The Alondra Oubré Academic Fraud Exposed

Alondra Oubré Wikipedia Scholar

As further proof that a specific violence gene common to Africans threatens the worldview of fundamentalist anthropologists, Wikipedia scholar Alondra Oubré became the latest anthropologist to post an error-riddled Internet screed against the warrior gene, monoamine oxidase A (MAOA). Oubré is the author of Instinct and Revelation: Reflections on the Origins of Numinous Perception and Race, Genes and Ability: Rethinking Ethnic Differences. She is also an expert at copying errors from Wikipedia into her writing. Her *Wikipedia* page lists her as a “newsmaker,” “prominent African American,” and an “African American achiever.” As an anti-science anthropologist, she joined her colleagues in writing another editorial against Nicholas Wade’s recent book, A Troublesome Inheritance, as well as the study of MAOA, a gene verified as causing violence in multiple meta-analyses. Unlike previous attacks on this science, no possibility exists that this is anything other than academic fraud. Oubré took false information from Wikipedia, for which I provide here the proof, and she deliberately lied about her source. I repeatedly requested an official correction from her editor, author and City University of New York professor Massimo Pigliucci, who refused to do so. What follows is a point-by-point refutation of Oubré’s work.

Wow! What a poorly researched Internet post! I hope you don’t mind if I post the factual corrections here for you.

“The most common variant, MAOA-4R, has four repeats and is associated with high-activity breakdown of neurotransmitters.”

I guess it would be true that MAOA-4R is the most common variant if everyone in the world was white.

“Up to this point, all of the studies on the MAOA gene had been conducted in Caucasians. That changed when researchers started investigating this gene in the Maori of New Zealand.”

No, here is a list of studies that looked at non-whites prior to that study: Sabol et al, Kunugi et al, Balciuniene et al, Gilad et al, Ono et al, Williams et al, Koen et al, Huang et al, Yu et al, Young et al, Widom & Brzustowicz, and Rosenberg et al.

“For many experts, this ethnic gap is the result of numerous environmental causes, including poverty.”

I think you should revise this sentence.

“It turned out that while 3R was found in 56% of Maori males, it occurred in 58% of African American males and 34% of European males.”

Notice how the African-American number is slightly lower than the source? Someone in Wikipedia has been tweaking the numbers at will. I don’t recommend that you rely so heavily on Wikipedia as your source for just this sort of reason. I also don’t recommend that you rely on that “study” by Lea and Chambers, which was the source of the “idiot test” copy-and-paste error that slandered Chinese men as having an MAOA-3R allele frequency of 77%.

“Interestingly, the press ignored studies indicating that the 3R variant occurred in 61% of Taiwanese males [15] and 56% of Chinese males [16].”

You switched your sources. Both samples were Taiwanese. You rounded 54.5% to 56%. That’s kind of sloppy.

“In the Add Health database, 5.5% of African American men, 0.9% of Caucasian men, and 0.00067% of Asian men have 2R.”

So, you took these numbers from Add Health, did you? No, you didn’t. I know because I calculated the number for Asian men and posted it on my blog and Wikipedia. Once again, the Wikipedia troll screwed up your numbers for white men by a factor of 9. The Asian allele frequency was based on eight studies. I only found one Asian with MAOA-2R in those studies, but I have since looked at other studies and revised the number upwards. I have been maintaining a table with my tabulated allele frequencies (without excluding any sample).

“This has led some popular writers to speculate that MAOA-2R might account for — or at least play a significant role in — the relatively higher rates of violent crime in African Americans. Not everyone agrees [21].”

If one writes, “Not everyone agrees,” it is good form to make sure that the source cited expresses some disagreement with what one wrote before “Not everyone agrees.”

“The rates of 2R are more than five times higher in African American males than in American white males, at least in the Add Health sample.”

Yeah, I guess 55 is more than 5. Damn that Wikipedia troll! Choe et al, which you cited, found it in 6% of black men and 0% of white men, so maybe it’s infinity times more common. Seriously, considering how rare it is in whites and Asians, why should we believe that those rare exceptions are actually genetically 100% white or Asian?

“Although genes affect individual differences in behavior, the effect of each individual gene is usually small.”

I think you meant to say “allele.” If the effects of individual genes are usually small, then missense mutations that completely shut off the gene and eliminate the protein should have little effect. Of course, you failed to mention the missense mutation specific to MAOA, which causes Brunner syndrome. The effect of Brunner syndrome on behavior is not small.

“The more common low-activity variant, 3R, interacts with adverse social effects such as childhood maltreatment. But other possible environmental factors, which conceivably could interact with the 2R, may not have been explored in-depth as yet.”

I think Fergusson et al did the most in-depth analysis of various environmental factors. Interestingly, the interaction effect of IQ on violence was more powerful than the interaction effect of childhood maltreatment. I’m afraid that you’ll have to look up for yourself whether African Americans differ from whites and Asians in average IQ because that is outside my area of expertise.

“Using PET imaging scans, these researchers found no correlation between MAOA brain levels and MAOA gene variants.”

However, Alia-Klein et al did find MAOA promoter effects on anger in an fMRI study. That study and Buckholtz et al found MAOA gene effects on the amygdala. Cerasa et al found that the gene influenced orbitofrontal cortical thickness with MRI. Buckholtz et al and Cerasa et al had much larger samples than the 34 men in Shumay et al. Shouldn’t you have mentioned those findings?

“Nonetheless, their results suggest that MAOA brain levels, which affect mood, are at least partially regulated by non-genetic factors — i.e., epigenetically.”

Of course, genes do influence epigenetics. In fact, the “environmental” interaction factors, like childhood maltreatment, might also have a component of heritability. Wong et al found that, compared to women, epigenetics of MAOA in men is minimal, low in variance, and high in hereditary influence. Pinsonneault et al was unable to detect any MAOA methylation in men. Philibert et al found less MAOA methylation in men and that MAOA methylation had no effect on antisocial personality disorder in men or women. That seems like a relevant finding.

“The jury is still out on whether 2R, the rare MAOA gene, acts independently of the environment (and independently of other genes) to shape antisocial personality traits.”

First of all, is MAOA-2R rare in Africans? A common definition of a rare allele is having an allele frequency less than 5%. It might not be rare in African-American men. We can extrapolate to the higher allele frequency in a population of Africans who are not racially mixed. All of the evidence we have on MAOA-2R, so far, suggests that it has a powerful effect independently of environment. The assumption is that this distinguishes MAOA-2R from MAOA-3R, which supposedly only has a gene-environment interaction effect. A recent meta-analysis of 31 studies actually disproved this and found that MAOA-3R has a slight effect on antisocial behavior independent of interaction factors.

Pigliucci allowed me to post this comment only so that others could harass me with baseless ad hominem, but he censored all of my other responses.

Exposing falsehoods about the warrior gene is nothing new for me, but this is different. It might be hard to believe that a respected scientist like Steven Pinker or an experienced writer like John Horgan would fall for the idiot test or that Scientific American, The Chronicle of Higher Education, and various journals and book publishers would reprint it. While one might not expect such incompetence from these sources, no evidence proves malfeasance. Also, Oubré’s mischaracterization of the science of MAOA epigenetics and brain imaging (also see Lei et al) is likely but not positively deceptive. In other words, she probably came across the evidence against her thesis and chose to keep it to herself, but one cannot absolutely demonstrate this as such. However, she unmistakingly lied when she attributed Wikipedia data to the Add Health subsample of the famous, widely used National Longitudinal Study of Adolescent Health database.

