"The Genetics of Success: How Single-Nucleotide Polymorphisms Associated With Educational Attainment Relate to Life-Course Development", Belsky et al 2016:
"A previous genome-wide association study (GWAS) of more than 100,000 individuals identified molecular-genetic predictors of educational attainment. We undertook in-depth life-course investigation of the polygenic score derived from this GWAS using the four-decade Dunedin Study (N = 918). There were five main findings. First, polygenic scores predicted adult economic outcomes even after accounting for educational attainments. Second, genes and environments were correlated: Children with higher polygenic scores were born into better-off homes. Third, children’s polygenic scores predicted their adult outcomes even when analyses accounted for their social-class origins; social-mobility analysis showed that children with higher polygenic scores were more upwardly mobile than children with lower scores. Fourth, polygenic scores predicted behavior across the life course, from early acquisition of speech and reading skills through geographic mobility and mate choice and on to financial planning for retirement. Fifth, polygenic-score associations were mediated by psychological characteristics, including intelligence, self-control, and interpersonal skill. Effect sizes were small. Factors connecting DNA sequence with life outcomes may provide targets for interventions to promote population-wide positive development.
This continuum, measured as a polygenic score (Chabris, Lee, Cesarini, Benjamin, & Laibson, 2015), has since been shown to predict educational attainment in cohorts on three continents and even differences in educational attainment between siblings in the same family (Conley et al., 2015; de Zeeuw et al., 2014; Domingue, Belsky, Conley, Harris, & Boardman, 2015; Rietveld, Esko, et al., 2014; Ward et al., 2014).
we ask three questions in the current article: (a) Do genetic discoveries for educational attainment predict outcomes beyond schooling? (b) If so, what are the developmental and behavioral pathways that connect differences in DNA sequences with divergent life outcomes? (c) Do psychological characteristics act as mediators of genetic associations? Although these questions may seem premature, it is important to ask them now, before technologies using genetics to predict social outcomes become possible.
We calculated polygenic scores according to the method described by Dudbridge (2013) using the PRsice software (Version 1.22; http://prsice.info/; Euesden, Lewis, & O’Reilly, 2015). To calculate the polygenic score for educational attainment, we matched genotypes from our data with GWAS results for educational attainment reported by the Social Science Genetic Association Consortium ( Rietveld et al., 2013) and used the approximately 2.3 million matched genotypes to score Dunedin Study members’ genetic predisposition to educational attainment.
We used all matched SNPs to compute polygenic scores, irrespective of nominal significance for their association with educational attainment. Scores ranged from −30.51 to 73.77 (M = 17.73, SD = 17.94) and were normally distributed in the Dunedin birth cohort. We standardized scores so that the mean was zero and the standard deviation was 1 (see Fig. S1 in the Supplemental Material available online).
Traits and abilities. We measured cognitive ability and cognitive development using the Peabody Picture Vocabulary Test (Dunn, 1965), administered when Dunedin Study members were 3 years old; the StanfordBinet Intelligence Scale (Terman & Merrill, 1960), administered when members were 5 years old; and the Wechsler Intelligence Scales for Children–Revised (WISC-R; Wechsler, 1974), administered when members were 7 to 13 years old.
Did individuals with higher polygenic scores achieve higher degrees? In replication of the original discovery about the genetics of educational attainment, our results showed that Dunedin cohort members with higher polygenic scores tended to go on to achieve higher degrees compared with peers who had lower scores (r = .15, p < .001; Fig. 1a). This correlation between polygenic score and educational attainment was nearly identical to the estimate from the original report (Rietveld et al., 2013). As in previous studies, the genetic effect was small in magnitude; for example, having a polygenic score 1 standard deviation above the mean was associated with a 19% increase in likelihood of completing a university degree (RR = 1.19, 95% confidence interval (CI) = [1.07, 1.32]).
