Race and IQ

Biology of the Sphingolipid Mutations

The sphingolipid storage mutations were probably favored and became common because of natural selection, yet we don’t see them in adjacent populations. We suggest that this is because the social niche favoring intelligence was key, rather than geographic location. It is unlikely that these mutations led to disease resistance in heterozygotes for two reasons. First, there is no real evidence for any disease resistance in heterozygotes (claims of TB resistance are unsupported) and most of the candidate serious diseases (smallpox, TB, bubonic plague, diarrheal diseases) affected the neighboring populations, that is people living literally across the street, as well as the Ashkenazim. Second and most important, the sphingolipid mutations look like IQ boosters.

The key datum is the effect of increased levels of the storage compounds. Glucosylceramide, the Gaucher storage compound, promotes axonal growth and branching (Schwartz et al., 1995). In vitro, decreased glucosylceramide results in stunted neurons with short axons while an increase over normal levels (caused by chemically inhibiting glucocerebrosidase) increases axon length and branching. There is a similar effect in Tay-Sachs (Walkley et al., 2000; Walkley, 2003): decreased levels of GM2 ganglioside inhibit dendrite growth, while an increase over normal levels causes a marked increase in dendritogenesis. This increased dendritogenesis also occurs in Niemann-Pick type A cells, and in animal models of TaySachs and Niemann-Pick.




Figure 1, from Schwartz et al. (1995) shows the effect of glucosylceramide, the sphingolipid that accumulates in Gaucher disease. These camera lucida drawings of cultured rat hippocampal neurons show the effect of fumonisin, which inhibits glucosylceramide synthesis, and of conduritol B-epoxide (CBE) which inhibits lysosomal glycocerebrosidase and leads to the accumulation of glucosylceramide, thus mimicking Gaucher disease. Decreased levels of glucosylceramide stunt neural growth, while increased levels caused increased axonal growth and branching. Figure 1: Glucosylceramide Increases Axon Growth, from Schwartz et al. (1995), reproduced by permission of the Journal of Biological Chemistry. Dendritogenesis appears to be a necessary step in learning.

Associative learning in mice significantly increases hippocampal dendritic spine density (Leuner et al., 2003), while enriched environments are also known to increase dendrite density (Holloway, 1966). It is likely that a tendency to increased dendritogenesis (in Tay-Sachs and Niemann-Pick heterozygotes) or to increased axonal growth and branching (in Gaucher heterozygotes) facilitates learning. Heterozygotes have half the normal amount of the lysosomal hydrolases and should show modest elevations of the sphingolipid storage compounds. A prediction is that Gaucher, Tay-Sachs, and Niemann-Pick heterozygotes will have higher tested IQ than control groups, probably on the order of 5 points. We do have strong but indirect evidence that one of these, Gaucher disease, does indeed increase IQ.



Tay-Sachs is caused by the absence of a vital enzyme called hexosaminidase-A (Hex-A). Without Hex-A, a fatty substance, or lipid, called GM2 ganglioside accumulates abnormally in cells, especially in the nerve cells of the brain. This ongoing accumulation causes progressive damage to the cells.


Professor Ari Zimran, who heads the Gaucher Clinic at the Shaare Zedek Medical Centre in Jerusalem, furnished us a list of occupations of 302 Gaucher patients. Because of the Israeli medical care system, these are essentially all the Gaucher patients in the country. Of the 255 patients who are not retired and not students, 81 are in occupations that ordinarily average IQ’s greater than 120. There are 13 academics, 23 engineers, 14 scientists, and 31 in other high IQ occupations like accountants, physicians, or lawyers. The government of Israel states that 1.35% of Israeli’s working age population are engineers or scientists, while in the Gaucher patient sample 37/255 or 15% are engineers or scientists.

Since Ashkenazim make up 60% of the workforce in Israel, a conservative base rate for engineers and scientists among Ashkenazim is 2.25% assuming that all engineers and scientists are Ashkenazim. With this rate, we expect 6 in our sample and we observe 37. The probability of 37 or more scientists and engineers in our sample, given a base rate of 2.25%, is approximately 4 x 10-19 . There are 5 physicists in the sample, while there is an equal number, 5, of unskilled workers. In the United States the fraction of people with undergraduate or higher degrees in physics is about one in one thousand. If this fraction applies even approximately to Israel the expected number of physicists in our sample is 0.25 while we observe 5. Gaucher patients are clearly a very high IQ subsample of the general population.





Are there Ashkenazi mutations other than these sphingolipid storage disorders that likely became common because of strong selection for IQ? There are several candidates. Ever since torsion dystonia among the Ashkenazim was first recognized, observers have commented on the unusual intelligence of patients. Flatau and Sterling (Eldridge, 1976) describe their first patient as showing “an intellectual development far exceeding his age”, and their second patient as showing “extraordinary mental development for his age.”

At least ten other reports in the literature have made similar comments. Eldridge (1970, 1976) studied 14 Jewish torison dystonia patients: he found that their average IQ before the onset of symptoms was 121, compared to an averge score of 111 in a control group of 14 unrelated Jewish children matched for age, sex, and school district. Riklan and colleagues found that 15 Jewish patients with no family history of dystonia (typical of DYT1 dystonia) had an average verbal IQ of 117 (Eldridge, 1979; Riklan et al., 1976).



Torsion dystonia is unusual among the Ashkenazi mutations in that it is caused by a lowpenetrance dominant rather than a recessive, so disease risk and any heterozygote advantage exist in the same individual. About 10% of heterozygotes have crippling muscular spasms (usually curable by modern neurosurgery), and such individuals seldom reproduced in the past. A net fitness advantage could have existed if healthy carriers had a greater than 10% fitness edge, presumably from increased intelligence. Risch (Risch et al., 1995) found that linkage data indicate that the DYT1 mutation came into existence around 300 years ago.

