Genetic and environmental determinants of IQ in black, white, and Hispanic Americans: a meta-analysis and a reanalysis (2023)

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Posted: September 15, 2014

Juan Fürst [1]


Summary:The authors performed a meta-analysis of the interactions between components of behavioral genetic variation (ACE) and race/ethnicity for cognitive ability. The differences between the variance components for black and white Americans were small, despite the large mean differences in test scores. Larger differences were found between Hispanics and non-Hispanic whites, although the results were based on only two studies. A new biometric analysis of the CNLSY survey was then performed and new meta-analytical results provided. The results were discussed in light of the bioecological model, which suggests that if subgroup scores are environmentally low, heritabilities will also decrease.

Key words: race, ethnicity, heredity, IQ, environment, ACE model, bioecological model


In behavioral genetics research, IQ variation is generally divided into three components: additive inheritance (h2), shared environment (C2) and non-shared environment (mi2🇧🇷 These are also known as ACE components.h2(Also known asa2) denotes genetic effects that act additively and independently of one another[2].C2refers to environmental influences that serve to make family members more alike whilemi2consists of those non-genetic effects that are not shared among family members but distinguish them from one another; if it is not fixedmi2includes measurement errors.C2mimi2are collectively referred to asenvironment🇧🇷 The basic biometric model assumes that environmental and genetic influences are additive, but that there can also be interactions between them and these can also be estimated (Plomin, et al., 2008).

The relative importance of genetic and environmental causes of IQ differences in white populations is a widely studied topic. The results clearly show that genetic variation is the main cause of individual differences in IQ after early childhood. The home environment has been shown to be a relatively small source of cognitive variation among whites. In general, genetic effects in early childhood explain less than 50% of IQ fluctuations, and the effect of shared environment is strong. As children age, genetic effects become more evident and shared environmental variations decrease. In adultsh2is in the range of 60-80 percent whileC2is small if not zero;mi2, which involves measurement error, explains the rest (McGue, et al., 1993; Tucker-Drob, et al., 2013).

Im Gegensatz dazu ist die Vererbbarkeit des IQ bei Afroamerikanern und Hispanoamerikanern weniger untersucht. Es gibt jedoch eine kleine Anzahl von Studien, die Erblichkeitsvergleiche zwischen weißen, schwarzen und hispanischen Proben ermöglichen. Lassen Sie uns diese Studien überprüfen, um festzustellen, ob die Größe der genetischen und Umweltparameter mit der Rasse/Ethnizität interagiert. Solche Analysen zeigen direkt nur die Quellen der Unterschiede innerhalb der Rassen an, haben aber nichtsdestotrotz wichtige Auswirkungen auf das Verständnis der Ursachen der mittleren Unterschiede zwischen Schwarzen und Weißen und Hispanoamerikanern und Weißen im IQ, die die Forschung durchweg einheitlich zeigt, etwa 1 und 0,7 Standard Abweichungen bzw. (Roth et al., 2001).[3]

It has been hypothesized that groups of individuals raised in cognitively depressed environments have lower heritabilities and higher environments than those raised in favored environments. This hypothesis led to the prediction that, assuming that social class and racial/ethnic IQ differences are primarily environmental, that social class and racial/ethnic groups with lower scores will have lower heritabilities than those with higher scores. 1971; Bronfenbrenner and Ceci, 1994; Guo and Stearns, 2002). There is some evidence that IQ heritability is lower for lower social classes, at least in the US (Turkheimer & Horn, 2014). There were no systematic reviews of racial and ethnic groups, and narrative reports came to opposite conclusions (cf. Jensen, 1998; Scarr, 1981).

Meta-Analysis: Method

We conducted a literature search for articles containing heritability estimates or kinship correlations that would allow calculating heritability estimates for racial and ethnic groups in the United States. ) and Rushton and Jensen (2005). We then performed a Boolean PsycINFO search for articles containing the terms "heritability" and "race/ethnicity/African-American/Black/Latino/Hispanic" and "cognitive/achievement". Our inclusion criteria were as follows: data must allow calculation of ACE estimates for some level of cognitive ability; The data had to allow calculation of ACE estimates for Black, Hispanic, or Asian peoplemiWhite to have a comparison group. When a study suggested that estimates that met the criteria were calculated but not reported, the authors were contacted and subgroup estimates requested. We identified seventeen studies that met at least one of our criteria, in whole or in part. Eight of these were excluded. The reasons for exclusion are shown in Table 1.

