Everything is heritable; Trzaskowski et al 2014:
"Environmental measures used widely in the behavioral sciences show nearly as much genetic influence as behavioral measures, a critical finding for interpreting associations between environmental factors and children's development. This research depends on the twin method that compares monozygotic and dizygotic twins, but key aspects of children's environment such as socioeconomic status (SES) cannot be investigated in twin studies because they are the same for children growing up together in a family. Here, using a new technique applied to DNA from 3000 unrelated children, we show significant genetic influence on family SES, and on its association with children's IQ at ages 7 and 12. In addition to demonstrating the ability to investigate genetic influence on between-family environmental measures, our results emphasize the need to consider genetics in research and policy on family SES and its association with children's IQ.
...Bivariate GCTA analysis also indicates the extent to which the phenotypic covariance between family SES and children's IQ is mediated genetically. The phenotypic correlation between family SES at age 7 and children's IQ at age 7 is 0.31. The genetic contribution to this covariance is 0.29 (Table 2). In other words, 94% of the correlation between family SES and children's IQ is mediated genetically.
...A new quantitative genetic method that uses DNA from unrelated individuals can solve this problem because it can assess genetic effects on between-family and between-school differences in children's outcomes. The method, called Genome-wide Complex Trait Analysis (GCTA), foregoes the identification of individual DNA variants to estimate the total genetic influence captured by genome-wide genotyping for a large sample of unrelated individuals whose genetic similarity is compared pair by pair (Yang, Lee, Goddard, & Visscher, 2011). The significance of GCTA is that it can estimate the net effect of genetic influence using DNA of unrelated individuals rather than using familial resemblance in groups of special family members who differ in genetic relatedness such as monozygotic and dizygotic twins. We applied GCTA based on children's genotypes to detect genetic influence on family SES as well as genetic mediation of the effect of family SES on children's cognitive development.
...The sample was drawn from the Twins Early Development Study (TEDS), which is a multivariate longitudinal study that recruited over 11,000 twin pairs born in England and Wales in 1994, 1995 and 1996 (Haworth, Davis, & Plomin, 2013). TEDS has been shown to be representative of the UK population (Kovas, Haworth, Dale, & Plomin, 2007). The project received approval from the Institute of Psychiatry ethics committee (05/Q0706/228) and parental consent was obtained prior to data collection. Cognitive data and buccal DNA were available for 3747 11- and 12-year-old children (one twin per family), whose first language was English and had no major medical or psychiatric problems. From that sample, 3665 DNA samples were successfully hybridized to Affymetrix GeneChip 6.0 SNP genotyping arrays using standard experimental protocols as part of the WTCCC2 project (Trzaskowski et al., 2013b). In addition to nearly 700,000 genotyped SNPs, more than one million other SNPs were imputed using IMPUTE v.2 software (Howie, Donnelly, & Marchini, 2009). 3152 DNA samples (1446 males and 1706 females) survived quality control criteria for ancestry, heterozygosity, relatedness, and hybridization intensity outliers. To control for ancestral stratification, we performed principal component analyses on a subset of 100,000 quality-controlled SNPs after removing SNPs in linkage disequilibrium (r2 > 0.2). Using the Tracy–Widom Test, we identified 8 axes with p < 0.05, which were used as covariates in our GCTA analyses. This is standard procedure in genome-wide association analyses to avoid artificial associations due to ethnic or other types of population stratification (Trzaskowski et al., 2013b); correcting for these covariates is also standard in GCTA in order to avoid this source of genetic similarity among individuals in the population (Yang et al.).
...Using SNP genotypes from the children's DNA, we found significant genetic influence on their families' SES when the children were age 2 and age 7 (Table 1). The GCTA estimates of heritability were 18% at age 2 and 19% at age 7; the similarity of results at age 2 and 7 is not a foregone conclusion because the correlation between family SES at the two ages is 0.75. These are underestimates of true heritability because GCTA is limited to detecting genetic influence due to additive effects of the common SNPs that are on current DNA microarrays such as the Affymetrix 6.0 GeneChip used in our study. That is, nonadditive effects and rarer DNA variants not tagged by common SNPs are missed in GCTA analysis. A novel aspect of the present study is that children cannot cause family SES — their genotypes only reflect the causal genotypic factors responsible for their parents' education and occupation. For this reason, parental genotypes, not available in the present study, would be expected to yield a higher GCTA estimate of the parents' own education and occupation, which comprise SES. This was the case for a recent GCTA report of a component of SES, adult educational attainment (Rietveld et al., 2013).
