Genetic Ancestry and General Cognitive Ability in a Sample of American Youths

Abstract

Black and Hispanic children in the United States have lower mean cognitive test scores than White children. The reasons for this are contested. The test score gap may be caused by socio-cultural factors, but the high heritability of g suggests that genetic variance might play a role. Differences between self-identified race or ethnicity (SIRE) groups could be the product of ancestral genetic differences. This genetic hypothesis predicts that genetic ancestry will predict g within these admixed groups. To investigate this hypothesis, we performed admixture-regression analyses with data from the Adolescent Brain Cognitive Development Cohort. Consistent with predictions from the genetic hypothesis, African and Amerindian ancestry were both found to be negatively associated with g. The association was robust to controls for multiple cultural, socioeconomic, and phenotypic factors. In the models with all controls the effects were as follows: (a) Blacks, African ancestry: b =-0.89, N = 1690; (b) Hispanics, African ancestry: b =-0.58, Amerindian ancestry: b =-0.86, N = 2021), and (c) a largely African-European mixed Other group, African ancestry: b =-1.08, N = 748). These coefficients indicate how many standard deviations g is predicted to change when an individual's African or Amerindian ancestry proportion changes from 0% to 100%. Genetic ancestry statistically explained the self-identified race and ethnicity (SIRE) differences found in the full sample. Lastly, within all samples, the relation between genetic ancestry and g was partially accounted for by cognitive ability and educational polygenic scores (eduPGS). These eduPGS were found to be significantly predictive of g within all SIRE groups, even when controlling for ancestry. The results are supportive of the genetic model.

Full text

MANKIND QUARTERLY 2021 62:1 186-216
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Genetic Ancestry and General Cognitive Ability in a
Sample of American Youths
John G.R. Fuerst*
Cleveland State University, USA
Ulster Institute for Social Research, London, UK
Meng Hu
Independent Researcher, France
Gregory Connor
Maynooth University, Ireland
* Corresponding author: email j122177@hotmail.com
Black and Hispanic children in the United States have lower mean
cognitive test scores than White children. The reasons for this are
contested. The test score gap may be caused by socio-cultural factors,
but the high heritability of g suggests that genetic variance might play a
role. Differences between self-identified race or ethnicity (SIRE) groups
could be the product of ancestral genetic differences. This genetic
hypothesis predicts that genetic ancestry will predict g within these
admixed groups. To investigate this hypothesis, we performed admixture-
regression analyses with data from the Adolescent Brain Cognitive
Development Cohort. Consistent with predictions from the genetic
hypothesis, African and Amerindian ancestry were both found to be
negatively associated with g. The association was robust to controls for
multiple cultural, socioeconomic, and phenotypic factors. In the models
with all controls the effects were as follows: (a) Blacks, African ancestry:
b = -0.89, N = 1690; (b) Hispanics, African ancestry: b = -0.58, Amerindian
ancestry: b = -0.86, N = 2021), and (c) a largely African-European mixed
Other group, African ancestry: b = -1.08, N = 748). These coefficients
indicate how many standard deviations g is predicted to change when an
individual's African or Amerindian ancestry proportion changes from 0%
to 100%. Genetic ancestry statistically explained the self-identified race
and ethnicity (SIRE) differences found in the full sample. Lastly, within all

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187
samples, the relation between genetic ancestry and g was partially
accounted for by cognitive ability and educational polygenic scores
(eduPGS). These eduPGS were found to be significantly predictive of g
within all SIRE groups, even when controlling for ancestry. The results are
supportive of the genetic model.
Keywords: Ancestry, Admixture, education, Intelligence, ABCD cohort
There are substantial differences in mean cognitive test scores between self-
identified racial and ethnic (SIRE) groups such as Whites, Hispanics, Blacks, and
Asians in the United States (Murray, 2021; Roth et al., 2017). These differences
are not attributable to psychometric bias, since cognitive test batteries typically
exhibit measurement invariance across American SIRE groups (Scheiber,
2016a,b; Warne, 2020a). As such, they represent real differences in latent
cognitive ability.
Although cognitive ability researchers disagree as to the causes of these
differences (Rindermann, Becker & Coyle, 2020), a number of explanatory factors
have been proposed. Most of these factors appeal to either cultural differences
between groups or differing social and economic circumstances such as
differences in poverty levels. Many researchers studying cognitive ability attribute
some part of the differences to genetics (Rindermann, Becker & Coyle, 2020).
Analyses of both nationally representative samples and cognitive battery
standardization data (Magnuson & Duncan, 2006; Weiss & Saklofske, 2020)
indicate that socioeconomic status (SES) can statistically explain a substantial
percentage of test score variance across SIRE groups. However, it is not clear to
what extent SES captures predominantly environmental or genetic causes, since
SES is related to both genetic and environmental differences within groups
(Belsky et al., 2018; Krapohl & Plomin, 2016; Rowe, Vesterdal & Rodgers, 1998).
Regardless, the data suggest that even after fully accounting for average SES
differences, there remains unexplained variance in cognitive test scores between
groups.
The genetic hypothesis is that differences in genes inherited from ancestors
play a significant role in causing the SIRE related variation in cognitive test
scores. Twin studies show non-trivial heritability for individual differences in
cognitive ability within ethnic groups (Pesta et al., 2020). SIRE groups differ in
European ancestry-based polygenic scores, and these scores are predictive of
cognitive ability within ethnic groups (Lasker et al., 2019). Taken together, these
results suggest that variation in cognitive ability among SIRE groups may be due
in part to allele frequency differences at trait-associated gene loci.

