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Its there a gene or genes for intelligence?

Its there a gene or genes for intelligence?


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I don't know almost nothing of genetics but I felt a little interested in the subject of genetics and its role in intelligence and i found to reports that talk about certain genes that contribute to intelligence

https://www.nature.com/news/smart-genes-prove-elusive-1.15858 https://www.newsweek.com/how-be-smart-intelligence-linked-500-genes-large-new-study-842454

What I don't understand is if only some people have this 500 genes or all human population has this genes?


Is IQ heritable?

Let me start by saying that there is no commonly agreed upon method to measure IQ (thank you @mdperry). Keeping this in mind…

Yes, studies have shown that IQ is heritable. Or rather, IQ has a heritability that differs from both 0 and 1, meaning that both environment variation and genetic variation plays a role in determining an individual's IQ. Studies show IQ heritability varying between 0.5 (ref) and 0.8 (ref). For more information, please have a look at this post.

To understand the concept of heritability, please have a look at Why is a heritability coefficient not an index of how “genetic” something is?.

What are the genes that contribute to it?

IQ is a polygenic trait, meaning that many loci (locus = position in the genome) are affecting the variation in IQ. For more information about those loci, please have a look at this post.

What I don't understand is if only some people have this 500 genes or all human population has this genes?

Your misunderstanding comes from a misunderstanding about the concepts of gene and allele. You should start by having a look at the definition of the three following terms

Below, I will not comment about the 500 genes claimed in this popular article and will just consider them as valid without considering the complication behind the methodology used for such estimation and about what original articles this popular article is citing.

With the exception of eventual copy number variation, the entire human population share these 500 genes but not everyone has the same allele at these 500 loci.


Gene Editing Won't Work on Complex Traits Like Intelligence

Last week, scientists gathered in Washington, DC for the International Summit on Human Gene Editing to discuss a technology called CRISPR-CAS9 , which can insert, remove and change the DNA of basically any organism. It is relatively simple, inexpensive and accurate, and it’s already being used in laboratories around the world to make cells and breed laboratory animals with modified DNA for the study of diseases.

CRISPR could also be used to modify DNA in human embryos, but the question is whether this should be allowed. Among the concerns scientists and bioethicists have highlighted are heritable gene modifications and the use of this technology to create “designer babies.” CRISPR provides new opportunities for disease treatment and prevention, but with unknown and potentially substantial risks that warrant an ethical discussion. And this discussion should be rooted in an understanding of what can and cannot be meaningfully edited.

I study the genetic prediction of complex diseases and traits. Research in my field has consistently shown that human traits and common diseases are not genetic enough to be predicted using DNA tests. For the same reasons, it will be impossible to successfully program the presence of traits in embryos.

Any concerns that CRISPR could taken a step further to enhance babies by selecting favorable traits such as intelligence and athleticism may be unwarranted.


Ronald Reagan Paved the Way for Donald Trump

If you want to understand why humans wage wars, there is a gene for that. Want to understand why men rape women? There is a gene for that. Want to understand why the “national characters” of East Asia, the West, and Africa are different? We have those genes covered too. Indeed, if we are to believe most popular media, there is a gene for just about every inequality and inequity in modern society.

Genetic determinism and its uglier cousin, social Darwinism, are making a comeback. Armed with large genomic datasets and an arsenal of statistical techniques, a small but vocal band of scientists are determined to hunt down the genetic basis of all we are and all we do.

The relationship between genetics and biological determinism is almost as old as the field itself. After all, one of the foremost modern institutes of genetics, the Cold Spring Harbor Laboratory, began as a eugenics institute whose activities included “lobbying for eugenic legislation to restrict immigration and sterilize `defectives,’ educating the public on eugenic health, and disseminating eugenic ideas widely.”

The latest wave of biological determinism continues this long history, but differs in a crucial way from the past. We are at the beginning of the genomics era — an era in which advances in molecular biology make it possible to precisely measure minute genetic differences between humans. Combined with the fact that we live in a new Gilded Age where a small global elite has access to, and needs justification for, unprecedented amounts of wealth and power, the conditions are ripe for a dangerous resurgence of biological determinism.

Today it costs $5000 to sequence a genome, to identify the 6 billion As, Cs, Ts, and Gs that define an individual’s DNA. Soon it will cost less — much less. We are told that this is a revolutionary moment. With access to detailed genetic information, medical professionals and genetic counselors will soon be able to identify the diseases we’re predisposed to, and help prevent or minimize their impact through “personalized medicine.”

The scientific knowledge extracted from these data is priceless. We are beginning to understand how viruses evolve, the genetic mutations that give rise to cancers, and the genetic basis of cellular identity. The sequencing revolution has allowed us to study the molecular basis of genetic regulation and identify amazing new players like non-coding RNAs and chromatin modifications. All of our ideas about biology are being reshaped.

One of the most striking results of the new sequencing studies is how similar humans actually are — we differ from each other only in 0.1% of our DNA. Yet this 0.1% of the genome gives rise to the variations we see between people in traits such as skin color, height, and proclivity for disease. An important goal of modern genetics is to relate a particular genomic variant to a specific trait or disease. To do so, scientists are developing powerful new statistical tools to analyze a wealth of sequence data from populations around the world.

The relationship between genes and observable traits is indisputable. Tall parents tend to have tall kids. Dark-haired parents have dark-haired kids. That traits are inherited has been clear since Mendel codified his famous Laws of Inheritance, inferred from statistical observations of over 29,000 pea plants. In classical Mendelian genetics, separate genes encoding for separate traits are passed independently from each other to their offspring. Thus, there is a clear mapping between genetic information, or genotype, and observable traits, or phenotype. A single gene (technically a locus or genetic location) encodes for a single trait and is not influenced by the other traits a person possesses. Furthermore, environmental factors have little influence on most Mendelian traits. Famous examples that fall into this framework include sickle-cell anemia and cystic fibrosis, each caused by a mutation to a specific gene.

However, it is now clear that the simple assumptions underlying Mendelian genetics are not applicable to most traits and diseases. Nearly all phenotypes, from height and eye color to diseases such as diabetes, emerge from extremely complex interactions between multiple genes (loci) and the environment. In contrast to Mendelian genetics, where one can easily identify the gene that encodes for a particular trait, for many traits there is no simple mapping from genotype to phenotype.

The sheer volume of DNA sequence data now available has convinced scientists they can overcome this challenge. To do so, they are developing new scientific and statistical tools geared toward analyzing and extracting genetic information from sequence data. The goal of these genome-wide association studies (GWAS) is to provide a blueprint for decoding the information contained in our DNA, and to identify the genetic basis of complex traits and diseases. GWAS are now a staple of modern population genetics. This is reflected in the astronomical increase in the number of published GWAS in the last decade, from single digits in 2005 to more than thirteen hundred to date. There are GWAS on body height, birth weight, Inflammatory Bowel Disease, how people respond to particular drugs or vaccines, cancers, diabetes, Parkinson’s disease, and more. There are actually so many GWAS that specialized viewers have been created to help scientists visualize the results of all these studies.

Given the increasing prevalence of GWAS, it is useful to explicate the basic logic underlying these studies. The concepts of phenotypic and genetic variations play a central role in GWAS. Phenotypic variation is defined as the variation of a trait in a population (such as the distribution of heights in the population of American men). Note that in order to define phenotypic variation, we must specify a population. This is an a priori choice that must be made to construct a statistical model. The choice of population is often an important source of bias where hidden social assumptions enter GWAS — this is especially true for studies that try to understand genetic variation across “racial” groups.