...A strength of our child-based design using children's genotypes in GCTA analyses rather than that of their parents is that it captures the genetic influence of family SES on the children themselves. This feature of the design enables the second stage of our analyses in which we conducted bivariate GCTA to determine the extent to which the well known link between family SES and cognitive development – about 0.30 in meta-analyses (Sirin, 2005) and in the present study – is mediated genetically. Because bivariate GCTA focuses on the genetic covariance between family SES and children's IQ, our analysis is limited to TEDS children for whom data are available for both variables. Despite the smaller samples, the bivariate GCTA heritability estimates for family SES (Table 2) are similar to the univariate estimates (Table 1): 21% at age 7 and 23% at age 12. For children's IQ, we find heritabilities of 28% at age 7 and 32% at age 12. These results from our bivariate analysis between family SES and children's IQ replicate our previously reported results showing significant GCTA heritability in our TEDS sample at ages 7 and 12 (Plomin et al., 2013; Trzaskowski, Yang, Visscher, & Plomin, 2013) as well as another study that reported significant GCTA heritability at age 11 (Davies et al., 2011).
The key result for the bivariate GCTA analysis is the genetic correlation, which indicates the extent to which the same genes affect family SES and children's IQ. The genetic correlation between family SES at age 7 and children's IQ at age 7 is near unity, indicating that the same genes affect both variables (Table 2). Despite the large standard error for GCTA estimates of genetic correlations, the genetic correlation is significantly greater than zero and not significantly lower than 1.0. We also conducted bivariate GCTA for family SES at age 7 and children's IQ at age 12 (family SES was not assessed at age 12). The GCTA genetic correlation was 0.66, which was again significantly greater than zero and not significantly lower than 1.0. Thus, these GCTA genetic correlations indicate that the same genes are largely responsible for genetic effects on family SES and children's IQ. This finding implies that when genes associated with children's IQ are identified, the same genes will also be likely to be associated with family SES. Although GCTA estimates of genetic variance and genetic covariance are biased in that they underestimate heritability to the extent that nonadditive effects and rare alleles are not included in the estimate, GCTA estimates of genetic correlations are unbiased because they are derived from the ratio of genetic covariance to genetic variance so that the bias in the numerator and denominator cancel out (Trzaskowski et al., 2013a).
...Our analysis provides the first DNA-based evidence that the well documented association between family SES and children's cognitive development, routinely interpreted as an environmental effect, is substantially mediated by genetic factors. Previous quantitative genetic research, largely using the twin design, has shown that most ‘environmental’ measures involve significant genetic influence and that associations between these environmental measures and children's development are mediated genetically (Plomin, 1994; Plomin et al., 2013; Vinkhuyzen et al., 2010). GCTA adds importantly to this body of research in two ways. First, because it uses DNA alone, GCTA sidesteps concerns about the twin design such as the equal environments assumption (Plomin, DeFries, Knopik, & Neiderhiser, 2013), which might be especially relevant to measures of family environment (Power et al., 2013). Second, many important environmental factors such as family SES cannot be studied using the twin design because they operate between families (family general) rather than within families (child specific). GCTA can be used to study between-family variables.
The main limitation of this study is the sample size. Although a sample of 3000 unrelated children with genome-wide genotypes and data on IQ and family SES is large by many standards, as noted earlier, GCTA has daunting demands for power. Our sample size is just on the cusp of being able to detect as significant the GCTA heritabilities of family SES, which is about 20%. It should be reiterated that GCTA heritability estimates are lower-limit estimates of twin heritability because GCTA is limited to detecting the additive effects of the common SNPs used in our genome-wide genotyping. On the other hand, because we found such high genetic correlations, we have good power to detect them. As noted earlier a sample of 3000 provides 80% power to detect a genetic correlation of 0.60.
...Specifically, our results bear on the extensive debate about social mobility, which has largely ignored the fact that parents and their offspring are genetically related (Saunders, 2012). Indeed, the correlation between parent and offspring SES is used as an index of intergenerational social mobility because it is assumed that SES advantages are transmitted environmentally from parent to offspring (Breen & Jonsson, 2005). For this reason, lower parent–offspring correlations are thought to indicate social mobility. From this environmental perspective, it follows that equal educational and occupational opportunities will result in equal outcomes between children so that parental SES would no longer have any effect on children's cognitive development, education or occupation. On the contrary, taking genetics into account suggests that higher parent–offspring correlations indicate social mobility. To the extent that genetics is important, parents and their offspring will be correlated; removing environmental sources of inequality will not remove this fundamental resemblance between parents and offspring."