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Most studies that investigate the source of group differences categorize
individuals by SIRE. However, SIRE reflects both genetic and sociocultural
factors. This makes interpretation of simple SIRE-based results ambiguous.
Admixture regression analyses, by including both SIRE and admixture variables
simultaneously, can quantify the association between genetic ancestry and
phenotype in admixed populations (Halder et al., 2015). These analyses test the
extent to which the genetic differences between SIRE groups are responsible for
observed trait differences.
If continental populations differ polygenically in trait-related phenotypes,
these continental differences are expected to contribute to individual differences
in admixed populations. In aggregate, alleles that vary between ancestral
populations will be associated with phenotype in admixed populations. Individuals
who have a larger proportion of their global ancestry from the ancestral group
with higher average frequencies of trait-enhancing alleles are likely to also have
higher polygenic scores and higher average values in the phenotype. As such,
associations between genetic ancestry and phenotype in admixed populations
would suggest that phenotypic differences between continental populations have
a genetic basis (Halder et al., 2015). This admixture regression methodology can
be extended by including polygenic scores (PGS) to see if PGS mediate the
association between g and ancestry.
In this way, admixture-regression analysis allows researchers to separate the
genetic element of SIRE from its cultural, behavioral, and psychosocial aspects,
which may alternatively be responsible for the observed phenotypic differences.
As Fang et al. (2019, p. 764) note: SIRE “acts as a surrogate to an array of social,
cultural, behavioral, and environmental variables” and so “stratifying on SIRE has
the potential benefits of reducing heterogeneity of these non-genetic variables
and decoupling the correlation between genetic and non-genetic factors.”
These analyses require a substantial degree of admixture in populations, and
are more robust when that admixture has taken place in the course of the last
seven to ten generations (Halder et al., 2015). In the USA, African and Hispanic
Americans meet these requirements. They exhibit a wide range of European,
African, and/or Amerindian ancestry due to admixture over the course of several
generations.
Thus we apply admixture analysis to examine if SIRE differences in general
cognitive ability (g) can be accounted for by genetic variation related to
continental ancestry. We hypothesize that g will be lower in African and Hispanic
American samples relative to European American samples. These differences
will be associated with African and Amerindian genetic ancestry within the SIRE
groups. Additionally, we hypothesize that the association between genetic
ancestry and g will be robust to controls for possible socio-environmental

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confounds and that genetic ancestry will also statistically account for the SIRE
differences found in the full sample. Finally, we hypothesize that current polygenic
scores for cognitive ability and education (eduPGS) will both predict individual
differences within SIRE groups and explain a portion of the effect of ancestry on
g.
Methods
1. Dataset
The Adolescent Brain Cognitive Development Study (ABCD) is a
collaborative longitudinal project involving 21 sites across the USA. ABCD is the
largest, longitudinal study of brain development and child health ever conducted
in the USA. It was created to research the psychological and neurobiological
bases of human development. At baseline, around 11,000 children aged 9-10
years were sampled, mostly from public and private elementary schools. A
probabilistic sampling strategy was used, with the goal of creating a broadly
representative sample of US children in that age range. Children with severe
neurological, psychiatric, or medical conditions were excluded. Children were
also excluded if they were not fluent in English or if their parents were not fluent
in either English or Spanish. Parents provided informed consent. For this study,
we utilized the baseline ABCD 3.01 data. We excluded individuals missing either
cognitive or admixture scores. We also excluded any individual identified as being
either Asian or Pacific Islander in order to focus on groups who were primarily of
African, European, and Amerindian ancestry. This left 10,370 children.
For the admixture-regression analyses, SIRE groups were delineated using
the ABCD race_ethnicity variable. This was a summary variable computed from
18 separate multiple choice questions asking about the child’s race (“What race
do you consider the child to be? Please check all that apply”) and one question
asking if the child is of Hispanic ethnicity (“Do you consider yourself
Hispanic/Latino/Latina?”). Children were classified into one of five mutually
exclusive groups: non-Hispanic White (White), non-Hispanic Black (Black),
Hispanic of any race (Hispanic), non-Hispanic Asian (Asian) or any other (Other).
The Other category included any non-Hispanic children who were reported to be
two or more racial groups. Because we dropped the Asian category, we were left
with four mutually exclusive SIRE groups.
2. Variables for admixture regression analyses
The following variables were used for the admixture regression analyses:

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1. g scores
ABCD baseline data contain the following cognitive subtests, the first seven
of which are from the NIH Toolbox® cognitive battery: Picture Vocabulary,
Flanker, List Sorting, Card Sorting, Pattern Comparison, Picture Sequence
Memory, Oral Reading Recognition, Wechsler Intelligence Scale for Children’s
Matrix Reasoning, The Little Man Test (efficiency score), The Rey Auditory Verbal
Learning Test (RAVLT), immediate recall, and RAVLT delayed recall. For details
about these measures, see Thompson et al. (2019).
We conducted multi-group confirmatory factor analysis (MGCFA) on these
subtests, as detailed in Supplementary File 1. Briefly, we first checked whether
outliers and missing data had any impact, and whether our results remained
strong after correction. We then conducted exploratory factor analysis and multi-
group confirmatory factor analysis on the aforementioned set of subtests as a
check for bias. After adjustment for age, we did not find any non-linear effects of
age. Adjustment for sex did not reveal any evidence of meaningful differences in
fit between the competing models, the g-model and the correlated factors model.
We find that a three broad factor model (memory, complex cognition, and
executive function) with g at the apex fits the data well. Moreover, strict
measurement invariance holds between SIRE groups. The best fitting model
(M6A, Table S2 of Supplementary File 1; CFI = .954, RMSEA = .044) was one in
which g alone explains SIRE group differences. We output the g-factor scores
from this model for use in the analyses. These score magnitudes are
approximately the same as those derived from exploratory factor analysis.
2. Socioeconomic status (SES)
We identified seven indicators of SES: financial adversity, area deprivation
index, neighborhood safety protocol, parental education, parental income,
parental marital status, and parental employment status. These are detailed in
Supplementary File 2. We submitted the seven SES indicators to Principal
Components Analysis (PCA). We used the R package PCAmixdata, which
handles mixed categorical and continuous data (Chavent et al., 2014). The first
unrotated component explained 42% of the variance. The PCA_1 loadings for the
seven SES indicators were as follows: financial adversity (.31), area deprivation
index (.49), neighborhood safety protocol (.31), parental education (.53), parental
income (.66), parental marital status (.42), and parental employment status
(0.21). More details and the correlation matrix for the SES indicators is provided
in Supplemental File 2.

