Heritability of IQ

Research on the heritability of intelligence quotient (IQ) inquires into the degree of variation in IQ within a population that is associated with genetic variation between individuals in that population. There has been significant controversy in the academic community about the heritability of IQ since research on the issue began in the late nineteenth century. ^{[1] [2]} Intelligence in the normal range is a polygenic trait, meaning that it is influenced by more than one gene, ^{[3] [4]} and in the case of intelligence at least 500 genes. ^[5] Further, explaining the similarity in IQ of closely related persons requires careful study because environmental factors may be correlated with genetic factors. Outside the normal range, certain single gene genetic disorders, such as phenylketonuria, can negatively affect intelligence. ^[6]

Estimates in the academic research of the heritability of IQ vary significantly by study and by study design. The general figure for heritability of IQ from behavioral genetic studies is about 0.5 across multiple studies in varying populations. ^{[7] : 172} However, alternative study designs using path analysis that explicitly account for cultural transmission have produced estimates around 0.3. ^[8] The relationship between heritability and age is uncertain, though most researchers believe that there is an increase in heritability over the course of the lifespan and that this increase reflects the importance of gene-environment correlations. Recent genetic research has come to more equivocal results, with estimates of heritability lower than those derived from twin studies, causing what is known as the "missing heritability problem".

Although IQ differences between individuals have been shown to have a hereditary component, it does not follow that disparities in IQ between groups have a genetic basis. ^{[9] [10] [11] [12]} The scientific consensus is that genetics does not explain average differences in IQ test performance between racial groups. ^{[13] [14] [15] [16]}

Heritability and caveats

Main article: Heritability

Heritability is a statistic used in the fields of breeding and genetics that estimates the degree of variation in a phenotypic trait in a population is explained by genetic variation between individuals in that population. ^[17] The concept of heritability can be expressed in the form of the following question: "What is the proportion of the variation in a given trait within a population that is not explained by the environment or random chance?" ^[18]

Estimates of heritability take values ranging from 0 to 1; a heritability estimate of 1 indicates that all variation in the trait in question is genetic and a heritability estimate of 0 indicates that none of the variation is genetic.

There are a number of caveats to consider when interpreting heritability:

• Heritability measures the proportion of variation in a trait that can be attributed to genes, and not the proportion of a trait caused by genes. ^[19] Thus, if the environment relevant to a given trait changes in a way that affects all members of the population equally, the mean value of the trait will change without any change in its heritability (because the variation or differences among individuals in the population will stay the same). This has evidently happened for height: the heritability of stature is high, but average heights continue to increase. ^[20] Thus, even in developed nations, a high heritability of a trait does not necessarily mean that average group differences are due to genes. ^{[20] [21]} Some have gone further, and used height as an example in order to argue that "even highly heritable traits can be strongly manipulated by the environment, so heritability has little if anything to do with controllability." ^[22]
• A common error is to assume that a heritability figure is necessarily unchangeable. The value of heritability can change if the impact of environment (or of genes) in the population is substantially altered. ^[20] If the environmental variation encountered by different individuals increases, then the heritability figure would decrease. On the other hand, if everyone had the same environment, then heritability would be 100%. The population in developing nations often has more diverse environments than in developed nations. This would mean that heritability figures would be lower in developing nations. Another example is phenylketonuria which previously caused intellectual disabilities in everyone who had this genetic disorder and thus had a heritability of 100%. Today, this can be prevented by following a modified diet, resulting in a lowered heritability. ^[23]
• A high heritability of a trait does not mean that effects such as learning are not involved. Vocabulary size, for example, is substantially heritable (and highly correlated with general intelligence) although every word in an individual's vocabulary is learned. In a society in which plenty of words are available in everyone's environment, especially for individuals who are motivated to seek them out, the number of words that individuals actually learn depends to a considerable extent on their genetic predispositions and thus heritability is high. ^[20]
• Contrary to popular belief, two parents of higher IQ will not necessarily produce offspring of equal or higher intelligence. Polygenic traits often appear less heritable at the extremes. A heritable trait is definitionally more likely to appear in the offspring of two parents high in that trait than in the offspring of two randomly selected parents. However, the more extreme the expression of the trait in the parents, the less likely the child is to display the same extreme as the parents. In fact, parents whose IQ is at either extreme are more likely to produce offspring with IQ closer to the mean (or average) than they are to produce offspring with high IQ. At the same time, the more extreme the expression of the trait in the parents, the more likely the child is to express the trait at all. For example, the child of two extremely tall parents is likely to be taller than the average person (displaying the trait), but unlikely to be taller than the two parents (displaying the trait at the same extreme). See also regression toward the mean. ^{[24] [25] [26]}

Methodology

See also: Behavioural genetics and Quantitative genetics

There are multiple types of studies that are designed to estimate heritability and other variance components, stemming from the field of biometrical genetics. ^[27] The variance, or more simply the differences in a trait within a population, can be partitioned into specific variance components that are based on particular decomposition models for the phenotype. First pointed out by Ronald Fisher and Sewall Wright, different sources of variance differ in their contribution to the resemblance between types of relatives. ^{[28] : 131}By making certain assumptions about the relationship between genes and environment, the field of quantitative genetics has developed equations that describe the expected correlation between relatives in terms of specific variance components (shared environment, additive genetic, etc.). ^{[28] : 163–167}

Correlation between relatives on a purely additive genetic model of phenotypic determination, from Coop 2020

One simple case is presented by twin studies. By making assumptions about genetic additivity and sources of environmental variation, researchers can estimate the heritability of a phenotype by comparing the resemblance of twins. ^{[28] : 581} Another method used to estimate heritability is adoption studies. Because adoptive parents and their adoptive children share a common environment, but not genetics, ^{[7] : 80–82} researchers in behavioral genetics have used data from adoptive families to estimate the heritability of IQ. ^{[30] [31]} Based on quantitative genetic theory, some researchers have taken a broader approach to analyzing familial resemblance. These researchers typically use path analysis ^[a] to fit models using larger sets of familiar correlations, incorporating cultural transmission, adoption, other complex living arrangements, and generalized assortative mating. ^{[35] [8]} However, with the transition into the postgenomic era, issues with controversial assumptions in previous models analyzing familial resemblance may be superseded by genetic analysis. ^[9] Methods have developed in genetic analysis to estimate the total heritability of a trait using large datasets with millions of genetic variants. ^{[36] [37]}

In the case of IQ, heritability estimates based on direct observation of molecular genetics have been significantly lower (around 10%) than those employing traditional methods (40–80%). ^{[38] [39]} This discrepancy has been termed the "missing heritability problem." ^[38] While researchers such as Robert Plomin and Sophie von Stumm contend that this gap will likely disappear with the acquisition of more genetic data, Eric Turkheimer and Lucas J. Matthews argue that it reveals a deeper set of methodological problem inherent in the concept of heritability itself, which will not be easy to overcome. ^[38]

Heritability Estimation Methods

Method	Gene-Environment Assumptions	Method Type	Originating Author
Classical Twin Design	Assumes equal environment between MZ and DZ twins, lack of GxE (gene-environment interactions), no nonadditive genetic variance (e.g., no dominance or epistasis); can be biased by assortative mating.	BG (Biometric Genetic)
Adoption Studies	Assumes genetic/environmental separation, and that the adoptive environment is not systematically different from the general population; can be biased by assortative mating.	BG (Biometric Genetic)
GREML-SNP	Assumes additive genetic effects, lack of genetic nurture, and that environmental relatedness does not scale with genetic relatedness; SNP effect sizes follow a normal distribution; misses rare variants; can be biased by assortative mating.	SNP-based	Yang et al. (2010) [40]
Within-Family GWAS	Isolates genetic effects within families; can still be biased by latent assortative mating, sibling indirect effects, ascertainment bias; may miss rare variants or low-frequency variants.	SNP-based	Possibly Benyamin et al. (2009) [41]
KINSHIP	Assumes fixed genetic effects across kin (relatives) and that environmental relatedness does not scale with genetic relatedness; can be biased by genetic nurture and assortative mating.	IBD-based	Zaitlen et al. (2013) [42]
SibReg	Does not assume anything about the environment; is biased by GxE (gene-environment interactions); can be biased by assortative mating.	IBD-based	Visscher et al. (2006) [43]
RDR	Does not assume any specific gene-environment relationship; misses ultra-rare variants; can be biased by assortative mating.	IBD-based	Young et al. (2017) [44]

Estimates

Although individual family studies that estimate the heritability of IQ vary greatly ^[b] , most family studies estimate the heritability of IQ in the range from 0.4 to 0.8 ^[11] and reviews of the literature typically summarize classical family design research with an estimate of 0.5. ^{[38] [39]} Issues with the methodology of family studies have long plagued the research field, ^{[46] [47]} but many researchers now believe that genome-wide association studies can provide less biased estimates of heritability. ^[48] These genomic studies provide estimates of heritability that are much lower than those derived from twin studies, leading to what is denoted as the "missing heritability problem". ^[49] The latest results from the best genomic methods are between 0.10 to 0.20, and researchers have proposed various theories to explain the gap. ^{[50] [51]}

Twin and family research

See also: Twin study and Adoption study

Twin studies

Twin studies are the most common method used to estimate the heritability of most traits. ^[52] Because monozygotic twins derive from one fertilized egg, they are largely genetically identical ^[c] , while dizygotic twins are expected to share 50% of their DNA. ^{[7] : 85–86} Twin studies compare the resemblance of monozygotic twins to the resemblance of dizygotic twins to estimate the heritability of trait using various models. ^[54]

Twin studies have shown greater resemblance between monozygotic twins than dizygotic twins. ^[7] This has lead most researchers in behavioral genetics to conclude that the heritability of IQ is in a range between 0.4 to 0.8. ^{[11] : 132  [55] : 143} However, it is not clear if all of the assumptions in twin studies hold true. ^[56] Critics typically focus on the equal environment assumption, which is the assumption that environmentally caused similarity is the same for both monozygotic and dizygotic twins. ^{[7] : 86  [57]} Genetic modeling has found that even in the complete absence of genetic factors for a trait, environmental similarity between monozygotic twins will cause heritability estimators to produce large estimates of genetic heritability. ^{[58] [59]} Researchers have produced different analyses of the equal environment assumption, some suggesting that the assumption is untenable for related traits like educational attainment. ^[60]

Twins reared apart have also been the subject of significant debate. ^{[61] [62] [63] [64]} The 2006 edition of Assessing adolescent and adult intelligence by Alan S. Kaufman and Elizabeth O. Lichtenberger reports correlations of 0.86 for identical twins raised together compared to 0.76 for those raised apart and 0.47 for siblings. ^[65] Theoretically, the correlation between twins reared apart is a direct estimate of heritability, as the only common factor between the twins would be the genetic variance. ^{[66] : 431} However, this makes an assumption of uncorrelated environments between twins, which may not be the case. ^{[67] [68] : 26  [69] : 18} For example, some researchers argue that twins experience similar education and this causes increased similarity in IQ. ^{[70] : 236–237  [71] : 67  [72]} Thomas Bouchard argues that some previous analyses of the data on twins reared apart are an abuse of statistical theory that he calls "psuedoanalysis". ^{[73] [61] : 146–147}

Adoption studies

Another study design researchers have used is adoption studies. In theory, "adoption creates sets of genetically related individuals who do not share a common family environment because they were adopted apart". ^{[7] : 80} Adoption studies have broadly found that the IQs of adoptees are more similar to their biological parents than that of their adoptive parents, ^[74] in tandem with findings that adoption greatly increases IQ. ^{[75] [76]} For example, one analysis of the Texas Adoption Project estimated heritability at 0.78. ^{[31] : 123} However, one recent adoption study estimate heritability for cognitive ability at 0.33. ^[77] Some critics point to assortative mating, range restriction ^{[78] [79]} and other complex family and social processes as providing issues in the interpretation of data from adoption studies. ^{[30] [80] [81]} Recent studies employing polygenic scores for educational attainment in adoptees have found a more complicated interaction between genes and environments. ^[82]

Other models

More broadly, researchers have analyzed the resemblance between various familial classes, such as parent-child correlations. In 1982, Bouchard and McGue reviewed such correlations reported in 111 original studies in the United States. The mean correlation of IQ scores between monozygotic twins was 0.86, between siblings 0.47, between half-siblings 0.31, and between cousins 0.15. ^[83] Some more basic model fitting studies are found in the behavioral genetics literature. Loehlin fit a model to the Bouchard and McGue data which estimated broad heritability at .58 from 'direct' methods and .47 from 'indirect' methods. ^{[84] [85]} Chipuer et al. analyzed this data using a LISREL model and estimated broad sense heritability as .51. ^[86] Other models successively add more components. In 1997, Devlin et al. also used these correlations to fit and compare different models and estimated that heritability was less than 50% in their best fitting models, finding that maternal effects were particularly important in their analysis. ^[1] Bouchard and McGue reviewed the literature in 2003, arguing that Devlin's conclusions about the magnitude of heritability is not substantially different from previous reports and that their conclusions regarding prenatal effects stands in contradiction to many previous reports. ^[87]

