The SAT Zombie Apocalypse

It’s the most wonderful time of the year: Halloween in an election year! ‘Tis the season for a bevy of fright flicks designed to put your girlfriend in your arms and a bad taste in your mouth. Granted, high-art horror, like Antichrist, Pan’s Labyrinth, and The Passion of the Christ, usually do not enter this season’s strategic lineup, but it is good to know that today’s zombies have taken up running because, really, who wants to see a bunch of fat zombies?

This year is special because a multitude of new Americans might very well bring to fruition one-party democracy, much like 20th-century Mexico. Pundits are lauding the Hispanic-American population boom, as if it were an accomplishment akin to putting a flag on the moon or Iwo Jima. Meanwhile, a few bitter white men are expected to vote for the Republican presidential candidate because they have no pity for the new majority.

Halloween season also brings scary stories about falling SAT scores. At Breitbart.com, Ben Shapiro lamented that “2012’s high school seniors have the worst SAT reading scores since 1972; they scored 486 on reading, out of a possible 800.” The Washington Post reported that the scores “reached a four-decade low.” The good news and the bad news are that the elite media are reporting falsehoods, again. Shapiro can take a deep breath because his copy-and-paste error (his Pinker error?) dropped ten points from the actual reading score. However, being too lazy to investigate the scores prior to 1972, the earliest year shown in the annual SAT report, gives the false impression that verbal scores were ever worse than now. In fact, reading scores before Woodstock stood far above the present.

A single year is unlikely to provide a momentous pivot in any of the multiple-decade trends, many of which, it seems, only my readers are aware. It is worth mentioning that the male mathematics advantage slightly grew despite the fact that the upper score limit held down an increased proportion of men relative to women.

Here are some updated graphs:

SAT examinees mirror the demographic changes in America, but racial score gaps (not counting Asians) declare their constancy. With its gargantuan participation levels of highly engaged students, the SAT offers relatively noise-free output and immunity to the growing criticism of differential motivation confounding IQ tests that lack incentives. Still, Alexander Abad-Santos, at the Atlantic’s blog, promised that the “sweeping assumption that minority test-takers are naturally worse than their non-minority counterparts at the ‘reading’ section doesn’t tell the entire story.” He read this assumption into words that the Post apparently removed from their article that “the declining national reading averages may in part reflect the ever widening pool of students who take the SAT…. Nearly half were minorities….” Abad-Santos seems unmoved by the new numbers. Perhaps the data require a fresh style of presentation—something that fits the spirit of the season. I think back to how the Centers for Disease Control produced an animated graph that defined the obesity epidemic as we know it. America has diversified but not with uniformity. Maybe this calls for a map that illustrates the score changes like an infectious outbreak—or a zombie apocalypse!

Forget the numbers. This night of the testing dread is in full color. White (and light pink) represent superior scores. Red is mid-range. Yellow and the more yellow shades of orange mark the worst states (and Washington, DC, the gold star).

Now, compare those results with this map of the percentage of white and Asian SAT takers out of those who identified their race.

State SAT scores do reflect demographic change, but also test participation. Maine is among the whitest states, but its SAT scores fell when it greatly increased the proportion of students who take the test. Delaware did the same. I have expressed some doubts about the effects on scores of participation rates for various groups, but racial and gender groups cannot achieve total participation in a test, like states can. That phenomenon pushes students to take part regardless of their ability or desire to attend college. Midwestern states seem to fare best, but those states emphasize the ACT rather than the SAT. In a state like Illinois, which currently has 100% ACT participation and 5% SAT participation among high-school graduates, a student who takes both tests probably outperforms most of his or her classmates. That student may want to take the SAT to apply to a more prestigious university outside the region.

According to the Washington Post, “questions about whether the SAT is biased in favor of middle-class and wealthy students have led many colleges and universities to use other gauges or to accept an alternative exam, the ACT, which edged out the SAT in 2012 for the first time…” With regard to racial groups, ACT scores act just like those of the SAT.

The ACT even had the same surge of people who would not respond to the racial identity question, except ACT non-responders peaked in 2007 instead of 2003.

Over the years, African Americans, Hispanic Americans, and Native Americans have fallen further behind whites on the ACT.

The map of ACT scores does show a developing North-South divide somewhat like the demographic map. However, it mostly looks red because the District of Columbia used to be such an outlier. Likewise, certain New England states with low ACT participation served as outliers at the other end.

To better account for state test preferences, I created a composite SAT-ACT map. I designed a crude linear formula based on a conversion table to convert ACT scores to SAT equivalents. Then I weighted each of the two tests according to the relative participation for each state. In accordance with the conversion table, I did not consider writing scores.