#inequality #economics #psychology #socialmobility
"Environmental measures used widely in the behavioral sciences show nearly as much genetic influence as behavioral measures, a critical finding for interpreting associations between environmental factors and children's development. This research depends on the twin method that compares monozygotic and dizygotic twins, but key aspects of children's environment such as socioeconomic status (SES) cannot be investigated in twin studies because they are the same for children growing up together in a family. Here, using a new technique applied to DNA from 3000 unrelated children, we show significant genetic influence on family SES, and on its association with children's IQ at ages 7 and 12. In addition to demonstrating the ability to investigate genetic influence on between-family environmental measures, our results emphasize the need to consider genetics in research and policy on family SES and its association with children's IQ.
...Bivariate GCTA analysis also indicates the extent to which the phenotypic covariance between family SES and children's IQ is mediated genetically. The phenotypic correlation between family SES at age 7 and children's IQ at age 7 is 0.31. The genetic contribution to this covariance is 0.29 (Table 2). In other words, 94% of the correlation between family SES and children's IQ is mediated genetically.
...A new quantitative genetic method that uses DNA from unrelated individuals can solve this problem because it can assess genetic effects on between-family and between-school differences in children's outcomes. The method, called Genome-wide Complex Trait Analysis (GCTA), foregoes the identification of individual DNA variants to estimate the total genetic influence captured by genome-wide genotyping for a large sample of unrelated individuals whose genetic similarity is compared pair by pair (Yang, Lee, Goddard, & Visscher, 2011). The significance of GCTA is that it can estimate the net effect of genetic influence using DNA of unrelated individuals rather than using familial resemblance in groups of special family members who differ in genetic relatedness such as monozygotic and dizygotic twins. We applied GCTA based on children's genotypes to detect genetic influence on family SES as well as genetic mediation of the effect of family SES on children's cognitive development.
...The sample was drawn from the Twins Early Development Study (TEDS), which is a multivariate longitudinal study that recruited over 11,000 twin pairs born in England and Wales in 1994, 1995 and 1996 (Haworth, Davis, & Plomin, 2013). TEDS has been shown to be representative of the UK population (Kovas, Haworth, Dale, & Plomin, 2007). The project received approval from the Institute of Psychiatry ethics committee (05/Q0706/228) and parental consent was obtained prior to data collection. Cognitive data and buccal DNA were available for 3747 11- and 12-year-old children (one twin per family), whose first language was English and had no major medical or psychiatric problems. From that sample, 3665 DNA samples were successfully hybridized to Affymetrix GeneChip 6.0 SNP genotyping arrays using standard experimental protocols as part of the WTCCC2 project (Trzaskowski et al., 2013b). In addition to nearly 700,000 genotyped SNPs, more than one million other SNPs were imputed using IMPUTE v.2 software (Howie, Donnelly, & Marchini, 2009). 3152 DNA samples (1446 males and 1706 females) survived quality control criteria for ancestry, heterozygosity, relatedness, and hybridization intensity outliers. To control for ancestral stratification, we performed principal component analyses on a subset of 100,000 quality-controlled SNPs after removing SNPs in linkage disequilibrium (r2 > 0.2). Using the Tracy–Widom Test, we identified 8 axes with p < 0.05, which were used as covariates in our GCTA analyses. This is standard procedure in genome-wide association analyses to avoid artificial associations due to ethnic or other types of population stratification (Trzaskowski et al., 2013b); correcting for these covariates is also standard in GCTA in order to avoid this source of genetic similarity among individuals in the population (Yang et al.).
...Using SNP genotypes from the children's DNA, we found significant genetic influence on their families' SES when the children were age 2 and age 7 (Table 1). The GCTA estimates of heritability were 18% at age 2 and 19% at age 7; the similarity of results at age 2 and 7 is not a foregone conclusion because the correlation between family SES at the two ages is 0.75. These are underestimates of true heritability because GCTA is limited to detecting genetic influence due to additive effects of the common SNPs that are on current DNA microarrays such as the Affymetrix 6.0 GeneChip used in our study. That is, nonadditive effects and rarer DNA variants not tagged by common SNPs are missed in GCTA analysis. A novel aspect of the present study is that children cannot cause family SES — their genotypes only reflect the causal genotypic factors responsible for their parents' education and occupation. For this reason, parental genotypes, not available in the present study, would be expected to yield a higher GCTA estimate of the parents' own education and occupation, which comprise SES. This was the case for a recent GCTA report of a component of SES, adult educational attainment (Rietveld et al., 2013).