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3. Child US-born
Parents were asked about the country of the child’s birth. We recoded this
variable as 1 for “United States” and 0 for all other responses.
4. Immigrant family
Parents were asked if anyone in the child’s family, including maternal or
paternal grandparents, was born outside of the United States. This variable was
coded as 1 for “Yes” and 0 for all other responses.
5. Nationality (Puerto Rican, Mexican, and Cuban)
If a child was reported to be Hispanic, parents were additionally asked about
the specific Latin American nation of origin (“Please choose the group that best
represents the child's Hispanic origin or ancestry”). Seventy percent of the
Hispanic children were reported as being either Mexican, Mexican American, or
Chicano (N = 1028), Puerto Rican (N = 210), or Cuban or Cuban American (N =
174). Dummy variables were created for these three nationality groups, with “1”
indicating “Yes” and “0” indicating “No”.
6. Frac_SIRE
Four dummy SIRE variables (Black, White, Native American, and Not
Otherwise Classified (NOC) were computed from the 18 questions asking about
the child’s specific race. The NOC SIRE group included those who were marked
as: “Other Race,” “Refused to answer,” or “Don’t Know.” These were then
recoded into interval variables in which individuals are assigned a SIRE fraction
ranging from 0 to 1 (Liebler & Halpern-Manners, 2008). These were calculated as
the value selected for each of the four groups (0 or 1) over the total number of
responses (0 to 4) chosen. For example, someone marked as only Black and
White would be assigned scores of (Black: ½; White: ½; Native_American: 0;
NOC = 0). This SIRE coding was used as it was previously found to be the most
predictive in models which also included genetic ancestry (Kirkegaard et al.,
2019).
7. Hispanic
For the admixture-regression analysis conducted on the full sample, we
additionally included a dummy variable for Hispanic ethnicity. This was coded as
“1” for “Hispanic” and “0” for not Hispanic. As the subsamples were either
Hispanic or non-Hispanic, this variable was not used in the subsample analyses.

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8. Ethnic attachment
Parents were given the Multigroup Ethnic Identity Measure-Revised (MEIM-
R) Survey. In this they were asked six Likert-scaled (1 = strongly agree; 5 =
strongly disagree) questions regarding their ethnic group: “I have spent time trying
to find out more about my ethnic group, such as its history, traditions, and
customs”, “I have a strong sense of belonging to my own ethnic group,” “I
understand pretty well what my ethnic group membership means to me,” “I have
often done things that will help me understand my ethnic background better,”, “I
have often talked to other people in order to learn more about my ethnic group,”,
“I feel a strong attachment towards my own ethnic group.” ABCD computed
MEIM-R summary scores, which we standardized. We treat this as a measure of
family ethnic-related culture. We only included this variable in the subsample
analyses. In these the members belonged to the same broad ethnic group (e.g.,
Black or Hispanic).
9. State racism
ABCD calculated state-level indicators of both racism and immigrant bias.
These were based on both implicit bias measures and state-level structural
variables. The two indicators correlated at r = .41 (N = 9386). We standardized
both measures (M = 0, SD = 1) and then averaged them and standardized the
resulting average.
10. Discrimination factor
In Year 1 follow-up, the children were asked 6 questions regarding perceived
ethnic, racial, national, or color based discrimination. The questions were as
follows: “In the past 12 months, have you felt discriminated against: because of
your race, ethnicity, or color?”, “In the past 12 months, have you felt discriminated
against: because you are (or your family is) from another country?”, “How often
do the following people treat you unfairly or negatively because of your ethnic
background?” (Teachers? Other adults outside school? Other students?), “I feel
that others behave in an unfair or negative way toward my ethnic group.” We
imputed missing data using the mice package (df, m = 5, maxit = 50, method =
'pmm', seed = 500). We used the mirt package in R to perform factor analysis on
the six questions. We then standardized and saved the factor scores.
11. Skin_color, P_Brown_Eye, P_Intermediate_Eye, P_Blue_Eye, P_Black_Hair,
P_Brown Hair, P_Red_or_Blond_Hair).
Conley and Fletcher (2017) have suggested that phenotypic-based
discrimination might mediate the association between cognitive ability and
genetic ancestry. This is called the colorism model (Hu et al., 2019). It can be

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tested by including indices of race-related phenotype into the regression models
to see if these capture the association between ancestry and cognitive ability. As
such, we include measures of eye, hair, and skin color. Skin, Hair, Eye color were
calculated based on the publicly available, “Hirisplex Eye, Hair and Skin Colour
DNA Phenotyping Webtool.” This tool and score calculations have been detailed
by Lasker et al. (2019). We additionally combined (summed) the red and blond
hair probabilities. Skin color was scaled as detailed in Lasker et al. (2019), with
higher scores representing darker color, and then standardized. The eye and hair
color variables represent the percent in the full sample with the specific color and
were left unstandardized to retain interpretability.
12. Admixture estimates
Imputing and genotyping was done by the ABCD Research Consortium
using Illumina XX. 516,598 variants survived the quality control. Before global
admixture estimation, we applied quality control using PLINK 1.9. We used only
directly genotyped, bi-allelic, autosomal SNP variants (494,433 before, 493,196
after lifting). We pruned variants for linkage disequilibrium at the 0.1 R² level using
PLINK 1.9 (--indep-pairwise 10000 100 0.1). This variant filtering was done in the
reference population dataset to reduce bias from sample non-representativeness.
99,642 variants were left after pruning. We merged the target samples from
ABCD with reference population data for the populations of interest. A k=5
solution with European, Amerindian, African, East Asian and South Asian
components provides the most comprehensive but parsimonious model of the US
population, capturing all the predominant ancestral backgrounds in the US
population. We merged our sample with relevant samples from 1000 Genomes
and from the HGDP to perform the cluster analysis and identify these k=5
components. The following populations from 1000 Genomes and from the HGDP
reference populations were excluded: Adygei, Balochi, Bedouin, Bougainville,
Brahui, Burusho, Druze, Hazara, Makrani, Mozabite, Palestinian, Papuan, San,
Sindhi, Uygur, Yakut. We excluded these populations because they were overly
admixed or because the individuals in the ABCD sample lacked significant
portions of these ancestries (e.g., Melanesians and San). We split the ABCD
target samples into 50 random subsets (222 persons each) and merged them
sequentially with the reference data. Admixture at k = 5 was run on each of the 50
merged subsets. This repeated subsetting was done to avoid skewing the
admixture algorithm to European ancestry which was predominant in the ABCD
sample.

