Every now and then I hear stories about us humans being far too dull to have done anything interesting in the past, and that the Pyramids or some such ancient government job creation scheme must have been the work of super-intelligent beings from outer space. Of course, this begs the question: by what process did these beings become super-intelligent?
However, in the way that, slightly to our surprise, we carry little bits of Neanderthal DNA and probably some early hominid adaptations to altitude and the like in our genomes, it might be the case that bright people carry bits of DNA of a sort not usually seen in common folk. For example, genius genes, genes for witty repartee and a gene for the intrinsic, uninstructed appreciation of Feynman diagrams.
In search of the miraculous, a group of researchers from the Plomin lab have analysed the Swedish Enlistment Battery test results of 3 million conscripts, from which they abstracted sibling and twins for analysis.
Nicholas G. Shakeshaft, Maciej Trzaskowski, Andrew McMillan, Eva Krapohl, Michael A. Simpson, Avi Reichenberg, Martin Cederlöf, Henrik Larsson, Paul Lichtenstein,Robert Plomin. Thinking positively: The genetics of high intelligence.
Intelligence 48 (2015) 123–132.
a key question is whether the genetic causes of high intelligence are qualitatively or quantitatively different from the normal distribution of intelligence. We report results from a sibling and twin study of high intelligence and its links with the normal distribution. We identified 360,000 sibling pairs and 9000 twin pairs from 3 million 18-year-old males with cognitive assessments administered as part of conscription to military service in Sweden between 1968 and 2010. We found that high intelligence is familial, heritable, and caused by the same genetic and environmental factors responsible for the normal distribution of intelligence. High intelligence is a good candidate for “positive genetics” — going beyond the negative effects of DNA sequence variation on disease and disorders to consider the positive end of the distribution of genetic effects.
But there is a little more to this than meets the eye. Very low intelligence is often caused by some “negative” genes which “interfere with the working of a naturally good brain, much as a bit of dirt may cause a first-rate chronometer to keep worse time than an ordinary watch ”. It is not impossible that a handful of “positive” booster genes are responsible for creating genius minds, even in Sweden.
In quantitative genetic studies (Nichols, 1984; Reichenberg et al., in preparation), a critical piece of evidence is that siblings of individuals with severe intellectual disability have an average IQ near 100, whereas siblings of those with mild intellectual disability have an average IQ of around 85, about one standard deviation below the population mean. In recent molecular genetic studies, rare non-inherited mutations appear to be a major source of severe intellectual disability
(Ellison, Rosenfeld, & Shaffer, 2013).
Naturally, given the big difference between familial and genetic retardation, then there might be a difference between normal wit and genius brightness. You will have noted the first part of the sentence is wrong, because familial retardation is also genetic, but “genetic” retardation is genetic plus genetic mutations. By the way, this also relates to presumed genetic differences in intelligence:
Another option is that genius is caused by some special hothouse training, in which young children are taught chess moves day and night, with calculus at lunchtime. Such kids would show genius despite their genetics: an environmental effect due to family circumstances.
In this study the top 5% of the sample were considered bright. The refined readers of this blog may be allowed a superior snigger (a snigger is laugh in a half-suppressed, typically scornful way).
The sample comprised 3 million 18-year-old Swedish males. From these, 363,905 families were identified containing at least two conscripted male siblings born in Sweden. From each family, we selected one twin pair if present (the youngest, if the
family contained more than one pair); if there were no twins, we selected the two male siblings closest to one another in age (the youngest, again, if two such pairs had the same age difference).
The authors tried two different techniques in their data analysis, and each raise interesting issues. For example, if you dichotomise population into “Very bright” vs everyone else, then the very bright group will have reduced variance, and variance is what you need when you are conducting analyses of variance.
The analysis which in my view has most power is DeFries–Fulker (DF) extremes analysis. Regression to the mean is a fascinating measure which has a strong genetic explanation and only a weak environmental explanation. Indeed, where two siblings are brought up in the same family, as is the case here, there is no really credible environmental explanation as to why one of a sibling pair should regress more to the mean than another. They have the same parents, the same house full or not full of books, and often go to the same schools.
(I will try to come back to this elsewhere, because genetic regression to the mean raises the question as to which mean you regress to: the national mean, or the mean for your genetic group?)
The dichotomous data – high intelligence versus the rest of the distribution – can be analysed by comparing the degree of concordance for MZ and DZ twins, and for non-twin siblings. Here, we used probandwise concordance: the proportion of “affected” individuals (i.e., those with a stanine score of 9, in this case) who have a twin or sibling who is also affected. This method indicates morbidity risk, i.e., the probability that a sibling or co-twin of someone in the high-intelligence group will also be in that group
As shown in Fig. 3, MZ co-twins of those in the high intelligence group regress to the population mean to a much smaller extent than do DZ co-twins, suggesting genetic
influence. As discussed in Methods, DF extremes analysis uses continuous data, and can estimate the genetic and environmental factors influencing the difference in mean intelligence between the two intelligence groups (high intelligence vs. the
rest of the population), by quantifying the differential regression to the mean for MZ and DZ co-twins of probands.
Fig 3 shows the general population properly distributed around the Standardised mean of 0.0. The fraternal twins of bright children (shown in blue) are at IQ 0.95 which is almost 1 sd above the mean. The identical twins of bright children (shown in light green) are at 1.47 which is almost one and a half sd above the mean. Bright children (actually young adults) have a mean of 1.98, almost precisely 2 sd above the mean, which is a shade higher than expected, if I have understood the inclusion criterion correctly and they were selecting IQ 124+ the top 5%.
As explained earlier, the authors find that the results strongly support the continuity hypothesis. Bright people are normal, and at the upper end of a normal distribution.
We found no support for the genetic Discontinuity Hypothesis that nonadditive genetic variance is greater for high intelligence, as suggested by the emergenesis hypothesis (Lykken, 1982, 2006).
So, dear reader, even if you are bright, you are very probably normal.
In terms of future research strategy, the authors say: selecting individuals of high intelligence might increase power for gene-hunting based on the simple hypothesis that high-intelligence individuals are enriched for intelligence-enhancing alleles and harbour few intelligence-depleting alleles. In other words, intellectual development can be disrupted by any and many mutations, including non-inherited (de novo) mutations, but high intelligence requires that everything works correctly. This hypothesis provided the rationale for a genome-wide case-control association study for cases with extremely high intelligence (IQ >150) compared to unselected control individuals (Spain et al., in preparation). However, in an initial report, this design does not appear to have found richer results either for identifying individual DNA variants, or for genomic approaches such as comparing the total number of rare variants (which generally have negative effects and might be expected to occur less frequently in the high-intelligence sample). Nonetheless, it is early days for the use of high-intelligence samples to increase power for gene-hunting.
Sometime soon this team will tell us if anything has come out of the detailed genomic studies of high intelligence being carried out in Beijing. Follow this blog.