The Genetics of Intelligence

Unless you’ve lived under a rock during the past three decades, you know that inherited DNA plays an important role in all human behavioral phenotypes, including intelligence. We must now focus on charting gene-by-environment reaction norms for every phenotype, thereby assessing the robustness of genetic control across different learning environments, which includes things like nutrition, parental effects, and schooling. A just-published Review (Plomin & von Stumm, 2018) shows us how that will be done. While not about behavioral genetics, the Review opens with that, so I’ll briefly summarize it before getting to “the new genetics” (also see an earlier blog, “Misunderstanding Heritability”).

As the authors report, “inherited differences in DNA sequence account for about half of the variance in measures of intelligence,” which means the heritability of intelligence is about 50%. Heritability is “The proportion of observed differences among individuals that can be attributed to inherited differences in genome sequences.” G represents DNA sequences, while the other part of the equation is E, the environment (writ large). G + E = 100%. Thus, if heritability (G) is 70%, then simple math rules apply, and E must be 30%…regardless of what that means in a biological sense. Heritability is not “inherited.” A child in impoverished Haiti undoubtedly inherits the same genes contributing to height as a child in America, but the heritability of height in Haiti could be 50% whereas it could be 90% in America. The difference is explained by the variation in E. Environmental factors impinging on the height of Haitian children are far more variable than those found in America, and if E goes up, G must go down. Now, substitute intelligence for height, which are both complex, continuous traits (bell curves graphically characterize the population).

The heritability of intelligence is undeniably about 50%, but the salient, biological question is, “Which genes?” Reaction norms require that information. That’s the subject of genome-wide association studies (GWAS), a specialty different than behavioral genetics. A GWAS uses single nucleotide polymorphisms (SNPs), or “single base pair differences in inherited DNA sequence between individuals” to find the needle (genes) in a haystack. Plomin and von Stumm’s Review discusses the failed history of finding genes accounting for the 50% heritability of intelligence; candidate genes account for only a miniscule amount of the 50% variation in intelligence. That’s a gap known as “missing heritability,” mostly resulting from small sample sizes, but it foretells the diminishing influence of behavioral genetics.

Finding the missing heritability will be difficult because, first, “genetic effects are extremely pleiotropic;” that is, genes interact dynamically, and not in linearly. Moreover, continuous traits are polygenic, involve many genes. The second difficulty is that many of the causal DNA sequences for intelligence variation “are in intergenic regions, which means that there are no ‘genes’ to trace through the brain to [intelligence].” Rather, the DNA sequences are in regulatory regions, sequences coding for molecules that regulate the expression of genes. “Third, the [genes that are found will] have miniscule effects—less than 0.05% of the variance—which means that hundreds of thousands of SNP associations are needed to account for the 50% heritability estimated by twin studies.”

The promise in “the new genetics of intelligence” is afforded by genome-wide polygenic scores (GPSs), which aggregate the miniscule effects of SNPs found in GWAS and require much smaller sample sizes. As the authors write, “we cannot resist the metaphor of the other ‘GPS’, global positioning system. We see IQ GPSs as a system to triangulate on the genetics of intelligence from all domains of the life sciences.” GPS works this way: GWAS results are used to determine which of the two alleles for a SNP is positively associated with the trait, called the ‘increasing allele’.” For each SNP, a genotypic score is created by adding the number of increasing alleles, which genotype score is then weighted by the SNP’s effect size (also obtained from GWAS). The GPS is the sum of the weighted genotypic scores. “GPSs are unique predictors in the behavioural sciences. They are an exception to the rule that correlations do not imply causation in the sense that there can be no backward causation when GPSs are correlated with traits. That is, nothing in our brains, behavior or environment changes inherited differences in our DNA sequence.” Moreover, GPSs “are exceptionally stable throughout the lifespan because they index inherited differences in our DNA sequence.” Thus, “a GPS derived from GWAS of intelligence in adulthood will predict adult intelligence just as well from DNA obtained at conception or birth as from DNA obtained in adulthood. By contrast, intelligence tests at birth cannot predict intelligence at age 18 years. At 2 years of age, infant intelligence tests predict less than 5% of the variance of intelligence in late adolescence.”

In summary, imagine obtaining each child’s DNA sequence (from inexpensive genetic screens) and aligning it with their scores on “specific cognitive abilities, such as verbal, spatial and memory abilities and specific cognitive skills taught in schools, for example reading, mathematics and language.” From this, the child’s GPS for each specific cognitive ability is compared to an average GPS for that skill. Thus, “these ability-specific GPSs could be used to create profiles of genetic strengths and weaknesses for individuals who could be targets for personalized prediction, prevention and intervention.” That’s a powerful tool, which, if properly applied in the sociopolitical arena (no easy task), will finally allow each child to thrive in a modern world where intelligence is the most important key to success.

References

Plomin, R., & von Stumm, S. (2018). The new genetics of intelligence. Nature Reviews Genetics. Published online 8 Jan 2018 https://www.nature.com/articles/nrg.2017.104

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