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13. First 20 Principal components
For the analysis of PGS predictivity within SIRE groups we controlled for the
first 20 ancestry principal components to take into account population structure
related effects. These components were generated by PLINK v1.90b6.8 when
computing polygenic scores.
14. eduPGS
For polygenic scores (PGS), we scored the genomes using PLINK
v1.90b6.8. For background, a polygenic score (PGS) “is an estimate of an
individual's genetic liability to a trait or disease, calculated according to their
genotype profile and relevant genome-wide association study (GWAS) data”
(Choi, Mak & O’Reilly, 2020). We used the genome-wide association study
(GWAS) results from Lee et al. (2018). Specifically, we used the multi-trait
analysis of genome-wide association study (MTAG) eduPGS SNPs (N = 8,898
variants in this sample) to compute eduPGS. The MTAG eduPGS were computed
using MTAG, a method for analyzing statistics from genome-wide association
studies (GWAS) on different but genetically correlated traits (e.g., education and
intelligence). These scores were based on cognitive ability (n = 257,841), hardest
math class taken (n = 430,445), and mathematical ability (n = 564,698) (Lee et
al., 2018). We use these PGS because previous research has shown them to
have trans-ethnic predictive validity in European, Hispanic, and African American
populations (Fuerst, Kirkegaard & Piffer, 2021; Lasker et al., 2019). Moreover,
common forms of bias were found not to account for the ancestry-related eduPGS
differences (Fuerst et al., 2021). Thus, we can say that this PGS plausibly
captures genetic effects between ancestry groups.
15. The NIH Toolbox® (NIHTBX) neuropsychological battery
For one validation analysis of the eduPGS which included Asians, we used
the NIHTBX summary scores. This was because we did not run MGCFA on the
small Asian samples and so did not have g scores for these groups. This battery
has been shown to be measurement invariant across American Black, Hispanic,
and White SIRE groups (Lasker et al., 2019). The effects of age and sex were
controlled for. We standardized the residuals.
3. Methods (Analyses)
We first present the descriptive statistics for the sample and the subsamples.
We then explore the bivariate relation between European admixture and g-
scores. We include both linear regression lines and loess lines in the regression
plots (based on the gg_scatter package in R). These analyses are descriptive and
do not take into account the complex structure of the data.

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After, we run a series of within-SIRE (Black, Hispanic, and Other) admixture-
regression analyses to control for potential environmental confounds. For these
analyses, we set European ancestry as a reference value with a value of zero.
Following Heeringa and Berglund’s (2021) recommendations, we use a multi-
level mixed effects three-level (site, family, individual) model. In this model,
recruitment site and family common factors are treated as random effects (i.e., as
random samples from a population). We further report the dense numeric matrix
results for the regression models in Supplementary File 3.
The pooled data with both the regular ABCD baseline sample and the pooled
twin samples were used. As Heeringa and Berglund (2021) note, the specification
replicates that used by the ABCD Data Exploration and Analysis Portal (DEAP).
Thus, the use of this multilevel model also aids in replication. For the regression
analyses on the SIRE subsamples, we ran four models. The first model includes
genetic ancestry and controls for both child and family immigrant status. The
second model adds a term for SIRE and ethnic attachment to capture SIRE
specific cultural effects. The third model adds terms to capture possible
discrimination related effects: state-level racism, child reported experiences of
discrimination, and race-related phenotype. The fourth model adds our general
SES variable. Geographic effects are controlled for by including study site as a
random effect in the model.
For the regression analyses, general cognitive ability scores (g-scores) are
used as the dependent variable. This variable was standardized (M = 0.00; SD =
1.00) in the full sample. As for the independent variables, both the ancestry and
fractional SIRE variables were left unstandardized. This allows the
unstandardized beta coefficients for these variables to be interpreted as the effect
of a change in 100 percent ancestry/SIRE identity on one standardized unit of
cognitive ability. The rationale for this method has been detailed elsewhere
(Lasker et al., 2019). The Child_USA_Born and Immigrant_Family dummy
variables were also left unstandardized to retain interpretability. The three eye
color and the three hair color variables, which represent probabilities that sum to
one in the full samples, are also not standardized to retain interpretability. The
remaining variables — ethnic attachment, state racism, discrimination factor, skin
color, and SES — are all standardized in the full sample. Thus, the
unstandardized B coefficient for these variables represents the change in g
induced by a change of one standard deviation in the independent variable.
Results
1. Descriptive statistics
The descriptive statistics for the total sample and the four SIRE subsamples are
shown in Table 1. Cohen’s d for the difference in g between Black and White