Table 1: List of included and excluded samples

Genetic and environmental determinants of IQ in black, white, and Hispanic Americans: a meta-analysis and a reanalysis (1)

Three of the studies, Vandenberg (1970), Osborne and Miele (1969), and Osborne and Gregor (1968), were superfluous with Osborne (1980) and were therefore excluded. A subsample of the study, the 4-year CPP sample used by Loehlin et al. (1975), contained redundant information on the Beaver et al. (2013) and therefore excluded; another by Loehlin et al. (1975) was excluded because it was a measure that was primarily motor development and not cognitive ability. In one study, zygosity was roughly derived from Scarr-Salaptek (1971). Another, Scarr et al. (1993), was based on a very unrepresentative sample of transracial adoptions and was therefore also excluded. Johnson and others. (2007) was excluded because it did not include a white reference group. Rhemtulla and Tucker-Drob (2012) were excluded because they did not report separate ACE estimates for different minority groups (Black, Hispanic, Asian, etc.). This left seven independent samples. Its characteristics (variance decomposition method used, number of kindreds involved, tests used, and mean age of individuals) are presented in Table 2.

Two of the seven samples were multiple estimates. One of them is number 2, where Beaver et al. (2013) decomposed the components of variance for the same 4- and 7-year-old children in the Collaborative Perinatal Project longitudinal survey. The other sample is #6, which includes three studies by different authors reporting four different sets of estimates based on data from the National Longitudinal Study of Adolescent Health (Add Health). For samples #2 and #6, we use mean ACE estimates weighted by the number of kin pairs.

Information on two of the samples came from personal communications. Kevin Beaver (personal communication October 3, 2013; personal communication September 24, 2013) provided ACE results related to Beaver et al. (2014). Sara Hart (personal communication, March 16, 2014) provided calculated twin correlations with respect to (but not in) Hart et al. (2013).

With respect to the calculation of the ACE estimates, according to standard practice, we standardize the values ​​so that the total sum variance is 1.00 and no values ​​of A, C or E are negative.

Table 2: Information on the study/sample

Genetic and environmental determinants of IQ in black, white, and Hispanic Americans: a meta-analysis and a reanalysis (2)

Meta-Analysis: Results

Calculations for each sample can be found in the supplemental file. The variance components for each sample are shown in Table 3 below. Unweighted and weighted means (by the total number of kindreds per sample) are provided.

Table 3: Meta-analytical results

Genetic and environmental determinants of IQ in black, white, and Hispanic Americans: a meta-analysis and a reanalysis (3)

Table 4 shows the black/white and Hispanic/white differences between mean ACE estimates. These differences were calculated by subtracting the black or Hispanic ACE components from the white; therefore, a positive difference indicates that the white variance component was larger. In Table 4, the number of pairs refers to the total number of kindred pairs for non-white groups.

Table 4: ACE x Race/Ethnicity Interactions: Difference Between Means

Genetic and environmental determinants of IQ in black, white, and Hispanic Americans: a meta-analysis and a reanalysis (4)

Table 5 shows the mean differences for the same groups. This is the preferred estimate because the mean of the differences, rather than the mean difference, controls the census-sample-specific effects. Relative to blacks and whites, the weighted racial difference n (by number of pairs of black relatives) inh2reaches 0.01, while the difference ofC2came out at 0.05. For Hispanics and Whites, the weighted racial difference n (by number of Hispanic cognates) inh2reaches 0.20, while the difference inC2came out at -0.24.

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Table 5: ACE × race/ethnic interactions: mean differences

Genetic and environmental determinants of IQ in black, white, and Hispanic Americans: a meta-analysis and a reanalysis (5)

Standardized differences in samples

We determined the size of the mean differences for the six samples. These are shown in Table 6. For samples with multiple tests, we weight standardized differences by the size of the peer sample. There are different methods of doing this, and different ones can produce slightly different results. No mean differences were reported for two of the kinship samples (#2 and #6), so we report the mean differences found across the entire survey sample. Overall, the mean Hispanic/White score difference (d-value) in these samples was similar to that reported nationally (0.7 vs. 0.7), while the mean Black-White d-value was slightly smaller (0.8 versus 1.0). 🇧🇷

Table 6: Differences in the values ​​in kinship samples.