GWAS try to statistically explain the observed phenotypic variation in terms of the genetic variation in the same population. This is where modern genomics shines. Whereas in the pre-genomic era one had to work hard to measure genetic variation at a single locus, now one can consult a readily available public database to get the genetic variation for thousands of individuals across the entire genome. Most GWAS focus on single-nucleotide polymorphisms (SNPs): DNA sequence variations that occur at a single base in the genome (e.g. AAGGCT vs. AAGTCT). Scientists have observed approximately 12 million SNPs in human populations. This number may seem incredibly large, but there are 6 billion bases in human DNA. So only 0.2% of all DNA bases exhibit any variation across all sampled human populations. For a trait such as height, there are about 180 SNPs known to contribute to human height variation.

The goal of GWAS is to relate genotypic variation to phenotypic variance. This is often expressed in a concept called heritability, which seeks to partition the phenotypic variance into a genetic and an environmental component. Roughly speaking, heritability is defined as the fraction of the phenotypic variation that we can ascribe to genetic variation. A heritability of zero means that all the phenotypic variance is environmental whereas a heritability of one means it is entirely genetic.

Behind the concept of heritability lies a whole world of simplifying assumptions about how biology works and how genes and environment interact, all filtered through increasingly complicated and obtuse statistical models. Heritability depends on the populations chosen and the environments probed by the experiments. Even the clean distinction between environment and genes is at some level artificial. As Richard Lewontin points out:

The very physical nature of the environment as it is relevant to organisms is determined by the organisms themselves. . . . A bacterium living in liquid does not feel gravity because it is so small . . . but its size is determined by its genes, so it is the genetic difference between us and bacteria that determines whether the force of gravity is relevant to us.

All this is to say that though heritability is a useful concept, it is an abstraction — one that depends entirely on the statistical models (with all their assumptions and prejudices) we use to define it.

Most importantly for our purposes, even for an extremely heritable trait such as height, the environment can drastically change the observed traits. For example, during the Guatemalan Civil War, US-backed death squads and paramilitaries brutalized the rural, indigenous population of Guatemala, resulting in widespread malnutrition. Many Mayan refugees fled to the United States to escape the violence. Comparing the heights of six- to twelve-year-old children of Guatemala Maya with American Maya, researchers found that the Americans were 10.24 centimeters taller than their Guatemalan counterparts, largely due to nutrition and access to healthcare. By comparison, the gene known to most influence height, the growth factor gene GDF5, is associated with changes in height of just 0.3 to 0.7 centimeters, and this only for participants with European ancestry.

Such dramatic environmental influences are commonplace. For example, the heritability of Type II diabetes, adjusted for age and Body Mass Index (BMI), is thought to be between 0.5 and 0.75 (a little less than that of height, but as discussed above, this number should be taken with a grain of salt). Currently, GWAS are able to explain only about 6% of this heritability, with no loci (genes) particularly predictive for whether an individual will develop diabetes. In contrast to genetics, an unhealthy BMI — a simple measure of how overweight a person is — increases the odds of developing diabetes nearly eightfold.

The same story holds for IQ — a staple of genetic studies on “intelligence.” Putting the validity of IQ tests aside for a moment, studies show a long and sustained increase in IQ scores over the course of the twentieth century (the Flynn Effect), pointing to the importance of environment rather than genetics in determining IQ.

Schizophrenia is another example. In his excellent blog Cross-Check, John Horgan discusses CMYA5, touted as the “schizophrenia gene” in the popular press. He points out that if you carry this gene, your risk of schizophrenia rises by just 0.07% to 1.07%. In contrast, “if you have a schizophrenic first-degree relative, such as a sibling, your probability of becoming schizophrenic is about 10%, which is more than 100 times the added risk of having the CMYA5 gene.” Such results are not uncommon. The field is actually very concerned about the lack of predictive power of GWAS (often discussed in the context of the “missing heritability” problem).

Despite the limited success of GWAS, it is doubtful that genetic determinist claims will abate in the near future. The main reason for this is the sheer volume of new genetic data currently being generated. This data deluge is a biological determinist’s wet dream. In case you think I am exaggerating, here’s a quote from a recent study on “the genetic architecture of economic and political preference” published in PNAS, a leading scientific journal. Not surprisingly, the SNPs they identified “explain only a small part of the total variance.” Far from discouraged, the authors conclude their abstract on an optimistic note:

These results convey a cautionary message for whether, how, and how soon molecular genetic data can contribute to, and potentially transform, research in social science. We propose some constructive responses to the inferential challenges posed by the small explanatory power of individual SNPS.

The sheer hubris speaks for itself. Given the difficulty of using GWAS to explain height — an easily quantified, easily measured trait — the absurdity of claiming to identify the genetic basis of ill-defined, temporally variable, hard-to-quantify traits such as intelligence, aggression, or political preference is patently clear.

Regardless, the genetic determinist’s playbook in the genomics era is clear: Collect mass quantities of sequence data. Find an ill-defined trait (like political preference). Find a gene that is statistically overrepresented in the sub-population that “possesses” that trait. Declare victory. Ignore the fact that these genes don’t really explain the phenotypic variance of the trait. Instead, claim that if we only had more data the statistics would all work out. Further generalize these results to the level of societies and claim they explain the fundamental genetic basis of human behavior. Write a press release and wait for the media to write glowing reviews. Repeat with another data set and another trait.

Biological determinism seems plausible precisely because it gives the illusion that it is grounded in scientific observation. No scientist disagrees that the basic building blocks of an organism are encoded in its genetic material, and that evolution, through some combination of genetic drift and selection, has shaped those genes. But trying to ascribe human behavior, whether eating a whole bag of potato chips or waging war, to a set of genes is clearly a quixotic exercise.

As Nigel Goldenfeld and Leo Kadanoff implore in a beautiful article discussing complex systems: “Use the right level of description to catch the phenomena of interest. Don’t model bulldozers with quarks.” While it is certainly true that all the properties of a bulldozer result from the particles that make it up, like quarks and electrons, it is useless to think about the properties of a bulldozer (its shape, its color, its function) in terms of those particles. The shape and function of a bulldozer are emergent properties of the system as a whole. Just as you can’t reduce the properties of a bulldozer to those of quarks, you can’t reduce the complex behaviors and traits of an organism to its genes. Marx made the same point when he stated that “merely quantitative differences beyond a certain point pass into qualitative changes.”

If the philosophical and scientific bases of genetic determinist claims are so problematic, why is such sloppy thinking rewarded with front-page articles in the New York Times Science section? To answer this, we must consider not just science, but politics.

We live in an era in which corporations make unprecedented profits, an elite few accumulate enormous wealth, and inequality is reaching levels approaching those of the Gilded Age. The contradictions between neoliberal capitalism and democratic impulses are continually exposed. The claims of equal opportunity underlying much of liberal thinking are becoming farce. The incongruity between what capitalism professes to be and the reality of capitalism is becoming increasingly apparent.

The appeal of biological determinism is that it offers plausible, scientific explanations for societal contradictions engendered by capitalism. If Type II diabetes is reduced to the problem of genetics (which it surely is to some degree), then we don’t have to think about the rise of obesity and its underlying causes: the agro-business monopoly, income inequality, and class-based disparities in food quality. Combine this with the prevalence of drug-based solutions to disease pushed by the pharmaceutical industry and it is no surprise that we are left with the impression that complex social phenomena are reducible to simple scientific fact.

Biological determinism, to paraphrase the great literary critic Roberto Schwarz, is a socially necessary illusion well-grounded in appearance. Much like art and literature, science “is historically shaped and . . . registers the social process to which it owes its existence.” Scientists inherit the prejudices of the societies in which they live and work. Nowhere is this more obvious than in the modern incarnation of biological determinism with its decidedly neoliberal assumptions about humans and societies.

The history of biology is littered with horrifying examples of the misuse of genetics (and evolutionary theory) to justify power and inequality: evolutionary justifications for slavery and colonialism, scientific explanations for rape and patriarchy, and genetic explanations for the inherent superiority of the ruling elite. We must work tirelessly to ensure that history does not repeat itself in the genomics era.