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Americans comes to 1.02 d. This represents a large effect by conventional
standards (Cohen, 1988) and is typically sized for measured g differences across
SIRE groups (Roth et al., 2017). The difference between Hispanic and White
Americans is 0.38 d, while that between Others and White Americans is 0.37 d.
These latter two differences represent small to medium sized effects (Cohen,
1988). The Hispanic-White difference is smaller than usually reported (e.g., Roth
et al., 2017). This could be due to the exclusion of children who were not fluent in
English.
Table 1. Total sample and subsample characteristics.
Total sample
Black
Hispanic
Other
White
M ± SD
M ± SD
M ± SD
M ± SD
M ± SD
Age (in Months)
119.0 ± 7.49
118.9 ± 7.28
118.6 ± 7.58
118.7 ± 7.40
119.2 ± 7.52
g
0.00 ± 1.00
-0.69 ± 1.07
-0.10 ± 0.99
-0.09 ± 1.09
0.24 ± 0.86
SES
0.00 ± 1.00
-0.98 ± 0.93
-0.36 ± 0.91
-0.40 ± 1.00
0.45 ± 0.75
frac_White_SIRE
0.73 ± 0.43
0.00 ± 0.00
0.67 ± 0.45
0.39 ± 0.25
1.00 ± 0.03
frac_Black_SIRE
0.20 ± 0.38
1.00 ± 0.04
0.07 ± 0.23
0.29 ± 0.26
0.00 ± 0.00
frac_Native_Amer._SIRE
0.02 ± 0.11
0.00 ± 0.00
0.03 ± 0.13
0.18 ± 0.28
0.00 ± 0.00
frac_Other_SIRE
0.06 ± 0.23
0.00 ± 0.04
0.23 ± 0.42
0.13 ± 0.34
0.00 ± 0.03
European_ancestry
0.75 ± 0.33
0.16 ± 0.11
0.60 ± 0.21
0.62 ± 0.25
0.98 ± 0.05
African_ancestry
0.18 ± 0.31
0.82 ± 0.11
0.10 ± 0.15
0.32 ± 0.26
0.01 ± 0.02
Amerindian_ancestry
0.06 ± 0.14
0.01 ± 0.02
0.28 ± 0.19
0.04 ± 0.09
0.01 ± 0.03
South_Asian_ancestry
0.00 ± 0.02
0.00 ± 0.01
0.01 ± 0.01
0.01 ± 0.05
0.00 ± 0.01
East_Asian_ancestry
0.01 ± 0.03
0.01 ± 0.02
0.01 ± 0.02
0.01 ± 0.07
0.00 ± 0.02
State_racism
0.00 ± 1.00
0.44 ± 0.93
-0.35 ± 0.93
0.26 ± 0.99
-0.04 ± 0.99
Discrim_fact
0.00 ± 1.00
0.47 ± 1.24
0.09 ± 1.04
0.19 ± 1.05
-0.19 ± 0.83
Ethnic_attachment
0.00 ± 1.00
0.38 ± 1.03
0.23 ± 1.01
0.05 ± 1.00
-0.19 ± 0.94
Skin_color
0.00 ± 1.00
1.32 ± 0.42
0.58 ± 0.80
0.36 ± 0.91
-0.62 ± 0.60
P_Brown_Eye
0.56 ± 0.41
0.97 ± 0.10
0.83 ± 0.28
0.73 ± 0.35
0.33 ± 0.35
P_Intermediate_Eye
0.08 ± 0.07
0.02 ± 0.03
0.06 ± 0.06
0.07 ± 0.06
0.10 ± 0.07
P_Blue_Eye
0.36 ± 0.41
0.02 ± 0.08
0.11 ± 0.25
0.20 ± 0.33
0.57 ± 0.39
P_Black_Hair
0.23 ± 0.24
0.55 ± 0.16
0.38 ± 0.23
0.28 ± 0.20
0.09 ± 0.10
P_Brown Hair
0.46 ± 0.18
0.43 ± 0.13
0.49 ± 0.16
0.54 ± 0.15
0.45 ± 0.20
P_Red_or_Blond_Hair
0.30 ± 0.29
0.02 ± 0.05
0.13 ± 0.18
0.18 ± 0.21
0.46 ± 0.26
Child_USA_Born
0.98 ± 0.15
0.98 ± 0.15
0.94 ± 0.24
0.98 ± 0.14
0.99 ± 0.12
Immigrant_family
0.28 ± 0.45
0.14 ± 0.35
0.73 ± 0.44
0.20 ± 0.40
0.18 ± 0.38
Puerto_Rican
0.10 ± 0.31
Mexican
0.51 ± 0.50
Cuban
0.09 ± 0.28
eduPGS
0.00 ± 1.00
-1.33 ± 0.57
-0.21 ± 0.76
-0.36 ± 0.85
0.50 ± 0.77
Note: Nationality variables (Mexico, Cuba, Puerto Rico) were only computed for
Hispanics.

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197
In this sample, parent-identified Whites are 98% European in ancestry (1%
African; 1% Amerindian). Since this group has little admixture, we relegate the
within SIRE admixture-regression analyses to the supplementary file. Both the
Black (82% African, 16% European, 1% Amerindian) and the Other (62%
European, 32% African, 4% Amerindian) groups are African-European admixed
groups. Hispanics additionally have a substantial Amerindian component (60%
European, 28% Amerindian, 10% African). Figure 1 shows the distribution of
ancestry by SIRE groups. These admixture percentages correspond with those
typically reported in the literature (e.g., Bryc et al., 2015).
Figure 1. Admixture triangle plot for SIRE groups in the ABCD sample.
2. Regression plots and admixture regression analyses
1. Black Americans
Figure 2 shows the regression plot for European ancestry and g-scores
among Black children. European ancestry is significantly (r = .10, N = 1690)
associated with g scores. The R boxplot function indicated 13 outliers. However,
removing these had no effect on the bivariate correlation (r = .10, N = 1677).
Additionally, the Loess regression line indicated a possible curvilinear relation
with a slight uptick in scores at the lowest European ancestry decile. Further
analysis showed that this was due to relatively high scores of individuals from
African immigrant families (MAfrican_immigrant = -.28, N = 60). Limiting our scope to
African Americans within US-born families raises the correlation to r = .13 (N =
1475); for African Americans with 2% to 80% European admixture, this correlation

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is r = .11 (N = 1635). These results are shown in Table S4 of Supplementary file
3 along with scores by African American subgroups. The full correlation matrices
are also provided in Supplementary File 3.
Figure 2. Regression plot of European ancestry and g in the Black American
subsample (N = 1690).
We next proceed to the admixture-regression analyses. Because the Black
SIRE category excludes multi-racial individuals, we do not include a term for
fraction SIRE in these models. As seen in Table 2, African ancestry is strongly
and significantly negatively related to cognitive ability in all four models.
Amerindian ancestry is also negatively related to g-scores; however, owing to the
low Amerindian admixture among non-Hispanic Blacks — and consequently the
high standard errors — these estimates are not reliable. Adding ethnic attachment
scores in Model 2 does not change the relationship with Amerindian and African
ancestry. As seen in Model 3, measures of racial discrimination do not mediate
the relation between g and ancestry. Of these variables added to Model 3, only
experiences of discrimination had a significant independent effect. Finally, as
seen in Model 4, while SES was significantly related to g, it did not substantially
attenuate the association between African ancestry and g (bAfrican ancestry = -1.08 to
-0.89).

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199
Table 2. Regression results for the effect of genetic ancestry on g Among Black
Americans (N = 1690). Shown are the beta coefficients (b) and p-values (p) from
the mixed effects models with recruitment site and family common factors treated
as random effects. The values in parentheses are standard errors. The marginal
and conditional R2 are provided at the bottom.