Even though I kept the same color scheme as the other SAT maps, the white and pink disappeared because the map reduces the influence of overachievers who take both tests when their state has a clear preference. A worsening North-South divide clearly presents itself.

I consider this the best and most reliable map, but participation issues limit even it. I controlled for the relative participation between the tests, but not for the overall participation rates. The states that moved to total or near total participation on one of the tests were Colorado, Delaware, Illinois, Kentucky, Louisiana, Maine, Michigan, Mississippi, North Dakota, Tennessee, Utah, and Wyoming. Only Delaware and Maine have chosen to go with the SAT. By contrast, Arizona sticks out for its extremely low participation on both tests. Clearly, Arizona has an unfair score advantage, and Iowa and California also do not have especially high testing rates, either. I decided to program a map that punishes the scores of states in which the combined participation percentages of both tests is less than 100% by a factor proportional to its deficit below 100%. The punishment is somewhat arbitrary and probably quite excessive, but at least it shows that the American Southwest has inflated scores. I consider this neither to be a “score” map nor truly “controlled,” so I reset the color ranges.

For clarity, here is a map just of the addition of the participation percentages for both tests without regard to scores. States like Florida are seeing high rates of participation on both tests because they have not settled upon a single standard.

State participation changes confound state scores in multiple ways, but a movement towards full participation on the ACT could settle this issue. States increasingly seem to favor the ACT over the SAT, which I suspect is partly due to the false impression that ACT scores are less racist. Demographic changes correspond to falling test scores, and one can see it, at least in terms of a North-South divide, on these maps. America is bearing a zombie apocalypse, which is sweeping the nation and coming for our brains.










Duckworth AL, Quinn PD, Lynam DR, Loeber R, & Stouthamer-Loeber M (2011). Role of test motivation in intelligence testing. Proceedings of the National Academy of Sciences of the United States of America, 108 (19), 7716-20 PMID: 21518867

Genes Dealt Made Asians Svelte

Another documentary has surfaced that leans on the apprehension or anticipation that genetics will confirm the intellectual advantages of certain racial groups over others. Realistically, I doubt Nature or The New York Times will break such a story. The media generally does not even address racial differences in the warrior gene. Why should anyone expect a mainstream science reporter to painstakingly calculate the cumulative effect of who-knows-how-many single nucleotide polymorphisms (SNPs) potentially to prove right Southern bigots? Nevertheless, curiosity abhors a pat tune, and I think questions of race naturally meld into one of the most basic existential questions: What does it mean to be human? In general, examinations of the genetics of obesity and intelligence would complement each other not only because both traits have complex genetic architectures, but also because obesity is a less controversial subject for many than intelligence, especially when these subjects intersect with race. So, an approach that gains acceptance for less contested phenotypes will streamline an IQ juggernaut. Since stepping on a scale is far simpler than measuring intelligence, temperament, personality, or behavior, that genome-wide association studies (GWAS) for body-mass index (BMI) are further along does not surprise.

So far, GWAS have identified 32 genetic loci for obesity. Different studies have used different SNPs to represent these loci. In order to compare diverse ethnic populations at these loci, I entered each SNP into the HapMap online database. Then, I selected the SNP from each locus for which HapMap provides the most information. HapMap has very thorough data for Northern Europeans, the Yoruba of Africa, Chinese people, and Japanese people. By multiplying the respective effect sizes of each SNP by each group’s allele frequency and adding the results for each group, I could graph a genetic index of obesity for each of those four groups. I also added the data from those four groups to data from less represented ethnic groups to create the following broader racial or ethnic designations. “Black” refers to the Yoruba, the Luhya, the Maasai, and African Americans. “Whites” are Northern Europeans and Italians. “East Asians” are Chinese and Japanese people, and the group “Asians” also adds people from India. The resulting graph suggests that Asians have a lower genetic risk for obesity.

For a more detailed picture of the full range of ethnic groups, I removed 7 of the 32 loci that had more limited data. This graph still seems to show less obesity propensity for Asians. In fact, graphs like this can serve as counterpoints to the social deconstruction of race, since ethnic groups within a continental racial group do tend to cluster together in allele frequencies. This fits with recent population genetics studies. For instance, a new study of natural selection in African populations found that “positive selection does not appear to have substantially shaped present-day allele frequency differences among the African populations in our dataset…. Our results agree with Coop et al (2009) and Pickrell et al (2009), who found that selective sweep signals tend to cluster by broad geographic and continental regions…”

Perhaps the similarity of genetic risk for white and black people should not surprise. Currently, in the United States, adult black women have nearly twice the prevalence of obesity as adult white women, but for the men no statistically significant difference exists. Therefore, I suspect that the unfortunate obesity epidemic among African-American women is a cultural phenomenon, rather than genetic destiny.