Results

IQ-related genes are enriched in multiple regions of chromosomes 7 and X

To address the genetic complexity of IQ score, we developed IQdb (http://IQdb.cbi.pku.edu.cn), a publicly available database for exploring IQ-associated human genes 7 . In IQdb, 158 experimentally verified genes from the literature serve as the core dataset, among which approximately 16% of the IQ-related genes are located on X chromosome. It is reported that only 3.4% of all human genes belong to X chromosome 12 . The obvious contrast may indicate that the IQ-related genes are over-represented on X chromosome. To clarify in more detailed which genomic regions contain greater numbers of IQ-related genes, we adopted a hypergeometric test based on the gene content of each cytoband on the human genome. The further enrichment analysis identified 10 genomic regions with significantly larger amount of IQ-related genes. Among 10 regions, 4 belong to X chromosome, including Xp, Xq, Xq28 and Xp11. A previous gene expression study showed that the genes on X chromosome are more likely to express in brain regions and reproductive tissues 13 . Our results may be consistent with the idea that genes on X chromosome may be associated with the quantitative IQ score 12 . Since X chromosome is important to sexual differentiation, the enrichment of IQ-related genes on X chromosome may provide an incentive to explore differences in the IQ scores between the sexes 14,15 .

Previous studies have identified 46 genomic regions involved in IQ using a linkage approach 7 . Though none of these regions are located on X chromosome, six of them are from chromosome 7 in humans (13.04%). Among the ten genomic regions with enriched IQ-related genes, six are not on X chromosome, including 7q, 7q11, 7q31, 8p12, 15q14, and17p13. Except for 8p12 16 , none of these genomic regions overlap with the genomic regions in the prior linkage studies. Three of six regions are from chromosome 7, accounting for 8.86% of the 158 IQ-related genes (Table 1). One previous study showed that 7q31–36 may be linked to verbal IQ based on 361 Australian and Dutch twins 17 . Our results may uncover complementary roles of the genes on chromosome 7 in general intelligence.

In summary, our analysis narrows the probable genomic regions to several likely candidates, providing a better understanding of an IQ-related genomics and a highly rational way to interpret differences such as gender. Notably, our gene enrichment based approach has identified multiple genomic regions in chromosome X, which was missed in previous linkage studies. This difference may be caused by the sample size of the population in linkage studies. The other potential reason is that most of genes on chromosome X in IQdb are collected based on single-gene-based functional studies. Genetic association is emerging as a large scale screening tool. These small proportions of genomic regions have permitted global examination of the genome/proteome on a larger population.

The enriched target genes in 158 IQ-related genes for transcription factors and miRNAs related to mental disorders

A fundamental problem in biological systems is discovering potential regulators for candidate genes, which may help us understand the entire volume of genetic information. Compared to enriched IQ-related genes, regulation does not teach us about the linear relationship with the physical chromosome, but the dynamic mechanisms of underlying environmental change.

The modifying effects of several types of regulatory genes are widely studied and can be predicted based on sequence features of their potential target genes. Transcription factor (TF) can influence gene expression through transcription activation or suppression of target genes with different binding efficiency in promoter regions 18 . The other major group of regulators, MicroRNA (miRNA), can regulate mRNA expression at the post-transcriptional level, such as degradation or translational repression by binding the target gene with small complementary sequences 19 . To identify potential upstream TFs and miRNAs as possible regulators of the 158 gene set, we performed enrichment analysis on both TF and miRNA targets. In total, we identified seven TFs likely to regulate the set of IQ-related genes. They are FOXF2, FOXO4, MAZ, MEF2A, NFIL3, TCF3 and TFAP4. NFIL3 is reportedly related to neuron disease 20 . Most remarkably, MEF2 is demonstrated to negatively regulate learning-induced structural plasticity and memory formation 21 . There are 16 target genes for MEF2 in 158 gene set, including ATXN1, BDNF, BRAF, DBH, DMD, DMPK, DRD3, GNAS, GRIN2B, IL1RAPL1, IL6R, NR3C2, PHOX2B, SNAP25, TMEM67 and TSC1. Based on pathway annotation, we found that six of them (BDNF, BRAF, DRD3, GNAS, GRIN2B and SNAP25) had functions in the neuronal system. In addition, the genes DRD3, GNAS and GRIN2B were related to dopaminergic synapse. These MEF2 targets in the IQ-related gene set may form the core transcriptional circuitry influencing memory formation related to IQ, which begs a further experimental validation.

Based on a similar approach, we found that 16 human miRNAs tended to regulate IQ-related genes. They were hsa-let-7i, hsa-mir-15b, hsa-mir-16, hsa-mir-181d, hsa-mir-195, hsa-mir-30a-3p, hsa-mir-30e-3p, hsa-mir-330, hsa-mir-374, hsa-mir-424, hsa-mir-429, hsa-mir-497, hsa-mir-515-5p, hsa-mir-519e, hsa-mir-522 and hsa-mir-96. Four of them, hsa-let-7i, hsa-miR-15b, hsa-miR-195 and hsa-miR-330, were related to mental disorders based on disease annotation. Both hsa-miR-15b and hsa-miR-195 belong to the miR-15 family, which has been up-regulated in the superior temporal gyrus and the dorsolateral prefrontal cortex in schizophrenics 22 . These two miRNAs can mediate wide gene silence in the cell. Among the 158 IQ-related genes, the targets of these two miRNAs are ADRB2, ATXN2, BDNF, GHR, IL1RAPL1, KCNJ2, MAP2K1, PAFAH1B1, RAF1, RELN, RPS6KA3, SIGMAR1, SLC6A4 and STX1A. Comparing this set with that of the MEF2 targets, only BDNF occurs in both sets of targets of mental-disorder-related TFs and miRNAs, which might highlight its central role in signaling pathways related to human intelligence and its potential role as the hub of regulatory circuits in the IQ-related genes 23 .

Reconstruction of the core pathway for IQ-related genes using known biological pathways

We are now able to specify several genomic regions and regulators as possible determinants of the IQ phenotype. We adopted a pathway reconstruction approach to describe more complex biological processes arranged in the form of a cascade of connected biochemical reactions or signaling transductions. Generally, molecular biological experimental technology enables us to identify physical and functional interactions between molecules in the cell. Plenty of signaling networks and pathways are summarized based on reliable experimental evidence. Generally speaking, the biological pathways are scattered in many databases and are often represented as diagrams. Recently, Pathway Commons integrated popular pathway databases and now provides a convenient plain-text-based convenient format for further pathway mining and reconstruction 24 . In order to utilize the available data, we adopted the Klein-Ravi Steiner algorithm to extract core interactions from the integrated human pathway data (see Methods). The reconstructed core pathway contains 97 genes and 129 fully connected pathway connections in total (Figure 1A). Among the 97 nodes, 62 are in the 158 IQ-related genes.

Network view of 158 IQ-related genes based on known pathway interaction data.

(A) Reconstructed biological network using 158 IQ-related genes as input by integrating gene-gene interaction from well-defined pathway data. The nodes in yellow (triangle) represent literature-based IQ-related genes nodes in red (circle) represent expanded genes based on pathway-interaction data. The size of each node represents the number of connection in this network. (B) The plot of degrees and number of nodes in the reconstructed IQ-specific network. (C) The histogram of path length in the reconstructed IQ-specific network. (D) The plot of closeness centralities and the number of neighbors in the reconstructed IQ-specific network.