Genetics researchers observed that cultural heritability and genetic heritability can produce similar patterns of observations between relatives in the 1970s. ^{[88] [89]} As a result, in a series of papers using models based on path analysis, a group of researchers from the University of Hawaii analyzed data collected on American families and estimated genetic heritability ranging from 0.30 to 0.34. ^{[8] [90] [91] [92] : 56–95} A later study by Otto et al. applied the path analysis method to the data collected by Bouchard and McGue and estimated heritability ranging from 0.29 to 0.42. ^{[8] [93]} Path analysis models have been subject to criticism for further issues with modeling assumptions and statistical procedures, ^{[94] [95] [96]} reflecting broader criticisms of structural equation modeling. ^{[97] [98]}

Molecular genetics

See also: Genome-wide association study, Polygenic score, and Missing heritability problem

These findings are compatible with the view that a large number of genes, each with only a small effect, contribute to differences in intelligence. ^[99]

A diagram showing the relationship between genomic relatedness and phenotypic correlation for a hypothetical trait, a method used to estimate heritability. From Kemper et al. 2021

Molecular genetic studies have been central to several major scientific advances relevant to heritability. The first is the advent of polygenic scores, which take the thousands of single-nucleotide polymorphisms (SNPs) that influence a given trait to form an estimated "genetic value" or "genetic propensity". ^[101] Most genome-wide association studies (GWAS) estimate a polygenic score (PGS) and describe its overall statistical contribution to variance for the population in question, i.e. providing a R² (coefficient of determination) for the polygenic score. ^{[102] : 251  [103]} This measures only the predictive power of the genetic variants in the study in question, a future study with larger sample size or more genetic variants tagged may produce a larger amount. ^[104] Consequently, a given polygenic score only captures "a fraction of SNP-heritability". ^[105] The second is the concept of SNP heritability, which is the total proportion of phenotypic variance explained by SNPs. ^{[37] [106] [107]}

Genomic studies have found significant correlations between IQ and various polygenic scores, ^[108] but the true size of this association is unclear. The largest published analysis for genetic contributions to IQ found that the polygenic score could explain 9.9% of the variance between families, ^{[108] [109]} while some later analyses estimated that polygenic scores can predict about 11% of the variance. ^[110] However, these analyses rely on differences in genetic variants between families which introduce biases from population stratification, assortative mating, and indirect genetic effects such as dynastic effects ^[111] or genetic nurture. ^{[112] [113] [114]} One study found that polygenic score prediction for cognitive traits was 60% greater between families than within families. ^[115]

Genomic studies have also estimated values of SNP heritability for IQ. Very early studies used a method called genome-wide complex trait analysis (GCTA) which calculates the overall genetic similarity (as indexed by the cumulative effects of all genotyped single nucleotide polymorphisms) between all pairs of individuals in a sample of unrelated individuals and then correlates this genetic similarity with phenotypic similarity across all the pairs. ^[116] Studies using this method reported relatively large estimates of heritability, ranging from 40% to 51%. ^[4] A later study by the same group estimated SNP heritability for different measures of cognitive ability at about 31%. ^[117] However, researchers later discovered population stratification and other environmental biases were major issues affecting basic analyses. ^{[118] [119]} As a result, recent research has sought to estimate the SNP heritability of traits, including IQ, using within-family genome-wide association studies, ^{[112] [113] [120]} though researchers have identified that these are not immune to interpretive issues. ^{[121] [122]} Some earlier estimates of the SNP heritability of IQ, derived from genome-wide association studies (GWAS), put the figure much lower, at around 10%. ^[38] For example, one recent published analysis estimated the within-family SNP heritability of cognitive function to be 14% ^[112] . A more recent unpublished preprint estimated the SNP heritability at about 19%. ^{[113] : 5}

Another set of methods attempts to generalize correlations between familial resemblance and genetic resemblance to estimate heritability. ^{[43] [100]} These methods are free from most environmental biases and have typically found that twin estimates of heritability are inflated. ^{[100] [44]} When tested on similar achievement phenotypes like educational attainment, these methods find that twin methods significantly inflate the value of heritability. ^[123]

The phrase 'missing heritability' is commonly used to describe both the gap between classically derived heritability estimates and the variance explained by variants ^[49] and "SNP" heritability. ^{[38] [124]} As the heritability estimates obtained by molecular genetic studies do not match earlier research from twin and family studies, researchers have proposed many theories as to explain these discrepancies for various traits ^{[50] [102] : 27, 111–112}, including but not limited ^[d] to rare variants, ^[129] untagged variants, ^[130] gene-environment interactions ^[131] or environmental confounding. ^{[8] [132] [133] [134] : 66–67} Some researchers believe that the gap for IQ heritability will be narrowed with larger, better-powered genome-wide studies and with whole-genome sequencing, ^{[39] : 4  [103] [108]} while others indicate that the gap may at least in part reflect overestimation of heritability by twin studies. ^{[8] [135]}

Environmental effects

Shared environment

See also: Causes of intelligence § Sociocultural

In biometrical studies that partition variance, "shared environment" refers to hypothetical factors that increase the similarity of relatives in a particular study design. ^{[136] [137] : 331-333} Researchers draw a distinction between "objectively shared environments" (features of the environment that relatives such as siblings share) and the "shared environment" in behavioral genetics studies, which measures only the factors that increase homogeneity. ^{[136] [138] [139]}

Researchers typically use the same designs to estimate shared environment effects as to estimate heritability: twin studies, adoption studies and family studies. ^[140] These methods often come to disparate conclusions about the effects of shared environment. ^{[140] [141]}

Some adoption studies show that by adulthood adoptive siblings aren't more similar in IQ than strangers, ^[142] while adult full siblings show an IQ correlation of 0.24. These findings have lead researchers such as Sandra Scarr and Robert Plomin to conclude that shared environment has minimal effect on intelligence in adulthood. ^{[140] : 1292  [143] : 27} Psychologists Ken Richardson and Sarah Norgate question the assumptions of adoption studies for IQ, highlighting issues with assumptions of additivity and randomization. ^[144] However, adoption studies have also shown that the process of adoption has a positive effect on IQ scores, ^{[75] [145]} and this is associated with factors such as the level of parental education and socioeconomic status of adoptive parents. ^{[146] [147]} In a 1991 paper, psychologist Eric Turkheimer tries to reconcile results from adoption studies that demonstrate large increases in IQ from adoption and those that show low correlations between non-biologically related relatives. He argued that early adoption studies first established that genetic effects influence behavioral phenotypes, while later studies from Schiff and colleagues demonstrate that environment has an influence. ^[148] Researchers Alexandra Burt and Wendy Johnson argue that the joint study of means and variance is necessary to understand the impact of environment. ^[149] In a 2024 study, Burt et al. propose that measurement error or noise might inhibit the identification of specific environmental factors that influence academic achievement. ^[150]

Relationship between correlation between brothers' IQ scores and age differences in Sundet et al. 2008

However, some studies of twins reared apart (e.g. Bouchard, 1990) find a significant shared environmental influence, of at least 10% going into late adulthood. ^[152] Jack Kaplan suggests that the evidence from twin studies and adoption studies are inconsistent, and that twin studies suggest a role for the effect of shared environment on intelligence in adulthood. ^[140] Researchers like Daniel Briley and Eliot Tucker-Drob have suggested that shared environment may have minimal or no effect on the variation in cognition in adulthood. ^[153] One unpublished meta-analysis of twin studies finds estimates of shared environment around 20% for 15-20 year olds, but results for adults no greater than 15%. ^{[154] [ better source needed ]} Researchers have proposed many different factors that could influence intelligence through the shared environment component. One 2018 paper by Laura Engelhardt and colleagues found that measures of socioeconomic status, school demographics and neighborhood socioeconomic status statistically account for most of shared environmental influences on several metrics of intelligence. ^[155]

Another method researchers have used to estimate the shared environmental component for intelligence is studies of siblings. In a large study, Kendler and colleagues used twins, step-siblings, and half-siblings to estimate the shared environment component of many behavioral phenotypes. They found shared environment components for IQ ranging from about 10% to 20%, depending on the relationship class. ^{[156] : Fig 1.} Another study found an association between similarity in age between siblings and similarity in IQ scores, suggesting familial environment influences on intelligence scores. ^[151]

A 2015 study by Johnathan Daw and colleagues argues that the additivity assumption of typical family studies is not correct and that measures of objectively measured shared environment could moderate the estimated nonshared environment component. They found that shared environments such as birth order, household size, sibling age dispersion, and school quality exhibit "large moderating effects on both verbal intelligence and academic achievement". They suggested that these results show how objectively shared environments can influence intelligence contra conclusions that objectively shared environments do not influence children's lives. ^[136]

Non-shared environment

As nonshared environmental variance is simply the leftover variance not accounted for by genetic variance and shared environmental variance, it encompasses any factors that cause differences between siblings and estimates vary based on the estimates of heritability and shared environment. ^{[157] : 22} Robert MCall argues nonshared environment contributes 15-25% of the variance of IQ scores. ^[158] Although parents treat their children differently, known measures of differential treatment explains only a small amount of non-shared environmental influence. ^{[139] [159]} One suggestion is that children react differently to the same environmental factors: that objectively shared measures of the environment can have different effects on siblings. ^{[139] : 79} One 1994 study of the National Longitudinal Survey of Youth found that a measure of the home environment accounted for nonshared environmental variance for several measures of intelligence. ^[160] Daw et al. suggest that nonshared environment is moderated by objectively shared environment. ^[136] More likely influences may be the impact of peers and other experiences outside the family. ^[20] For example, siblings grown up in the same household may have different friends and teachers and even contract different illnesses. This factor may be one of the reasons why IQ score correlations between siblings decreases as they get older. ^[161]

Some authors highlight measurement error as one source of variance attributed to the nonshared environment, as measurement error is captured in the nonshared environment term. ^{[162] [163]} Another factor that produces differences between individuals is stochastic variation, often attributed to nonlinear epigenetic processes. ^{[164] [165]}

Dickens and Flynn model

Dickens and Flynn (2001) argued that the "heritability" figure includes both a direct effect of the genotype on IQ and also indirect effects where the genotype changes the environment, in turn affecting IQ. That is, those with a higher IQ tend to seek out stimulating environments that further increase IQ. The direct effect can initially have been very small but feedback loops can create large differences in IQ. In their model an environmental stimulus can have a very large effect on IQ, even in adults, but this effect also decays over time unless the stimulus continues. ^[166] This model could be adapted to include possible factors, like nutrition in early childhood, that may cause permanent effects.

The Flynn effect is the increase in average intelligence test scores by about 0.3% annually, resulting in the average person today scoring 15 points higher in IQ compared to the generation 50 years ago. ^[167] This effect can be explained by a generally more stimulating environment for all people. Some scientists have suggested that such enhancements are due to better nutrition, better parenting and schooling, as well as exclusion of the least intelligent people from reproduction. However, Flynn and a group of other scientists share the viewpoint that modern life implies solving many abstract problems which leads to a rise in their IQ scores. ^[167]

Indirect genetic effects

Various molecular genetic studies have identified an effect of alleles not carried by an individual, but some relative, on their phenotype. ^[168] These have been varyingly referred to as "dynastic effects", ^[169] "non-transmitted coefficients", ^[170] "interpersonal genetic effects", ^[171] "genetic nurture", ^[172] or more broadly as "indirect genetic effects". ^[173]

Kong ^[174] reports that, "Nurture has a genetic component, i.e. alleles in the parents affect the parents' phenotypes and through that influence the outcomes of the child." These results were obtained through a meta-analysis of educational attainment and polygenic scores of non-transmitted alleles. Although the study deals with educational attainment and not IQ, these two are strongly linked. ^[175]

Influences

Heritability and socioeconomic status

See also: Scarr–Rowe effect and Causes of intelligence § Family

Hypotheses about the relationship between the value of heritability and socioeconomic status originated with Sandra Scarr's 1971 article Race, Social Class, and IQ. ^[176] Scarr notes that existing theories of environmental disadvantage explaining the relationship between social class and IQ predict that more advantaged groups will show greater genetic variance as the advantaged groups don't experience as many detrimental environmental effects. her study, she identified statistical interactions between socioeconomic environment and genetic variance, finding that genetic influences on IQ were stronger in higher socioeconomic groups where environmental conditions allowed genetic potential to be more fully expressed, whereas in lower socioeconomic groups, adverse environments constrained this expression and reduced the impact of genetic differences. ^{[11] [176]} Other than some minor criticisms and a partial replication, the topic was not broached again until the 1996 APA report "Intelligence: Knowns and Unknowns", ^[11] which stated that:

"We should note, however, that low-income and non-white families are poorly represented in existing adoption studies as well as in most twin samples. Thus it is not yet clear whether these studies apply to the population as a whole. It remains possible that, across the full range of income and ethnicity, between-family differences have more lasting consequences for psychometric intelligence." ^[20]

Relationship between the genetic variance component and socioeconomic status in Turkheimer et al. 2003