A relevant criticism of my genetic racial comparisons is that the GWAS that identified these genes were conducted in Europeans. Moreover, Chinese people have allele frequencies of zero for 5 of the loci, and Japanese people have allele frequencies of zero for those 5 and one more. If those loci would not be identified in Chinese or Japanese obesity GWAS, one could certainly imagine that those GWAS could identify obesity-causing alleles which whites or Africans lack. Therefore, I recreated the first graph minus those 6 loci to attempt a more fair comparison.

The racial genetic risk gap is lessened but is still very much present.

A different set of five loci (four for the detailed ethnicity breakdown graph) affect extreme obesity risk, with extreme obesity defined as an adult BMI of greater than or equal to 40 or a childhood BMI greater than or equal to 99 percent of the age and gender cohort. In the case of extreme obesity, Asians appear to be at greater risk than whites. Japanese people, in particular, apparently possess a sumo-sized extreme obesity risk, despite having low overall genetic obesity risk.

Three SNPs affect body fat composition, as measured by bioimpedance analysis and dual energy X-ray absorptiometry. One of the alleles is a member of the 32 obesity loci. Another was found to affect body fat percentage in Europeans but not Indians. The third, IRS1, has an allele that raises body fat but paradoxically lowers type 2 diabetes risk in men, seemingly by shifting fat storage to the layer just beneath the skin where it is less harmful. Asians are much less likely to have that allele, which could help explain why studies are finding that nonoverweight Chinese people have high rates of metabolic abnormalities more commonly associated with obesity. Specifically, one-third of nonoverweight Chinese people have at least one metabolic risk factor.

GWAS have found fourteen SNPs so far for waist-to-hip ratio after controlling for BMI, age, and sex. The detailed ethnicity breakdown bar graph includes eleven of them. These graphs do not show strong racial or ethnic differences, but perhaps these alleles further contribute to unhealthy fat distribution in Asians.

The overriding concern that troubles this form of analysis is that the totality of the molecular genetics of any of these phenotypes is still so poorly detailed that the known loci account for almost none of the genetic heritability determined by twins studies and the like. The obesity GWAS used a quarter of a million subjects to lay out just 2 to 4 percent of the estimated heritability. The GWAS for waist-to-hip ratio used 190,000 subjects to account for 2 to 5 percent of the estimated heritability. The three body-fat SNPs using 76,000 subjects explain a mere 0.25% of body fat composition heritability. Despite such low levels of explained variance, this genetic data accurately samples the whole of which it is part. Belsky et al recently demonstrated this to be the case, using the same method of calculating an obesity genetic risk score applied to individuals rather than groups. In fact, the effect size of the genetic risk index correlated only slightly less than familial risk based on each individual’s parents’ BMI, and their genetic risk index did not even include 3 of the 32 loci. Also, as the graphs below reveal, the genetic risk was not merely a subset of the parental risk. The two risk scores (listed as high or low for being one standard deviation above or below the mean, respectively) could not completely match the predictive quality of a risk based on the two in combination.

Presumably individual and population differences in important characteristics have some comprehensible root cause or causes. Regardless of the precise contributions to polygenic trait evolution from natural selection, the Founder effect, deleterious mutations, and so on, the order of allele identification is sufficiently independent of these forces, and the effect sizes are sufficiently distributed so as to make, I predict, nearly any genetic index a representative sample. If I am wrong, then at least I have started a scalable database as additional loci trickle in.

The Latest Intelligence on Intelligence

The concern about applicability to non-Europeans has greater salience, considering recent findings about rare SNPs. These GWAS only consider the independent effects of common SNPs, not the effects of rare SNPs or the “non-additive” genetic effects of the interactions between genes (called epistasis). A pair of studies recently addressed rare SNPs in the journal Science. One determined that 86% of the 500,000 SNPs found with “deep sequencing” of the protein-coding exomes were “rare,” meaning that their less common allele frequency was less than 0.5%. Rare SNPs were mostly race-specific and mostly recent deleterious mutations. Among the 1,351 European Americans (EA), 65% of all of the SNPs were race-specific. Among the 1,088 African Americans (AA), the percentage was 72%. One Native American (NA) was also examined. Below is a diagram depicting the population overlap of these SNPs and a bar graph detailing the proportion that was race-specific by allele frequency.

Research into the genetics of intelligence might also face this dilemma. A new hypothesis from Gregory Cochran suggests that deleterious mutations determine a postulated genetic component of racial IQ gaps, with the driving force being temperature’s acceleration of the mutation rate or differences in paternal age. The authors of these studies try to explain the differences with population-size dynamics. Population growth amplifies the number of the mutations or “derived alleles” present per individual. Natural selection lowers the proportion of mutations that are “functional” or “non-synonymous,” meaning that such mutations change the protein for which the DNA codes and are usually deleterious. Recent population bottlenecks, like the exodus from Africa of Eurasians, both amplify derived alleles and only allow a shorter period of natural selection for those alleles.