Interestingly, the idiot test almost constitutes a photographic negative of this fraud. The original copy-and-paste error by Rod Lea and Geoffrey Chambers first appeared in a scientific journal—perhaps not a highly respected journal, but a journal nonetheless—and subsequently spread to the public through mass media. This time, misinformation sprouted from the lowly, anonymously edited Wikipedia and traveled up the media food chain to scientific blogs. The Wikipedia page for MAOA originally contained correct information that I and other responsible agents copied correctly from peer-reviewed studies. Then, someone identified only by their Internet Protocol address, 76.78.226.57, began altering the data. One can observe from this person’s contributions page, that he or she has a history of altering numbers in Wikipedia that relate to immigration and ethnicity helter-skelter without providing new sources. On March 22nd, the offender made three unsourced edits to the same group of numbers on the MAOA page. At 2:25, the change was as follows:

Two minutes later, another change occurred:

At 23:55, the offender changed the same numbers, again:

This allowed for an error of an order of magnitude in Oubré’s numbers. So, who is 76.78.226.57? Is she Oubré? Is he Pigliucci? Who knows? Maybe he is Eric Holder. Nobody who knows is saying.

Massimo Pigliucci “Editor-in-Chief”

To prevent confusion among the lay public, I politely asked Scientia Salon editor-in-chief Pigliucci for an official correction in this e-mail:

You posted my corrections for "The Extreme Warrior gene: a reality check" as a comment. However, the errors were quite serious, such as claiming that data from Wikipedia (which was false information) actually came from the National Longitudinal Study of Adolescent Health. Such errors should not only be addressed by an outsider's comment. As the editor-in-chief of Scientia Salon, you should see that someone actually investigates my claims and posts a complete correction, if true. Alternatively, you could direct me to the appropriate authority within your site who handles corrections.

Pigliucci could not bother himself with more than a curt reply.

your comment has been published, so I’m not sure what additional action you expect from me, or why.

I tried repeatedly.

Yes or no, did Alondra Oubré falsely claim on your site that information she took from Wikipedia had actually come from the Add Health subsample of the National Longitudinal Study of Adolescent Health? If yes, was the information from Wikipedia all accurate? If she falsely attributed false information from Wikipedia, why do you refuse to post an official correction at the end of her piece, as any reputable source of information would? I noted numerous other errors, but this one in particular seems especially egregious because it reveals a lack of integrity and provides a conduit for anyone to make up information on Wikipedia and disseminate it through disreputable blogs.

No reply came.

As shown by her citations, Oubré obviously intended her perversion of MAOA science as a rebuttal to Wade. Less than three weeks prior, Pigliucci spent forty-two minutes in a podcast expatiating the standard semantic criticism that has amounted to basically the entirety of the fundamentalist anthropologist attack on Wade’s book. They call Wade a racist, and, in modern civilization, racists might be preferred to pedophiles but are considered far worse than necrophiliacs, cannibals, terrorists, zombies, Democrats, rapists, and even boy-band alumnae. Wade spent a good portion of his book criticizing white supremacy and called a book by JP Rushton racist in an interview. If idiotic anthropologists label every prestigious intellectual with whom they disagree a white supremacist, then the desire to be white supremacist among average white folks will grow like the tuition rates that young people pay to hear idiotic anthropologists bloviate. The colloquial definition of racism is the belief that average ability and tendency differences (stereotypes) exist between peoples grouped by place of origin. Heritability mathematics has nothing to do with it. Therefore, all anti-racists are racists. Actually, I would like to see the forces of good defeat white supremacy, which is why I know that the perceptual and strategic superiority lies with the recognition that the face of neo-Nazi white supremacy is the one covered in tattoos.










Alia-Klein N, Goldstein RZ, Tomasi D, Woicik PA, Moeller SJ, Williams B, Craig IW, Telang F, Biegon A, Wang GJ, Fowler JS, & Volkow ND (2009). Neural mechanisms of anger regulation as a function of genetic risk for violence. Emotion (Washington, D.C.), 9 (3), 385-96 PMID: 19485616

Balciuniene J, Syvänen AC, McLeod HL, Pettersson U, & Jazin EE (2001). The geographic distribution of monoamine oxidase haplotypes supports a bottleneck during the dispersion of modern humans from Africa. Journal of molecular evolution, 52 (2), 157-63 PMID: 11231895

Buckholtz JW, Callicott JH, Kolachana B, Hariri AR, Goldberg TE, Genderson M, Egan MF, Mattay VS, Weinberger DR, & Meyer-Lindenberg A (2008). Genetic variation in MAOA modulates ventromedial prefrontal circuitry mediating individual differences in human personality. Molecular psychiatry, 13 (3), 313-24 PMID: 17519928

Cerasa A, Cherubini A, Quattrone A, Gioia MC, Magariello A, Muglia M, Manna I, Assogna F, Caltagirone C, & Spalletta G (2010). Morphological correlates of MAO A VNTR polymorphism: new evidence from cortical thickness measurement. Behavioural brain research, 211 (1), 118-24 PMID: 20303364

Ficks CA, & Waldman ID (2014). Candidate Genes for Aggression and Antisocial Behavior: A Meta-analysis of Association Studies of the 5HTTLPR and MAOA-uVNTR. Behavior genetics PMID: 24902785

Fergusson DM, Boden JM, Horwood LJ, Miller A, & Kennedy MA (2012). Moderating role of the MAOA genotype in antisocial behaviour. The British journal of psychiatry : the journal of mental science, 200 (2), 116-23 PMID: 22297589

Gilad Y, Rosenberg S, Przeworski M, Lancet D, & Skorecki K (2002). Evidence for positive selection and population structure at the human MAO-A gene. Proceedings of the National Academy of Sciences of the United States of America, 99 (2), 862-7 PMID: 11805333

Huang YY, Cate SP, Battistuzzi C, Oquendo MA, Brent D, & Mann JJ (2004). An association between a functional polymorphism in the monoamine oxidase a gene promoter, impulsive traits and early abuse experiences. Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology, 29 (8), 1498-505 PMID: 15150530

Koen L, Kinnear C, Corfield V, Emsley R, Jordaan E, Keyter N, Moolman-Smook J, Stein D, & Niehaus D (2004). Violence in male patients with schizophrenia: risk markers in a South African population Australian and New Zealand Journal of Psychiatry, 38 (4), 254-259 DOI: 10.1111/j.1440-1614.2004.01338.x

Kunugi H, Ishida S, Kato T, Tatsumi M, Sakai T, Hattori M, Hirose T, & Nanko S (1999). A functional polymorphism in the promoter region of monoamine oxidase-A gene and mood disorders. Molecular psychiatry, 4 (4), 393-5 PMID: 10483059

Lei H, Zhang X, Di X, Rao H, Ming Q, Zhang J, Guo X, Jiang Y, Gao Y, Yi J, Zhu X, & Yao S (2014). A Functional Polymorphism of the MAOA Gene Modulates Spontaneous Brain Activity in Pons. BioMed research international, 2014 PMID: 24971323

Ono H, Shirakawa O, Nishiguchi N, Nishimura A, Nushida H, Ueno Y, & Maeda K (2002). No evidence of an association between a functional monoamine oxidase a gene polymorphism and completed suicides. American journal of medical genetics, 114 (3), 340-2 PMID: 11920860

Philibert RA, Gunter TD, Beach SR, Brody GH, & Madan A (2008). MAOA methylation is associated with nicotine and alcohol dependence in women. American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics, 147B (5), 565-70 PMID: 18454435

Pinsonneault JK, Papp AC, & Sadée W (2006). Allelic mRNA expression of X-linked monoamine oxidase a (MAOA) in human brain: dissection of epigenetic and genetic factors. Human molecular genetics, 15 (17), 2636-49 PMID: 16893905