A 1999 study by David Rowe and colleagues replicated the effect in the National Longitudinal Study of Adolescent Health, finding that among children from poorly-educated parents 'shared environment' was associated with most of the variance, while among children from well-educated parents, genes were associated with most of the variance. ^{[11] [178]} Turkheimer and colleagues (2003) argued that the proportions of IQ variance attributable to genes and environment vary with socioeconomic status. They found that in a study on seven-year-old twins, in impoverished families, 60% of the variance in early childhood IQ was accounted for by the shared family environment, and the contribution of genes is close to zero; in affluent families, the result is almost exactly the reverse. ^[177] The name "Scarr-Rowe effect" was proposed for this proposed interaction between heritability and socioeconomic status by Eric Turkheimer in 2009 paper presentation. ^[179] Later studies found more equivocal results, some failing to replicate the finding in different locations. A 2016 meta-analysis found that the effects varied by country, with moderate interactions in studies from the United States, while the effects were zero or reversed in studies from Australia and Western Europe. ^[180] A large study published the following year, however, found negligible impacts using data from twins in Florida. ^{[181] [182]}

Recent research has focused on the relationship between socioeconomic status and heritability using genomic data. One 2020 study on educational attainment, a closely related but not identical phenotype, ^[183] found that polygenic scores had greater predictive value in lower socioeconomic status quartiles. ^[184] A 2021 study similarly found an increase in SNP heritability with increasing socioeconomic deprivation; lower socioeconomic status was associated with higher heritability. ^[185] One 2024 study used polygenic indices and found that the Scarr-Rowe hypothesis "lacks empirical support" and suggested an alternative theory, the compensatory advantage hypothesis, that heritability was lower among populations with higher heritability. ^[186]

Heritability and age

Twin correlations over development (data from Wilson 1983 )

The interaction between heritability and age over the course of development has been the interest of scientists for many years. Perhaps first described by Ronald Wilson, ^[188] researchers have noted changes in kinship correlations over the life course. Particularly in the case of twins, the correlation between monozygotic twins remains relatively constant, while the correlation between dizygotic twins decreases. ^{[188] : 923} Some early attempts at modeling this change have equivocal results, with Rao et al. finding that heritability increased in adulthood in one model, but not in another, ^[90] while a later study by Devlin et al. did not find support for age moderation of heritability. ^[1] Most researchers in behavioral genetics now believe that estimates of heritability from classical family studies (adoption, twin studies) increase as individuals age, ^{[7] : 179–183  [189]} . One review described a "linearly increasing heritability of intelligence from infancy (20%) through adulthood (60%)". ^{[190] [191]} There are two possible explanations for why measured heritability might increase over the course of the lifespan: that new genetic factors are activated (genetic innovation) or that previous genetic factors explain more variance (genetic amplification). ^{[190] [192]} Behavioral genetics researchers believe that genetic amplification is most supported by the evidence, ^[193] where the same genetic factors that influence IQ at one age are involved later, but produce larger and larger phenotypic effects over time. ^{[7] : 182  [192]} One theory proposed to explain the apparent increase in heritability values is a reciprocal effects model, where environments and genes become correlated over the course of development. ^{[166] [191] [194]} These models incorporate phenotype to environment associations that violate the independence assumption in traditional behavioral genetic models that may overestimate heritability. ^{[195] [196]} Another proposed explanation for why heritability can increase over the course of development is gene-environment interactions. ^[197]

Recent molecular genetics research has also investigated the interaction between genetics or heritability and age over the course of development. One 2018 genome-wide association study found "comparable heritability across age". ^[109] Another study found that the within-family estimate heritability was 0.12 in childhood, 0.13 in adolescence and 0.15 in adulthood. ^[198]

Implications

Development

Some researchers, especially those that work in fields like developmental systems theory, have criticized the concept of heritability as misleading or meaningless. Douglas Wahlsten and Gilbert Gottlieb argue that the prevailing models of behavioral genetics are too simplistic by not accounting for gene-environment interactions. ^[199] Stephen Ceci also highlights the issues with this assumption, noting that they were first raised by Jane Loevinger in 1943. ^{[55] : 139–141  [200]} They assert that the idea of partitioning variance makes no sense when environments and genes interact and argue that such interaction is ubiquitous in human development. ^{[201] [202]} They highlight their belief that heritability analysis requires a hidden assumption they call the "separation of causes", which isn't borne out by biological reality or experimental research. ^[203] Such researchers argue that the notion of heritability gives the false impression that "genes have some direct and isolated influence on traits", rather than another developmental resource that a complex system uses over the course of ontogeny. ^[201]

Between-group heritability

Main article: Race and intelligence

Although IQ differences between individuals have been shown to have a hereditary component, it does not follow that differences in average IQ test performance between racial and ethnic groups have a genetic basis. ^{[10] [11] [14]} The scientific consensus is that genetics does not explain average differences in IQ test performance between racial groups. ^{[204] [205] [206] [14] [207]} Growing evidence indicates that environmental factors, not genetic ones, explain the racial IQ gap. ^{[14] [207] [208]}

Arguments in support of a genetic explanation of racial differences in average IQ are sometimes fallacious. For instance, some hereditarians have cited as evidence the failure of known environmental factors to account for such differences, or the high heritability of intelligence within races. ^[10] In 1972, John DeFries attempted to connect "between group heritability" or h²_B to "within group heritability" or h²_W in an equation ${\displaystyle h_{B}^{2}\approx h_{W}^{2}{\frac {(1-t)r}{(1-r)t}}}$, where r is the intraclass genetic correlation among members of the same group, t is corresponding phenotypic intraclass correlation. ^[209] However, this equation is tautological as the term r is defined in reference to variance among and between groups, meaning r, if anything, depends on h²_B. ^{[209] [10] [210]} Geneticists Joshua Schraiber and Michael Edge showed that even if this quantity r is known, the quantity h²_B is still consistent with "infinitely many configurations of genetic differences among populations". ^[209] They conclude:

Perfect knowledge of within-group heritability provides no information about between-group heritability. Crucially, even if the heritability of between-group differences is estimated correctly, it leaves the direction of the genetic and environmental components of phenotypic difference unclear.

In 2017, the editorial board of Nature to issued a statement differentiating current research on the genetics of intelligence from the racist pseudoscience which it acknowledged has dogged intelligence research since its inception. ^[211] It characterized the idea of genetically determined differences in intelligence between races as definitively false. ^[211] Analysis of polygenic scores sampled from the 1000 Genomes Project has likewise found no evidence that intelligence was under diversifying selection in Africans and Europeans, suggesting that genetic differences cannot be a significant factor in the observed Black-White gap in average IQ test performance. ^[212]

See also

• Human intelligence  – Human capacity or ability to acquire, apprehend and apply knowledge
• Outline of human intelligence  – Overview of and topical guide to human intelligence
• Sex differences in intelligence  – Area of scientific research
• Impact of health on intelligence
• Behavioral epigenetics  – Study of epigenetics' influencing behavior

Footnotes

1. For the full series of papers developing the method, see ^{[32] [33] [34]}
2. For example, individual studies have estimated values as low as "not significantly different from zero" ^[45] to as high as over .8.
3. Strictly speaking, monozygotic twins are not purely genetically identical due to somatic mosaicism and mutations. ^[53]
4. Some other proposed explanations that have largely been discounted include gene-gene interactions like epistasis ^{[125] [126] : 52-53}  and epigenetics, ^{[127] [128]}