It turns out that deleterious mutations are more likely to be rare SNPs in African Americans than in European Americans.

Consequently, a study with a smaller sample, such as Lohmueller et al, will tend to find a higher proportion of deleterious-to-synonymous mutations in Europeans than Africans. For just this reason, a genetic index comparison of common SNPS for intelligence along the lines of what I have done for obesity might underestimate the genetic component of the IQ gap between black people and white people, until later research with higher sample sizes take into account rare alleles.

On the other hand, the African exodus bottleneck seems to have increased homozygosity (matching pairs) of deleterious mutations in Europeans. Although Africans seem to have more deleterious mutations per person overall, perhaps their genetic diversity and the possible recessive quality of these mutations help balance out that effect. The graph below shows the number of homozygous pairs that are synonymous (S), non-synonymous (NS), possibly damaging (PO), and probably damaging (PR).

Moreover, MacArthur et al found much higher numbers of deleterious mutations in Asians and Africans than Europeans, but detailed follow-up determined many of these to be false positives. Thus, whites and Asians each had an equal number of true loss-of-function variants per person (104). Africans still had more (122), but each group had a roughly equal number of homozygous pairs.

Another study that postulated a significant epistatic component to heredity used an equation based on twins studies to estimate how much of the variability of different phenotypes owes to the additive effects of SNPs (and, therefore, resulting from the sort of alleles that I am tracking). The equation result was closer to zero as the influence of those effects rose. BMI between the ages of 30 and 39 was about as close to zero (-4) as “performance IQ” (5), fitness (4), and exercise participation (5), and quite closer than general IQ (-10). BMI between the ages of 20 and 29 (18) was not as close but was still the same distance from zero as verbal IQ (-18). For comparison, birth weight was -73, and having fainting spells not in response to blood was -63. Since GWAS for IQ have already found that common SNPs account for about half of its variability, which is the bulk of its heritable component, and since those equation results showed comparable results for BMI and IQ, my approach might work fairly well for both obesity and intelligence. Nevertheless, I cannot yet reconcile that with the research on exome rare SNPs.

Steve Hsu, who recently became the Michigan State University vice president for research and graduate studies, is working on an IQ GWAS and has offered some amazing revelations in a recent presentation. He appears to endorse deleterious mutations as the major genetic contribution to individual IQ differences, and he estimates that having 39 such mutations lowers IQ 15 points (one standard deviation), about 10,000 IQ SNPs exist, and removal of such mutations could raise IQ perhaps as much as 30 standard deviations. If today’s geniuses have IQs above about 145, one can hardly imagine the potential of a person whose IQ is over 500. Of course, no IQ test today could verify such a level, but after humanity creates those dorks, maybe they could invent one. Hsu points to embryo selection as a realistic means of consumer-driven eugenics. He seems to think that Asian societies might be amenable to this approach, but he hopes that “progressive governments will make this procedure free for everyone.” Perhaps his work with the Beijing Genomics Institute will help identify IQ SNPs specific to Asians.

Society is used to a somewhat sporadic quality to genius because extraordinary intelligence often benefits from favorable epistasis, but embryo selection would raise the “additive” IQ potential, so the children and grandchildren of these people would invariably be super-geniuses, as well. I could conjecture about the implications of this form of eugenics. The potential to spread genius far and wide could negate a key reactionary theme, while bolstering a liberal intellectual elite. “Elite” might become a tenuous term, as genius might no longer incur reward and professional status, that is, if embryo selection becomes ubiquitous. Such circumstances multiply leftist agitants. That this might occur concomitant with global warming and automation’s realization of Marx’s “labor-saving devices” prophecy could precipitate a re-birth of Communism. At least initially, willingness to abort many healthy embryos will be a major determinant of participation, making for a far-left leading edge.

Then again, when living embodiments of eugenics ideology make quaint the quasi-religious adjective, “gifted,” an entire right-wing historical narrative will march to the fore. Intelligence will become a choice of responsible parents, and liberals will grow frustrated in their attempts to invite evolution-disbelieving African Americans.

For some, other races will always be “the other,” regardless of IQ, and universal genius promotion might sooner reach out to the family-pet community. Don’t laugh! If humans can reach a new brilliance beyond superlatives, who could say how far into the animal kingdom human intellect could penetrate? If scientists can resurrect Neandertals or Denisovans, those creatures might even share some of the target IQ variants with modern humans. All this existential tumult will owe to a movement that began with elites’ innumerable abortions. Personhood will never be the same.









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