Genetic and environmental determinants of IQ in black, white, and Hispanic Americans: a meta-analysis and a reanalysis (6)

If a bioecological model were correct, one would expect d-values ​​to be positively correlated with differences in heritability, such that with higher d-values, the population with lower values ​​will have lower test heritability. Unfortunately, we cannot determine with certainty whether this is the case from our samples, as they differ in the age of the participants and the type of test, differences that would falsify such an analysis and cannot be controlled due to the lack of available samples . .

Impact of excluding other published studies

Because the above results are based on a limited number of samples, it is important to consider the impact of our inclusion criteria. Of the excluded studies, the Minnesota Transracial Adoption Study by Scarr et al. (1993) and an 8-month twin study described in Loehlin et al. (1975) may have produced some data. The adoption study was not included because it had a wide range of different estimates and because the interracial adoption project made the sample too unrepresentative. However, it is worth noting that Scarr and colleagues concluded that "Black/mixed race children adopted into middle-class white families appear to have the same degree of genetic influences on individual differences in their intellectual achievement as children from the most populations of the USA United States. Joined." United States and Western Europe. Examination of children was excluded because the Bayley scale tests used are primarily measures of motor development. However, this study showed similar components of variance between blacks and whites; heritability estimates in this study were 64% for blacks and 60% for whites, so including the results of these studies would not have significantly changed the results of our meta-analysis on blacks versus whites.

In addition, we found a study by Rhemtulla and Tucker-Drob (2012) using 800 pairs of siblings to investigate whether there was a variance × race/ethnicity interaction component for math tests in the birth cohort of the Longitudinal Early Childhood Study (ECLS-B). The authors found a heritability × SES interaction but no significant heritability × race/ethnicity. (EITHERh2for whites it was 0.37 and theh2for non-whites it was 0.35). Unfortunately, the results were not broken down by specific non-white racial/ethnic groups. Instead, the authors compared whites with non-whites and mixed races; The latter groups were approximately 40% Black, 40% Hispanic, 7.5% Asian, and 2.5% Native American or Pacific Islander and 10% mixed race. Based on information provided in the NCES summary reports for this survey, white and non-white groups differed by approximately 0.5 standard deviations on the math test. The sample was not included because the differences were not broken down by specific racial/ethnic groups; however, if this were the case, the results would likely not have significantly altered our meta-analysis findings regarding blacks and whites and would likely have reduced the differences between Hispanic and whites, since no statistically significant interactions were found between ACE and race/ethnicity. The ACE estimates reported by Rhemtulla (personal communication, August 1, 2014) are presented in Table 7.

Table 7: Standardized and non-standardized results according to Rhemtulla and Tucker-Drob (2012)

Genetic and environmental determinants of IQ in black, white, and Hispanic Americans: a meta-analysis and a reanalysis (7)

As noted above, we excluded the results of one study because it did not include a white reference group. Johnson and others. (2007) reported a sex- and age-adjusted heritability of 0.73 for an abstract reasoning test administered to 476 Hispanics who had family members with Alzheimer's disease. This heritability was lower when scores were adjusted for education, but such an adjustment is inappropriate because educational differences are partly a consequence of IQ differences. This study did not show particularly low heritability for Hispanics.

new analysis

One of the samples in our meta-analysis, Rowe and Cleveland (1996), was based on the first waves of the CNLSY survey. As the authors noted, kinship sample sizes were small, and at this time it was impossible to produce reliable estimates of ACE for Hispanics. We decided to repeat the analysis for all waves using the data already published.

Data and method for CNLSY analysis

CNLSY is a longitudinal study evaluating the children of women participating in the NLSY79 study. It is estimated that approximately 95 percent of the children of NLSY79 women are enrolled in CNLSY. Most of them were born in the 1980s and 1990s. CNLSY participants completed a series of cognitive tests. The tests used are Digit Range Forward (DSF), Digit Range Backward (DSB); the Peabody proficiency tests in mathematics (PIAT-M), reading recognition (PIAT-RR) and reading comprehension (PIAT-RC); and the Peabody Picture Vocabulary Test (PPVT). The tests were conducted between 1986 and 2010, when participants were approximately 3 to 13 years old.

