Low socioeconomic status (SES) children perform on average worse on intelligence tests than children from higher SES backgrounds, but the developmental relationship between intelligence and SES has ...not been adequately investigated. Here, we use latent growth curve (LGC) models to assess associations between SES and individual differences in the intelligence starting point (intercept) and in the rate and direction of change in scores (slope and quadratic term) from infancy through adolescence in 14,853 children from the Twins Early Development Study (TEDS), assessed 9 times on IQ between the ages of 2 and 16years. SES was significantly associated with intelligence growth factors: higher SES was related both to a higher starting point in infancy and to greater gains in intelligence over time. Specifically, children from low SES families scored on average 6 IQ points lower at age 2 than children from high SES backgrounds; by age 16, this difference had almost tripled. Although these key results did not vary across girls and boys, we observed gender differences in the development of intelligence in early childhood. Overall, SES was shown to be associated with individual differences in intercepts as well as slopes of intelligence. However, this finding does not warrant causal interpretations of the relationship between SES and the development of intelligence.
•IQ growth trajectories were modeled in British children from age 2 to 16years.•Children's socioeconomic background (SES) was associated with IQ growth.•High and low SES children differed by 6 IQ points at age 2.•By age 16, this IQ difference between high and low SES children had tripled.
Background
The explosion caused by the fusion of quantitative genetics and molecular genetics will transform behavioural genetic research in child and adolescent psychology and psychiatry.
Methods
...Although the fallout has not yet settled, the goal of this paper is to predict the next 10 years of research in what could be called behavioural genomics.
Results
I focus on three research directions: the genetic architecture of psychopathology, causal modelling of gene‐environment interplay, and the use of DNA as an early warning system.
Conclusion
Eventually, whole‐genome sequencing will be available for all newborns, which means that behavioural genomics could potentially be applied ubiquitously in research and clinical practice.
Three transformative developments for behavioural genomic research in the next 10 years are described. First, behavioural genomic research will use dimensional measures to reveal the hierarchical genetic architecture of psychopathology. A second direction for behavioural genomic research will be to investigate causa models of GE interplay. Third, polygenic scores will be used as an early warning system in childhood to predict profiles of adult psychopathology, which will eventually transform clinical practice.
Genetic specificity of face recognition Shakeshaft, Nicholas G.; Plomin, Robert
Proceedings of the National Academy of Sciences - PNAS,
10/2015, Volume:
112, Issue:
41
Journal Article
Peer reviewed
Open access
Specific cognitive abilities in diverse domains are typically found to be highly heritable and substantially correlated with general cognitive ability (g), both phenotypically and genetically. Recent ...twin studies have found the ability to memorize and recognize faces to be an exception, being similarly heritable but phenotypically substantially uncorrelated both with g and with general object recognition. However, the genetic relationships between face recognition and other abilities (the extent to which they share a common genetic etiology) cannot be determined from phenotypic associations. In this, to our knowledge, first study of the genetic associations between face recognition and other domains, 2,000 18- and 19-year-old United Kingdom twins completed tests assessing their face recognition, object recognition, and general cognitive abilities. Results confirmed the substantial heritability of face recognition (61%), and multivariate genetic analyses found that most of this genetic influence is unique and not shared with other cognitive abilities.
In the context of current concerns about replication in psychological science, we describe 10 findings from behavioral genetic research that have replicated robustly. These are "big" findings, both ...in terms of effect size and potential impact on psychological science, such as linearly increasing heritability of intelligence from infancy (20%) through adulthood (60%). Four of our top 10 findings involve the environment, discoveries that could have been found only with genetically sensitive research designs. We also consider reasons specific to behavioral genetics that might explain why these findings replicate.