References

1. Devlin, B.; Daniels, Michael; Roeder, Kathryn (1997). "The heritability of IQ". Nature. 388 (6641): 468–71. Bibcode:1997Natur.388..468D. doi: 10.1038/41319 . PMID   9242404. S2CID   4313884.
2. Rose, Steven P R (June 2006). "Commentary: Heritability estimates—long past their sell-by date". International Journal of Epidemiology. 35 (3): 525–527. doi: 10.1093/ije/dyl064 . PMID   16645027.
3. Alice Marcus. 2010. Human Genetics: An Overview. Alpha Science section 14.5
4. Davies, G.; Tenesa, A.; Payton, A.; Yang, J.; Harris, S. E.; Liewald, D.; Deary, I. J. (2011). "Genome-wide association studies establish that human intelligence is highly heritable and polygenic". Molecular Psychiatry. 16 (10): 996–1005. doi:10.1038/mp.2011.85. PMC   3182557 . PMID   21826061.
5. Association, New Scientist staff and Press. "Found: more than 500 genes that are linked to intelligence". New Scientist. Archived from the original on 2019-12-13. Retrieved 2018-11-29.
6. Robert J. Sternberg; Elena Grigorenko (2002). The general factor of intelligence . Lawrence Erlbaum Associates. pp.  260–261. ISBN   978-0-8058-3675-2.^{[ page needed ]}
7. Plomin, Robert; DeFries, John C.; Knopik, Valerie S.; Neiderhiser, Jenae M. (24 September 2012). Behavioral Genetics. Worth Publishers. pp. 195–196. ISBN   978-1-4292-4215-8 . Retrieved 4 September 2013.
8. Feldman, Marcus W.; Ramachandran, Sohini (2018-02-12). "Missing compared to what? Revisiting heritability, genes and culture". Philosophical Transactions of the Royal Society B: Biological Sciences. 373 (1743) 20170064. doi:10.1098/rstb.2017.0064. PMC   5812976 . PMID   29440529.
9. Visscher, Peter M.; Hill, William G.; Wray, Naomi R. (2008). "Heritability in the genomics era – concepts and misconceptions" . Nature Reviews Genetics. 9 (4): 255–266. doi:10.1038/nrg2322. PMID   18319743. S2CID   690431.
10. Mackenzie, Brian (1984). "Explaining race differences in IQ: The logic, the methodology, and the evidence". American Psychologist. 39 (11): 1214–1233. doi:10.1037/0003-066X.39.11.1214. Archived from the original on 2021-02-02. Retrieved 2021-01-29.
11. Nisbett, Richard E.; Aronson, Joshua; Blair, Clancy; Dickens, William; Flynn, James; Halpern, Diane F.; Turkheimer, Eric (2012). "Intelligence: New findings and theoretical developments". American Psychologist. 67 (2): 130–159. doi:10.1037/a0026699. ISSN   1935-990X. PMID   22233090.
12. Mitchell, Kevin (2 May 2018). "Why genetic IQ differences between 'races' are unlikely: The idea that intelligence can differ between populations has made headlines again, but the rules of evolution make it implausible". The Guardian. Archived from the original on 29 June 2020. Retrieved 13 June 2020.
13. Panofsky, Aaron; Dasgupta, Kushan; Iturriaga, Nicole (28 September 2020). "How White nationalists mobilize genetics: From genetic ancestry and human biodiversity to counterscience and metapolitics". American Journal of Physical Anthropology. 175 (2): 387–398. doi: 10.1002/ajpa.24150 . PMC   9909835 . PMID   32986847. [T]he claims that genetics defines racial groups and makes them different, that IQ and cultural differences among racial groups are caused by genes, and that racial inequalities within and between nations are the inevitable outcome of long evolutionary processes are neither new nor supported by science (either old or new).
14. Nisbett, Richard E.; Aronson, Joshua; Blair, Clancy; Dickens, William; Flynn, James; Halpern, Diane F.; Turkheimer, Eric (2012). "Group differences in IQ are best understood as environmental in origin" (PDF). American Psychologist. 67 (6): 503–504. doi:10.1037/a0029772. ISSN   0003-066X. PMID   22963427. Archived (PDF) from the original on 23 January 2015. Retrieved 22 July 2013.
15. Nisbett, Eric Turkheimer, Kathryn Paige Harden, and Richard E. (2017-05-18). "Charles Murray is once again peddling junk science about race and IQ". Vox. Retrieved 2025-10-30. All three of us are academic psychologists who have studied human intelligence, and it is our contention that Murray's views do not represent the consensus in our field.
16. Bird, Kevin A. (2021). "No support for the hereditarian hypothesis of the Black–White achievement gap using polygenic scores and tests for divergent selection". American Journal of Physical Anthropology. 175 (2): 465–476. Bibcode:2021AJPA..175..465B. doi:10.1002/ajpa.24216. ISSN   1096-8644. PMID   33529393.
17. Wray N, Visscher P (2008). "Estimating Trait Heritability". Nature Education. 1 (1): 29. Archived from the original on 2 August 2015. Retrieved 24 July 2015.
18. Gazzaniga MS, Heatherton TF, Halpern DF (February 2015). Psychological science (5th ed.). New York: W.W. Norton. ISBN   978-0-393-26313-8. OCLC   908409996.
19. Moore, David S.; Shenk, David (2017). "The heritability fallacy". WIREs Cognitive Science. 8 (1–2) e1400. doi:10.1002/wcs.1400. ISSN   1939-5086. PMID   27906501.
20. Neisser, Ulric; Boodoo, Gwyneth; Bouchard, Thomas J. Jr.; Boykin, A. Wade; Brody, Nathan; Ceci, Stephen J.; Halpern, Diane F.; Loehlin, John C.; et al. (1996). "Intelligence: Knowns and unknowns". American Psychologist. 51 (2): 77–101. doi:10.1037/0003-066X.51.2.77.
21. Brooks-Gunn, Jeanne; Klebanov, Pamela K.; Duncan, Greg J. (1996). "Ethnic Differences in Children's Intelligence Test Scores: Role of Economic Deprivation, Home Environment, and Maternal Characteristics". Child Development. 67 (2): 396–408. doi:10.2307/1131822. JSTOR   1131822. PMID   8625720.
22. Johnson, Wendy; Turkheimer, Eric; Gottesman, Irving I.; Bouchard Jr., Thomas J. (2009). "Beyond Heritability: Twin Studies in Behavioral Research". Current Directions in Psychological Science. 18 (4): 217–20. doi:10.1111/j.1467-8721.2009.01639.x. PMC   2899491 . PMID   20625474.
23. Haworth, Claire; Davis, Oliver (2014). "From observational to dynamic genetics". Frontiers in Genetics. 5: 6. doi: 10.3389/fgene.2014.00006 . ISSN   1664-8021. PMC   3896969 . PMID   24478793.
24. Strachan, Tom; Read, Andrew (2011). Human Molecular Genetics, Fourth Edition . New York: Garland Science. pp.  80–81. ISBN   978-0-8153-4149-9.
25. Humphreys, Lloyd G. (1978). "To understand regression from parent to offspring, think statistically". Psychological Bulletin. 85 (6): 1317–1322. doi:10.1037/0033-2909.85.6.1317. PMID   734015.
26. Schacter, Daniel, Daniel Gilbert, and Daniel Wegner. "Intelligence." Psychology. 2 ed. New York: Worth Publishers, 2010. 405-406. Print.
27. Evans, David M.; Gillespie, N. A.; Martin, N. G. (2002-10-01). "Biometrical genetics". Biological Psychology. 61 (1): 33–51. doi:10.1016/S0301-0511(02)00051-0. ISSN   0301-0511. PMID   12385668.
28. Lynch, Michael (1998). Genetics and Analysis of Quantitative Traits. Sinauer Associates. ISBN   978-0-87893-481-2.
29. Coop, Graham (21 September 2020). Population and Quantitative Genetics (3rd ed.).
30. Richardson, Ken; Norgate, Sarah H. (2006). "A Critical Analysis of IQ Studies of Adopted Children". Human Development. 49 (6): 319–335. doi:10.1159/000096531. ISSN   0018-716X.
31. Loehlin, John C.; Horn, Joseph M.; Willerman, Lee (1999). "Heredity, environment, and IQ in the Texas Adoption Project". In Sternberg, Robert J. (ed.). Intelligence, heredity, and environment (Repr ed.). Cambridge: Cambridge Univ. Press. pp. 126–160. ISBN   978-0-521-46904-3.
32. Rice, J.; Cloninger, C. R.; Reich, T. (November 1978). "Multifactorial inheritance with cultural transmission and assortative mating. I. Description and basic properties of the unitary models". American Journal of Human Genetics. 30 (6): 618–643. ISSN   0002-9297. PMC   1685878 . PMID   747189.
33. Cloninger, C. R.; Rice, J.; Reich, T. (March 1979). "Multifactorial inheritance with cultural transmission and assortative mating. II. a general model of combined polygenic and cultural inheritance". American Journal of Human Genetics. 31 (2): 176–198. ISSN   0002-9297. PMC   1685756 . PMID   453202.
34. Cloninger, C. R.; Rice, J.; Reich, T. (May 1979). "Multifactorial inheritance with cultural transmission and assortative mating. III. Family structure and the analysis of separation experiments". American Journal of Human Genetics. 31 (3): 366–388. ISSN   0002-9297. PMC   1685778 . PMID   572636.
35. Rao, D. C.; Morton, N. E.; Cloninger, C. R. (April 1979). "Path analysis under generalized assortative mating: I. Theory". Genetics Research. 33 (2): 175–188. doi:10.1017/S0016672300018310. ISSN   1469-5073. PMID   478296.
36. Zaitlen, Noah; Kraft, Peter (2012-10-01). "Heritability in the genome-wide association era". Human Genetics. 131 (10): 1655–1664. doi:10.1007/s00439-012-1199-6. ISSN   1432-1203. PMC   3432754 . PMID   22821350.
37. Tang, Mingsheng; Wang, Tong; Zhang, Xuefen (2022-03-14). "A review of SNP heritability estimation methods". Briefings in Bioinformatics. 23 (3) bbac067. doi:10.1093/bib/bbac067. ISSN   1467-5463. PMID   35289357. Archived from the original on 2025-08-26.
38. Matthews, Lucas J.; Turkheimer, Eric (2022-06-01). "Three legs of the missing heritability problem". Studies in History and Philosophy of Science. 93: 183–191. doi:10.1016/j.shpsa.2022.04.004. ISSN   0039-3681. PMC   9172633 . PMID   35533541.
39. Plomin, Robert; von Stumm, Sophie (8 January 2018). "The new genetics of intelligence". Nature Reviews. Genetics. 19 (3): 148–159. doi:10.1038/nrg.2017.104. ISSN   1471-0064. PMC   5985927 . PMID   29335645.
40. Yang, Jian; Benyamin, Beben; McEvoy, Brian P.; Gordon, Scott; Henders, Anjali K.; Nyholt, Dale R.; Madden, Pamela A.; Heath, Andrew C.; Martin, Nicholas G.; Montgomery, Grant W.; Goddard, Michael E.; Visscher, Peter M. (July 2010). "Common SNPs explain a large proportion of the heritability for human height". Nature Genetics. 42 (7): 565–569. doi:10.1038/ng.608. ISSN   1546-1718. PMC   3232052 . PMID   20562875.
41. Benyamin, Beben; Visscher, Peter M.; McRae, Allan F. (February 2009). "Family-based genome-wide association studies". Pharmacogenomics. 10 (2): 181–190. doi:10.2217/14622416.10.2.181. ISSN   1744-8042. PMID   19207019.
42. Zaitlen, Noah; Kraft, Peter; Patterson, Nick; Pasaniuc, Bogdan; Bhatia, Gaurav; Pollack, Samuela; Price, Alkes L. (May 2013). "Using extended genealogy to estimate components of heritability for 23 quantitative and dichotomous traits". PLOS Genetics. 9 (5) e1003520. doi: 10.1371/journal.pgen.1003520 . ISSN   1553-7404. PMC   3667752 . PMID   23737753.
43. Visscher, Peter M.; Medland, Sarah E.; Ferreira, Manuel A. R.; Morley, Katherine I.; Zhu, Gu; Cornes, Belinda K.; Montgomery, Grant W.; Martin, Nicholas G. (2006-03-24). "Assumption-Free Estimation of Heritability from Genome-Wide Identity-by-Descent Sharing between Full Siblings". PLOS Genetics. 2 (3) e41. doi: 10.1371/journal.pgen.0020041 . ISSN   1553-7404. PMC   1413498 . PMID   16565746.
44. Young, Alexander I.; Frigge, Michael L.; Gudbjartsson, Daniel F.; Thorleifsson, Gudmar; Bjornsdottir, Gyda; Sulem, Patrick; Masson, Gisli; Thorsteinsdottir, Unnur; Stefansson, Kari; Kong, Augustine (September 2018). "Relatedness disequilibrium regression estimates heritability without environmental bias". Nature Genetics. 50 (9): 1304–1310. doi:10.1038/s41588-018-0178-9. ISSN   1546-1718. PMC   6130754 . PMID   30104764.
45. Adams, B.; Ghodsian, M.; Richardson, K. (September 1976). "Evidence for a low upper limit of heritability of mental test performance in a national sample of twins". Nature. 263 (5575): 314–316. Bibcode:1976Natur.263..314A. doi:10.1038/263314a0. ISSN   1476-4687. PMID   986563.
46. Panofsky, Aaron (2014). Misbehaving Science: Controversy and the Development of Behavior Genetics. University of Chicago Press. ISBN   978-0-226-05845-0.
47. Meyer, Michelle N; Appelbaum, Paul S.; Benjamin, Daniel J.; Callier, Shawneequa; Comfort, Nathaniel; Conley, Dalton; Freese, Jeremy; Garrison, Nanibaa' A; Hammonds, Evelyn M.; Harden, K. Paige; Lee, Sandra Soo-Jin; Martin, Alicia R.; Martschenko, Daphne Oluwaseun; Neale, Benjamin M.; et., al (20 April 2023). "Wrestling with Social and Behavioral Genomics: Risks, Potential Benefits, and Ethical Responsibility". The Hastings Center Report. 53 (S1): S2 –S49. doi:10.1002/hast.1477. PMC   10433733 . PMID   37078667.
48. Yang, Jian; Zeng, Jian; Goddard, Michael E.; Wray, Naomi R.; Visscher, Peter M. (1 September 2017). "Concepts, estimation and interpretation of SNP-based heritability". Nature Genetics. 49 (9): 1304–1310. doi:10.1038/ng.3941. ISSN   1546-1718. PMID   28854176.
49. Young, Alexander I. (2019-06-24). "Solving the missing heritability problem". PLOS Genetics. 15 (6) e1008222. doi: 10.1371/journal.pgen.1008222 . ISSN   1553-7404. PMC   6611648 . PMID   31233496.
50. Génin, Emmanuelle (2020-01-01). "Missing heritability of complex diseases: case solved?". Human Genetics. 139 (1): 103–113. doi:10.1007/s00439-019-02034-4. ISSN   1432-1203. PMID   31165258.
51. Rizzi, Thais S.; Posthuma, Danielle (2013). "Genes and Intelligence". In Reisberg, Daniel (ed.). The Oxford handbook of cognitive psychology. Oxford library of psychology. New York Oxford: Oxford University Press. pp. 834–836. ISBN   978-0-19-997830-4.
52. Polderman, Tinca J. C.; Benyamin, Beben; de Leeuw, Christiaan A.; Sullivan, Patrick F.; van Bochoven, Arjen; Visscher, Peter M.; Posthuma, Danielle (July 2015). "Meta-analysis of the heritability of human traits based on fifty years of twin studies". Nature Genetics. 47 (7): 702–709. doi:10.1038/ng.3285. ISSN   1546-1718. PMID   25985137.
53. Charney, Evan (2012-10-24). "Behavior genetics and postgenomics". Behavioral and Brain Sciences. 35 (5): 331–358. doi:10.1017/S0140525X11002226. ISSN   0140-525X. PMID   23095378.
54. Verweij, Karin J. H.; Mosing, Miriam A.; Zietsch, Brendan P.; Medland, Sarah E. (2012), Elston, Robert C.; Satagopan, Jaya M.; Sun, Shuying (eds.), "Estimating Heritability from Twin Studies", Statistical Human Genetics: Methods and Protocols, vol. 850, Totowa, NJ: Humana Press, pp. 151–170, doi:10.1007/978-1-61779-555-8_9, ISBN   978-1-61779-555-8, PMID   22307698
55. Ceci, Stephen (1 September 1996). On Intelligence: A Biological Treatise on Intellectual Development (1st ed.). Harvard University Press. ISBN   978-0-674-63456-5.
56. Stenberg, Anders (2013-03-01). "Interpreting estimates of heritability – A note on the twin decomposition". Economics & Human Biology. 11 (2): 201–205. doi:10.1016/j.ehb.2012.05.002. ISSN   1570-677X. PMID   22676967.