The CNLSY participants differed significantly in year of birth, but their test scores were obtained at a similar age. It could be that the Flynn effect skews the results and increases the scores of younger children. In fact, there is a correlation between age and ability in CNLSY, with those born later tending to do better. In their CNLSY analysis, Ang et al. (2010) found that the age effect is mainly explained by the fact that women with higher IQ tend to have children later than women with lower IQ. Evidence of the Flynn effect was only found for the PIAT-M test. This indicates that the Flynn effect cannot significantly skew our results.

CNLSY uses three race/ethnicity categories: Black, Hispanic (any race), and Non-Hispanic, Non-Black. This means that the sample we refer to as "Whites" includes all non-Hispanic non-Blacks; H. a small number of non-whites, mostly Asians and Native Americans, in addition to the large majority of whites. Each child's race should be the same as his mother's. For behavioral genetic analyses, we used theNlsyLink's R Package🇧🇷 The package makes it easy to decompose test result variations into additive genetic (A), shared environmental (C), and non-shared environmental (E) components. We use structural equation models to fit the ACE models to the family data.

The classic twin design based on MZ (identical) and DZ (fraternal) twins raised together is the workhorse of behavioral genetics. Unfortunately, CNLSY was not designed for twin analysis and there are only a few pairs of twins among the participants. However, since the sample includes practically all the children of thousands of mothers, there is a large number of sibling and half-sibling pairs. Because there are many siblings and other relatives in NLSY79, many of her CNLSY children are cousins ​​with each other, including first cousins ​​and more distant cousins. The NlsyLinks package uses theexpected ratio coefficientsbetween these pairs (0.5 siblings, 0.25 half-siblings, 0.125 first cousins, etc.) to estimate the ACE components.

We use two different approaches to estimate the genetic and environmental components. The first is the sibling model, which is based solely on pairs of full siblings and half siblings. The second is the extended kinship model, which includes everything from full brothers to distant cousins. Peers are not independent in the sense that each person can be a sibling of one or more people and a cousin of one or more people.

ACE components were estimated separately for White, Black, and Hispanic samples. The estimation method is based on the assumption of equal sibling environments, i. H. Full siblings and half siblings should share trait relevant environments equally. If this assumption is violated, the heredity estimate may be overestimated.

Technical limitations include our inability to calculate confidence intervals, although given our large samples this is not a major problem. We provide ACE estimates for all samples and do not perform tests to see if other models (e.g. AE models) would fit better. Since all three variance components for IQ are generally present in the age groups examined, this model assumption appears plausible.

Analysis I: Sibling model, age-adjusted

In our analysis I, only full sibling and half sibling couples who spent at least part of their childhood in the same household are considered. Cognitive abilities show a significant shared environmental influence in children, such that ACE components cannot be reliably estimated when sibling pairs are reared separately. A limitation of our research was that we could not verify whether each couple spent their entire childhood together. It is assumed that the primes used in Analysis II were not generated together.

The following analyzes present the results divided into six age groups: 2-4, 4-6, 6-8, 8-10, 10-12 and 12-14 years. For convenience, we will refer to these groups as 3, 5, 7, 9, 11 and 13 year olds respectively. In our analyses, we used standardized test scores calculated using nationally standardized samples, with the exception of digit range tests, for which raw scores were used. Within each age group, test results were ranked by age. Not all tests were used in all age groups, for example 3-year-olds only took the TVFP. Because of the longitudinal nature of CNLSY, each included child was able to contribute multiple test scores to our analysis. However, not all eligible children were tested in every biennial round of testing. Most of the analyzes in this post-use test evaluate the results described above, but for an alternative approach to using the results see Analyzes III and IV below.

Standardized estimates of ACE for Whites, Blacks, and Hispanics by age group and test for the sibling model are included in the supplemental file.Gramm-Values ​​were also calculated for 11-year-olds, i.e. the mean andGramm-The results of the scores can be compared. For reasons discussed below, the estimates for individual tests appear to be too noisy, and thus too little attention should be paid to racial differences in the ACE components in any given test. Rather, we are interested in differences in mean ACE changes between studies and age groups. This meta-analytic approach reduces the effects of sample variance and other artifacts and should provide reasonably accurate estimates of population effect sizes.