Rosenberg S, Templeton AR, Feigin PD, Lancet D, Beckmann JS, Selig S, Hamer DH, & Skorecki K (2006). The association of DNA sequence variation at the MAOA genetic locus with quantitative behavioural traits in normal males. Human genetics, 120 (4), 447-59 PMID: 16896926

Sabol S, Hu S, & Hamer D (2014). A functional polymorphism in the monoamine oxidase A gene promoter Human Genetics, 103 (3), 273-279 DOI: 10.1007/s004390050816

Widom CS, & Brzustowicz LM (2006). MAOA and the "cycle of violence:" childhood abuse and neglect, MAOA genotype, and risk for violent and antisocial behavior. Biological psychiatry, 60 (7), 684-9 PMID: 16814261

Williams RB, Marchuk DA, Gadde KM, Barefoot JC, Grichnik K, Helms MJ, Kuhn CM, Lewis JG, Schanberg SM, Stafford-Smith M, Suarez EC, Clary GL, Svenson IK, & Siegler IC (2003). Serotonin-related gene polymorphisms and central nervous system serotonin function. Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology, 28 (3), 533-41 PMID: 12629534

Wong CC, Caspi A, Williams B, Craig IW, Houts R, Ambler A, Moffitt TE, & Mill J (2010). A longitudinal study of epigenetic variation in twins. Epigenetics : official journal of the DNA Methylation Society, 5 (6), 516-26 PMID: 20505345

Young SE, Smolen A, Hewitt JK, Haberstick BC, Stallings MC, Corley RP, & Crowley TJ (2006). Interaction between MAO-A genotype and maltreatment in the risk for conduct disorder: failure to confirm in adolescent patients. The American journal of psychiatry, 163 (6), 1019-25 PMID: 16741202

Yu YW, Tsai SJ, Hong CJ, Chen TJ, Chen MC, & Yang CW (2005). Association study of a monoamine oxidase a gene promoter polymorphism with major depressive disorder and antidepressant response. Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology, 30 (9), 1719-23 PMID: 15956990

The Warrior Gene, Back from the Grave

Recently I read a phenomenal book called A Troublesome Inheritance by Nicholas Wade. It has science and sociological “speculation,” but most of the speculation actually just cited the speculation of other writers. I would say the scholarship was relatively good compared to other works of popular science. I assumed that critical appraisal would universally approve, but I would like to report that this was not entirely true. The book was actually panned by a group of people called “anthropologists,” who are almost like real scientists with their own journals and everything. Anthropologists only had one criticism: they wished Wade had scratched every use of the word “race” and written in “population,” which is ironic because a recent survey of anthropologists determined that “racist” was their second most commonly used word after “the.” It is troubling that while anthropologists have taken their courageous stand against racism, they are as of yet blind to the scourge of populationism. I think it was Confucius who said, “We have come so far but have so much farther to go.” I think “further” would have been more grammatically correct, but distance is a metaphor, and “farther” resembles a parallel construction.

Nicholas Wade took up my cause of drawing attention to an allele of monoamine oxidase A, "the warrior gene," that is rare in non-Africans and thought to predispose one to violence. However, an anthropologist named Jennifer Raff succeeded in invading the field of genetics, and she countered our claim by posting a study by Vassos et al.

"MAO-A’s effects (as well as those of any other candidate gene known at this point) appear to be very, very minor (if they even exist at all)…"

Good gravy! You mean to tell me that all that research that I have been following all these years has been debunked by a single study?! Well, I decided that before I take the drastic step of erasing half of my blog, I should actually read the study. Then, I read the other study that was on basically the same subject and published at almost the same time but that came to the opposite conclusion. Then, I magnanimously offered up my very own blog as a moderated forum for a discussion with the correspondence authors of each study. They did not respond to my request, so I shall do what I usually do and post the empty-chair interview questions.

Questions posed to Dr. Evangelos Vassos:

1. Your study mentions “increased aggressive behavior in … MAOA knockout mice.” Why does your study make no mention of the equivalent MAOA knockout condition, Brunner syndrome, in humans?
2. Your study concludes that “it is unlikely that few candidate genes explain a complex behavior like aggression” and “aggression and even violence are complex behaviors.” Does the existence of Brunner syndrome contradict your conclusions?
3. For MAOA in females, your study included Guo et al, “The VNTR 2 repeat in MAOA and delinquent behavior in adolescence and young adulthood: associations and MAOA promoter activity.” Unlike other studies on MAOA, that study obviously defined MAOA-2R as a distinct allele rather than another “low-activity allele” paired with MAOA-3R. How did your study account for this difference? Why did your study not note that Guo et al found a main effect of MAOA-2R, for which no attempt at replication with a different sample has been attempted?
4. Have you heard of the meta-analysis by Ficks and Waldman, “Candidate genes for aggression and antisocial behavior: A meta-analysis of association studies of the 5HTTLPR and MAOA-uVNTR”? Would you care to comment on how that study’s meta-analysis for MAOA differs from your meta-analysis?
5. Unlike Ficks and Waldman, your meta-analysis elected to include studies that used clinical psychiatric patients with mental illnesses and substance abuse problems. In fact, I count that 12 of the 17 studies on MAOA in males used such clinical patients. What is your rationale for this approach?
6. Supplementary Table 1 shows sample sizes for Gerra et al and Koller et al that suggest that your study did not consider the effects of MAOA on the aggression or hostility of each study’s control sample. It appears that you excluded the control samples and only considered the effects on the alcoholic or heroin-dependent subjects. Why is that?
7. Your meta-analysis found studies partly by scanning “reference lists of all included studies,” but these reference lists included multiple studies that Ficks and Waldman included but not your meta-analysis, specifically Manuck et al, Jacob et al, Beitchman et al, and Kim-Cohen et al. Manuck et al was listed in 4 of the 17 studies for MAOA in males. Ficks and Waldman were able to include 31 MAOA studies despite excluding studies of other psychiatric disorders, and neither meta-analysis included Williams et al (2003), Rosenberg et al (2006), Nilsson et al (2007), or Kuepper et al (2013). Why were these studies not included?
8. Like other negative studies of MAOA, your meta-analysis criticizes the entire project of candidate-gene behavioral genetics, saying “Our study provides evidence that the candidate gene approach has not succeeded in identifying genes associated with these outcomes. This is consistent with recent observations in the field that candidate gene studies of human characteristics and complex diseases at large have failed to produce consistent and clinically useful findings.” Ficks and Waldman included more studies for MAOA and found a modest positive effect, consistent with other lines of evidence (MAOA knockout mice, Brunner syndrome, gene-environment interaction, brain imaging, etc.). Should their meta-analysis have made just as strong of a judgment in favor of the usefulness of a candidate-gene approach to studying behavioral genetics? Why do only negative studies reflect upon this issue?
9. Would you be willing to have your responses appear unedited on my personal blog, The Unsilenced Science?