Biological pathway enrichment analysis is one of the most practical ways to mine underlying molecular mechanisms in complex cellular processes 25 . Further functional enrichment analysis showed that 97 genes in our reconstructed map were enriched in 30 biological pathways (Table 2). In terms of neuron-related function, there are three identified pathways: “Neurotrophin signaling”, “Long-term potentiation” and “GnRH signaling”. Interestingly, the majority of the remaining 27 pathways are related to various signaling events, including signaling in cancer, ErbB receptor, TRAIL, proteoglycan syndecan, IFN-gamma, PI3K, MAPK, TSH, Kit receptor, TCR, IL-3 and hepatocyte Growth Factor Receptor. Besides neurohormone GnRH, which is produced in a neural cell and released at its neural terminal, the map indicated that five additional hormones may have an effect. These are androgen, endothelins, glypican, leptin and prolactin. In summary, our reconstructed map revealed multiple paths related to several known signaling pathways, suggesting potential cellular mechanisms which have not been presented on the topic of signaling transduction, as far as we know. Hormone related pathways, including an androgen reception signaling pathway related to development of male secondary sex characteristics, might suggest interesting and new components related to sex difference, extending our current knowledge.

The arrangement and structure of the nodes in a complex system, such as a biological network, often follow specific rules which may be closely related to the function in this system 11 . To decompose the reconstructed maps, topological analysis was conducted (Figure 1B–D). Generally, the number of connections at each node is represented as the degree in a network 11 . As shown in Figure 1B, the degrees of all molecules in the reconstructed map follow a power law distribution: P(k)

kb , where P(k) is the probability that a molecule connects with k molecules and b has an estimated value of 1.602. Therefore the majority of molecules in our map are sparsely connected. In contrast, a small fraction of molecules are more likely to be connected. In total, there are 11 molecules with at least five connections. They are PRKACA (14), CREB1 (9), TP53 (8), SOS1 (8), JAK2 (7), PTPN11 (7), PIK3CA (6), CREBBP (6), CDC42 (6), RAF1 (5) and GNAI3 (5). Among these, four are in 158 IQ-related gene set, namely SOS1, PTPN11, CREBBP and RAF1. The remaining seven molecules are appended through pathways connected to the 158 genes.

The hub nodes in a network often serve as common connections to mediate information transduction along a short path. Thus, they often play a prominent role in biological network. In our map, gene PRKACA is the most connected, showing 14 connections. PRKACA (protein kinase, cAMP-dependent, catalytic, alpha) plays a fundamental role in various cellular functions related to 76 KEGG and 59 REACTOME pathways including cell cycle, apoptosis, signaling transduction, gap junction and interaction with HIV and the immune system. In addition, PRKACA involves many neurological processes such as Long-term potentiation, GnRH signaling, Nicotine Activity on Dopaminergic Neurons, addiction (Amphetamine, Cocaine and Morphine), synapse activities (Cholinergic, Dopaminergic, Glutamatergic and Serotonergic synapse). Previous studies demonstrates that cAMP-dependent protein kinases are involved in the associative learning of the Drosophila (fruit fly) 26 . Additionally, cAMP/cAMP-dependent protein kinases in the hippocampal region are reported to be related to a late memory consolidation phase of aversively motivated learning in rats 27 . Moreover, cAMP-dependent protein kinases can also cooperate with CaMKII in the H3 receptor to regulate the synthesis and release of histamine 28 . In addition to PRKACA, CREB1 (cAMP response element-binding protein 1), the second-most connected gene in our reconstructed map, can also bond to cAMP response elements in DNA. This gene has been reported to facilitate the formation of long-term memory. Furthermore, CREB1 interacts with BDNF and NTRK2 to form a core pathway in depression 29 . In spite of this volume of evidence for the role of cAMP-dependent protein kinases and cAMP response element-binding protein in cognitive-related process, there has been no association between cAMP-dependent protein kinases or cAMP response element-binding protein with IQ. These overlaps with many known signaling cascades with IQ-related genes, which might provide a clue for complex signaling cross-talk centered by PRKACA/CREB1, which is in IQdb.

In addition to the cAMP related molecules, there are at least two cancer related genes identified by our pathway reconstruction approach. One is the most well-known tumor suppressor TP53. In fact, there is evidence that is play a role in IQ-related mental disorders such as schizophrenia 30 . Furthermore, the other oncogene, PIK3CA, is also included in our final map and is known to influence several psychiatric processes 31,32,33 . The next three most-connected genes (JAK2, CDC42 and GNAI3) have also been reported to be related to cognitive disorders or related neuronal functions. JAK2 is related to cognitive impairment in the mouse model 34 . CDC42 is associated with neurofibromatosis and mental retardation 35 . In spite of the fact that there is no direct evidence for its role in cognitive process, GNAI3 takes part in negative regulation of synapse transmission, long-term depression and axon guidance according to KEGG pathway annotation. In summary, of the seven hub molecules in our reconstructed map related to IQ, at least six (85.71%) of them are reported in the literature to be potentially associated with IQ or other cognitive processes. This high relevance not only demonstrates the accuracy of pathway-based reconstruction approach to identify critical molecules, but also provides a fully-connected signaling pathway worthy of further investigation.

Though our reconstructed map is an unoriented signaling pathway, it provides many testable molecules in a typical small-world network involved in cognitive processes, where their degrees follow a power-law distribution. Our further topologic analysis on short path (Figure 1C) and closeness centrality (Figure 1D) shows that the reconstructed map is relatively compact. The path length represents how many steps are between one node and other nodes 11 . As shown in Figure 1C, the majority of nodes in the map are easily reached from another node in three to six steps. The closeness centrality is used to reveal the shortest step from one node to another 11 . As shown in Figure 1D, the nodes with more neighbors tend to have higher closeness centrality.

A connected drug-target network in the core pathway of IQ-related genes

Based on the reconstructed pathway map, we further identified enriched drug targets in this map. Using the enriched drug targets as inputs, we combined the drugs and their targets to form a drug-target network. As shown in Figure 2A, there are ten enriched drugs, namely dopamine, nitric oxide, L-tyrosine, methamphetamine, norepinephrine, glutathione, amphetamine, tetrahydrobiopterin, apomorphine and somatropin recombinant. Except for somatropin recombinant, all have been reported to be active in nervous systems or mental functions according to pharmacodynamics annotation in DrugBank 36 . Interestingly, six of them interact with two critical neurotransmitters (dopamine and norepinephrine) systems that regulate mood and behavior. These include amphetamine, apomorphine, dopamine, L-tyrosine and norepinephrine. Dopamine is a neurotransmitter responsible for various behavior and cognition activities, such as whose found in reward-driven learning systems. Dopamine is also linked to many neurological disorders such as Parkinson's disease, psychosis and schizophrenia 37 . Norepinephrine plays a critical role in decision making and can affect attention it is also used as anti-depressant and anti-schizophrenic 38,39 . L-tyrosine is one of the precursors in the synthesis of dopamine and norepinephrine and can be used to treat depression, improve memory and enhance mental alertness according to DrugBank annotation 36 . Amphetamine can stimulate central adrenergic receptors to release norepinephrine and a high dosage of amphetamine is reported to help release dopamine. Methamphetamine, a related entity, is neurotoxic to dopamine transporters and is often used to mark dopamine terminal laboratory animals. Since it is involved in dopamine systems, methamphetamine has been reported to be associated with slower motor function and memory deterioration 40 . Apomorphine is a dopamine agonists and is used to treat Parkinson's disease based on DrugBank annotation, according to DrugBank annotation 36 .

Network view of drug-target interaction based on reconstructed IQ-specific network.

(A) Drug-gene interaction network of IQ-related genes from our reconstructed pathway (see Figure 1). The nodes in blue (triangle) represent the enriched drugs in the reconstructed IQ-specific network (Figure 1) nodes in grey (circle) represent the target of the drugs. The links between drugs and genes represent the drug-target relationships. The size of each node represents the number of connection in this network. (B) The plot of degrees and number of nodes in the IQ-related gene-drug interaction network. (C) The histogram of path length in the IQ-related gene-drug interaction network. (D) The plot of closeness centralities and the number of neighbors in the IQ-related gene-drug interaction network.