57. Richardson, Ken; Norgate, Sarah (2005). "The equal environments assumption of classical twin studies may not hold". British Journal of Educational Psychology. 75 (3): 339–350. doi:10.1348/000709904X24690. ISSN   2044-8279. PMID   16238870.
58. Guo, Sun-Wei (1999). "The Behaviors of Some Heritability Estimators in the Complete Absence of Genetic Factors". Human Heredity. 49 (4): 215–228. doi:10.1159/000022878. ISSN   0001-5652. PMID   10436384.
59. Guo, Sun-Wei (2001). "Does Higher Concordance in Monozygotic Twins Than in Dizygotic Twins Suggest a Genetic Component?". Human Heredity. 51 (3): 121–132. doi:10.1159/000053333. ISSN   0001-5652. PMID   11173963.
60. Bingley, Paul; Cappellari, Lorenzo; Tatsiramos, Konstantinos (5 Jul 2024). On the Origins of Socioeconomic Inequalities: Evidence from Twin Families (Report). Archived from the original on 8 July 2025. Retrieved 13 October 2025.
61. Bouchard, Thomas (1999). "IQ similarity in twins reared apart: Findings and responses to critics". In Sternberg, Robert J. (ed.). Intelligence, heredity, and environment (Repr ed.). Cambridge: Cambridge Univ. Press. ISBN   978-0-521-46904-3.
62. Segal, Nancy L. (2021). Deliberately Divided: Inside the Controversial Study of Twins and Triplets Adopted Apart. Rowman & Littlefield Publishing Group. ISBN   978-1-5381-3285-2.
63. Joseph, Jay (2014-11-20). The Trouble with Twin Studies: A Reassessment of Twin Research in the Social and Behavioral Sciences (1 ed.). Routledge. doi:10.4324/9781315748382. ISBN   978-1-315-74838-2.
64. Farber, Susan L. (1981). Identical twins reared apart: a reanalysis. New York: Basic Books. ISBN   978-0-465-03228-0.
65. Kaufman, Alan S.; Lichtenberger, Elizabeth (2006). Assessing Adolescent and Adult Intelligence (3rd ed.). Hoboken (NJ): Wiley. ISBN   978-0-471-73553-3.^{[ page needed ]}
66. Pedersen, N. L.; Lichtenstein, P.; Plomin, R.; DeFaire, U.; McClearn, G. E.; Matthews, K. A. (1989). "Genetic and environmental influences for type A-like measures and related traits: a study of twins reared apart and twins reared together". Psychosomatic Medicine. 51 (4): 428–440. doi:10.1097/00006842-198907000-00006. ISSN   0033-3174. PMID   2772107.
67. Joseph, Jay (2022). "A Reevaluation of the 1990 "Minnesota Study of Twins Reared Apart" IQ Study". Human Development. 66 (1): 48–65. doi:10.1159/000521922. ISSN   0018-716X.
68. Nisbett, Richard E. (2009). Intelligence and How to Get It: Why Schools and Cultures Count. W. W. Norton & Company. ISBN   978-0-393-07141-2.
69. Kempthorne, Oscar (1978). "A Biometrics Invited Paper: Logical, Epistemological and Statistical Aspects of Nature-Nurture Data Interpretation". Biometrics. 34 (1): 1–23. doi:10.2307/2529584. ISSN   0006-341X. JSTOR   2529584.
70. Bronfenbrenner, Urie, ed. (2005). Making human beings human: bioecological perspectives on human development. The Sage program on applied developmental science. Thousand Oaks: Sage Publications. ISBN   978-0-7619-2711-2.
71. Kamin, Leon (2012). The Science and Politics of I.Q.. Hoboken: Taylor and Francis. ISBN   978-0-89859-129-3.
72. Horvath, Jared C.; Fabricant, Katie (2025-07-01). "IQ differences of identical twins reared apart are significantly influenced by educational differences". Acta Psychologica. 257 105072. doi:10.1016/j.actpsy.2025.105072. ISSN   0001-6918. PMID   40403412.
73. Bouchard, Thomas J. (March 1982). "Identical Twins Reared Apart: Reanalysis or Pseudo-analysis?". Contemporary Psychology: A Journal of Reviews. 27 (3): 190–191. doi:10.1037/021001.
74. Turkheimer, Eric (November 1991). "Individual and group differences in adoption studies of IQ". Psychological Bulletin. 110 (3): 392–405. doi:10.1037/0033-2909.110.3.392. ISSN   1939-1455. Archived from the original on 2025-07-10.
75. van IJzendoorn, Marinus H.; Juffer, Femmie; Poelhuis, Caroline W. Klein (2005). "Adoption and Cognitive Development: A Meta-Analytic Comparison of Adopted and Nonadopted Children's IQ and School Performance". Psychological Bulletin. 131 (2): 301–316. doi:10.1037/0033-2909.131.2.301. ISSN   1939-1455. PMID   15740423. Archived from the original on 2025-09-16.
76. Kendler, Kenneth S.; Turkheimer, Eric; Ohlsson, Henrik; Sundquist, Jan; Sundquist, Kristina (2015-04-14). "Family environment and the malleability of cognitive ability: A Swedish national home-reared and adopted-away cosibling control study". Proceedings of the National Academy of Sciences. 112 (15): 4612–4617. Bibcode:2015PNAS..112.4612K. doi: 10.1073/pnas.1417106112 . PMC   4403216 . PMID   25831538.
77. Beauchamp, Jonathan; Schmitz, Lauren; McGue, Matt; Lee, James (30 November 2023). Nature-Nurture Interplay: Evidence from Molecular Genetic and Pedigree Data in Korean American Adoptees (Report). SSRN. doi:10.2139/ssrn.4491976. SSRN   4491976 . Retrieved 30 October 2025.
78. Stoolmiller, Mike (1998-11-01). "Correcting Estimates of Shared Environmental Variance for Range Restriction in Adoption Studies Using a Truncated Multivariate Normal Model". Behavior Genetics. 28 (6): 429–441. doi:10.1023/A:1021685211674. ISSN   1573-3297. PMID   9926612.
79. Stoolmiller, Mike (1999). "Implications of the restricted range of family environments for estimates of heritability and nonshared environment in behavior–genetic adoption studies". Psychological Bulletin. 125 (4): 392–409. doi:10.1037/0033-2909.125.4.392. ISSN   1939-1455. PMID   10414224.
80. Kaplan, Jack S. (2012). "The effects of shared environment on adult intelligence: A critical review of adoption, twin, and MZA studies". Developmental Psychology. 48 (5): 1292–1298. doi:10.1037/a0028133. ISSN   1939-0599. PMID   22746221.
81. Burt, S. Alexandra; O'Keefe, Patrick; Johnson, Wendy; Thaler, Daniel; Leve, Leslie D.; Natsuaki, Misaki N.; Reiss, David; Shaw, Daniel S.; Ganiban, Jody M.; Neiderhiser, Jenae M. (May 2024). "The Detection of Environmental Influences on Academic Achievement Appears to Depend on the Analytic Approach". Behavior Genetics. 54 (3): 252–267. doi:10.1007/s10519-024-10179-w. ISSN   1573-3297. PMID   38587720.
82. Cheesman, Rosa; Hunjan, Avina; Coleman, Jonathan R. I.; Ahmadzadeh, Yasmin; Plomin, Robert; McAdams, Tom A.; Eley, Thalia C.; Breen, Gerome (2020-05-01). "Comparison of Adopted and Nonadopted Individuals Reveals Gene–Environment Interplay for Education in the UK Biobank". Psychological Science. 31 (5): 582–591. doi:10.1177/0956797620904450. ISSN   0956-7976. PMC   7238511 . PMID   32302253.
83. Bouchard, Thomas J.; McGue, Matthew (1981). "Familial Studies of Intelligence: A Review". Science. 212 (4498): 1055–9. Bibcode:1981Sci...212.1055B. doi:10.1126/science.7195071. PMID   7195071.
84. Loehlin, John C. (1989). "Partitioning environmental and genetic contributions to behavioral development". American Psychologist. 44 (10): 1285–1292. doi:10.1037/0003-066X.44.10.1285. ISSN   1935-990X. PMID   2679255.
85. Plomin, Robert; Loehlin, John C. (1989-05-01). "Direct and indirect IQ heritability estimates: A puzzle". Behavior Genetics. 19 (3): 331–342. doi:10.1007/BF01066162. ISSN   1573-3297. PMID   2667514.
86. Chipuer, Heather M.; Rovine, Michael J.; Plomin, Robert (1990-01-01). "LISREL modeling: Genetic and environmental influences on IQ revisited". Intelligence. 14 (1): 11–29. doi:10.1016/0160-2896(90)90011-H. ISSN   0160-2896.
87. Bouchard, Thomas J.; McGue, Matt (January 2003). "Genetic and environmental influences on human psychological differences". Journal of Neurobiology. 54 (1): 4–45. doi: 10.1002/neu.10160 . PMID   12486697.
88. Shen, Hao; Feldman, Marcus W. (2021-10-16). "Cultural versus biological inheritance: A retrospective view of Cavalli-Sforza and Feldman (1973)". Human Population Genetics and Genomics. 1 (1) 0003: 1–18. doi:10.47248/hpgg2101010003. ISSN   2770-5005.
89. Benning, John W.; Carlson, Jedidiah; Smith, Olivia S.; Shaw, Ruth G.; Harpak, Arbel (2025-07-11). "Confounding Fuels Misinterpretation in Human Genetics". bioRxiv   10.1101/2023.11.01.565061 .
90. Rao, D. C.; Morton, N. E.; Lalouel, J. M.; Lew, R. (April 1982). "Path analysis under generalized assortative mating: II. American I.Q." Genetics Research. 39 (2): 187–198. doi:10.1017/S0016672300020875. ISSN   1469-5073. PMID   7084667.
91. Rice, J.; Cloninger, C. R.; Reich, T. (January 1980). "Analysis of behavioral traits in the presence of cultural transmission and assortative mating: Applications to IQ and SES". Behavior Genetics. 10 (1): 73–92. doi:10.1007/BF01067320. ISSN   0001-8244. PMID   7425997.
92. Goldberger, Arthur (February 1978). Models and Methods in the IQ Debate: Part I (PDF) (Report). Archived (PDF) from the original on 23 May 2024. Retrieved 17 October 2025.
93. Otto, Sarah P; Feldman, Marcus W.; Christiansen, Freddy B. (1995). Genetic and Cultural Inheritance of Continuous Traits (Report). Retrieved 13 October 2025.
94. Karlin, S.; Cameron, E. C.; Chakraborty, R. (July 1983). "Path analysis in genetic epidemiology: a critique". American Journal of Human Genetics. 35 (4): 695–732. ISSN   0002-9297. PMC   1685747 . PMID   6349335.
95. Wright, S. (July 1983). "On "Path analysis in genetic epidemiology: a critique"". American Journal of Human Genetics. 35 (4): 757–768. ISSN   0002-9297. PMC   1685743 . PMID   6881145.
96. Cloninger, C. R.; Rao, D. C.; Rice, J.; Reich, T.; Morton, N. E. (July 1983). "A defense of path analysis in genetic epidemiology". American Journal of Human Genetics. 35 (4): 733–756. ISSN   0002-9297. PMC   1685729 . PMID   6349336.
97. Goldberger, Arthur S. (2005), Andrews, Donald W. K.; Stock, James H. (eds.), "Structural Equation Models in Human Behavior Genetics", Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg, Cambridge: Cambridge University Press, pp. 11–26, doi:10.1017/cbo9780511614491.003, ISBN   978-0-521-84441-3 , retrieved 2025-10-14
98. Gifford, Fred (1989-06-01). "Complex genetic causation of human disease: Critiques of and rationales for heritability and path analysis". Theoretical Medicine. 10 (2): 107–122. doi:10.1007/BF00539877. ISSN   1573-1200. PMID   2675372.
99. Chabris, C. F.; Hebert, B. M.; Benjamin, D. J.; Beauchamp, J.; Cesarini, D.; Van Der Loos, M.; Johannesson, M.; Magnusson, P. K. E.; Lichtenstein, P.; Atwood, C. S.; Freese, J.; Hauser, T. S.; Hauser, R. M.; Christakis, N.; Laibson, D. (2012). "Most Reported Genetic Associations with General Intelligence Are Probably False Positives". Psychological Science. 23 (11): 1314–23. doi:10.1177/0956797611435528. PMC   3498585 . PMID   23012269.
100. Kemper, Kathryn E.; Yengo, Loic; Zheng, Zhili; Abdellaoui, Abdel; Keller, Matthew C.; Goddard, Michael E.; Wray, Naomi R.; Yang, Jian; Visscher, Peter M. (2021-02-16). "Phenotypic covariance across the entire spectrum of relatedness for 86 billion pairs of individuals". Nature Communications. 12 (1): 1050. Bibcode:2021NatCo..12.1050K. doi:10.1038/s41467-021-21283-4. ISSN   2041-1723. PMC   7886899 . PMID   33594080.
101. Rosenberg, Noah A; Edge, Michael D; Pritchard, Jonathan K; Feldman, Marcus W (2018-12-27). "Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences". Evolution, Medicine, and Public Health. 2019 (1): 26–34. doi:10.1093/emph/eoy036. ISSN   2050-6201. PMC   6393779 . PMID   30838127. Archived from the original on 2025-07-27.
102. Mills, Melinda; Barban, Nicola; Tropf, Felix C. (2020). An introduction to statistical genetic data analysis. Cambridge, Massachusetts London, England: The MIT Press. ISBN   978-0-262-53838-1.
103. Plomin, Robert; von Stumm, Sophie (January 2022). "Polygenic scores: prediction versus explanation". Molecular Psychiatry. 27 (1): 49–52. doi:10.1038/s41380-021-01348-y. ISSN   1476-5578. PMC   8960389 . PMID   34686768.
104. Cesarini, David; Visscher, Peter M. (2017-02-01). "Genetics and educational attainment". npj Science of Learning. 2 (1): 4. Bibcode:2017npjSL...2....4C. doi:10.1038/s41539-017-0005-6. ISSN   2056-7936. PMC   6220209 . PMID   30631451.
105. Pingault, Jean-Baptiste; Allegrini, Andrea G.; Odigie, Tracy; Frach, Leonard; Baldwin, Jessie R.; Rijsdijk, Frühling; Dudbridge, Frank (2022). "Research Review: How to interpret associations between polygenic scores, environmental risks, and phenotypes". Journal of Child Psychology and Psychiatry. 63 (10): 1125–1139. doi:10.1111/jcpp.13607. ISSN   1469-7610. PMC   9790749 . PMID   35347715.
106. Speed, Doug; Cai, Na; Johnson, Michael R.; Nejentsev, Sergey; Balding, David J. (July 2017). "Reevaluation of SNP heritability in complex human traits". Nature Genetics. 49 (7): 986–992. doi:10.1038/ng.3865. ISSN   1546-1718. PMC   5493198 . PMID   28530675.
107. Huang, Jinguo; Kleman, Nicole; Basu, Saonli; Shriver, Mark D; Zaidi, Arslan A (2025-08-06). Li, Y (ed.). "Interpreting SNP heritability in admixed populations". Genetics. 230 (4) iyaf100. doi:10.1093/genetics/iyaf100. ISSN   1943-2631. PMC   12273224 . PMID   40404285.
108. Oxley, Florence A. R.; Wilding, Kirsty; von Stumm, Sophie (2024-11-01). "DNA and IQ: Big deal or much ado about nothing? – A meta-analysis". Intelligence. 107 101871. doi:10.1016/j.intell.2024.101871. ISSN   0160-2896.
109. Savage, Jeanne E.; Jansen, Philip R.; Stringer, Sven; Watanabe, Kyoko; Bryois, Julien; de Leeuw, Christiaan A.; Nagel, Mats; Awasthi, Swapnil; Barr, Peter B.; Coleman, Jonathan R. I.; Grasby, Katrina L.; Hammerschlag, Anke R.; Kaminski, Jakob A.; Karlsson, Robert; Krapohl, Eva (July 2018). "Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence". Nature Genetics. 50 (7): 912–919. doi:10.1038/s41588-018-0152-6. ISSN   1546-1718. PMC   6411041 . PMID   29942086.
110. Procopio, Francesca; Liao, Wangjingyi; Rimfeld, Kaili; Malanchini, Margherita; von Stumm, Sophie; Allegrini, Andrea G.; Plomin, Robert (February 2025). "Multi-polygenic score prediction of mathematics, reading, and language abilities independent of general cognitive ability". Molecular Psychiatry. 30 (2): 414–422. doi:10.1038/s41380-024-02671-w. ISSN   1476-5578. PMC   11746139 . PMID   39085392.
111. Nivard, Michel G.; Belsky, Daniel W.; Harden, K. Paige; Baier, Tina; Andreassen, Ole A.; Ystrøm, Eivind; van Bergen, Elsje; Lyngstad, Torkild H. (2024-01-15). "More than nature and nurture, indirect genetic effects on children's academic achievement are consequences of dynastic social processes". Nature Human Behaviour. 8 (4): 771–778. doi:10.1038/s41562-023-01796-2. ISSN   2397-3374. PMC   11569812 . PMID   38225408.