Did individuals with higher polygenic scores go on to achieve socioeconomic success beyond schooling? Adult socioeconomic attainments of Dunedin Study members were measured using data from structured interviews about jobs, income, wealth, and financial difficulties and by conducting administrative-record searches of governmental and credit-bureau databases. Factor analysis of these multiple measures was used to compute an adult-attainment-factor score (see Table S2 and Fig. S2 in the Supplemental Material). By midlife, individuals with higher polygenic scores tended to be more socioeconomically successful: They held more prestigious occupations, earned higher incomes, had accumulated more assets, reported fewer difficulties paying their expenses, relied less on social-welfare benefits, and had higher credit scores (r = .13, p < .001 for the adult-attainment factor; Fig. 1b). It may seem unsurprising that a polygenic score that predicted educational attainment also continued to predict success after education was complete. However, less than half of the genetic association with adult attainment was accounted for by educational attainment; when we repeated our genetic analysis of the adult-attainment factor and included education as a covariate, the adjusted effect size was .07 (p = .035). Genetic effect sizes for the individual attainment measures and effect sizes after adjustment for educational attainment are shown in Fig. S3 in the Supplemental Material.
Parents’ SES was measured from repeated assessments conducted when the cohort members were growing up (i.e., during their first 15 years of life; see the Supplemental Material). Our findings point to a gene-environment correlation: The polygenic score for educational attainment was stratified by childhood SES such that children with higher polygenic scores tended to have grown up in families with higher SES, whereas children with lower polygenic scores tended to have grown up in families with lower SES (r = .13, p < .001).
Three interrelated outcomes were considered: the Dunedin Study member’s educational attainment; their attained adult SES, measured as occupational prestige (in parallel to the status of their parents); and their adult-attainment-factor score. Children with higher polygenic scores tended to attain more regardless of whether they began life in a family that was well-off or one that was socially disadvantaged (more education: r = .10, p = .002; more prestigious occupations: r = 0.11, p < .001; higher adult-attainment-factor scores: r = .11, p = .002). Figure 2 shows associations between the polygenic score and adult attainment in groups of Dunedin Study members with low, middle, and high childhood SES. Dunedin Study data confirmed that children with higher polygenic scores had grown up in families with more socioeconomic resources (Krapohl & Plomin, 2016). But the data also showed that even for children born into socially disadvantaged circumstances, higher polygenic scores predicted upward social mobility.
Children with higher polygenic scores acquired reading skills at younger ages. Study members’ reading skill was assessed with the Burt Word Reading Test at each measurement session from ages 7 to 18 years. We used longitudinal multilevel growth models to test genetic associations with the model intercept and linear and quadratic slopes of change in reading over time (see Fig. S6 in the Supplemental Material). The model intercept captured the cohort mean reading score at age 7 (b = 30.50). The linear-slope term captured average annual change in reading score from age 7 to age 18 (b = 12.50). The quadratic-slope term captured deceleration of change; that is, it captured the convexity of the trajectory across childhood (b = −0.60). All model terms were statistically significant (p < .001). We tested genetic influence on growth by modeling intercept and slope terms of the growth curve as functions of the polygenic score and covariates. Polygenic score coefficients measured the effect of a 1-standard-deviation difference in polygenic score on reading at age 7 (intercept), on the linear change per year in reading score between the ages of 7 and 18 (linear slope), and on the deceleration of that change with increasing age (quadratic slope). Growth-curve modeling found that by age 7, children with higher polygenic scores were already stronger readers (intercept: b = 2.79, SE = 0.57, p < .001). Thereafter, these children improved their performance at a faster rate (linear slope: b = 0.25, SE = 0.09, p = 0.005) and reached their peak performance at an earlier age (quadratic slope: b = −0.03, SE = 0.01, p < .001; Fig. 3).
Dunedin Study members with higher polygenic scores were more financially planful. At ages 32 and 38, friends and relatives who knew each Dunedin Study member well reported about the member’s ability to manage money (96% response rate). In addition, Dunedin Study members were interviewed about financial building blocks (investments and retirement savings) and saving behaviors; scores on financial building blocks and savings behavior scales were averaged to calculate a financial planfulness score (see the Supplemental Material). Dunedin Study members with higher polygenic scores were rated by their informants as having fewer difficulties managing their money (r = −.08, p = .013) and were more financially planful on average (r = .09, p = .008). These findings show that in addition to acquiring academic credentials and professional experience to command higher earnings, Dunedin Study members with higher polygenic scores tended to be better managers of their financial resources.
Multivariate twin research suggests that the heritability of educational attainment reflects genetic influences on noncognitive skills as well as intelligence (Krapohl et al., 2014). We found molecular evidence to support this hypothesis: Children’s polygenic scores for educational attainment were correlated with their noncognitive self-control and interpersonal skills as well as with their IQ scores. Our “top-down” approach, working from an adult phenotype backward in development toward a DNA sequence, yielded findings that suggest behavioral mechanisms for genetic influences on educational attainment."