Table 8 shows the weighted mean changes in ACE as a function of forward digit span, backward digit span, PPVT, PIAT math, PIAT reading recognition, and PIAT reading comprehension. The sample sizes for each test/age combination correlate by >0.98 between runs, yielding approximately equal weights for the same tests between runs. Estimates were calculated as the weighted root mean square of the ACE estimates; that is, first the ACE components were transformed into Pearson correlations, their mean values ​​calculated and the results transformed into variances in turn. It should be noted that the samples are not independent as each individual can contribute many test results.

Table 8: Mean results for the siblings of the same age model

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Genetic and environmental determinants of IQ in black, white, and Hispanic Americans: a meta-analysis and a reanalysis (8)

These heritability estimates (0.43–0.50) can be considered typical for the age range of our sample (3–13 years), but the estimates for the common environment (0.04–0.08) are slightly off and less than expected. In these samples and with this method, the non-genetic effects are predominantly of the non-divided variety (0.35-0.41). More relevant for our purposes, heritability appears to be slightly higher in Whites and Hispanics than in Blacks, while the influence of shared environment is slightly higher in Blacks.

Gramm- Results were derived from Digit Span Forward and Backward, PIAT-M, PIAT-RR, PIAT-RC and PPVT test scores at age 11. The results are shown in the supplementary file. The genetic variations were 0.61 for Whites, 0.55 for Blacks, and 0.60 for Hispanics. The fact that theGrammScores are based on multiple tests and should make the results reasonably reliable.

Analysis II: Extended kinship model, age adjustment

Standardized estimates of ACE for Whites, Blacks, and Hispanics by age group and evidence for the extended kinship model are presented in the Supplementary File. The mean ACE changes from this analysis are presented in Table 9.

Table 9: Average results for the extended model with age adjustment

Genetic and environmental determinants of IQ in black, white, and Hispanic Americans: a meta-analysis and a reanalysis (9)

In contrast to sibling analysis, heritability estimates for Hispanics, and especially blacks, are higher than those for whites. the 11 yearsGrammscores showed the same pattern: genetic variations were 0.55 for Whites, 0.86 for Blacks, and 0.88 for Hispanics. These results are also shown in the supplementary file.

Analysis III: Mean scores between rounds, sibling model

There are different approaches to obtain ACE estimates from the large and heterogeneous collection of test results available in the CNLSY data. Above we estimate the ACE components for each test and age group separately and average the obtained variance components. Alternatively, you can average the test scores across all tests, age groups, and test rounds and use the average scores in your ACE analyses. Because each participant typically completed several different tests at different ages, these averages reflect, to some extent, the longitudinal stability of mental ability.

Therefore, we averaged each participant's standard scores across all rounds of testing (1986-2010) for each test (total scores, PIAT-M, PIAT-RR, PIAT-RC, and PPVT). Also, we chargeGrammScores for each individual based on standard scores averaged over the years. Table 10 shows the results of applying the ACE sister model.

Table 10: Round and cross sibling model results

Genetic and environmental determinants of IQ in black, white, and Hispanic Americans: a meta-analysis and a reanalysis (10)

Analysis IV: Round averages, extended relationship model

We repeated analysis III using the extended relatedness test. The results are shown in Table 11.

Table 11: Results of the extended cross round model

Genetic and environmental determinants of IQ in black, white, and Hispanic Americans: a meta-analysis and a reanalysis (11)

As in Analyzes I and II, the extended kinship model suggests that heritability is higher in Blacks and to some extent Hispanics than in Whites, while the sibling model suggests that heritability is lower in Blacks.

The results of the four analyzes are summarized in Table 12.

Table 12. Summary of CNLSY-ACE results.

Genetic and environmental determinants of IQ in black, white, and Hispanic Americans: a meta-analysis and a reanalysis (12)

New discussion on CNLSY analysis

The pattern of different heritabilities and environments across different tests discovered in our research does not necessarily indicate that the determinants of different abilities are significantly variable. There are several weaknesses in the data and methods used that tend to make any individual parameter estimates very noisy. These weaknesses include:

1) Random sampling errors. This is especially true for tests and age groups with small sample sizes.