112. Howe, Laurence J.; Nivard, Michel G.; Morris, Tim T.; Hansen, Ailin F.; Rasheed, Humaira; Cho, Yoonsu; Chittoor, Geetha; Ahlskog, Rafael; Lind, Penelope A.; Palviainen, Teemu; van der Zee, Matthijs D.; Cheesman, Rosa; Mangino, Massimo; Wang, Yunzhang; Li, Shuai (May 2022). "Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects". Nature Genetics. 54 (5): 581–592. doi:10.1038/s41588-022-01062-7. ISSN   1546-1718. PMC   9110300 . PMID   35534559.
113. Tan, Tammy; Jayashankar, Hariharan; Guan, Junming; Nehzati, Seyed Moeen; Mir, Mahdi; Bennett, Michael; Agerbo, Esben; Ahlskog, Rafael; Anapaz, Ville Pinto de Andrade (2024-10-04). "Family-GWAS reveals effects of environment and mating on genetic associations". medRxiv   10.1101/2024.10.01.24314703 .
114. Border, Richard; Wang, Jeremy; Caggiano, Christa; Sankararaman, Sriram; Schork, Andrew J.; Turley, Patrick; Young, Alexander S.; Benjamin, Daniel J.; Dahl, Andy W. (2024-10-18). "Simple models of non-random mating and environmental transmission bias standard human genetics statistical methods". bioRxiv   10.1101/2024.10.16.618755 .
115. Selzam, Saskia; Ritchie, Stuart J.; Pingault, Jean-Baptiste; Reynolds, Chandra A.; O'Reilly, Paul F.; Plomin, Robert (2019-08-01). "Comparing Within- and Between-Family Polygenic Score Prediction". The American Journal of Human Genetics. 105 (2): 351–363. doi:10.1016/j.ajhg.2019.06.006. ISSN   0002-9297. PMC   6698881 . PMID   31303263.
116. Yang, Jian; Lee, S. Hong; Goddard, Michael E.; Visscher, Peter M. (2011-01-07). "GCTA: A Tool for Genome-wide Complex Trait Analysis". The American Journal of Human Genetics. 88 (1): 76–82. doi:10.1016/j.ajhg.2010.11.011. ISSN   0002-9297. PMC   3014363 . PMID   21167468.
117. Davies, G.; Marioni, R. E.; Liewald, D. C.; Hill, W. D.; Hagenaars, S. P.; Harris, S. E.; Ritchie, S. J.; Luciano, M.; Fawns-Ritchie, C.; Lyall, D.; Cullen, B.; Cox, S. R.; Hayward, C.; Porteous, D. J.; Evans, J. (June 2016). "Genome-wide association study of cognitive functions and educational attainment in UK Biobank (N=112 151)". Molecular Psychiatry. 21 (6): 758–767. doi:10.1038/mp.2016.45. ISSN   1476-5578. PMC   4879186 . PMID   27046643.
118. Haworth, Simon; Mitchell, Ruth; Corbin, Laura; Wade, Kaitlin H.; Dudding, Tom; Budu-Aggrey, Ashley; Carslake, David; Hemani, Gibran; Paternoster, Lavinia; Smith, George Davey; Davies, Neil; Lawson, Daniel J.; J. Timpson, Nicholas (2019-01-18). "Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis". Nature Communications. 10 (1): 333. Bibcode:2019NatCo..10..333H. doi:10.1038/s41467-018-08219-1. ISSN   2041-1723. PMC   6338768 . PMID   30659178.
119. Dandine-Roulland, Claire; Bellenguez, Céline; Debette, Stéphanie; Amouyel, Philippe; Génin, Emmanuelle; Perdry, Hervé (2016-05-25). "Accuracy of heritability estimations in presence of hidden population stratification". Scientific Reports. 6 (1) 26471. Bibcode:2016NatSR...626471D. doi:10.1038/srep26471. ISSN   2045-2322. PMC   4879529 . PMID   27220488.
120. Morris, Tim T.; Davies, Neil M.; Hemani, Gibran; Smith, George Davey (2020-04-15). "Population phenomena inflate genetic associations of complex social traits". Science Advances. 6 (16) eaay0328. Bibcode:2020SciA....6..328M. doi:10.1126/sciadv.aay0328. PMC   7159920 . PMID   32426451.
121. Howe, Laurence J.; Evans, David M.; Hemani, Gibran; Smith, George Davey; Davies, Neil M. (2022-07-07). "Evaluating indirect genetic effects of siblings using singletons". PLOS Genetics. 18 (7) e1010247. doi: 10.1371/journal.pgen.1010247 . ISSN   1553-7404. PMC   9262210 . PMID   35797272.
122. Veller, Carl; Coop, Graham M. (2024-04-11). "Interpreting population- and family-based genome-wide association studies in the presence of confounding". PLOS Biology. 22 (4) e3002511. doi: 10.1371/journal.pbio.3002511 . ISSN   1545-7885. PMC   11008796 . PMID   38603516.
123. Young, Alexander I.; Frigge, Michael L.; Gudbjartsson, Daniel F.; Thorleifsson, Gudmar; Bjornsdottir, Gyda; Sulem, Patrick; Masson, Gisli; Thorsteinsdottir, Unnur; Stefansson, Kari; Kong, Augustine (September 2018). "Relatedness disequilibrium regression estimates heritability without environmental bias". Nature Genetics. 50 (9): 1304–1310. doi:10.1038/s41588-018-0178-9. ISSN   1546-1718. PMC   6130754 . PMID   30104764.
124. Oxley, Florence A. R.; Wilding, Kirsty; von Stumm, Sophie (2024-11-01). "DNA and IQ: Big deal or much ado about nothing? – A meta-analysis". Intelligence. 107 101871. doi:10.1016/j.intell.2024.101871. ISSN   0160-2896.
125. Zuk, Or; Hechter, Eliana; Sunyaev, Shamil R.; Lander, Eric S. (2012-01-24). "The mystery of missing heritability: Genetic interactions create phantom heritability". Proceedings of the National Academy of Sciences. 109 (4): 1193–1198. Bibcode:2012PNAS..109.1193Z. doi: 10.1073/pnas.1119675109 . PMC   3268279 . PMID   22223662.
126. Conley, Dalton; Fletcher, Jason (2017). The genome factor: what the social genomics revolution reveals about ourselves, our history, and the future. Princeton: Princeton university press. ISBN   978-0-691-16474-8.
127. Bourrat, Pierrick; Lu, Qiaoying; Jablonka, Eva (2017). "Why the missing heritability might not be in the DNA". BioEssays. 39 (7) 1700067. doi:10.1002/bies.201700067. ISSN   1521-1878. PMID   28582595.
128. Trerotola, Marco; Relli, Valeria; Simeone, Pasquale; Alberti, Saverio (2015-07-28). "Epigenetic inheritance and the missing heritability". Human Genomics. 9 (1): 17. doi: 10.1186/s40246-015-0041-3 . ISSN   1479-7364. PMC   4517414 . PMID   26216216.
129. Zuk, Or; Schaffner, Stephen F.; Samocha, Kaitlin; Do, Ron; Hechter, Eliana; Kathiresan, Sekar; Daly, Mark J.; Neale, Benjamin M.; Sunyaev, Shamil R.; Lander, Eric S. (2014-01-28). "Searching for missing heritability: Designing rare variant association studies". Proceedings of the National Academy of Sciences. 111 (4): E455 –E464. Bibcode:2014PNAS..111E.455Z. doi: 10.1073/pnas.1322563111 . PMC   3910587 . PMID   24443550.
130. Gymrek, Melissa; Goren, Alon (2021-09-24). "Missing heritability may be hiding in repeats". Science. 373 (6562): 1440–1441. Bibcode:2021Sci...373.1440G. doi:10.1126/science.abl7794. PMID   34554784.
131. Kaprio, J. (2012). "Twins and the mystery of missing heritability: the contribution of gene–environment interactions". Journal of Internal Medicine. 272 (5): 440–448. doi:10.1111/j.1365-2796.2012.02587.x. ISSN   1365-2796. PMC   4422871 . PMID   22934540.
132. Shen, Hao; Feldman, Marcus W. (2020-10-13). "Genetic nurturing, missing heritability, and causal analysis in genetic statistics". Proceedings of the National Academy of Sciences. 117 (41): 25646–25654. Bibcode:2020PNAS..11725646S. doi: 10.1073/pnas.2015869117 . PMC   7568332 . PMID   32989124.
133. Chaufan, Claudia; Joseph, Jay (2013-04-01). "The 'Missing Heritability' of Common Disorders: Should Health Researchers Care?". International Journal of Health Services. 43 (2): 281–303. doi:10.2190/HS.43.2.f. ISSN   0020-7314. PMID   23821906.
134. Richardson, Ken (2017). Genes, brains, and human potential: the science and ideology of intelligence. Columbia scholarship online. New York: Columbia University Press. ISBN   978-0-231-54376-7.
135. Richardson, Ken; Jones, Michael C. (2019-12-01). "Why genome-wide associations with cognitive ability measures are probably spurious". New Ideas in Psychology. 55: 35–41. doi:10.1016/j.newideapsych.2019.04.005. ISSN   0732-118X.
136. Daw, Jonathan; Guo, Guang; Harris, Kathie Mullan (July 2015). "Nurture net of nature: Re-evaluating the role of shared environments in academic achievement and verbal intelligence". Social Science Research. 52: 422–439. doi:10.1016/j.ssresearch.2015.02.011. ISSN   1096-0317. PMC   4888873 . PMID   26004471.
137. Harold, Goldsmith (1993). "Nature–nurture issues in the behavioral genetics context: Overcoming barriers to communication.". In Plomin, Robert; McClearn, Gerald E. (eds.). Nature, Nurture, and Psychology. American Psychological Association. ISBN   978-1-55798-202-5.
138. "Shared and Nonshared Environment", The SAGE Encyclopedia of Lifespan Human Development, 2455 Teller Road, Thousand Oaks, California 91320: SAGE Publications, Inc., 2018, doi:10.4135/9781506307633.n741, ISBN   978-1-5063-0765-7 , retrieved 2025-10-28
139. Turkheimer, E.; Waldron, M. (January 2000). "Nonshared environment: a theoretical, methodological, and quantitative review". Psychological Bulletin. 126 (1): 78–108. doi:10.1037/0033-2909.126.1.78. ISSN   0033-2909. PMID   10668351.
140. Kaplan, Jack S. (2012). "The effects of shared environment on adult intelligence: A critical review of adoption, twin, and MZA studies". Developmental Psychology. 48 (5): 1292–1298. doi:10.1037/a0028133. ISSN   1939-0599. PMID   22746221.
141. Plomin, R.; Loehlin, J. C. (May 1985). "Direct and indirect IQ heritability estimates: a puzzle". Behavior Genetics. 19 (3): 331–342. doi:10.1007/BF01066162. ISSN   0001-8244. PMID   2667514.
142. Bouchard Jr, TJ (1998). "Genetic and environmental influences on adult intelligence and special mental abilities". Human Biology. 70 (2): 257–79. PMID   9549239.
143. Scarr, Sandra (1999). "Behavior-Genetic and Socialization theories of intelligence". In Sternberg, Robert J. (ed.). Intelligence, heredity, and environment (Repr ed.). Cambridge: Cambridge Univ. Press. ISBN   978-0-521-46904-3.
144. Richardson, Ken; Norgate, Sarah H. (2006). "A Critical Analysis of IQ Studies of Adopted Children". Human Development. 49 (6): 319–335. doi:10.1159/000096531. ISSN   0018-716X. JSTOR   26763905.
145. Duyme, Michel; Dumaret, Annick-Camille; Tomkiewicz, Stanislaw (1999-07-20). "How can we boost IQs of "dull children"?: A late adoption study". Proceedings of the National Academy of Sciences. 96 (15): 8790–8794. Bibcode:1999PNAS...96.8790D. doi: 10.1073/pnas.96.15.8790 . PMC   17595 . PMID   10411954.
146. Kendler, Kenneth S.; Turkheimer, Eric; Ohlsson, Henrik; Sundquist, Jan; Sundquist, Kristina (2015-04-14). "Family environment and the malleability of cognitive ability: A Swedish national home-reared and adopted-away cosibling control study". Proceedings of the National Academy of Sciences. 112 (15): 4612–4617. Bibcode:2015PNAS..112.4612K. doi: 10.1073/pnas.1417106112 . PMC   4403216 . PMID   25831538.
147. Capron, Christiane; Duyme, Michel (1996-05-01). "Effect of socioeconomic status of biological and adoptive parents on WISC-R subtest scores of their French adopted children". Intelligence. 22 (3): 259–275. doi:10.1016/S0160-2896(96)90022-7. ISSN   0160-2896.
148. Turkheimer, Eric (November 1991). "Individual and group differences in adoption studies of IQ". Psychological Bulletin. 110 (3): 392–405. doi:10.1037/0033-2909.110.3.392. ISSN   1939-1455.
149. Burt, S. Alexandra; Johnson, Wendy (2023-03-01). "Joint Consideration of Means and Variances Might Change the Understanding of Etiology". Perspectives on Psychological Science. 18 (2): 416–427. doi:10.1177/17456916221096122. ISSN   1745-6916. PMID   36027892.
150. Burt, S. Alexandra; O'Keefe, Patrick; Johnson, Wendy; Thaler, Daniel; Leve, Leslie D.; Natsuaki, Misaki N.; Reiss, David; Shaw, Daniel S.; Ganiban, Jody M.; Neiderhiser, Jenae M. (2024-05-01). "The Detection of Environmental Influences on Academic Achievement Appears to Depend on the Analytic Approach". Behavior Genetics. 54 (3): 252–267. doi:10.1007/s10519-024-10179-w. ISSN   1573-3297. PMID   38587720.
151. Sundet, Jon Martin; Eriksen, Willy; Tambs, Kristian (September 2008). "Intelligence correlations between brothers decrease with increasing age difference: evidence for shared environmental effects in young adults". Psychological Science. 19 (9): 843–847. doi:10.1111/j.1467-9280.2008.02166.x. ISSN   1467-9280. PMID   18947347.
152. Bouchard, Thomas J.; Lykken, David T.; McGue, Matthew; Segal, Nancy L.; Tellegen, Auke (1990). "Sources of Human Psychological Differences: The Minnesota Study of Twins Reared Apart". Science. 250 (4978): 223–8. Bibcode:1990Sci...250..223B. CiteSeerX   10.1.1.225.1769 . doi:10.1126/science.2218526. PMID   2218526.
153. Briley, Daniel A.; Tucker-Drob, Elliot M. (February 2017). "Comparing the Developmental Genetics of Cognition and Personality over the Life Span". Journal of Personality. 85 (1): 51–64. doi:10.1111/jopy.12186. ISSN   1467-6494. PMC   4670606 . PMID   26045299.
154. Dickens, William T.; Protzko, John (2015-10-23), The Heritability of Cognitive Ability Across the Lifespan: A Meta-Analysis of Twins Studies (SSRN Scholarly Paper), Rochester, NY, SSRN   2679002, 2679002, retrieved 2025-10-28
155. Engelhardt, Laura E.; Church, Jessica A.; Paige Harden, K.; Tucker-Drob, Elliot M. (Aug 2018). "Accounting for the shared environment in cognitive abilities and academic achievement with measured socioecological contexts". Developmental Science. 22 (1) e12699. doi:10.1111/desc.12699. ISSN   1467-7687. PMC   6294670 . PMID   30113118.
156. Kendler, Kenneth S.; Ohlsson, Henrik; Lichtenstein, Paul; Sundquist, Jan; Sundquist, Kristina (2019-01-01). "The Nature of the Shared Environment". Behavior Genetics. 49 (1): 1–10. doi:10.1007/s10519-018-9940-0. ISSN   1573-3297. PMID   30536082.
157. Plomin, Robert; Chipuer, Heather; Neiderhiser, Jenae M. (1994). "Behavioral genetic evidence for the importance of nonshared environment" (PDF). In Hetherington, E. Mavis; Reiss, David; Plomin, Robert (eds.). Separate Social Worlds of Siblings (1st ed.). Routledge. ISBN   978-1-138-98166-9.
158. McCall, Robert B. (1983). "Environmental Effects on Intelligence: The Forgotten Realm of Discontinuous Nonshared Within-Family Factors". Child Development. 54 (2): 408–415. doi:10.2307/1129701. ISSN   0009-3920. JSTOR   1129701. PMID   6872632.
159. Plomin, R; Asbury, K; Dunn, J (2001). "Why are children in the same family so different? Nonshared environment a decade later". Canadian Journal of Psychiatry. 46 (3): 225–33. doi: 10.1177/070674370104600302 . PMID   11320676.
160. Rodgers, Joseph Lee; Rowe, David C.; May, Kim (1994-09-01). "DF analysis of NLSY IQ/achievement data: Nonshared environmental influences". Intelligence. 19 (2): 157–177. doi:10.1016/0160-2896(94)90011-6. ISSN   0160-2896.
161. Schacter, Daniel; Gilbert, Daniel; Wegner, Daniel (2010). Psychology (2nd ed.). New York: Worth Publishers. p.  408. ISBN   978-1-4292-3719-2.
162. Plomin, Robert (September 2024). "Nonshared environment: Real but random". JCPP Advances. 4 (3) e12229. doi:10.1002/jcv2.12229. ISSN   2692-9384. PMC   11472802 . PMID   39411468.
163. Chen, Gang; Moraczewski, Dustin; Taylor, Paul A. (2025-04-02). "Improving accuracy and precision of heritability estimation in twin studies through hierarchical modeling: reassessing the measurement error assumption". Frontiers in Genetics. 16 1522729. doi: 10.3389/fgene.2025.1522729 . ISSN   1664-8021. PMC   11999965 . PMID   40242473.