He suggests that a high reproductive variance may have decreased the effective Ashkenazi population size, resulting in drift, but as we pointed earlier, this proposed mechanism strengthens selection far more than drift. The high gene frequency and recent origin of the DYT1 are signs of positive selection, while the many observations of increased intelligence among people with ITD strongly suggests that increased fitness resulted from increased intelligence. Non-classic congenital adrenal hyperplasia (CAH) is another mutation that is unusually common among the Ashkenazim and has been reported to increase IQ. At least seven studies show high IQ in CAH patients, parents, and siblings, ranging from 107 to 113. Parents are obligate carriers and 2/3rds of siblings are carriers. There is also SES elevation in patient families (Nass and Baker, 1991). This mainly applies to the milder forms of CAH; there is no apparent IQ advantage in seriously ill patients like saltwasters. The Ashkenazi gene frequency is almost 20% (New and Wilson, 1999).

Past research suggests that within-family Flynn trends exist and correlate with observed patterns (1, 8). The IQ difference between scored siblings was shown to shrink with the age difference in periods of rising cohort IQs (as the Flynn effect counteracts the first-born birth order advantage) and to increase with the age difference in a period with declining cohort IQs (8). Building on this result, we use population-covering administrative data registers from Norway to estimate within-family Flynn effects across 30 birth cohorts and examine whether these estimates recover the full magnitude of, variation in, and reversal of the Flynn effects seen in average cohort scores. The Norwegian data have been extensively used in intelligence research (1, 4, 9⇓–11) and provide a particularly useful dataset for our purposes given the roughly symmetric positive and negative trends across the 1962–1991 cohorts (Fig. 1A). Based on data from birth cohorts born before 1985, prior research has reported this as a slowdown or leveling off of the Norwegian Flynn effect (9), but the additional cohorts included in our data strongly indicate that it is in fact a reversal.


Fig. 1.
Average IQ score by birth year (A) and distribution of IQ scores (B). IQ scores are computed from stanine scores (s) using the conversion IQ = 100 + 7.5 × (s − 5). In A, the shaded region depicts 95% confidence intervals around the cohort mean score. n = 736,808.

The analysis is made possible by the comprehensive coverage of administrative data for the native-born population. This enables us to precisely identify family relationships, birth order, and siblings without ability scores from military conscription testing. Precise controls of birth order are necessary for estimation of within-family trends, as prior research shows that IQ relates inversely to sibling order (12⇓–14). Ignoring birth order would induce omitted-variable bias with the order effect falsely attributed to later birth years, in turn causing negative bias in trend estimates. Information on unscored individuals is required to correct for changes in selection into ability testing over time, which otherwise will bias trend estimates.


Fig. 2.
Within-family estimates of Flynn effects. The sample underlying estimates in A consists of all families with at least two scored brothers (n = 355,438), B of families with scored brothers in the first two parities (n = 215,514), and C of siblings born 1962–1991 in all families with sons in the first two parities (n = 236,934). Two-brother samples exclude twins and brothers born the same year. The dashed line depicts the trend for firstborn sons (n = 320,739 in A and B and 353,476 in C). Confidence intervals are computed from SEs clustered within families. The shaded region in C covers percentile values from the posterior distribution of the Bayesian model.

Results
The research question is whether within-family variation can recover the population Flynn trend apparent across families. This requires an appropriate comparison curve showing the across-family variation in IQ scores. The simplest such curve is the curve of observed means for all firstborn children by birth year, as it obviates the need for statistical controls for birth order.

The coefficients from the standard fixed-effects model estimated on data for all scored siblings closely track the across-family variation in IQ scores throughout most of the data period (Fig. 2A). In particular, the within-family estimates confirm the positive Flynn trend during the first half of the observation period, with positive and statistically significant Flynn effects for the birth years 1962–1975. For this period, the average within-family Flynn effect is 0.26 IQ point per year (SI Appendix, Table S3, column 3), similar to the 0.28 estimated annual gain for full-scale IQ from a metaanalysis based on 271 independent samples from 31 countries (2).

The fixed-effects model correctly identifies the turning point of the Flynn effect and indicates a decline for post-1975 birth cohorts. For the cohorts born in the latter half of the 1980s, however, the across-family decline in cohort IQ exceeds what the within-family estimates recover. Between the 1975 and 1991 cohorts, the average annual decline estimated using within-family variation is attenuated by almost two-thirds relative to the across-family trend: −0.08 IQ point per year versus −0.23 point per year (SI Appendix, Table S3, columns 1 and 3).

One source for this divergence of within-family estimates and across-family Flynn trends in the decline period may be sample selection bias induced by conditioning the data on siblings with a valid IQ score. If selection into scoring is increasing over time, this generates a positive bias in trend estimates as families showing a decline are disproportionately removed from the sample. Conscription test coverage declined substantially for cohorts born after 1980, with coverage rates falling from 93% in 1980 to 83% in 1991 (Fig. 3A). This decline in coverage was selective and partly based on characteristics associated with intelligence: Focusing on families with sons in the first two parities and plotting the share of unscored younger siblings by the observed IQ score of the older brother, lower scoring firstborns were more likely to have unscored younger brothers (Fig. 3B). The problem is exacerbated toward the end of our data window: Among the 1987–1991 birth cohorts, fully 30% of those whose older sibling scored in the bottom IQ bracket have missing IQ scores. As sibling scores are correlated, this implies that low-ability males are less likely to be scored, and that the selection was stronger for the cohorts born in the late 1980s than for those from the 1960s and 1970s.