The six drugs that affect the two critical neurotransmitters dopamine and norepinephrine covered 66.7% of the target genes (32 genes) in the drug-target map (Figure 2A). Since we constructed this map through enriched drugs, this drug-target map is mostly centered by drugs. Therefore, the connectivities of drugs are higher than drug target genes as shown in Figure 2B. Using topological analysis, most of the nodes can be reached from another in two to four steps (Figure 2C). Since the majority of targets and drugs are related to dopamine and norepinephrine system, this network is also very compact based on closeness centralities (Figure 2D). In summary, our enrichment and network-based analysis shows that the dopamine and norepinephrine systems are critical to IQ-related genes, which may provide more insight into the cognitive process from an IQ aspect.


Genetic Science Ethics

1. An agricultural company has found a way to make tomatoes 50% larger by splicing new genes into the tomatoes. Will you:

a. Buy the tomatoes and have no problem eating them
b. Probably not buy or eat the tomatoes
c. Protest the company. It's not right to fool with mother nature!

2. You've found out that the child you (or your wife) carries has the gene for Tay-Sachs. Babies born with this disorder develop brain damage and usually die in childhood. What will you and your spouse do?

a. Test the fetus in early pregnancy to see if it has the disease
b. Do not test for the disease, because there is nothing that can be done anyway
c. Consider alternate reproduction strategies, like adoption or embryo screening.

3. Pet cloning is now available from a new biotech company. Your dog, Charlie, died last year but you can choose to clone your pet for about 20,000 dollars. What do you do?

a. Find a strand of Charlie's hair as soon as possible. Can't wait to see Charlie again!
b. Let sleeping dogs lie. (Don't clone Charlie)
c. Use the money to donate to a local shelter and get your next pup from a rescue.


4. A company can now create test tube babies according to parent specifications. The company can make sure that your child has all the traits you desire - hair color, intelligence, athletic ability, etc….. What do you do?

a. Sign me up, I want my child to be perfect.
b. I'd rather let nature take its course.
c. We might adjust some things to make sure the child is healthy (and has no genetic abnormalities).

5. Your family is known to have Huntington's disease. Huntington's is a disease that causes its victims to slowly lose their ability to speak, walk and function. Ultimately, Huntington's causes death. There is a test that will tell you whether you have the gene for Huntington's disease. What do you do?

a. I would take the test, so that I would be better prepared for the future.
b. I would not want to know.

6. You learn that you need a kidney transplant, but there are no donors available. A doctor suggests that you make a clone of yourself, so that the kidney would be a perfect match. What do you do?

a. One is enough of me, I'll wait for a donor.
b. Clone myself, two is better than one.

7. A government organization is proposing to have all citizens of the US submit a sample of their hair so that their DNA can be scanned and kept on file. Each person's DNA would be kept in a national database so that police could access the DNA when a crime was committed. Do you?

a. Support this legislation
b. Oppose this legislation
c. Suggest the legislation be modified, only people with criminal records should be scanned.

8. A company will do a DNA analysis if you submit a saliva sample. The analysis will tell you about genetic abnormalities and connect you with other people you are related to. The test costs $100.00. What do you do?

a. I would take the test, I would love to learn about my family
b. I would not want to know about relatives
c. I would want to know my relatives, but not about genetic abnormalities

9. An experimental procedure would allow you to add genes to your body. You can order certain genes, like a smart gene, or an athletic gene, or a musical ability gene. What do you do?

a. We would have the procedure done, I'd like to be better in school and at sports.
b. We would not have the procedure done, people should accept themselves the way they are

10. An insurance company is requiring individuals to get genetic testing performed to determine whether they have a higher risk of heart disease, cancer, or other diseases. They are requiring that all people wanting health insurance be tested. What do you do?

a. find another insurance company, that information is none of their business
b. submit my DNA for a test, I'd like to know anyway.
c. file a lawsuit against the company

11. Final Discussion

As a group, it is unlikely you agreed on every answer, but there was probably some overlap in your opinion with others. Write a short position paragraph that summarizes the areas that your group agrees upon as procedures that would be wrong or unethical and those procedures that would be right (or ethical) in certain circumstances.


Conclusion

In this article we have argued that four of the most well-worn objections to genetic modification of human beings – the freedom argument, the giftedness argument, the authenticity argument, and the uniqueness argument – rely heavily on deterministic assumptions. These arguments assume that, despite conclusive evidence to the contrary, most traits are strongly determined by genetics (or that individuals believe, and act as if, this is the case). These deterministic assumptions are demonstrably false. Utilizing such false assumptions to support an argument that genetic modification is inherently objectionable exploits the public's worst fears and perpetuates misunderstandings concerning basic human biology and genetics.

So far, most of the debates about the ethics of genetic modification have stayed largely within the confines of academia and have not had a major or lasting impact on public policy. But the day is coming – we think sooner rather than later – when political leaders will be compelled to make difficult choices concerning genetic modification. Thus, the need for well reasoned scientific policy is increasingly a pressing one. Such policy cannot be built on logical or biological errors and misunderstandings: it should rest on a clear and accurate understanding of the best scientific evidence available. As we have seen, the four critiques of genetic modification examined in this paper are frequently used to portray genetic modification as inherently objectionable and immoral, and they are frequently accompanied by the policy suggestion of a ban on further scientific research and development.

Having demonstrated that these non-consequentialist arguments rely on faulty determinist assumptions, we suggest that the public and scientific policy debate should concern itself instead with alternative arguments that address concerns about risk, safety, social and economic consequences, and justice. These alternative, consequentalist arguments tend to support the view that genetic modification is not inherently immoral but that the morality of genetic modification depends on its implementation and its use by individuals and society, and on the consequences produced therein. While predicting the consequences of genetic modification is a speculative exercise we conclude that it is this approach, one free from foundational misconceptions about the deterministic role of genetics in individual development, that is considerably more likely to bear fruit in developing the effective and sustainable science policies that will be so urgently required in the years to come.


Cells of Intelligence

Ever since Ramón y Cajal postulated his neuron doctrine of information processing calling neurons 𠇋utterflies of the soul” (Cajal, 1893), neuroscience has agreed that the basis of human intelligence must lie in neurons or networks of neurons. However, the neuroscientific search for the biological basis of intelligence has so far focused almost exclusively on the macroscopic brain level and genetics of intelligence, leaving a large gap of knowledge at cellular level.

We assume that our mind functions through the activity of 86 billion neurons (Herculano-Houzel, 2012) and their connections, that form principal building blocks for coding, processing, and storage of information in the brain and ultimately give rise to cognition (Salinas and Sejnowski, 2001). Given the astronomic number of neuronal connections (Drachman, 2005), even the slightest change in efficiency of information processing by neurons can translate into large differences in cognitive ability. Indeed, one of the most robust and replicable associations in behavioral psychology is that of intelligence with mental processing speed, measured by reaction times by human test subjects (Vernon, 1983 Barrett et al., 1986). However, very few studies attempted to answer the question whether the activity and structure of single human neurons support human intelligence and how faster mental processing can be brought about by properties of cells in our brain.

This knowledge gap is not surprising: the access to neurons in the living human brain is very limited and most of what is known about the function of neurons comes from laboratory animal research. During the past decades, the use of brain tissue resected during neurosurgical treatment of epilepsy or tumors has opened new avenues for studying the human brain on the cellular level (Molnár et al., 2008 Testa-Silva et al., 2010, 2014 Verhoog et al., 2013, 2016). To gain access to affected deep brain structures, neurosurgeons resect overlaying non-pathological neocortex that can be transported to the lab for further investigation. In combination with cognitive testing prior to surgery, this approach offers great opportunity to study neuronal function in relation to human intelligence. Such use of living human brain tissue from neurosurgery cannot be substituted by other techniques: post-mortem tissue is generally not suitable for physiological studies (but see Kramvis et al., 2018), while brain imaging studies lack the necessary cellular precision.