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2. Hispanic Americans
Figure 3 shows the regression plot for European ancestry and g scores
among Hispanic children. As seen, European ancestry is significantly (r = .23, N
= 2021) associated with g scores. While the R boxplot function indicates that there
are 23 outliers, removing these had little effect on the bivariate correlation (r = .22,
N = 1998). The Loess regression line suggests a possible slight uptick in scores
at the lowest European ancestry decile. However, the 95% confidence intervals
of this line (not shown) overlapped with the linear regression line.
Figure 3. Regression plot of European ancestry and g in the Hispanic American
subsample (N = 2021).
For the Hispanic admixture-regression analyses, we include a term for race
because the Hispanic ethnic category is inclusive of all self-identified racial
groups. As shown in Table 3, both Amerindian and African ancestry are strongly
negatively associated with g in the first three models. Adding SIRE ethnic identity
and the ethnic attachment variable in Model 2 had little effect on the beta for
Amerindian ancestry. Doing so increases the effect of African ancestry. In Model
3, both skin color and experiences of discrimination have significant independent
effects on g, but these variables only slightly attenuated the relation between g
and Amerindian and African ancestry. However, as seen in Model 4, SES
attenuated the effect of Amerindian and African ancestry (Model 3: bafrican ancestry =
-0.96 →Model 4: bafrican ancestry = -0.58; Model 3: bamerindian ancestry = -1.37 →Model

FUERST, J.G.R., et al. GENETIC ANCESTRY AND GENERAL COGNITIVE ABILITY
201
4: bamerindian ancestry = -0.86). Nonetheless, the magnitudes of the Amerindian and
African ancestry effects remained medium to large in size and statistically
significant.
Table 3. Regression results for the effect of genetic ancestry on g among
Hispanic American children (N = 2021). Shown are the beta coefficients (b) and
p-values (p) from the mixed effects models with recruitment site and family
common factors treated as random effects. The values in parentheses are
standard errors. The marginal and conditional R2 are provided at the bottom.

MANKIND QUARTERLY 2021 62:1
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3. Other Americans
Figure 4 shows the regression plot for European ancestry and g-scores
among the Other group. European ancestry is significantly (r = .19, N = 748)
associated with g scores. Eight outliers were identified using the R boxplot
function. Removing these had little effect on the correlation (r = .16, N = 740). The
Loess regression line show a slight uptick in scores at the lowest European
ancestry decile. However, the 95% confidence intervals of this line overlapped
with the linear regression line. The correlation matrix is provided in the
Supplementary File.
Figure 4. Regression plot of European ancestry and g in Other American
subsample (N = 748).
As for Hispanics, we include a term for race because the Other American
ethnic category is inclusive of all self-identified racial groups. As seen in Table 4,
both coefficients for Amerindian and African ancestry show a strong negative
association with g from Model 1 through Model 4. Adding SIRE ethnic identity and
the ethnic attachment variable in Model 2 has little effect on the beta for
Amerindian ancestry. Doing so increases the effect of African ancestry. In Model
3, only experiences of discrimination has a significant independent effect on g.
The discrimination variables did not attenuate the relation between g and
Amerindian and African ancestry. As seen in Model 4, SES moderately
attenuated the effects of Amerindian and African ancestry (Model 3: bafrican ancestry

FUERST, J.G.R., et al. GENETIC ANCESTRY AND GENERAL COGNITIVE ABILITY
203
= -1.38 →Model 4: bafrican ancestry = -1.08; Model 3: bamerindian ancestry = -1.51 →Model
4: bamerindian ancestry = -1.09). Nonetheless, the magnitudes of these ancestry effects
remained large in magnitude and statistically significant.
Table 4. Regression results for the effect of genetic ancestry on g among Other
Americans children (N = 748). Shown are the beta coefficients (b) and p-values
(p) from the mixed effects models with recruitment site and family common factors
treated as random effects. The values in parentheses are standard errors. The
marginal and conditional R2 are provided at the bottom.

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4. White Americans
We do not report the admixture regression results for the 5911 non-Hispanic
White Americans. These results are unreliable owing to the low dispersion in
African and Amerindian ancestry within this SIRE group (see Table 1). Thus, we
relegate these results to the supplemental material. Briefly, though, in Model 4 for
this subsample, both African ancestry (bAfrican ancestry = -.85, p = .110) and
Amerindian ancestry (bAmerindian ancestry = -.96) also have large negative effects.
However, this effect is only statistically significant for Amerindian ancestry (p =
.012).
5. Full sample
The results above indicate that factors associated with genetic ancestry are
related to g within SIRE groups. These findings also suggest that these same
factors explain differences between SIRE groups (Halder et al., 2015). Using the
full sample, we examine this implication. The relation between European ancestry
and g for the full sample is shown in Figure 5. As expected, there is a strong
positive association for the SIRE groups between ancestry and g (r = .36; N =
10370). Because the range of ancestry is not restricted — restriction of range
attenuates correlations — the correlation is high. In this plot, we again see the
uptick at the lowest decile of European admixture. This is due to the relatively
high scores of children of recent African immigrants.
Figure 5. Regression plot of European ancestry and g in the full sample (N =
10370).

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Table 5. Regression results for the effect of ancestry on cognitive ability in the
full sample (N = 10,370). Shown are the beta coefficients (b) and p-values (p) from
the mixed effects models with recruitment site and family common factors treated
as random effects. The values in parentheses are standard errors. The marginal
and conditional R2 are provided at the bottom.