Questions posed to Courtney Ficks:

1. For MAOA, your study included Guo et al, “The VNTR 2 repeat in MAOA and delinquent behavior in adolescence and young adulthood: associations and MAOA promoter activity.” Unlike other studies on MAOA, that study obviously defined MAOA-2R as a distinct allele rather than another “low-activity allele” paired with MAOA-3R. How did your study account for this difference? Why did your study not note that Guo et al found a main effect of MAOA-2R, for which no attempt at replication with a different sample has been attempted?
2. Have you heard of the meta-analysis by Vassos, Collier, and Fazel, “Systematic meta-analyses and field synopsis of genetic association studies of violence and aggression”? Would you care to comment on how that study’s meta-analysis for MAOA differs from your meta-analysis?
3. It appears that their study was published by a journal with a higher impact factor than yours (15 versus 3 in 2012), but they elected to include studies that used clinical psychiatric patients with mental illnesses and substance abuse problems and included fewer studies on MAOA. Why is your study in a journal with a lower impact factor? Is it possible that the outcome of their study was ideologically favored over that of your study?
4. Did you consider including the studies by Williams et al (2003), Rosenberg et al (2006), Nilsson et al (2007), or Kuepper et al (2013)? If so, why were these studies not included?
5. Like other negative studies of MAOA, Vassos et al criticized the entire project of candidate-gene behavioral genetics, saying “Our study provides evidence that the candidate gene approach has not succeeded in identifying genes associated with these outcomes. This is consistent with recent observations in the field that candidate gene studies of human characteristics and complex diseases at large have failed to produce consistent and clinically useful findings.” Your study found a modest positive effect, consistent with other lines of evidence (MAOA knockout mice, Brunner syndrome, gene-environment interaction, brain imaging, etc.). Why didn’t your study conclude with just as strong of a judgment in favor of the usefulness of a candidate-gene approach to studying behavioral genetics? Why do only negative studies reflect upon this issue?
6. Your study claimed that “there is growing evidence that we must be wary of” gene-environment interaction findings. However, Byrd and Manuck published a meta-analysis last year that seemed to show a robust gene-environment interaction for MAOA and childhood maltreatment. Some GxE interaction studies for MAOA have had positive results for fairly common environmental factors, like an IQ less than 85, high cerebral spinal fluid free testosterone, and poverty. Is it possible that the totality of suspected environmental factors are so common that your finding of a modest main effect was actually picking up these effects, even though you didn’t try to isolate them? Should those factors be control variables in this type of research?
7. Would you be willing to have your responses appear unedited on my personal blog, The Unsilenced Science?

I completely empathize with the decisions of Ficks and Vassos to ignore the interview requests. Ficks probably did not want to answer a question that would insult a prestigious journal, like Molecular Psychiatry. Dr. Vassos probably didn’t feel like answering because he must know that his study is complete garbage, and he doesn’t want to talk about it.










Ficks CA, & Waldman ID (2014). Candidate Genes for Aggression and Antisocial Behavior: A Meta-analysis of Association Studies of the 5HTTLPR and MAOA-uVNTR. Behavior genetics PMID: 24902785

Vassos E, Collier DA, & Fazel S (2014). Systematic meta-analyses and field synopsis of genetic association studies of violence and aggression. Molecular psychiatry, 19 (4), 471-7 PMID: 23546171

Parents’ Income Poorly Predicts SAT Score

Abstract
Parents’ annual income lacks statistical significance as a predictor of state SAT scores when additional variables are well controlled. Spearman rank correlation coefficients reveal parents’ income to be a weaker predictor of average SAT scores for each income bracket within each state than parents’ education level as a predictor of average SAT scores for each education level within each state. Multiple linear regression of state SAT scores with covariates for sample size, state participation, year, and each possible combination of ordinal variables for parents’ income, parents’ education, and race shows income to lack statistical significance in 49% of the iterations with greater frequency of insignificance among iterations with higher explained variance. Cohen’s d comparisons of the yearly individual SAT advantage of having educated parents show a fairly consistently increasing positive relationship over time, whereas similar analysis of the yearly individual SAT advantage of having high-income parents shows variability somewhat coinciding with the business cycle.

Read the whole study at Open Differential Psychology.

See below for important excerpts and extra super-awesome graphs.

“Sackett et al (2009) recounted a series of accusations that the SAT merely measures family wealth. The College Board’s announcement of 2016 SAT reforms has stirred anew claims that 'the only persistent statistical result from the SAT is the correlation between high income and high test scores' (Botstein, 2014). Thus, income as an important predictor of SAT scores somewhat fits a view critical of the SAT, which is that financial resources and class privilege unduly enable higher SAT achievement. If the education component of socioeconomic status dominates over the income component, then the relationship between socioeconomic status and scores might instead more accurately reflect a family’s values towards education and a hereditary influence shared between test performance and educability.”

“This study seeks to thoroughly parse the effects of multiple covariates, including family income, parents’ highest education level, and potential confounding variables specific to state or multiple-year comparisons. To do this, full advantage will be taken from all sixteen years of state data.”

Income p-values without race as a covariate (p-values are shown on an inverse logarithmic scale)

Income p-values with race as a covariate (p-values are shown on an inverse logarithmic scale)

“The racial variable was the most consistently significant variable of these three ordinal variables for composite scores and subtests, which speaks to its independence from socioeconomic status. Race also explained much of the SAT advantage that appeared to be attributable to parents’ income prior to the addition of the racial variable in iterations with low income thresholds simultaneous with the education cutoff being graduate degree.”

Critical-reading and mathematics standardized coefficients compared to adjusted R² values, organized by education first, race within each educational category, and income within each racial category

“Parents’ income has a significant association with SAT scores, but parents’ education is consistently stronger, and regression with effective controls for race, education, and other factors, usually suppresses the income variable to insignificance. The income variable achieved significance when the education threshold was high school diploma most likely because so few parents were dropouts that education was no longer effectively controlled, and parents’ income became a proxy variable for parents’ education…. Part of this dominance could result from heritability in test performance corresponding to parents’ educational attainment, given the high heritability estimates from twins studies for high-stakes standardized exams in the UK and the Netherlands (Bartels et al, 2002; Shakeshaft et al, 2013).”

“Figure 1 seems to contradict Dixon- Román et al in finding that the racial variable had its greatest influence at the highest education level and at high income levels.”

“Asian Americans have historically high average mathematics subtest scores but lower verbal/critical-reading average scores than the white majority…. Despite their likely small average verbal disadvantage and small population in many states, this study’s consistent regression results for Asian race match verifiable individual SAT-score phenomena. A study with fewer observations, a much smaller represented sample, or fewer or poorly chosen covariates might not have achieved that level of definition, but, fundamentally, states do not take the SAT; people do.”

Cohen’s d SAT advantage of having parents’ annual income above $60,000

“Family educational advantage seems to evince virtually undeviating growth as a predictor of SAT scores, but financial advantage seems to grow as the economy worsens. Rather than postulate that times of economic difficulty almost immediately make wealthy people smarter, one should focus on the exclusivity of the income category…. The declining relative income advantage on the mathematics subtest compared to the critical-reading subtest also could be related to structural changes to the economy since the decline of the high-technology boom of the 1990’s, which also fits this interpretation of persistence within families.”

For those readers who do not have a heart condition, I recommend the spirited and colorful statistics debate in the open peer-review forum. One may also find there data supplements of state data that required many months of typing out the data from 816 state reports into a database, which makes a fun toy.










nooffensebut (2014). Parents’ Income is a Poor Predictor of SAT Score Open Differential Psychology, 1-19

Ariel Investments. (2010). The Ariel Investments 2010 black investor survey: Saving and investing among higher income African-American and white Americans. Retrieved April 1, 2014 from http://www.arielinvestments.com/landmark-surveys/

Balf, T. (2014). The story behind the SAT overhaul. New York Times. Retrieved March 25, 2014 from http://www.nytimes.com/2014/03/09/magazine/the-story-behind-the-sat-overhaul.html.

Bartels M, Rietveld MJ, Van Baal GC, & Boomsma DI (2002). Heritability of educational achievement in 12-year-olds and the overlap with cognitive ability. Twin research : the official journal of the International Society for Twin Studies, 5 (6), 544-53 PMID: 12573186

Botstein, L. (2014). College president: SAT is part hoax, part fraud. Time. Retrieved March 25, 2014 from http://time.com/15199/college-president-sat-is-part-hoax-and-part-fraud/.