...A strength of our child-based design using children's genotypes in GCTA analyses rather than that of their parents is that it captures the genetic influence of family SES on the children themselves. This feature of the design enables the second stage of our analyses in which we conducted bivariate GCTA to determine the extent to which the well known link between family SES and cognitive development – about 0.30 in meta-analyses (Sirin, 2005) and in the present study – is mediated genetically. Because bivariate GCTA focuses on the genetic covariance between family SES and children's IQ, our analysis is limited to TEDS children for whom data are available for both variables. Despite the smaller samples, the bivariate GCTA heritability estimates for family SES (Table 2) are similar to the univariate estimates (Table 1): 21% at age 7 and 23% at age 12. For children's IQ, we find heritabilities of 28% at age 7 and 32% at age 12. These results from our bivariate analysis between family SES and children's IQ replicate our previously reported results showing significant GCTA heritability in our TEDS sample at ages 7 and 12 (Plomin et al., 2013; Trzaskowski, Yang, Visscher, & Plomin, 2013) as well as another study that reported significant GCTA heritability at age 11 (Davies et al., 2011).
The key result for the bivariate GCTA analysis is the genetic correlation, which indicates the extent to which the same genes affect family SES and children's IQ. The genetic correlation between family SES at age 7 and children's IQ at age 7 is near unity, indicating that the same genes affect both variables (Table 2). Despite the large standard error for GCTA estimates of genetic correlations, the genetic correlation is significantly greater than zero and not significantly lower than 1.0. We also conducted bivariate GCTA for family SES at age 7 and children's IQ at age 12 (family SES was not assessed at age 12). The GCTA genetic correlation was 0.66, which was again significantly greater than zero and not significantly lower than 1.0. Thus, these GCTA genetic correlations indicate that the same genes are largely responsible for genetic effects on family SES and children's IQ. This finding implies that when genes associated with children's IQ are identified, the same genes will also be likely to be associated with family SES. Although GCTA estimates of genetic variance and genetic covariance are biased in that they underestimate heritability to the extent that nonadditive effects and rare alleles are not included in the estimate, GCTA estimates of genetic correlations are unbiased because they are derived from the ratio of genetic covariance to genetic variance so that the bias in the numerator and denominator cancel out (Trzaskowski et al., 2013a).
...Our analysis provides the first DNA-based evidence that the well documented association between family SES and children's cognitive development, routinely interpreted as an environmental effect, is substantially mediated by genetic factors. Previous quantitative genetic research, largely using the twin design, has shown that most ‘environmental’ measures involve significant genetic influence and that associations between these environmental measures and children's development are mediated genetically (Plomin, 1994; Plomin et al., 2013; Vinkhuyzen et al., 2010). GCTA adds importantly to this body of research in two ways. First, because it uses DNA alone, GCTA sidesteps concerns about the twin design such as the equal environments assumption (Plomin, DeFries, Knopik, & Neiderhiser, 2013), which might be especially relevant to measures of family environment (Power et al., 2013). Second, many important environmental factors such as family SES cannot be studied using the twin design because they operate between families (family general) rather than within families (child specific). GCTA can be used to study between-family variables.
The main limitation of this study is the sample size. Although a sample of 3000 unrelated children with genome-wide genotypes and data on IQ and family SES is large by many standards, as noted earlier, GCTA has daunting demands for power. Our sample size is just on the cusp of being able to detect as significant the GCTA heritabilities of family SES, which is about 20%. It should be reiterated that GCTA heritability estimates are lower-limit estimates of twin heritability because GCTA is limited to detecting the additive effects of the common SNPs used in our genome-wide genotyping. On the other hand, because we found such high genetic correlations, we have good power to detect them. As noted earlier a sample of 3000 provides 80% power to detect a genetic correlation of 0.60.
...Specifically, our results bear on the extensive debate about social mobility, which has largely ignored the fact that parents and their offspring are genetically related (Saunders, 2012). Indeed, the correlation between parent and offspring SES is used as an index of intergenerational social mobility because it is assumed that SES advantages are transmitted environmentally from parent to offspring (Breen & Jonsson, 2005). For this reason, lower parent–offspring correlations are thought to indicate social mobility. From this environmental perspective, it follows that equal educational and occupational opportunities will result in equal outcomes between children so that parental SES would no longer have any effect on children's cognitive development, education or occupation. On the contrary, taking genetics into account suggests that higher parent–offspring correlations indicate social mobility. To the extent that genetics is important, parents and their offspring will be correlated; removing environmental sources of inequality will not remove this fundamental resemblance between parents and offspring."
#inequality #economics #psychology #socialmobility