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To examine if SIRE differences can be accounted for by genetic ancestry,
we construct a new set of regression models using the full sample. As seen in
Table 5 in the first two models, Model 1a and Model 1b, we include only genetic
ancestry variables or alternatively SIRE variables along with controls for migrant
status. As seen in Model 2, none of the SIRE values remain significant after
adding genetic ancestry to the model. These results indicate that ancestry-
associated factors account for the SIRE differences in g. We additionally include
a Model 3, which adds the cultural, socioeconomic, and phenotypic indices. As
seen in Model 3, these variables attenuated the effect of African and Amerindian
ancestry (Model 2: bafrican ancestry = -1.31 →Model 3: bafrican ancestry = -0.80; Model 2:
bamerindian ancestry = -1.57 →Model 3: bamerindian ancestry = -0.86), but the ancestry effects
remain large. Note that the effects of East Asian and South Asian ancestry are
insignificant because there is little variance in these ancestry components. This
is because we excluded everyone identified as Asian and Pacific Islander.
Finally, we can check the extent to which eduPGS can explain ancestry
effects. Before doing so, we verify that eduPGS are associated with g within each
of the SIRE groups. In doing so, we include controls for the first 20 genetic
principal components or, alternatively, continental ancestry (with European
ancestry left as the reference). Moreover, we run the analysis both using all
families and using only singleton families (i.e., families with only one child). The
full results are provided in the supplementary material. The results are
summarized in Table 6. As previously found, the eduPGS by g associations are
attenuated among African Americans, but not among Hispanic and Other
Americans (Fuerst et al., 2021). Nonetheless, eduPGS are significantly
associated with g within all SIRE groups.
Table 6. Validities (b) of eduPGS by American SIRE groups from multilevel
regression models with g as the dependent variable and PGS as a predictor.
Controls
Sample
Black
Hispanic
Other
White
20 PCs
Full Sample
0.17
0.27
0.32
0.26
Ancestry
Full Sample
0.16
0.27
0.32
0.27
20 PCs
Singletons
0.19
0.29
0.26
0.27
Ancestry
Singletons
0.18
0.29
0.28
0.26
Note: Sample sizes for the full samples are: Black (N = 1690), Hispanic (N = 2021), Other
(N = 748) and White (N = 5911). The sample sizes for the singletons subsamples are:
Black (N = 1159), Hispanic (N = 1516), Other (N = 505), and White (N = 3674). All betas
are statistically significant at the p < .01 level. Singletons = single child families.
For further validation of the PGS, we correlated the eduPGS with the NIHTBX
summary scores which we had for all groups, including Asians. We computed

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207
mean scores for all ABCD SIRE subgroups and combinations with N ≥ 50. There
were 17 such groups. We then correlated the eduPGS with the mean subgroup
test scores. This correlation came to r = .93. Thus, we conclude, in line with
Chande et al. (2020), that “the general concordance seen between genetically
inferred (predicted) phenotypic differences and the observed differences for
anthropometric traits, or known prevalence differences in the case of disease
traits, supports the approach taken here” (p. 1525-6), despite concerns raised in
the literature. The regression plot is shown in Figure 6. The number of individuals
in each SIRE group is represented by the size of the associated data point.
Figure 6. Regression plot of eduPGS and NIHTBX scores for the 17 largest SIRE
groups in the ABCD sample, with SIRE group sample sizes represented by the
size of the data points.
Next, we include the PGS in the model, starting with Model 3 from Table 5.
Comparing Model 3 and Model 4 (which adds eduPGS) of Table 7, we see that
eduPGS explains a substantial portion of the residual effect of African and
Amerindian ancestry after controls for SES.
Table 7. Regression results for the effect of eduPGS and ancestry on cognitive
ability in the full sample. Shown are the beta coefficients (b) and p-values (p) from
the mixed effects models with recruitment site and family common factors treated

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as random effects. The values in parentheses are standard errors. The marginal
and conditional R2 are provided at the bottom.

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We also examine the individual SIRE subsample results for eduPGS. The
model adds eduPGS to the respective Model 4s for each SIRE group (i.e., the
model with potential environmental factors included). The full results are provided
in the Supplementary File. These results are summarized in Table 8. Specifically,
Table 8 shows the effects for Amerindian and African ancestry on g with possible
environmental controls. These come from the fourth models of Tables 2, 3, 4, and
S8 and the third model from Table 5. It next shows the effects when eduPGS is
added. As seen, eduPGS accounts for a portion of the ancestry by g association
in all SIRE subsamples.
Table 8. Effects (b) of Amerindian and African ancestry on g in multi-level models
with environmental controls (Model 4/3), and multi-level models with
environmental controls and eduPGS (Model 5).
Amerindian
African
Black
Model 4
-3.46
-0.89
Model 5
-3.15
-0.69
Hispanic
Model 4
-0.86
-0.58
Model 5
-0.55
-0.16
Other
Model 4
-1.09
-1.08
Model 5
-0.55
-0.48
White
Model 4
-0.96
-0.85
Model 5
-0.65
-0.32
Full
Model 4
-0.86
-0.79
Model 5
-0.58
-0.40
It is conceptually possible that our eduPGS are just capturing global ancestry
effects. Our ancestry components are based on more SNPs. Moreover, they are
not weighted by trait-associations which will attenuate the association with
ancestry. As such this is unlikely. However, to test this possibility we created
pseudoPGS. To do so, we used PLINK v1.90b6.8 to select random sets of 8,898
variants to match the eduPGS. Then we randomly assigned the eduPGS beta
weights (from Lee et al., 2018) to the respective sets of SNPs.
Following this procedure, we create 10 pseudo eduPGS scores. This
procedure produced PGS with the same set of SNPs as the SNPs used to
calculate genetic ancestry, but randomized trait-association information. The full
results are provided in Supplementary File 3, Table S17. Unlike the real PGS,
these pseudoPGS had no validity independent of genetic ancestry. This is
because of the random assignment of eduPGS betas to the SNP frequencies
resulting in poor indices of ancestry. Generally, we conclude that PGS will not