The Key Role of Pyramidal Neurons

Genetic studies indicate that expression of genes associated with intelligence accumulates in cortical pyramidal neurons (Savage et al., 2018 Coleman et al., 2019). Comparisons of key cellular properties of pyramidal neurons across species may offer insights into functional significance of such differences for human cognition. In fact, human tissue used in research always comes from higher-order association areas, typically temporal cortex, in order to spare primary sensory and language functions of the patient. These are exactly the areas implicated by brain imaging in human intelligence. Which properties of pyramidal neurons from temporal cortex stand out when compared across species?

First, the structure of pyramidal cells is different (Elston and Fujita, 2014): compared to rodents and macaques, human layer 2/3 pyramidal cells have threefold larger and more complex dendrites (Mohan et al., 2015). Moreover, these large dendrites also receive two times more synapses than rodent pyramidal neurons (DeFelipe et al., 2002).

Apart from structural differences, human pyramidal neurons display a number of unique functional properties. human excitatory synapses recover 3𠄴 times faster from depression than synapses in rodent cortex, have more speedy action potentials and transfer information at up to nine times higher rate than mouse synapse (Testa-Silva et al., 2014). In addition, adult human neurons can associate synaptic events in a much wider temporal window for plasticity (Testa-Silva et al., 2010 Verhoog et al., 2013). These differences across species may suggest evolutionary pressure on both dendritic structure and neuronal function in temporal lobe and emphasize specific adaptations of human pyramidal cells in cognitive functions these brain areas perform.

Recently, these differences in human pyramidal neuron function and structure were linked to the intelligence scores and anatomical structure of temporal lobes from the same subjects (Goriounova et al., 2018 Figure 3). The results showed that high IQ scores associated with larger temporal cortical thickness in neurosurgery patients, as in healthy subjects (Choi et al., 2008). Furthermore, thicker temporal cortex linked to larger, more complex dendrites of human pyramidal neurons. Incorporating these realistic dendritic morphologies into computational model showed that larger model neurons were able to process synaptic inputs with higher temporal precision. Improved information transfer by model neurons was due to faster action potentials in larger cells. Finally, as predicted by the model, experimental recordings of action potential spiking in human pyramidal neurons demonstrated that individuals with higher IQ scores were able to sustain fast action potentials during neuronal activity. These findings provide the first evidence that human intelligence is associated with larger and more complex neurons and faster action potentials and more efficient synaptic information transfer (Goriounova et al., 2018).

Figure 3. A cellular basis of human intelligence. Higher IQ scores associate with larger dendrites, faster action potentials during neuronal activity and more efficient information tracking in pyramidal neurons of temporal cortex. The figure is based on the results from Goriounova et al. (2018).

Connecting Levels: Genes, Cells, Networks and Brain Areas

Pyramidal cells, especially in superficial layers of multimodal integration areas such as temporal or frontal cortex, are main integrators and accumulators of synaptic information. Larger dendrites can physically contain more synaptic contacts and process more information. Indeed, dendrites of human pyramidal neuron receive twice as many synapses than those in rodents (DeFelipe et al., 2002). The increasing information integration capacity of these brain areas is also reflected in a gradient in complexity of pyramidal cells across cortical areas�lls have increasingly larger dendrites in regions involved in higher-order cortical processing (Elston et al., 2001 Jacobs et al., 2001 Elston, 2003 Elston and Fujita, 2014 van den Heuvel et al., 2015). Both in humans and other primates, cortico-cortical whole-brain connectivity positively correlates with the size of pyramidal cell dendrites (Scholtens et al., 2014 van den Heuvel et al., 2015).

Overall, larger dendritic length in human neurons compared to other species, and in particular elongation of their basal dendritic terminals (Deitcher et al., 2017) would enable these cells to use branches of their dendritic tree as independent computational compartments. Recently, Eyal et al. (2016, 2018) have provided new insights into signal processing and computational capabilities of the human pyramidal cells by testing their detailed models including excitatory synapses, dendritic spines, dendritic NMDA- and somatic spikes (Eyal et al., 2018). The results show that particularly large number of basal dendrites in human pyramidal cells and elongation of their terminals compared to other species result in electrical decoupling of the basal terminals from each other. Similar observations were also recently made by dendritic recordings from human layer 5 pyramidal neurons (Beaulieu-Laroche et al., 2018). In this way, human dendrites can function as multiple, semi-independent subunits and generate more dendritic NMDA- spikes independently and simultaneously, compared to rat temporal cortex (Eyal et al., 2014). Dendritic spikes through NMDA receptors are an essential component of behaviorally relevant computations in neurons. In mice, manipulation of these spikes lead to decreased orientation selectivity of visual cortical neurons linking the function of dendrites to visual information processing by neurons (Smith et al., 2013). Furthermore, larger dendrites have an impact on excitability of cells (Vetter et al., 2001 Bekkers and Häusser, 2007) and determine the shape and rapidity of action potentials (Eyal et al., 2014). Increasing the size of dendritic compartments in silico lead to acceleration of action potential onset and increased encoding capability of neurons (Eyal et al., 2014 Goriounova et al., 2018). In addition, compared to mouse, human pyramidal neurons in superficial layers show more hyperpolarization activated currents that facilitate excitability of these cells (Kalmbach et al., 2018).

Thus, larger dendrites equip cells with many computational advantages necessary for rapid and efficient integration of large amounts of information. The fact that the larger and faster human neurons in temporal cortex link to intelligence (Goriounova et al., 2018) provides evidence that there is a continuum of these cellular properties across the human population. At the high end of the IQ score distribution, pyramidal cells of individuals with high IQ receive more synaptic inputs and are able to achieve higher resolution of synaptic integration by processing these multiple synaptic inputs separately and simultaneously. As cells are constantly bombarded by a large load of incoming signals during cognitive activity, the neuron has to relay these multiple inputs into output. Human neurons of individuals with higher IQ are able to translate these inputs into action potentials—output signal of the cell—much more efficiently, transfer more information and sustain fast action potential firing compared to lower IQ subjects. These findings harmonize well with genetic and imaging studies identifying metabolic rate as an important correlate of intelligence (Haier et al., 1988 Savage et al., 2018).

Finally, genetic studies of intelligence also implicate genes supporting dendritic structure in human cognitive ability. Clustering of candidate genes from GWAS of educational attainment in gene sets with known biological function identified gene sets involved in cerebral cortex morphology and specifically in dendrites and dendritic spine organization (Okbay et al., 2016). Furthermore, the strongest emerging genetic association with intelligence established by Sniekers et al. (2017) and later replicated in a much larger sample (Coleman et al., 2019) is in an intronic region of the FOXO3 gene and its promoter. The FOXO3 gene is part of the insulin/insulin-like growth factor 1 (IGF-1) signaling pathway (Costales and Kolevzon, 2016). Notably, IGF-I was shown to increase branching and dendritic size in rat primary somatosensory cortex, specifically in pyramidal cells in superficial cortical layers (Niblock et al., 2000). Low IGF-1 levels have also been associated with poor cognitive function during aging (Aleman et al., 1999 Tumati et al., 2016) and a less integrated functional network of connected brain areas (Sorrentino et al., 2017). Thus, individual differences in dendritic elaboration in pyramidal cells are subject to genetic control, go accompanied by functional adaptations in these cells and underlie human variability in intelligence.