164. Molenaar, Peter C. M.; Boomsma, Dorret I.; Dolan, Conor V. (1993-11-01). "A third source of developmental differences". Behavior Genetics. 23 (6): 519–524. doi:10.1007/BF01068142. ISSN   1573-3297. PMID   8129693.
165. Vogt, Günter (2020-01-01), Levine, Herbert; Jolly, Mohit Kumar; Kulkarni, Prakash; Nanjundiah, Vidyanand (eds.), "9 - Disentangling the environmentally induced and stochastic developmental components of phenotypic variation", Phenotypic Switching, Academic Press, pp. 207–251, ISBN   978-0-12-817996-3 , retrieved 2025-10-30
166. Dickens, William T.; Flynn, James R. (2001). "Heritability estimates versus large environmental effects: The IQ paradox resolved". Psychological Review. 108 (2): 346–69. CiteSeerX   10.1.1.139.2436 . doi:10.1037/0033-295X.108.2.346. PMID   11381833.
167. Schacter, Daniel; Gilbert, Daniel; Wegner, Daniel (2010). Psychology (2nd ed.). New York: Worth Publishers. pp.  409–10. ISBN   978-1-4292-3719-2.
168. Bijma, P. (January 2014). "The quantitative genetics of indirect genetic effects: a selective review of modelling issues". Heredity. 112 (1): 61–69. Bibcode:2014Hered.112...61B. doi:10.1038/hdy.2013.15. ISSN   1365-2540. PMC   3860160 . PMID   23512010.
169. Brumpton, Ben; Sanderson, Eleanor; Heilbron, Karl; Hartwig, Fernando Pires; Harrison, Sean; Vie, Gunnhild Åberge; Cho, Yoonsu; Howe, Laura D.; Hughes, Amanda; Boomsma, Dorret I.; Havdahl, Alexandra; Hopper, John; Neale, Michael; Nivard, Michel G.; Pedersen, Nancy L. (2020-07-14). "Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses". Nature Communications. 11 (1): 3519. Bibcode:2020NatCo..11.3519B. doi:10.1038/s41467-020-17117-4. ISSN   2041-1723. PMID   32665587.
170. Young, Alexander I.; Nehzati, Seyed Moeen; Benonisdottir, Stefania; Okbay, Aysu; Jayashankar, Hariharan; Lee, Chanwook; Cesarini, David; Benjamin, Daniel J.; Turley, Patrick; Kong, Augustine (June 2022). "Mendelian imputation of parental genotypes improves estimates of direct genetic effects". Nature Genetics. 54 (6): 897–905. doi:10.1038/s41588-022-01085-0. ISSN   1546-1718. PMC   9197765 . PMID   35681053.
171. Benjamin, Daniel J.; Cesarini, David; Turley, Patrick; Young, Alexander Strudwick (2024-05-06). Social-Science Genomics: Progress, Challenges, and Future Directions (Report). National Bureau of Economic Research.
172. Wang, Biyao; Baldwin, Jessie R.; Schoeler, Tabea; Cheesman, Rosa; Barkhuizen, Wikus; Dudbridge, Frank; Bann, David; Morris, Tim T.; Pingault, Jean-Baptiste (2021-09-02). "Robust genetic nurture effects on education: A systematic review and meta-analysis based on 38,654 families across 8 cohorts". American Journal of Human Genetics. 108 (9): 1780–1791. doi:10.1016/j.ajhg.2021.07.010. ISSN   1537-6605. PMC   8456157 . PMID   34416156.
173. Nivard, Michel G.; Belsky, Daniel W.; Harden, K. Paige; Baier, Tina; Andreassen, Ole A.; Ystrøm, Eivind; van Bergen, Elsje; Lyngstad, Torkild H. (April 2024). "More than nature and nurture, indirect genetic effects on children's academic achievement are consequences of dynastic social processes". Nature Human Behaviour. 8 (4): 771–778. doi:10.1038/s41562-023-01796-2. ISSN   2397-3374. PMC   11569812 . PMID   38225408.
174. Kong, Augustine; Thorleifsson, Gudmar; Frigge, Michael L.; Vilhjalmsson, Bjarni J.; Young, Alexander I.; Thorgeirsson, Thorgeir E.; Benonisdottir, Stefania; Oddsson, Asmundur; Halldorsson, Bjarni V.; Masson, Gisli; Gudbjartsson, Daniel F.; Helgason, Agnar; Bjornsdottir, Gyda; Thorsteinsdottir, Unnur; Stefansson, Kari (25 January 2018). "The nature of nurture: Effects of parental genotypes". Science. 359 (6374): 424–428. Bibcode:2018Sci...359..424K. doi: 10.1126/science.aan6877 . PMID   29371463.
175. Deary, Ian J.; Strand, Steve; Smith, Pauline; Fernandes, Cres (January 2007). "Intelligence and educational achievement". Intelligence. 35 (1): 13–21. doi:10.1016/j.intell.2006.02.001.
176. Scarr-Salapatek, Sandra (1971-12-24). "Race, Social Class, and IQ". Science. 174 (4016): 1285–1295. Bibcode:1971Sci...174.1285S. doi:10.1126/science.174.4016.1285. PMID   5167501.
177. Turkheimer, Eric; Haley, Andreana; Waldron, Mary; d'Onofrio, Brian; Gottesman, Irving I. (2003). "Socioeconomic status modifies heritability of iq in young children". Psychological Science. 14 (6): 623–8. doi:10.1046/j.0956-7976.2003.psci_1475.x. PMID   14629696. S2CID   11265284.
178. Rowe, D. C.; Jacobson, K. C.; Van den Oord, E. J. (1999). "Genetic and environmental influences on vocabulary IQ: parental education level as moderator". Child Development. 70 (5): 1151–1162. doi:10.1111/1467-8624.00084. ISSN   0009-3920. PMID   10546338.
179. Giangrande, Evan J.; Turkheimer, Eric (May 2022). "Race, Ethnicity, and the Scarr-Rowe Hypothesis: A Cautionary Example of Fringe Science Entering the Mainstream". Perspectives on Psychological Science: A Journal of the Association for Psychological Science. 17 (3): 696–710. doi:10.1177/17456916211017498. ISSN   1745-6924. PMID   34793248.
180. Tucker-Drob, Elliot M.; Bates, Timothy C. (Feb 2016). "Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence". Psychological Science. 27 (2): 138–149. doi:10.1177/0956797615612727. ISSN   1467-9280. PMC   4749462 . PMID   26671911.
181. Figlio, David N.; Freese, Jeremy; Karbownik, Krzysztof; Roth, Jeffrey (2017-12-19). "Socioeconomic status and genetic influences on cognitive development". Proceedings of the National Academy of Sciences. 114 (51): 13441–13446. Bibcode:2017PNAS..11413441F. doi: 10.1073/pnas.1708491114 . PMC   5754768 . PMID   29133413.
182. Mills, Melinda C.; Tropf, Felix C. (2020-07-30). "Sociology, Genetics, and the Coming of Age of Sociogenomics". Annual Review of Sociology. 46 (46): 553–581. doi:10.1146/annurev-soc-121919-054756. ISSN   0360-0572.
183. Krapohl, Eva; Rimfeld, Kaili; Shakeshaft, Nicholas G.; Trzaskowski, Maciej; McMillan, Andrew; Pingault, Jean-Baptiste; Asbury, Kathryn; Harlaar, Nicole; Kovas, Yulia; Dale, Philip S.; Plomin, Robert (2014-10-21). "The high heritability of educational achievement reflects many genetically influenced traits, not just intelligence". Proceedings of the National Academy of Sciences. 111 (42): 15273–15278. Bibcode:2014PNAS..11115273K. doi: 10.1073/pnas.1408777111 . PMC   4210287 . PMID   25288728.
184. Mostafavi, Hakhamanesh; Harpak, Arbel; Agarwal, Ipsita; Conley, Dalton; Pritchard, Jonathan K; Przeworski, Molly (2020-01-30). "Variable prediction accuracy of polygenic scores within an ancestry group". eLife. 9 e48376. doi: 10.7554/eLife.48376 . ISSN   2050-084X. PMC   7067566 . PMID   31999256.
185. Rask-Andersen, Mathias; Karlsson, Torgny; Ek, Weronica E.; Johansson, Åsa (July 2021). "Modification of Heritability for Educational Attainment and Fluid Intelligence by Socioeconomic Deprivation in the UK Biobank". American Journal of Psychiatry. 178 (7): 625–634. doi:10.1176/appi.ajp.2020.20040462. ISSN   0002-953X. PMID   33900812.
186. Ghirardi, Gaia; Gil-Hernández, Carlos J.; Bernardi, Fabrizio; van Bergen, Elsje; Demange, Perline (2024-08-01). "Interaction of family SES with children's genetic propensity for cognitive and noncognitive skills: No evidence of the Scarr-Rowe hypothesis for educational outcomes". Research in Social Stratification and Mobility. 92 100960. doi:10.1016/j.rssm.2024.100960. ISSN   0276-5624. PMC   11364161 . PMID   39220821.
187. Wilson, Ronald S. (1983). "The Louisville Twin Study: Developmental Synchronies in Behavior". Child Development. 54 (2): 298–316. doi:10.2307/1129693. ISSN   0009-3920.
188. Bouchard, Thomas J. (October 2013). "The Wilson Effect: the increase in heritability of IQ with age". Twin Research and Human Genetics: The Official Journal of the International Society for Twin Studies. 16 (5): 923–930. doi:10.1017/thg.2013.54. ISSN   1832-4274. PMID   23919982.
189. Haworth, C. M. A.; Wright, M. J.; Luciano, M.; Martin, N. G.; de Geus, E. J. C.; van Beijsterveldt, C. E. M.; Bartels, M.; Posthuma, D.; Boomsma, D. I.; Davis, O. S. P.; Kovas, Y.; Corley, R. P.; Defries, J. C.; Hewitt, J. K.; Olson, R. K. (2 June 2009). "The heritability of general cognitive ability increases linearly from childhood to young adulthood". Molecular Psychiatry. 15 (11): 1112–1120. doi:10.1038/mp.2009.55. ISSN   1476-5578. PMC   2889158 . PMID   19488046.
190. Plomin, Robert; DeFries, John C.; Knopik, Valerie S.; Neiderhiser, Jenae M. (2016-01-01). "Top 10 Replicated Findings From Behavioral Genetics". Perspectives on Psychological Science. 11 (1): 3–23. doi:10.1177/1745691615617439. ISSN   1745-6916. PMC   4739500 . PMID   26817721.
191. Reis, André; Spinath, Frank M. (2025-01-24). "The Genetics of Intelligence". Deutsches Arzteblatt International. 122 (2): 38–42. doi:10.3238/arztebl.m2024.0236. ISSN   1866-0452. PMC   12416016 . PMID   39635948.
192. Briley, Daniel A.; Tucker-Drob, Elliot M. (1 July 2013). "Explaining the increasing heritability of cognitive ability across development: a meta-analysis of longitudinal twin and adoption studies". Psychological Science. 24 (9): 1704–1713. doi:10.1177/0956797613478618. ISSN   1467-9280. PMC   3954471 . PMID   23818655.
193. Plomin, R.; Deary, I. J. (February 2015). "Genetics and intelligence differences: five special findings". Molecular Psychiatry. 20 (1): 98–108. doi:10.1038/mp.2014.105. ISSN   1476-5578. PMC   4270739 . PMID   25224258.
194. Kendler, Kenneth S.; Jaffree, Sara; Romer, Daniel, eds. (2011). "The Social Dynamics of the Expression of Genes for Cognitive Ability". The dynamic genome and mental health: The role of genes and environments in youth development. USA: Oxford University Press. pp. 103–127. ISBN   978-0-19-973796-3.
195. Beam, Christopher R.; Pezzoli, Patrizia; Mendle, Jane; Burt, S. Alexandra; Neale, Michael C.; Boker, Steven M.; Keel, Pamela K.; Klump, Kelly L. (Feb 2022). "How nonshared environmental factors come to correlate with heredity". Development and Psychopathology. 34 (1): 321–333. doi:10.1017/S0954579420001017. ISSN   1469-2198. PMC   8081739 . PMID   33118912.
196. Beam, Christopher R.; Turkheimer, Eric (Feb 2013). "Phenotype-environment correlations in longitudinal twin models". Development and Psychopathology. 25 (1): 7–16. doi:10.1017/S0954579412000867. ISSN   1469-2198. PMC   5746192 . PMID   23398749.
197. Fischbein, Siv (1978-12-01). "Heredity-Environment Interaction in the Development of Twins". International Journal of Behavioral Development. 1 (4): 313–322. doi:10.1177/016502547800100402. ISSN   0165-0254.
198. Lin, Yujing; Procopio, Francesca; Keser, Engin; Kawakami, Kaito; Rimfeld, Kaili; Malanchini, Margherita; Plomin, Robert (2025-06-23). "Polygenic Score Prediction Within and Between Sibling Pairs for Intelligence, Cognitive Abilities, and Educational Traits From Childhood to Early Adulthood". Intelligence & Cognitive Abilities. 1 (1): 5–19.
199. Wahlsten, Douglas; Gottlieb, Gilbert (1999). "The invalid separation of effects of nature and nurture: Lessons from animal experimentation". In Sternberg, Robert J. (ed.). Intelligence, heredity, and environment (Repr ed.). Cambridge: Cambridge Univ. Press. ISBN   978-0-521-46904-3.
200. Loevinger, J. (December 1943). "On the proportional contributions of differences in nature and in nurture to differences in intelligence". Psychological Bulletin. 40 (10): 725–756. doi:10.1037/h0053767. ISSN   1939-1455.
201. Moore, David S.; Shenk, David (January 2017). "The heritability fallacy" (PDF). WIREs Cognitive Science. 8 (1–2) e1400. doi:10.1002/wcs.1400. ISSN   1939-5078. PMID   27906501.
202. Tabery, James (31 October 2023). Beyond Versus: The Struggle to Understand the Interaction of Nature and Nurture. The MIT Press. ISBN   978-0-262-54960-8.
203. Wahlsten, Douglas (July 1994). "The intelligence of heritability". Canadian Psychology / Psychologie Canadienne. 35 (3): 244–260. doi:10.1037/0708-5591.35.3.244. ISSN   1878-7304.
204. Ceci, Stephen; Williams, Wendy M. (1 February 2009). "Should scientists study race and IQ? YES: The scientific truth must be pursued". Nature. 457 (7231): 788–789. Bibcode:2009Natur.457..788C. doi: 10.1038/457788a . PMID   19212385. S2CID   205044224. There is an emerging consensus about racial and gender equality in genetic determinants of intelligence; most researchers, including ourselves, agree that genes do not explain between-group differences.
205. Hunt, Earl (2010). Human Intelligence. Cambridge University Press. p. 447. ISBN   978-0-521-70781-7. OL   24384631M – via Open Library. [N]o genes related to the difference in cognitive skills across the various racial and ethic groups have ever been discovered.
206. Mackintosh, N. J. (2011). IQ and human intelligence (2nd ed.). Oxford: Oxford University Press. pp. 334–338, 344. ISBN   978-0-19-958559-5. OCLC   669754008. OL   25211083M – via Open Library.
207. Kaplan, Jonathan Michael (January 2015). "Race, IQ, and the search for statistical signals associated with so-called "X"-factors: environments, racism, and the "hereditarian hypothesis"". Biology & Philosophy. 30 (1): 1–17. doi:10.1007/s10539-014-9428-0. ISSN   0169-3867. S2CID   85351431.
208. Nevid 2014, p. 271.
209. Schraiber, Joshua G.; Edge, Michael D. (2024-03-19). "Heritability within groups is uninformative about differences among groups: Cases from behavioral, evolutionary, and statistical genetics". Proceedings of the National Academy of Sciences of the United States of America. 121 (12) e2319496121. Bibcode:2024PNAS..12119496S. doi:10.1073/pnas.2319496121. ISSN   1091-6490. PMC   10962975 . PMID   38470926.
210. Feldman, M. W.; Lewontin, R. C. (1975-12-19). "The heritability hang-up". Science (New York, N.Y.). 190 (4220): 1163–1168. Bibcode:1975Sci...190.1163F. doi:10.1126/science.1198102. ISSN   0036-8075. PMID   1198102.
211. "Intelligence research should not be held back by its past". Nature. 545 (7655): 385–386. 25 May 2017. Bibcode:2017Natur.545R.385.. doi: 10.1038/nature.2017.22021 . PMID   28541341. S2CID   4449918.
212. Bird, Kevin A. (2 February 2021). "No support for the hereditarian hypothesis of the Black–White achievement gap using polygenic scores and tests for divergent selection" . American Journal of Physical Anthropology . 175 (2): 465–476. Bibcode:2021AJPA..175..465B. doi:10.1002/ajpa.24216. ISSN   0002-9483. PMID   33529393 . Retrieved 1 November 2024– via Wiley Online Library.