2) Systematic sampling errors. Some tests were carried out selectively. For example, the PIAT-RC test was only performed if the subject scored high enough on the PIAT-RR.

3) Poor psychometric properties. Some test score distributions were essentially abnormal, and the range property of the scales used may have been impaired more than normal in cognitive testing. Reliability differed between the tests. See Winship (2003) for a discussion of the psychometric shortcomings of the tests used in CNLSY.

4) Non-representative samples. Our analyzes were based on all sibling and cousin pairs in CNLSY. Because the original NLSY79 contained too many samples of, for example, poor people, the children in the CNLSY cannot be considered representative of the US population.

5) Uncertainty related to the behavioral genetic models used. Sibling and extended kinship models produce slightly different parameter estimates.

One might think that little can be gleaned from these results because of these problems, but we would argue that meta-analytical averaging of parameter estimates (or test results, as in Analyzes III and IV) greatly enhances and cancels out the signal in the data. Mistake. I'll go right away. The results do not appear to be heavily skewed towards race/ethnicity, at least for issues 1-4. Individual test estimates can be affected by the above shortcomings, but since we have dozens of different sets of ACE estimates for each group, there is reason to believe that the overall results are largely reliable.

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Unfortunately, it seems clear that either the sibling model or the extended kinship model (or both) is breed biased. Depending on the estimates we choose, heritability is similar, lower, or higher in whites compared to non-whites; the differences are too large and systematic to be due to sampling error. It is very difficult to say which method is the most reliable or how bad the distortion is. We hypothesize that the problem is a lack of peer independence (ie, each individual may be a member of multiple kinship pairs) combined with the fact that the frequency of different kinship types differs between races.

New metaanalytic tools

The results of our CNLSY analysis varied depending on whether a kinship model or an extended kinship model was used and how the effect of age was accounted for (either by attempting to perform a regression or by matching pairs by age). Including some of the model results would have significant implications for our meta-analyses. But even if the results that most closely matched an ACE × race/ethnicity interaction prediction were included, the meta-analytic differences would still not show a large interaction. Possible results, with Rowe and Cleveland's (Sample #5) CNLSY results replacing ours, are shown in Table 13 below.

Table 13. ACE × Race/Ethnicity Interactions: Mean Differences Using CNLSY (Method II, Siblings and Extended Models)

Genetic and environmental determinants of IQ in black, white, and Hispanic Americans: a meta-analysis and a reanalysis (13)

In conclusion, our meta-analysis suggests that there are not major differences between white and black Americans in the relative importance of genetic and environmental determinants of IQ. Regarding Hispanics and whites, the situation is more unclear due to the limited number of studies and small sample size. Presumably, the immigrant generation and language would be confusing here, as first-generation Hispanic scores are likely to drop significantly due to language bias (see Fuerst, 2014).

Regardless, genes explain about half of the IQ variation in the three groups for which data were available, while shared and non-shared environmental effects explain the other half. Because the available samples included mostly children and adolescents, these estimates are in line with expectations from other studies. Due to the limited number of studies, the results cannot be considered absolutely positive and we cannot exclude the possibility that there are minor differences in ACE parameters between breeds.

In what is probably the most well-known study on the interaction between heredity and socioeconomic status (SES), Turkheimer et al. (2003) found that the heritability of IQ in a sample of 7-year-old children was approximately zero at the low end of the SES scale, with shared environmental influences explaining most of the IQ variance. At the high end of the SES scale, the results were more or less the opposite, with strong genetic influences and weak environmental influences. The sample analyzed by Turkheimer and his colleagues was 54% black and 43% white, leading to the interpretation that the study has direct implications for understanding the difference in IQ between blacks and whites. Since inheritance in poor families is zero and blacks are disproportionately poor, genes can have little to do with blacks' lower average IQ. It turns out that Turkheimer's study provides no evidence for this theory. Turkheimer et al. It did not provide separate estimates of the ACE parameters for blacks and whites, and such estimates have not been published for the sample in question (the Collaborative Perinatal Project). However, Kevin Beaver and colleagues calculated these estimates relative to Beaver et al. (2013) who used the same sample. (Beaver et al. included MZ and DZ twins, full siblings, half siblings, and twins with uncertain zygosity, while Turkheimer et al. used only MZ and DZ twins.) The estimates were not included in the published article, but by Castor obtained, they were included in the above meta-analysis and are presented in Table 14 with 95% confidence intervals.