How do these findings on cellular and genetic level translate to macroscale findings in brain imaging? One of the most robust finding in brain imaging is that cortical thickness and volume associate with intelligence (Haier et al., 2004 Colom et al., 2006, 2009 Narr et al., 2007 Choi et al., 2008 Karama et al., 2009). Reconstruction of cortical column at nanoscale resolution shows that cortical volume consists largely of dendritic and axonal processes with 7-fold greater number of axons over dendrites (Kasthuri et al., 2015), only a small proportion of this volume is occupied by cell bodies. The dendrites and axons are structures that mediate synaptic plasticity, store information and continue to grow and change during lifetime. Indeed, during normal postnatal development cortical areas follow a similar pattern: dendrites show continuous growth that is accompanied by increased cortical volume and decreased neuronal densities (Huttenlocher, 1990). In addition, frontal cortical areas that are more shaped by age and experience show a slower time course of these changes compared to primary visual areas that have an earlier critical period (Huttenlocher, 1990). In line with this prolonged development, dendritic trees in human temporal lobe continue to grow throughout maturity and into the old age. In 80-year-olds dendritic trees are more extensive than at the age of 50, with most of the difference resulting from increases in the number and average length of terminal segments of the dendritic tree. The link between dendritic size and cognition is emphasized by the fact that in senile dementia, dendritic trees are less extensive, largely because their terminal segments are fewer and shorter (Buell and Coleman, 1979).

Also, within human cortex, a gradient of dendritic complexity exists across cortical areas. Higher order association areas that store and process more complex information contain neurons with larger and more complex dendrites compared to primary sensory areas. At the same time neuronal cell body density is lower in cortical association areas compared to primary sensory areas (Buell and Coleman, 1979 DeFelipe et al., 2002 Elston, 2003).

A recent study by Genç et al. (2018) used multi-shell diffusion tensor imaging to estimate parieto-frontal cortical dendritic density in relation to human cognition. This study found that higher scores in cognitive tests correlated with lower values of neurite density (Genç et al., 2018). As neurite density decreases go together with the increases of dendrite length (Huttenlocher, 1990), the results obtained by Genç et al. (2018) may indicate that parieto-frontal cortical areas in individuals with higher intelligence have less densely packed neurons, and imply that these neurons have larger dendrites. Taking the results of Genç et al. (2018) and Goriounova et al. (2018) together suggests that the neuronal circuitry associated with higher intelligence is organized in a sparse and efficient manner. Larger and more complex pyramidal neurons are more dispersed in cortical space and occupy larger cortical volume.


Genetic Variants Build a Smarter Brain

Researchers have yet to understand how genes influence intelligence, but a new study takes a step in that direction. An international team of scientists has identified a network of genes that may boost performance on IQ tests by building and insulating connections in the brain.

Intelligence runs in families, but although scientists have identified about 20 genetic variants associated with intelligence, each accounts for just 1% of the variation in IQ scores. Because the effects of these genes on the brain are so subtle, neurologist Paul Thompson of the University of California, Los Angeles, devised a new large-scale strategy for tackling the problem. In 2009, he co-founded the ENIGMA Network, an international consortium of researchers who combine brain scanning and genetic data to study brain structure and function.

Earlier this year, Thompson and his colleagues reported that they had identified genetic variants associated with head size and the volume of the hippocampus, a brain structure that is crucial for learning and memory. One of these variants was also weakly associated with intelligence. Those carrying it scored on average 1.29 points better on IQ tests than others, making it one of the strongest candidate intelligence genes so far.

The researchers have now used the same strategy to identify more genetic variants associated with brain structure and IQ. In the new study, they analyzed brain images and whole-genome data from 472 Australians, including 85 pairs of identical twins, 100 pairs of nonidentical twins, and their nontwin siblings. They identified 24 genetic variations within six different genes, all of which were linked to differences in the structural integrity of major brain pathways. "We measured the insulation of the neural pathways," says Thompson. "This affects how fast nervous impulses are routed around the brain. If the pathways are insulated poorly, the brain functions less efficiently and is less resistant to disease."

Many of the genes were already known, but "most haven't been linked to brain integrity before," says Thompson. He adds that the genes "help to make cell membranes and connections" in pathways that are involved in spatial abilities and working memory, which allows us to store information for short periods of time while performing mental tasks.

The researchers also found that some of the variants are associated with intelligence, in that individuals carrying them performed several points better on standardized IQ tests than others. The variants seem to amplify each other's effects, so that possessing more than one provided a synergistic IQ boost, the team reports online today in the Journal of Neuroscience. "We found a whole range of genetic variants that affect the impact of other variants," says Thompson, "and we are beginning to understand the guiding principles of these gene networks."

The researchers used a "highly sophisticated method" that simplifies the statistics involved by identifying gene networks rather than individual variants, says human geneticist Silvia Paracchini of the University of St. Andrews in the United Kingdom, who was not involved in the study.

She questions how robustly the experiments were designed, however, and says that the number of participants was relatively small for a study of this kind. "I would like to see the findings replicated, with further evidence from larger samples."

Epidemiologist Sarah Medland of the Queensland Institute of Medical Research in Australia adds another note of caution: Most large-scale genetic studies replicate their findings using preexisting sets of data, Medland says, but "There was no replication here." But that may be because there are no other appropriate data sets. Medland says she knows of only one other study that collected both IQ scores and the same kind of brain imaging data, and that "the data probably aren't comparable."


The Guardian view on intelligence genes: going beyond the evidence

H umans are fascinated by the source of their failings and virtues. This preoccupation inevitably leads to an old debate: whether nature or nurture moulds us more. A revolution in genomics has poised this as a modern political question about the character of our society: if personalities are hard-wired into our genes, what can governments do to help us? This is a big, creepy “if” over which the spectre of eugenics hovers. It feels morally questionable yet claims of genetic selection by intelligence are making headlines.

This is down to “hereditarian” science, a field dominated in this country by Robert Plomin, a psychologist at King’s College London. His latest paper claimed “differences in exam performance between pupils attending selective and non-selective schools mirror the genetic differences between them”. With such a billing the work was predictably greeted by a raft of absurd claims about “genetics determining academic success”. What the research revealed was the rather less surprising result: the educational benefits of selective schools largely disappear once pupils’ innate ability and socio-economic background were taken into account. It is a glimpse of the blindingly obvious – and there’s nothing to back strongly either a hereditary or environmental argument.

Yet Professor Plomin’s paper does say children are “unintentionally genetically selected” by the school system. Such a claim, as one geneticist put it, “could have been lifted right out of The Bell Curve”. This is a reference to a 1994 US book that retailed a form of highbrow racism in which white people’s success was ascribed to higher average IQs. Professor Plomin endorsed the book’s data but not its authors’ conclusions. Central to hereditarian science is a tall claim: that identifiable variations in genetic sequences can predict an individual’s propensity to learn, reason and solve problems. This is problematic on many levels. A teacher could not seriously tell a parent their child has a low genetic tendency to study when external factors clearly exist. Unlike-minded academics say the heritability of human traits is scientifically unsound. At best there is a weak statistical association and not a causal link between DNA and intelligence. Yet sophisticated statistics are used to create an intimidatory atmosphere of scientific certitude.

Buried beneath the science is a troubling policy prescription. We are all individually different and unaware of what our lot in life is to be. Ignorant of our talents, we are predisposed to maximise the welfare of the worst-off in case we end up at the bottom of society – even if this means burdening the successful. What if genetic testing forewarned us of our fates would the load be shared differently? Almost certainly so – and to the detriment of the poor. If intelligence is largely inherited and the reason for failure then attempts to remedy it are doomed. This is what is so pernicious and incendiary about these ideas: they conceal a tendentious opinion about compensatory social programmes.

While there’s an undoubted genetic basis to individual difference, it is wrong to think that socially defined groups can be genetically accounted for. The fixation on genes as destiny is surely false too. Medical predictability can rarely be based on DNA alone environment matters too. Something as complex as intellect is likely to be affected by many factors beyond genes. If hereditarians want to advance their cause it will require more balanced interpretation and not just acts of advocacy.