"A previous genome-wide association study (GWAS) of more than 100,000 individuals identified molecular-genetic predictors of educational attainment. We undertook in-depth life-course investigation of the polygenic score derived from this GWAS using the four-decade Dunedin Study (N = 918). There were five main findings. First, polygenic scores predicted adult economic outcomes even after accounting for educational attainments. Second, genes and environments were correlated: Children with higher polygenic scores were born into better-off homes. Third, children’s polygenic scores predicted their adult outcomes even when analyses accounted for their social-class origins; social-mobility analysis showed that children with higher polygenic scores were more upwardly mobile than children with lower scores. Fourth, polygenic scores predicted behavior across the life course, from early acquisition of speech and reading skills through geographic mobility and mate choice and on to financial planning for retirement. Fifth, polygenic-score associations were mediated by psychological characteristics, including intelligence, self-control, and interpersonal skill. Effect sizes were small. Factors connecting DNA sequence with life outcomes may provide targets for interventions to promote population-wide positive development.
This continuum, measured as a polygenic score (Chabris, Lee, Cesarini, Benjamin, & Laibson, 2015), has since been shown to predict educational attainment in cohorts on three continents and even differences in educational attainment between siblings in the same family (Conley et al., 2015; de Zeeuw et al., 2014; Domingue, Belsky, Conley, Harris, & Boardman, 2015; Rietveld, Esko, et al., 2014; Ward et al., 2014).
we ask three questions in the current article: (a) Do genetic discoveries for educational attainment predict outcomes beyond schooling? (b) If so, what are the developmental and behavioral pathways that connect differences in DNA sequences with divergent life outcomes? (c) Do psychological characteristics act as mediators of genetic associations? Although these questions may seem premature, it is important to ask them now, before technologies using genetics to predict social outcomes become possible.
We calculated polygenic scores according to the method described by Dudbridge (2013) using the PRsice software (Version 1.22; http://prsice.info/; Euesden, Lewis, & O’Reilly, 2015). To calculate the polygenic score for educational attainment, we matched genotypes from our data with GWAS results for educational attainment reported by the Social Science Genetic Association Consortium ( Rietveld et al., 2013) and used the approximately 2.3 million matched genotypes to score Dunedin Study members’ genetic predisposition to educational attainment.
We used all matched SNPs to compute polygenic scores, irrespective of nominal significance for their association with educational attainment. Scores ranged from −30.51 to 73.77 (M = 17.73, SD = 17.94) and were normally distributed in the Dunedin birth cohort. We standardized scores so that the mean was zero and the standard deviation was 1 (see Fig. S1 in the Supplemental Material available online).
Traits and abilities. We measured cognitive ability and cognitive development using the Peabody Picture Vocabulary Test (Dunn, 1965), administered when Dunedin Study members were 3 years old; the StanfordBinet Intelligence Scale (Terman & Merrill, 1960), administered when members were 5 years old; and the Wechsler Intelligence Scales for Children–Revised (WISC-R; Wechsler, 1974), administered when members were 7 to 13 years old.
Did individuals with higher polygenic scores achieve higher degrees? In replication of the original discovery about the genetics of educational attainment, our results showed that Dunedin cohort members with higher polygenic scores tended to go on to achieve higher degrees compared with peers who had lower scores (r = .15, p < .001; Fig. 1a). This correlation between polygenic score and educational attainment was nearly identical to the estimate from the original report (Rietveld et al., 2013). As in previous studies, the genetic effect was small in magnitude; for example, having a polygenic score 1 standard deviation above the mean was associated with a 19% increase in likelihood of completing a university degree (RR = 1.19, 95% confidence interval (CI) = [1.07, 1.32]).
Did individuals with higher polygenic scores go on to achieve socioeconomic success beyond schooling? Adult socioeconomic attainments of Dunedin Study members were measured using data from structured interviews about jobs, income, wealth, and financial difficulties and by conducting administrative-record searches of governmental and credit-bureau databases. Factor analysis of these multiple measures was used to compute an adult-attainment-factor score (see Table S2 and Fig. S2 in the Supplemental Material). By midlife, individuals with higher polygenic scores tended to be more socioeconomically successful: They held more prestigious occupations, earned higher incomes, had accumulated more assets, reported fewer difficulties paying their expenses, relied less on social-welfare benefits, and had higher credit scores (r = .13, p < .001 for the adult-attainment factor; Fig. 1b). It may seem unsurprising that a polygenic score that predicted educational attainment also continued to predict success after education was complete. However, less than half of the genetic association with adult attainment was accounted for by educational attainment; when we repeated our genetic analysis of the adult-attainment factor and included education as a covariate, the adjusted effect size was .07 (p = .035). Genetic effect sizes for the individual attainment measures and effect sizes after adjustment for educational attainment are shown in Fig. S3 in the Supplemental Material.
