (2017): 46-57.
27. Giuliani, Cristina, et al. “Epigenetic variability across human populations: a focus on DNA methylation profiles of the KRTCAP3, MAD1L1 and BRSK2 genes.” Genome biology and evolution 8.9 (2016): 2760-2773.
28. Boks, Marco P., et al. “The relationship of DNA methylation with age, gender and genotype in twins and healthy controls.” PloS one 4.8 (2009): e6767.