Buchmann, C., Condron, D., & Roscigno, V. (2010). Shadow Education, American Style: Test Preparation, the SAT and College Enrollment Social Forces, 89 (2), 435-461 DOI: 10.1353/sof.2010.0105

Dixon-Román, E.J., Everson, H.T., & McArdle, J.J. (2013). Race, poverty and SAT scores: Modeling the influences of family income on black and white high school students’ SAT performance. Teachers College Record, 115 (4), 1-33

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

Duncan, J., Seitz, R.J., Kolodny, J., Bor, D., Herzog, H., Ahmed, A., Newell, F.N., & Emslie, H. (2000). A Neural Basis for General Intelligence Science, 289 (5478), 457-460 DOI: 10.1126/science.289.5478.457

Everson, H.T., and Millsap, R.E. (2004). Beyond individual differences: Exploring school effects on SAT scores. (RR-2004-3). New York: College Board.

MacCallum RC, Wegener DT, Uchino BN, & Fabrigar LR (1993). The problem of equivalent models in applications of covariance structure analysis. Psychological bulletin, 114 (1), 185-99 PMID: 8346326

Marioni RE, Davies G, Hayward C, Liewald D, Kerr SM, Campbell A, Luciano M, Smith BH, Padmanabhan S, Hocking LJ, Hastie ND, Wright AF, Porteous DJ, Visscher PM, & Deary IJ (2014). Molecular genetic contributions to socioeconomic status and intelligence. Intelligence, 44 (100), 26-32 PMID: 24944428

Neter, J., Wasserman, W., and Kutner, M.H. (1983). Applied Linear Regression Models. Homewood, IL: Richard D. Irwin, Inc.

Prescott, B.T., and Bransberger, P. (2012). Knocking at the College Door: Projections of High School Graduates (eighth edition). Boulder, CO: Western Interstate Commission for Higher Education.

Sackett PR, Kuncel NR, Arneson JJ, Cooper SR, & Waters SD (2009). Does socioeconomic status explain the relationship between admissions tests and post-secondary academic performance? Psychological bulletin, 135 (1), 1-22 PMID: 19210051

Sackett PR, Kuncel NR, Beatty AS, Rigdon JL, Shen W, & Kiger TB (2012). The role of socioeconomic status in SAT-grade relationships and in college admissions decisions. Psychological science, 23 (9), 1000-7 PMID: 22858524

Schmitt N, Keeney J, Oswald FL, Pleskac TJ, Billington AQ, Sinha R, & Zorzie M (2009). Prediction of 4-year college student performance using cognitive and noncognitive predictors and the impact on demographic status of admitted students. The Journal of applied psychology, 94 (6), 1479-97 PMID: 19916657

Shakeshaft NG, Trzaskowski M, McMillan A, Rimfeld K, Krapohl E, Haworth CM, Dale PS, & Plomin R (2013). Strong genetic influence on a UK nationwide test of educational achievement at the end of compulsory education at age 16. PloS one, 8 (12) PMID: 24349000

Trzaskowski M, Harlaar N, Arden R, Krapohl E, Rimfeld K, McMillan A, Dale PS, & Plomin R (2014). Genetic influence on family socioeconomic status and children's intelligence. Intelligence, 42 (100), 83-88 PMID: 24489417

US Census Bureau. (1990). Asians and Pacific Islanders in the United States. Retrieved March 23, 2014 from https://www.census.gov/prod/cen1990/cp3/cp-3-5.pdf.

US Census Bureau. (2013). Asian/Pacific American Heritage Month: May 2013. Retrieved March 24, 2014 from https://www.census.gov/newsroom/releases/pdf/cb13ff-09_asian.pdf.

Dr. Kevin Beaver the Apostle

In the discussion following my previous video about monoamine oxidase A, I noted a new study with lead author Dr. Kevin Beaver. The study closely resembles his previous study of the 2-repeat allele, my analysis of which is well on its way to becoming my most popular blog essay. Once again, a small subset of African-American men from the National Longitudinal Study of Adolescent Health served as a comparison group for the eight African-American men, who possess this rare allele of the upstream promoter for MAOA and who had complete phenotype data. This time, the specific behaviors of shooting or stabbing replaced measures of psychopathic personality, arrests, and incarceration as the outcomes of interest. Despite the small sample size, the results were significant (odds ratio = 12.89, p < 0.05) because those with the allele had a fifty-percent chance of shooting or stabbing someone. For African-American men without this allele, the risk was only seven percent. Those with MAOA-2R were also more likely to have victimized multiple people based on their greater likelihood of admitting stabbing or shooting during multiple “waves” of study follow-up.

I still have concerns about population substructure. In a mixed population like African Americans, alleles associated with African ancestry like MAOA-2R might correlate with many other African alleles, and because MAOA-2R is so much more common in African Americans than whites and Asians, scientists have studied no other sample and no other race for behavioral phenotypes specifically for this allele. However, Guo et al previously demonstrated the functional differences between MAOA-2R and the other alleles in vitro.

This study bares the imprint of my influence. I previously sent Beaver a list of questions that reflected beliefs of mine about the research on this gene. Here was one such question:

In your studies of MAOA, you used the convention of including the 2-repeat and 3-repeat alleles in the category MAOA-L, but Guo et al “The integration of genetic propensities into social-control models of delinquency and violence among male youths” and Guo et al “The VNTR 2 repeat in MAOA and delinquent behavior in adolescence and young adulthood: associations and MAOA promoter activity” found that the 2-repeat allele had twice as much effect on violent delinquency as either the 3-repeat or 4-repeat alleles and that the 2-repeat allele had more effect than the dopamine genes DAT1 and DRD2. How do you justify following the MAOA-L convention rather than studying the 2-repeat allele separately?

In this study, Beaver et al make the following similar remark:

[A]lmost all of the prior research examining the effects of MAOA on antisocial behaviors has pooled the 2-repeat allele together with the 3-repeat allele. As the results of this study indicate, however, this approach may be misguided as the most powerful effects may be found within the 2-repeat allele and combining the 2-repeat allele with the 3-repeat allele may attenuate the main effects of MAOA.

I raised another concern in a separate question:

A disproportionate number of studies on MAOA and antisocial personality disorder were negative (Saito et al, Koller et al, Parsian et al, Lu et al, and Prichard et al). Why should antisocial personality disorder be a focus of genetic research? Should not the aggression or impulsive criteria of antisocial personality disorder be considered separately in genetic studies?

Beaver et al echo my concern:

Using an additive scale of antisocial behaviors may mask important heterogeneity that exists between the individual behaviors and MAOA genotype such that MAOA may be related to certain types of antisocial behaviors, but not others. As a result, to further unpack the nexus between MAOA genotype and serious violence, the current study examines only extreme violence as measured by shooting and stabbing behaviors.

Neuroskeptic, who has been a reader of the Unsilenced Science, responded to the study by saying, “Hmmm,” to which neurogeneticist Dr. Kevin Mitchell responded, “Grrrrr,” undoubtedly while pounding his chest. University of North Carolina geneticist Dr. Patrick Sullivan wrote, “I would have rejected wo review. Studies like these have not taught us much.” By “studies like these,” I assume he means the hundreds of corroborating studies pertaining to the MAOA gene, its enzyme, and its metabolites, but dismissive flippancy from genetics professors is the sign of the times. Mitchell passed along my recent video to his fellow GWAS Jihadists “based on the title,” which is clearly a Christmas miracle. I, for one, generally do not consider candidate-gene studies with large effect sizes to be evidence of no effect at all “by historical analogy.” In the past, interesting studies would lead to attempts to replicate the finding. Often some attempts would support, others would not, and a meta-analysis of all pertinent results would provide the final word. Now, some attempts support, and every attempt that does not support the original finding is evidence of a broader dysfunction in medicine or science, which are to blame for the media’s hype. Only a select few GWAS Jihadists have the moral courage to dismiss every positive finding with a self-righteous fist pound on the lectern. Nevertheless, Vimaleswaran et al determined that candidate-gene studies, which follow hypotheses about specific genes, show “evidence for enrichment” when compared to genome-wide association studies (for obesity) such that “the candidate gene approach retains some value.” Tielbeek et al attempted to test MAOA in a GWAS that only examined single nucleotide polymorphisms and found no effects for antisocial behavior. Of course, such a study could not directly examine the VNTR promoters that have drawn so much interest to this gene.