Table 14: Results of the Collaborative Perinatal Project

Genetic and environmental determinants of IQ in black, white, and Hispanic Americans: a meta-analysis and a reanalysis (14)

While the analysis by Turkheimer et al. shows that there ish2-SES for IQ, it is clear that there is noneh2-Racial interaction in the same sample. Heredity is similar and significant across all breeds, as is environmental impact. The same phenomenon as ACE × SES but no ACE × race/ethnicity effect was found in the Rhemtulla et al. analyzed ECLS found. (2012) and those of Rowe et al. (1997) (but see Guo and Stearns, 2002).

The bioecological model and related models propose that "environmental handicaps" reduce genotype-phenotype correlations. From the perspective of such models, our results are curious. Proponents have been very explicit, if not precise, about their predictions. For example, Scarr-Salapatek (1971) noted:

The term "environmental deprivation" denotes the largely undefined set of environmental factors associated with poverty that prevent an organism from achieving its optimal development...

The environmental disadvantage hypothesis posits that lower-class whites and most blacks live in oppressed conditions (19, 20) for IQ development. In summary, the disability hypothesis states that: (i) unspecified environmental factors affect IQ development, thus causing the observed differences in mean IQ levels among children of different social classes and races; (ii) blacks have more biological and social disadvantages than whites; and (iii) if disadvantage were evenly distributed between social classes and racial groups, social class and racial correlations with IQ would disappear. The environmental disadvantage hypothesis predicts that IQ scores within advantaged groups will have higher proportions of genetic variance and lower proportions of environmental variance than IQ scores of disadvantaged groups. Environmental disadvantages are thought to reduce genotype-phenotype correlation (21) in lower class groups and in the Black group in general.

Because genotype-phenotype correlations are comparable across racial and ethnic groups in the US, this implies that groups with lower scores are at a genetic, not an environmental, disadvantage. The matter is difficult to determine because no clear quantitative prediction of the effect of environmental depression on biometric variance components has been offered.

Alternatively, the results may imply that the bioecological model's key prediction is wrong. Perhaps "ecological disadvantage" between groups does not result in significantly reducing heritability within disadvantaged groups. Or, as Scarr-Salapatek (1971) noted as one possibility, it may be that 'environmental disadvantages' apply only to one population or the other and therefore do not affect the relative magnitude of genetic influence. As Scarr-Salapatek (1971) observed:

If all black children in a white-dominated society are disadvantaged to an unknown extent by being raised black, and no white child is so disadvantaged, it is impossible to assess genetic and environmental differences between races. Only if black children could be raised as white and vice versa could the effects of different rearing environments on the genotype distribution of the two breeds be assessed.

This last possibility is, in our opinion, very unlikely when it comes to differences between blacks and whites. Readers are referred to the discussion in Dalliard (2014).

Overall, our meta-analysis (Table 13) shows that IQ heritability is roughly similar among Black, Hispanic, and White Americans. While we cannot rule out subtle differences in these population parameters, we can rule out the notion that the determinants of IQ differences in black populations are radically different from those in white populations.


[1]Corresponding author:

[2]It is likely that some of the genetic variation in IQ is non-additive (Vinkhuyzen et al., 2012), but due to data and model limitations, most studies assume additivity. We follow this convention in our analyses. In practice, the additive genetic component estimates we report may involve some interactive genetic effects; This is probably particularly true for estimates based on Falconer's formula, which tend to be closer to total genetic influence than additive genetic influence (Falconer and Mackay, 1996).

[3]These provide rough characterizations of the magnitudes of differences typically found. A more detailed description would account for variability by age, birth cohort, and immigrant generation; It should be noted that Dickens and Flynn (2006) found evidence of an interaction between age and birth cohort in relation to the White/Black difference, while Fuerst (2014) found evidence of an interaction between immigrant generations in relation to the difference between Hispanic and Black found whites. .


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