Sources

• Campbell, Frances A; Ramey, Craig T; Pungello, Elizabeth; Sparling, Joseph; Miller-Johnson, Shari (2002). "Early Childhood Education: Young Adult Outcomes From the Abecedarian Project". Applied Developmental Science. 6: 42–57. doi:10.1207/s1532480xads0601_05. S2CID   71602425.
• Cooper, R. S. (2005). "Race and IQ: Molecular Genetics as Deus ex Machina". American Psychologist. 60 (1): 71–76. CiteSeerX   10.1.1.624.5059 . doi:10.1037/0003-066X.60.1.71. PMID   15641923.
• Daley, C. E.; Onwuegbuzie, A. J. (2011). "Race and Intelligence". In Sternberg, R.; Kaufman, S. B. (eds.). The Cambridge Handbook of Intelligence. Cambridge New York: Cambridge University Press. pp. 293–306. ISBN   978-0-521-51806-2.
• Nevid, Jeffrey S. (2014-01-17). Essentials of Psychology: Concepts and Applications. Cengage Learning. ISBN   978-1-285-75122-1. Archived from the original on 2023-03-20. Retrieved 2018-01-30.
• Reichman, Nancy E. (2005). "Low birth weight and school readiness". The Future of Children. 15 (1): 91–116. doi:10.1353/foc.2005.0008. ISSN   1054-8289. PMID   16130543. S2CID   23345980.

Further reading

• Johnson, Wendy; Penke, Lars; Spinath, Frank M. (July 2011). "Understanding Heritability: What it is and What it is Not". European Journal of Personality. 25 (4): 287–294. doi:10.1002/per.835. S2CID   41842465.
• Johnson, Wendy (10 June 2010). "Understanding the Genetics of Intelligence". Current Directions in Psychological Science. 19 (3): 177–182. doi:10.1177/0963721410370136. S2CID   14615091.
• Scott Barry Kaufman (October 17, 2013). "The Heritability of Intelligence: Not What You Think". Scientific American. Retrieved 20 October 2013.
• Newson, Ainsley; Williamson, Robert (1999). "Should We Undertake Genetic Research on Intelligence?" Bioethics 13(3-4), 327–342. doi:10.1111/1467-8519.00161

External links

• McGue, Matt (5 May 2014). "Introduction to Human Behavioral Genetics". Coursera. Retrieved 10 June 2014. Free Massively Open Online Course on human behavior genetics by Matt McGue of the University of Minnesota, including unit on genetics of human intelligence