Eight is a small number of cases, but it is approximately the same as the number of cases of Brunner syndrome when that diagnosis was established in 1993, a diagnosis that only recently came into use for two additional families. When I first confronted the GWAS Jihadists, I asked them if their disbelief in the gene-environment interaction for MAOA-3R extended to a disbelief in Brunner syndrome. They defensively denied reaching that conclusion. The lesson is obvious: in order to establish the effect of MAOA-2R on violence as a trustworthy scientific finding, this allele’s effect must have an eponym. But which scientist should the disease immortalize? Will it be Guo syndrome or Beaver syndrome? (How about nooffensebut syndrome?) We could follow the example of entomologists, Quentin Wheeler and Kelly Miller, and name this after the greatest president of my lifetime: George W. Bush syndrome. However, as any graduate of medical school can attest, eponyms are evil. The study of genetics as it pertains to social sciences might gain the respectability of physics if it follows physics naming conventions. Just as flavors of quarks have creative names like strange and charm, the disease with symptoms of shooting people and stabbing people caused by the allele MAOA-2R should be called sunshine syndrome.










Beaver KM, Barnes JC, & Boutwell BB (2013). The 2-Repeat Allele of the MAOA Gene Confers an Increased Risk for Shooting and Stabbing Behaviors. The Psychiatric quarterly PMID: 24326626

Piton A, Redin C, & Mandel JL (2013). XLID-causing mutations and associated genes challenged in light of data from large-scale human exome sequencing. American journal of human genetics, 93 (2), 368-83 PMID: 23871722

Tielbeek JJ, Medland SE, Benyamin B, Byrne EM, Heath AC, Madden PA, Martin NG, Wray NR, & Verweij KJ (2012). Unraveling the genetic etiology of adult antisocial behavior: a genome-wide association study. PloS one, 7 (10) PMID: 23077488

Vimaleswaran KS, Tachmazidou I, Zhao JH, Hirschhorn JN, Dudbridge F, & Loos RJ (2012). Candidate genes for obesity-susceptibility show enriched association within a large genome-wide association study for BMI. Human molecular genetics, 21 (20), 4537-42 PMID: 22791748

The Stupid Stupidity Surrounding the Warrior Gene, MAOA, is Stupid

Byrd AL, & Manuck SB (2013). MAOA, Childhood Maltreatment, and Antisocial Behavior: Meta-analysis of a Gene-Environment Interaction. Biological psychiatry PMID: 23786983

Why YouTube Sucks Episode II – The Phantom Menace

I must apologize for taking a break from expanding the MAOA bibliography to interject myself into a YouTube debate about heredity.

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.









Belsky DW, Moffitt TE, Houts R, Bennett GG, Biddle AK, Blumenthal JA, Evans JP, Harrington H, Sugden K, Williams B, Poulton R, & Caspi A (2012). Polygenic risk, rapid childhood growth, and the development of obesity: evidence from a 4-decade longitudinal study. Archives of pediatrics & adolescent medicine, 166 (6), 515-21 PMID: 22665028

Flegal KM, Carroll MD, Kit BK, & Ogden CL (2012). Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999-2010. JAMA : the journal of the American Medical Association, 307 (5), 491-7 PMID: 22253363

Gordon-Larsen P, Adair LS, Meigs JB, Mayer-Davis E, Herring A, Yan S, Zhang B, Shufa D, & Popkin BM (2012). Discordant Risk: Overweight and cardiometabolic risk in Chinese adults Gordon-Larsen: Overweight and Cardiometabolic Risk in China. Obesity (Silver Spring, Md.) PMID: 22714089

Granka, JM, Henn, BM, Gignoux, CR, Kidd, JM, Bustamante, CD, & Feldman, MW (2012). Limited Evidence for Classic Selective Sweeps in African Populations Genetics DOI: 10.1534/genetics.112.144071

Heid IM, Jackson AU, Randall JC, Winkler TW, Qi L, Steinthorsdottir V, Thorleifsson G, Zillikens MC, Speliotes EK, Mägi R, Workalemahu T, White CC, Bouatia-Naji N, Harris TB, Berndt SI, Ingelsson E, Willer CJ, Weedon MN, Luan J, Vedantam S, Esko T, Kilpeläinen TO, Kutalik Z, Li S, Monda KL, Dixon AL, Holmes CC, Kaplan LM, Liang L, Min JL, Moffatt MF, Molony C, Nicholson G, Schadt EE, Zondervan KT, Feitosa MF, Ferreira T, Lango Allen H, Weyant RJ, Wheeler E, Wood AR, MAGIC, Estrada K, Goddard ME, Lettre G, Mangino M, Nyholt DR, Purcell S, Smith AV, Visscher PM, Yang J, McCarroll SA, Nemesh J, Voight BF, Absher D, Amin N, Aspelund T, Coin L, Glazer NL, Hayward C, Heard-Costa NL, Hottenga JJ, Johansson A, Johnson T, Kaakinen M, Kapur K, Ketkar S, Knowles JW, Kraft P, Kraja AT, Lamina C, Leitzmann MF, McKnight B, Morris AP, Ong KK, Perry JR, Peters MJ, Polasek O, Prokopenko I, Rayner NW, Ripatti S, Rivadeneira F, Robertson NR, Sanna S, Sovio U, Surakka I, Teumer A, van Wingerden S, Vitart V, Zhao JH, Cavalcanti-Proença C, Chines PS, Fisher E, Kulzer JR, Lecoeur C, Narisu N, Sandholt C, Scott LJ, Silander K, Stark K, Tammesoo ML, Teslovich TM, Timpson NJ, Watanabe RM, Welch R, Chasman DI, Cooper MN, Jansson JO, Kettunen J, Lawrence RW, Pellikka N, Perola M, Vandenput L, Alavere H, Almgren P, Atwood LD, Bennett AJ, Biffar R, Bonnycastle LL, Bornstein SR, Buchanan TA, Campbell H, Day IN, Dei M, Dörr M, Elliott P, Erdos MR, Eriksson JG, Freimer NB, Fu M, Gaget S, Geus EJ, Gjesing AP, Grallert H, Grässler J, Groves CJ, Guiducci C, Hartikainen AL, Hassanali N, Havulinna AS, Herzig KH, Hicks AA, Hui J, Igl W, Jousilahti P, Jula A, Kajantie E, Kinnunen L, Kolcic I, Koskinen S, Kovacs P, Kroemer HK, Krzelj V, Kuusisto J, Kvaloy K, Laitinen J, Lantieri O, Lathrop GM, Lokki ML, Luben RN, Ludwig B, McArdle WL, McCarthy A, Morken MA, Nelis M, Neville MJ, Paré G, Parker AN, Peden JF, Pichler I, Pietiläinen KH, Platou CG, Pouta A, Ridderstråle M, Samani NJ, Saramies J, Sinisalo J, Smit JH, Strawbridge RJ, Stringham HM, Swift AJ, Teder-Laving M, Thomson B, Usala G, van Meurs JB, van Ommen GJ, Vatin V, Volpato CB, Wallaschofski H, Walters GB, Widen E, Wild SH, Willemsen G, Witte DR, Zgaga L, Zitting P, Beilby JP, James AL, Kähönen M, Lehtimäki T, Nieminen MS, Ohlsson C, Palmer LJ, Raitakari O, Ridker PM, Stumvoll M, Tönjes A, Viikari J, Balkau B, Ben-Shlomo Y, Bergman RN, Boeing H, Smith GD, Ebrahim S, Froguel P, Hansen T, Hengstenberg C, Hveem K, Isomaa B, Jørgensen T, Karpe F, Khaw KT, Laakso M, Lawlor DA, Marre M, Meitinger T, Metspalu A, Midthjell K, Pedersen O, Salomaa V, Schwarz PE, Tuomi T, Tuomilehto J, Valle TT, Wareham NJ, Arnold AM, Beckmann JS, Bergmann S, Boerwinkle E, Boomsma DI, Caulfield MJ, Collins FS, Eiriksdottir G, Gudnason V, Gyllensten U, Hamsten A, Hattersley AT, Hofman A, Hu FB, Illig T, Iribarren C, Jarvelin MR, Kao WH, Kaprio J, Launer LJ, Munroe PB, Oostra B, Penninx BW, Pramstaller PP, Psaty BM, Quertermous T, Rissanen A, Rudan I, Shuldiner AR, Soranzo N, Spector TD, Syvanen AC, Uda M, Uitterlinden A, Völzke H, Vollenweider P, Wilson JF, Witteman JC, Wright AF, Abecasis GR, Boehnke M, Borecki IB, Deloukas P, Frayling TM, Groop LC, Haritunians T, Hunter DJ, Kaplan RC, North KE, O'Connell JR, Peltonen L, Schlessinger D, Strachan DP, Hirschhorn JN, Assimes TL, Wichmann HE, Thorsteinsdottir U, van Duijn CM, Stefansson K, Cupples LA, Loos RJ, Barroso I, McCarthy MI, Fox CS, Mohlke KL, & Lindgren CM (2010). Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nature genetics, 42 (11), 949-60 PMID: 20935629