Parents’ SES was measured from repeated assessments conducted when the cohort members were growing up (i.e., during their first 15 years of life; see the Supplemental Material). Our findings point to a gene-environment correlation: The polygenic score for educational attainment was stratified by childhood SES such that children with higher polygenic scores tended to have grown up in families with higher SES, whereas children with lower polygenic scores tended to have grown up in families with lower SES (r = .13, p < .001).
Three interrelated outcomes were considered: the Dunedin Study member’s educational attainment; their attained adult SES, measured as occupational prestige (in parallel to the status of their parents); and their adult-attainment-factor score. Children with higher polygenic scores tended to attain more regardless of whether they began life in a family that was well-off or one that was socially disadvantaged (more education: r = .10, p = .002; more prestigious occupations: r = 0.11, p < .001; higher adult-attainment-factor scores: r = .11, p = .002). Figure 2 shows associations between the polygenic score and adult attainment in groups of Dunedin Study members with low, middle, and high childhood SES. Dunedin Study data confirmed that children with higher polygenic scores had grown up in families with more socioeconomic resources (Krapohl & Plomin, 2016). But the data also showed that even for children born into socially disadvantaged circumstances, higher polygenic scores predicted upward social mobility.
Children with higher polygenic scores acquired reading skills at younger ages. Study members’ reading skill was assessed with the Burt Word Reading Test at each measurement session from ages 7 to 18 years. We used longitudinal multilevel growth models to test genetic associations with the model intercept and linear and quadratic slopes of change in reading over time (see Fig. S6 in the Supplemental Material). The model intercept captured the cohort mean reading score at age 7 (b = 30.50). The linear-slope term captured average annual change in reading score from age 7 to age 18 (b = 12.50). The quadratic-slope term captured deceleration of change; that is, it captured the convexity of the trajectory across childhood (b = −0.60). All model terms were statistically significant (p < .001). We tested genetic influence on growth by modeling intercept and slope terms of the growth curve as functions of the polygenic score and covariates. Polygenic score coefficients measured the effect of a 1-standard-deviation difference in polygenic score on reading at age 7 (intercept), on the linear change per year in reading score between the ages of 7 and 18 (linear slope), and on the deceleration of that change with increasing age (quadratic slope). Growth-curve modeling found that by age 7, children with higher polygenic scores were already stronger readers (intercept: b = 2.79, SE = 0.57, p < .001). Thereafter, these children improved their performance at a faster rate (linear slope: b = 0.25, SE = 0.09, p = 0.005) and reached their peak performance at an earlier age (quadratic slope: b = −0.03, SE = 0.01, p < .001; Fig. 3).
Dunedin Study members with higher polygenic scores were more financially planful. At ages 32 and 38, friends and relatives who knew each Dunedin Study member well reported about the member’s ability to manage money (96% response rate). In addition, Dunedin Study members were interviewed about financial building blocks (investments and retirement savings) and saving behaviors; scores on financial building blocks and savings behavior scales were averaged to calculate a financial planfulness score (see the Supplemental Material). Dunedin Study members with higher polygenic scores were rated by their informants as having fewer difficulties managing their money (r = −.08, p = .013) and were more financially planful on average (r = .09, p = .008). These findings show that in addition to acquiring academic credentials and professional experience to command higher earnings, Dunedin Study members with higher polygenic scores tended to be better managers of their financial resources.
Multivariate twin research suggests that the heritability of educational attainment reflects genetic influences on noncognitive skills as well as intelligence (Krapohl et al., 2014). We found molecular evidence to support this hypothesis: Children’s polygenic scores for educational attainment were correlated with their noncognitive self-control and interpersonal skills as well as with their IQ scores. Our “top-down” approach, working from an adult phenotype backward in development toward a DNA sequence, yielded findings that suggest behavioral mechanisms for genetic influences on educational attainment."
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