MANKIND QUARTERLY 2021 62:1
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necessarily capture effects of global ancestry. This finding suggests that our
eduPGS are in fact capturing causal genetic effects on g both within and between
ancestries.
Discussion
Genetic ancestry measures provide very powerful scientific value in studying
SIRE differences in g. Using ancestry allows one to examine how the trait varies
by genetic ancestry within self-identified racial and ethnic groups. Doing so offers
a potential solution to the problem of decomposing genetic and environmental
variance (Halder et al., 2015). Admixture regression has been widely applied to
medical and behavioral traits. This includes type 2 diabetes (Cheng et al., 2013),
asthma (Salari et al., 2005), blood pressure (Klimentidis et al., 2012), and sleep
depth (Halder et al., 2015). Admixture regression has a natural application to
studying g.
Here we apply this technique to examine SIRE differences in g. We find that
African and Amerindian ancestry are strongly negatively associated with general
cognitive ability among African, Hispanic, and other American subsamples. This
replicates previous research which showed that genetic ancestry predicts
cognitive ability, independent of socioeconomic status and phenotypic
discrimination variables which are the usual suspects (Kirkegaard et al., 2019;
Lasker et al., 2019; Warne, 2020). The importance of such analyses within SIRE
groups is that they shed light on the cause of g differences between SIRE groups
with respect to similarities in developmental processes (Rowe, Vazsonyi &
Flannery, 1994).
The ancestry effects are consistent in direction across subsamples and hold
after controlling for a wide array of economic and social factors, including migrant
status, SIRE, ethnic attachment, measures of discrimination, phenotypic indices
of race, and general SES. These results suggest that African, Hispanic, and other
groups have inherited alleles from their African and Amerindian ancestors which
make them liable to lower levels of g. In fact, as seen in Table 5 (Model 2), 100%,
76%, 81%, and 100% of the respective Black, Native American, Other, and
Hispanic SIRE effects were explained by genetic ancestry. This association
between genetic ancestry and g suggests a partial genetic basis for observed
SIRE differences.
This inference is supported by additional findings based on the eduPGS
analyses. These polygenic scores were found to be predictive of g within SIRE
groups controlling for the first 20 principal components and for ancestry.
Moreover, they explain a substantial portion of the ancestry effects both in the full
sample and all subsamples. Also, they were almost perfectly correlated with SIRE
group means in cognitive ability (r = .93). The most parsimonious explanation for

FUERST, J.G.R., et al. GENETIC ANCESTRY AND GENERAL COGNITIVE ABILITY
211
this, given the apparent absence of obvious forms of confounding (Fuerst et al.,
2021), would seem to be that eduPGS are capturing causal effects of genes on g
both within and between ancestry groups and thus also SIRE groups. Firm
conclusions, though, will require a better understanding of the relation between
polygenic scores and ancestry (Lawson et al., 2020; Fuerst et al., 2021).
It is worth emphasizing that our g scores were from a confirmatory factor
model in which strict factorial invariance (SFI) held between SIRE groups. SFI
entails that the differences between SIRE groups have the same psychometric
meaning as the differences between individuals within these groups (i.e., the
scores are psychometrically unbiased). Moreover, SFI implies that the causes of
group differences are a subset of the causes of the individual differences within
groups (Lubke et al., 2003; Dalliard, 2014). In this sample of children, individual
differences in general cognitive ability are largely due to genes (Freis et al., 2020).
It should be noted that the polygenic scores represent genetic variation that
is caused by common alleles, not genetic variation that is caused by rare alleles
under mutation-selection balance. The causal alleles that are tapped by polygenic
scores are ancient. Most were already polymorphic 60,000 years ago when
people left Africa and spread all over Eurasia. Today’s racial allele frequency
differences are the cumulative effects of selection and genetic drift acting over
more than 2,000 generations, while rare variants under mutation-selection
balance are much younger, no more than one or two millennia or even less.
Therefore it is predictable that genetic race differences that evolved over a long
time are differences in polygenic scores but not necessarily differences in
mutational load. The latter are the result of strength of selection during the last
centuries.
Overall, the results suggest that genetic variants related to general cognitive
ability vary between source genetic populations and have a causal effect on
intelligence. Because individuals within SIRE groups differ in their proportion of
African, European and Amerindian ancestors, general cognitive ability varies by
genetic ancestry within SIRE groups.
Limitations
This study advances over previous studies in that we used a diverse national
sample, a good measure of g, multiple indices of racial discrimination including
multiple race-associated phenotypes, and a composite index of SES based on
seven different indices. Moreover, our multilevel model controlled for the effects
of geography. Unfortunately, our index of skin color was imperfect. However, it
seems unlikely that skin color discrimination is a significant immediate cause of g
differences among 9-10 year old children. Such color discrimination explanations
usually propose labor market based discrimination (Hersch, 2011), which would

MANKIND QUARTERLY 2021 62:1
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be captured by our index of SES. Regardless, admixture-regression results can
only provide indirect evidence for a genetic hypothesis because there could be
unmeasured environmental factors that are related to both ancestry and cognitive
ability.
While the results also show that educational and intelligence-related
polygenic scores can account for some of the effects of ancestry on g, these
results are only tentative. It is not certain that these PGS are capturing genetic
effects, at least between ancestries (Fuerst et al., 2021). Thus these results do
not provide definitive evidence for a genetic hypothesis. However, following the
methodology of genetic epidemiology, admixture regression analyses are just a
first step in elucidating the genetic and environmental causes of group
differences.
Author contributions
All analyses were conducted by JGRF. MH and GC helped prepare the
manuscript.
Acknowledgments
Data used in the preparation of this article were obtained from the Adolescent
Brain Cognitive Development (ABCD) Study (https://abcdstudy.org), held in the
NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to
recruit more than 10,000 children aged 9-10 and follow them over 10 years into
early adulthood. The ABCD Study® is supported by the National Institutes of
Health and additional federal partners under award numbers U01DA041048,
U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037,
U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028,
U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025,
U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089,
U24DA041123, U24DA041147. A full list of supporters is available at
https://abcdstudy.org/federal-partners.html. A listing of participating sites and a
complete listing of the study investigators can be found at
https://abcdstudy.org/consortium_members/. ABCD consortium investigators
designed and implemented the study and/or provided data, but did not
necessarily participate in the analysis or writing of this report. This manuscript
reflects the research results and interpretations of the authors alone and may not
reflect the opinions or views of the NIH or ABCD consortium investigators. The
ABCD data repository grows and changes over time. The ABCD data used in this
report came from Version 3.01. The raw data are available at
https://nda.nih.gov/edit_collection.html?id=2573. Instructions on how to create an

FUERST, J.G.R., et al. GENETIC ANCESTRY AND GENERAL COGNITIVE ABILITY
213
NDA study are available at https://nda.nih.gov/training/modules/study.html).
Additional support for this work was made possible from supplements to
U24DA041123 and U24DA041147, the National Science Foundation (NSF
2028680), and Children and Screens: Institute of Digital Media and Child
Development Inc.
Additionally, we thank Jordan Lasker for guidance with the MGCFA analysis
code.
All supplemental files are available online at:
http://www.mankindquarterly.org/files/papers/62/1/11_sup_x0CHZFC.pdf
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