Hsu FC, Lenchik L, Nicklas BJ, Lohman K, Register TC, Mychaleckyj J, Langefeld CD, Freedman BI, Bowden DW, & Carr JJ (2005). Heritability of body composition measured by DXA in the diabetes heart study. Obesity research, 13 (2), 312-9 PMID: 15800289

Kilpeläinen TO, Zillikens MC, Stančákova A, Finucane FM, Ried JS, Langenberg C, Zhang W, Beckmann JS, Luan J, Vandenput L, Styrkarsdottir U, Zhou Y, Smith AV, Zhao JH, Amin N, Vedantam S, Shin SY, Haritunians T, Fu M, Feitosa MF, Kumari M, Halldorsson BV, Tikkanen E, Mangino M, Hayward C, Song C, Arnold AM, Aulchenko YS, Oostra BA, Campbell H, Cupples LA, Davis KE, Döring A, Eiriksdottir G, Estrada K, Fernández-Real JM, Garcia M, Gieger C, Glazer NL, Guiducci C, Hofman A, Humphries SE, Isomaa B, Jacobs LC, Jula A, Karasik D, Karlsson MK, Khaw KT, Kim LJ, Kivimäki M, Klopp N, Kühnel B, Kuusisto J, Liu Y, Ljunggren O, Lorentzon M, Luben RN, McKnight B, Mellström D, Mitchell BD, Mooser V, Moreno JM, Männistö S, O'Connell JR, Pascoe L, Peltonen L, Peral B, Perola M, Psaty BM, Salomaa V, Savage DB, Semple RK, Skaric-Juric T, Sigurdsson G, Song KS, Spector TD, Syvänen AC, Talmud PJ, Thorleifsson G, Thorsteinsdottir U, Uitterlinden AG, van Duijn CM, Vidal-Puig A, Wild SH, Wright AF, Clegg DJ, Schadt E, Wilson JF, Rudan I, Ripatti S, Borecki IB, Shuldiner AR, Ingelsson E, Jansson JO, Kaplan RC, Gudnason V, Harris TB, Groop L, Kiel DP, Rivadeneira F, Walker M, Barroso I, Vollenweider P, Waeber G, Chambers JC, Kooner JS, Soranzo N, Hirschhorn JN, Stefansson K, Wichmann HE, Ohlsson C, O'Rahilly S, Wareham NJ, Speliotes EK, Fox CS, Laakso M, & Loos RJ (2011). Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile. Nature genetics, 43 (8), 753-60 PMID: 21706003

Lohmueller KE, Indap AR, Schmidt S, Boyko AR, Hernandez RD, Hubisz MJ, Sninsky JJ, White TJ, Sunyaev SR, Nielsen R, Clark AG, & Bustamante CD (2008). Proportionally more deleterious genetic variation in European than in African populations. Nature, 451 (7181), 994-7 PMID: 18288194

MacArthur DG, Balasubramanian S, Frankish A, Huang N, Morris J, Walter K, Jostins L, Habegger L, Pickrell JK, Montgomery SB, Albers CA, Zhang ZD, Conrad DF, Lunter G, Zheng H, Ayub Q, DePristo MA, Banks E, Hu M, Handsaker RE, Rosenfeld JA, Fromer M, Jin M, Mu XJ, Khurana E, Ye K, Kay M, Saunders GI, Suner MM, Hunt T, Barnes IH, Amid C, Carvalho-Silva DR, Bignell AH, Snow C, Yngvadottir B, Bumpstead S, Cooper DN, Xue Y, Romero IG, 1000 Genomes Project Consortium, Wang J, Li Y, Gibbs RA, McCarroll SA, Dermitzakis ET, Pritchard JK, Barrett JC, Harrow J, Hurles ME, Gerstein MB, & Tyler-Smith C (2012). A systematic survey of loss-of-function variants in human protein-coding genes. Science (New York, N.Y.), 335 (6070), 823-8 PMID: 22344438

Meyre D, Delplanque J, Chèvre JC, Lecoeur C, Lobbens S, Gallina S, Durand E, Vatin V, Degraeve F, Proença C, Gaget S, Körner A, Kovacs P, Kiess W, Tichet J, Marre M, Hartikainen AL, Horber F, Potoczna N, Hercberg S, Levy-Marchal C, Pattou F, Heude B, Tauber M, McCarthy MI, Blakemore AI, Montpetit A, Polychronakos C, Weill J, Coin LJ, Asher J, Elliott P, Järvelin MR, Visvikis-Siest S, Balkau B, Sladek R, Balding D, Walley A, Dina C, & Froguel P (2009). Genome-wide association study for early-onset and morbid adult obesity identifies three new risk loci in European populations. Nature genetics, 41 (2), 157-9 PMID: 19151714

Scherag A, Dina C, Hinney A, Vatin V, Scherag S, Vogel CI, Müller TD, Grallert H, Wichmann HE, Balkau B, Heude B, Jarvelin MR, Hartikainen AL, Levy-Marchal C, Weill J, Delplanque J, Körner A, Kiess W, Kovacs P, Rayner NW, Prokopenko I, McCarthy MI, Schäfer H, Jarick I, Boeing H, Fisher E, Reinehr T, Heinrich J, Rzehak P, Berdel D, Borte M, Biebermann H, Krude H, Rosskopf D, Rimmbach C, Rief W, Fromme T, Klingenspor M, Schürmann A, Schulz N, Nöthen MM, Mühleisen TW, Erbel R, Jöckel KH, Moebus S, Boes T, Illig T, Froguel P, Hebebrand J, & Meyre D (2010). Two new Loci for body-weight regulation identified in a joint analysis of genome-wide association studies for early-onset extreme obesity in French and german study groups. PLoS genetics, 6 (4) PMID: 20421936

Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, Lango Allen H, Lindgren CM, Luan J, Mägi R, Randall JC, Vedantam S, Winkler TW, Qi L, Workalemahu T, Heid IM, Steinthorsdottir V, Stringham HM, Weedon MN, Wheeler E, Wood AR, Ferreira T, Weyant RJ, Segrè AV, Estrada K, Liang L, Nemesh J, Park JH, Gustafsson S, Kilpeläinen TO, Yang J, Bouatia-Naji N, Esko T, Feitosa MF, Kutalik Z, Mangino M, Raychaudhuri S, Scherag A, Smith AV, Welch R, Zhao JH, Aben KK, Absher DM, Amin N, Dixon AL, Fisher E, Glazer NL, Goddard ME, Heard-Costa NL, Hoesel V, Hottenga JJ, Johansson A, Johnson T, Ketkar S, Lamina C, Li S, Moffatt MF, Myers RH, Narisu N, Perry JR, Peters MJ, Preuss M, Ripatti S, Rivadeneira F, Sandholt C, Scott LJ, Timpson NJ, Tyrer JP, van Wingerden S, Watanabe RM, White CC, Wiklund F, Barlassina C, Chasman DI, Cooper MN, Jansson JO, Lawrence RW, Pellikka N, Prokopenko I, Shi J, Thiering E, Alavere H, Alibrandi MT, Almgren P, Arnold AM, Aspelund T, Atwood LD, Balkau B, Balmforth AJ, Bennett AJ, Ben-Shlomo Y, Bergman RN, Bergmann S, Biebermann H, Blakemore AI, Boes T, Bonnycastle LL, Bornstein SR, Brown MJ, Buchanan TA, Busonero F, Campbell H, Cappuccio FP, Cavalcanti-Proença C, Chen YD, Chen CM, Chines PS, Clarke R, Coin L, Connell J, Day IN, den Heijer M, Duan J, Ebrahim S, Elliott P, Elosua R, Eiriksdottir G, Erdos MR, Eriksson JG, Facheris MF, Felix SB, Fischer-Posovszky P, Folsom AR, Friedrich N, Freimer NB, Fu M, Gaget S, Gejman PV, Geus EJ, Gieger C, Gjesing AP, Goel A, Goyette P, Grallert H, Grässler J, Greenawalt DM, Groves CJ, Gudnason V, Guiducci C, Hartikainen AL, Hassanali N, Hall AS, Havulinna AS, Hayward C, Heath AC, Hengstenberg C, Hicks AA, Hinney A, Hofman A, Homuth G, Hui J, Igl W, Iribarren C, Isomaa B, Jacobs KB, Jarick I, Jewell E, John U, Jørgensen T, Jousilahti P, Jula A, Kaakinen M, Kajantie E, Kaplan LM, Kathiresan S, Kettunen J, Kinnunen L, Knowles JW, Kolcic I, König IR, Koskinen S, Kovacs P, Kuusisto J, Kraft P, Kvaløy K, Laitinen J, Lantieri O, Lanzani C, Launer LJ, Lecoeur C, Lehtimäki T, Lettre G, Liu J, Lokki ML, Lorentzon M, Luben RN, Ludwig B, MAGIC, Manunta P, Marek D, Marre M, Martin NG, McArdle WL, McCarthy A, McKnight B, Meitinger T, Melander O, Meyre D, Midthjell K, Montgomery GW, Morken MA, Morris AP, Mulic R, Ngwa JS, Nelis M, Neville MJ, Nyholt DR, O'Donnell CJ, O'Rahilly S, Ong KK, Oostra B, Paré G, Parker AN, Perola M, Pichler I, Pietiläinen KH, Platou CG, Polasek O, Pouta A, Rafelt S, Raitakari O, Rayner NW, Ridderstråle M, Rief W, Ruokonen A, Robertson NR, Rzehak P, Salomaa V, Sanders AR, Sandhu MS, Sanna S, Saramies J, Savolainen MJ, Scherag S, Schipf S, Schreiber S, Schunkert H, Silander K, Sinisalo J, Siscovick DS, Smit JH, Soranzo N, Sovio U, Stephens J, Surakka I, Swift AJ, Tammesoo ML, Tardif JC, Teder-Laving M, Teslovich TM, Thompson JR, Thomson B, Tönjes A, Tuomi T, van Meurs JB, van Ommen GJ, Vatin V, Viikari J, Visvikis-Siest S, Vitart V, Vogel CI, Voight BF, Waite LL, Wallaschofski H, Walters GB, Widen E, Wiegand S, Wild SH, Willemsen G, Witte DR, Witteman JC, Xu J, Zhang Q, Zgaga L, Ziegler A, Zitting P, Beilby JP, Farooqi IS, Hebebrand J, Huikuri HV, James AL, Kähönen M, Levinson DF, Macciardi F, Nieminen MS, Ohlsson C, Palmer LJ, Ridker PM, Stumvoll M, Beckmann JS, Boeing H, Boerwinkle E, Boomsma DI, Caulfield MJ, Chanock SJ, Collins FS, Cupples LA, Smith GD, Erdmann J, Froguel P, Grönberg H, Gyllensten U, Hall P, Hansen T, Harris TB, Hattersley AT, Hayes RB, Heinrich J, Hu FB, Hveem K, Illig T, Jarvelin MR, Kaprio J, Karpe F, Khaw KT, Kiemeney LA, Krude H, Laakso M, Lawlor DA, Metspalu A, Munroe PB, Ouwehand WH, Pedersen O, Penninx BW, Peters A, Pramstaller PP, Quertermous T, Reinehr T, Rissanen A, Rudan I, Samani NJ, Schwarz PE, Shuldiner AR, Spector TD, Tuomilehto J, Uda M, Uitterlinden A, Valle TT, Wabitsch M, Waeber G, Wareham NJ, Watkins H, Procardis Consortium, Wilson JF, Wright AF, Zillikens MC, Chatterjee N, McCarroll SA, Purcell S, Schadt EE, Visscher PM, Assimes TL, Borecki IB, Deloukas P, Fox CS, Groop LC, Haritunians T, Hunter DJ, Kaplan RC, Mohlke KL, O'Connell JR, Peltonen L, Schlessinger D, Strachan DP, van Duijn CM, Wichmann HE, Frayling TM, Thorsteinsdottir U, Abecasis GR, Barroso I, Boehnke M, Stefansson K, North KE, McCarthy MI, Hirschhorn JN, Ingelsson E, & Loos RJ (2010). Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nature genetics, 42 (11), 937-48 PMID: 20935630

Tennessen JA, Bigham AW, O'Connor TD, Fu W, Kenny EE, Gravel S, McGee S, Do R, Liu X, Jun G, Kang HM, Jordan D, Leal SM, Gabriel S, Rieder MJ, Abecasis G, Altshuler D, Nickerson DA, Boerwinkle E, Sunyaev S, Bustamante CD, Bamshad MJ, Akey JM, Broad GO, Seattle GO, & NHLBI Exome Sequencing Project (2012). Evolution and functional impact of rare coding variation from deep sequencing of human exomes. Science (New York, N.Y.), 337 (6090), 64-9 PMID: 22604720

Zuk O, Hechter E, Sunyaev SR, & Lander ES (2012). The mystery of missing heritability: Genetic interactions create phantom heritability. Proceedings of the National Academy of Sciences of the United States of America, 109 (4), 1193-8 PMID: 22223662