Genetic selection is a way of exerting influence over others, “the ultimate collective control of human destinies” as HG Wells, who like many intellectuals of his time was a fan of eugenics, said. Knowledge becomes power and power requires a sense of responsibility. In understanding cognitive ability, we must not elevate discrimination to a science: allowing people to climb the ladder of life only as far as their cells might suggest. This will need a more sceptical eye on the science. As technology progresses, we all have a duty to make sure that we shape a future that we would want to find ourselves in.


No, Research Has Not Established That You Inherited Your Intelligence From Your Mother

A garbled post from a website called Second Nexus has gone viral in my feeds (and possibly yours), likely because of its eye-catching headline claim that “New Research Establishes That Intelligence Is Inherited From The Mother.” The piece is bylined “Editorial Staff,” presumably because everyone was too embarrassed to put a real name on it.

The premise of the post seems to be that science has traced “intelligence genes” to the X chromosome and that:

children are more likely to inherit intelligence from their mothers because intelligence genes are located on the X chromosomes (and mothers have two).

Mothers do tend to have two X chromosomes, but they aren't identical chromosomes, and of course, they got one of them from their fathers. Mothers generally pass only one X to their children (after the two X chromosomes engage a little genetic swapping themselves), and those children in turn receive the second sex chromosome (X or Y) from their fathers. Whatever is on the X can pass from mother to child or father to (usually) daughter, but the two X chromosomes the mother has aren't the same and don't at all automatically double the odds of inheriting a specific variant.

Shutterstock. No, that's not a remotely accurate representation of DNA.

But there's more. You may know that many people walk around with two of these huge chromosomes whereas others of us seem to function just fine with one. The doubled “gene dosage” for people with two (or more) X chromosomes is adjusted downward in a clever way: each cell turns off most of one X or the other. So inheriting an X-linked gene variant isn't a guarantee that it will even be used because some cells might just shut it down.

And everyone has at least one X chromosome. Mother Nature, like Oprah, doles them out to her entire studio audience--i.e., all of us: And you get an X chromosome, and you get an X chromosome! And so do you!

Finally, when you inherit an X chromosome (which you have done if you are reading this), short of a major deletion, you’re getting whatever genes are on that chromosome, linked to intelligence or not. Of course, the Editorial Staff really are referencing gene variants that might be linked to intelligence, not just genes.

So let’s get this out of the way: Intelligence is complicated. While maybe half of our intelligence as we currently define and measure it is inherited, that proportion is in turn fractured into many many genetic variants scattered across our genomes. These variants operate together in various ways to form what we view as intelligence. And each of those fragments of heredity that contributes is itself subject to a host of environmental factors, both in its immediate molecular world and inputs to the whole organism, that will influence function. And that influence continues after birth as an ongoing mutual interplay of gene variants and environment. It’s layer upon layer upon layer of interacting pieces. So no. Not just your mother. Not just the X chromosome. Not even just genes.

After its simplistic presentation of chromosomal genetics, this hot mess of an article then digresses into post-birth interactions of mother and child, before wheeling back again to the X chromosome. The jumbled word salad begins with:

In this way, researchers discovered that there are conditioned genes which only activate when inherited from the mother and that are crucial to the proper development of the embryo.

While “conditioned” genes sounds like they should have shiny hair or toned bodies, I think what they really mean is gene sequences that are registered as being of maternal origin. Thing is, we also require genes complementarily tagged as being of paternal origin for this all to work out. The Editorial Staff continue:

Scientists hypothesized that genes essential to the development of the embryo would also have a significant impact brain function in the lives of animals and people.

You don’t have to be a developmental biologist or even a biologist or maybe even a person to figure out that anything critical to the progression of embryonic development might have some kind of effect on the brains of "animals and people." But I guess that it sounds impressive.

The “Editorial Staff” (who seem each to have written one paragraph of this, individually assembling their contribution using randomly selected refrigerator poetry magnets) go on to say:

Researchers found that embryos survived when normal embryonic cells were maintained. When they manipulated the rest, they created several genetically modified laboratory mice that did not develop in the same way. Mice that received an extra dose of maternal genes developed larger heads and brains, but smaller bodies. By contrast, mice that received an additional dose of paternal genes had smaller brains, but larger bodies.

What they’re really describing (I think) is the results of a 1996 paper that reports using mouse embryos that were a mix of cells, some carrying double paternal genomes and some carrying double maternal genomes. Some parts of the mouse brain that developed carried far more of one than the other whereas other parts of the brain showed a reverse pattern. The senior author on that paper, Cambridge University neuroscientist Barry E. Keverne, himself wrote in 2013 that some of the findings may have been the result of a “failure of these (double paternal) cells to thrive and survive when they reach the developing cortex."

The Editorial Staff continue:

Researchers have not found paternal genes in the cerebral cortex, where humans develop advanced cognitive functions such as intelligence, thought, language, and painting.

With consideration for the lack of citations or context, I think that they’re still referring to those mice, and what they mean is that double-paternal cells in the mice in that 1996 study tended to be excluded from these brain areas. But of course, no one who understands this material would write that researchers have not found paternal genes in the cerebral cortex, giving the impression that somehow, our cerebral cortex excises with surgical precision those parts of the cellular genome that are paternally inherited. And, of course…painting? What?

The post itself took its trashcan spark from two places, the first a blog at a site called Psychology Spot. The Psychology Spot post is in turn a dumpster fire of poor information about genetics and embryonic development, citing 14 references to support its hodgepodge of claims. Of these citations, only one was published this decade (in 2012) and relates to maternal support in the first years of life, not brain genetics. There is no “new research” at all here.

Most of the other papers cited are 20 or 30 years old, and one predates the first Star Wars movie. They primarily deal with the discovery that an embryo requires both a paternal and a maternal genome for appropriate development and that in the absence of these complementary genomes, development fails.

As the second source for the splashy “intelligence is inherited from the mother” claim, the Second Nexus Editorial Staff link us to…Cosmopolitan magazine. Oddly enough, in her brief summary, Cosmo writer Lauren Smith sources her material back to…Psychology Spot, touting it as citing “new research” when it emphatically does not.

Then, at the bottom of the Cosmopolitan post, yet another source is linked: Good Housekeeping. A click on that just takes the reader to the same Lauren Smith-authored post featured on the Cosmo site.

This tautological cross-referencing gets almost as tangled as the genetics of intelligence. The "story" traces from what appears to be a translated post at Psychology Spot to Good Housekeeping to Cosmopolitan to Second Nexus, which cites Psychology Spot and Cosmo as its sources and then ships straight to you by way of Facebook. That Psychology Spot post alone appears to have had 2.2 million Facebook shares.

The explanation for the virality despite the hot mess and inaccuracies? Those headlines. It’s an irresistible invitation to humblebragging, whether you have a mother and think you’re a genius or you are a mother and think your children are geniuses or you’re feeling feminist and want to stake a claim that women bring the smarts to this world. That’s a pretty solid built-in audience ready to click. and share.

The only thing that truly makes sense here is that every single one of these posts uses an image of a little girl in giant glasses and adds a similar headline, one designed to get at that enormous ready-made audience:

  • New Research Confirms That Kids Get Their Intelligence From Mom: One more thing to add to the "Moms Are Awesome" list
  • New Research Confirms That Kids Get Their Intelligence From Mom: Science just proved what your mom has been saying all along.
  • NEW RESEARCH ESTABLISHES THAT INTELLIGENCE IS INHERITED FROM THE MOTHER
  • Did you know that intelligence is inherited from mothers?

Yet the claims in those headlines--and the articles that follow--are just rubbish, and pretty obviously so. Which just goes to show that no matter how smart we are or where we got those smarts, confirmation bias will be what makes us click. and share.