Polygenic scores are a popular tool for prediction of complex traits. However, prediction estimates in samples of unrelated participants can include effects of population stratification, assortative ...mating, and environmentally mediated parental genetic effects, a form of genotype-environment correlation (rGE). Comparing genome-wide polygenic score (GPS) predictions in unrelated individuals with predictions between siblings in a within-family design is a powerful approach to identify these different sources of prediction. Here, we compared within- to between-family GPS predictions of eight outcomes (anthropometric, cognitive, personality, and health) for eight corresponding GPSs. The outcomes were assessed in up to 2,366 dizygotic (DZ) twin pairs from the Twins Early Development Study from age 12 to age 21. To account for family clustering, we used mixed-effects modeling, simultaneously estimating within- and between-family effects for target- and cross-trait GPS prediction of the outcomes. There were three main findings: (1) DZ twin GPS differences predicted DZ differences in height, BMI, intelligence, educational achievement, and ADHD symptoms; (2) target and cross-trait analyses indicated that GPS prediction estimates for cognitive traits (intelligence and educational achievement) were on average 60% greater between families than within families, but this was not the case for non-cognitive traits; and (3) much of this within- and between-family difference for cognitive traits disappeared after controlling for family socio-economic status (SES), suggesting that SES is a major source of between-family prediction through rGE mechanisms. These results provide insights into the patterns by which rGE contributes to GPS prediction, while ruling out confounding due to population stratification and assortative mating.
Most evidence to date highlights the importance of genetic influences on the liability to autism and related traits. However, most of these findings are derived from clinically ascertained samples, ...possibly missing individuals with subtler manifestations, and obtained estimates may not be representative of the population.
To establish the relative contributions of genetic and environmental factors in liability to autism spectrum disorder (ASD) and a broader autism phenotype in a large population-based twin sample and to ascertain the genetic/environmental relationship between dimensional trait measures and categorical diagnostic constructs of ASD.
We used data from the population-based cohort Twins Early Development Study, which included all twin pairs born in England and Wales from January 1, 1994, through December 31, 1996. We performed joint continuous-ordinal liability threshold model fitting using the full information maximum likelihood method to estimate genetic and environmental parameters of covariance. Twin pairs underwent the following assessments: the Childhood Autism Spectrum Test (CAST) (6423 pairs; mean age, 7.9 years), the Development and Well-being Assessment (DAWBA) (359 pairs; mean age, 10.3 years), the Autism Diagnostic Observation Schedule (ADOS) (203 pairs; mean age, 13.2 years), the Autism Diagnostic Interview-Revised (ADI-R) (205 pairs; mean age, 13.2 years), and a best-estimate diagnosis (207 pairs).
Participants underwent screening using a population-based measure of autistic traits (CAST assessment), structured diagnostic assessments (DAWBA, ADI-R, and ADOS), and a best-estimate diagnosis.
On all ASD measures, correlations among monozygotic twins (range, 0.77-0.99) were significantly higher than those for dizygotic twins (range, 0.22-0.65), giving heritability estimates of 56% to 95%. The covariance of CAST and ASD diagnostic status (DAWBA, ADOS and best-estimate diagnosis) was largely explained by additive genetic factors (76%-95%). For the ADI-R only, shared environmental influences were significant (30% 95% CI, 8%-47%) but smaller than genetic influences (56% 95% CI, 37%-82%).
The liability to ASD and a more broadly defined high-level autism trait phenotype in this large population-based twin sample derives primarily from additive genetic and, to a lesser extent, nonshared environmental effects. The largely consistent results across different diagnostic tools suggest that the results are generalizable across multiple measures and assessment methods. Genetic factors underpinning individual differences in autismlike traits show considerable overlap with genetic influences on diagnosed ASD.
Significance Differences among children in educational achievement are highly heritable from the early school years until the end of compulsory education at age 16, when UK students are assessed ...nationwide with standard achievement tests General Certificate of Secondary Education (GCSE). Genetic research has shown that intelligence makes a major contribution to the heritability of educational achievement. However, we show that other broad domains of behavior such as personality and psychopathology also account for genetic influence on GCSE scores beyond that predicted by intelligence. Together with intelligence, these domains account for 75% of the heritability of GCSE scores. These results underline the importance of genetics in educational achievement and its correlates. The results also support the trend in education toward personalized learning.
Because educational achievement at the end of compulsory schooling represents a major tipping point in life, understanding its causes and correlates is important for individual children, their families, and society. Here we identify the general ingredients of educational achievement using a multivariate design that goes beyond intelligence to consider a wide range of predictors, such as self-efficacy, personality, and behavior problems, to assess their independent and joint contributions to educational achievement. We use a genetically sensitive design to address the question of why educational achievement is so highly heritable. We focus on the results of a United Kingdom-wide examination, the General Certificate of Secondary Education (GCSE), which is administered at the end of compulsory education at age 16. GCSE scores were obtained for 13,306 twins at age 16, whom we also assessed contemporaneously on 83 scales that were condensed to nine broad psychological domains, including intelligence, self-efficacy, personality, well-being, and behavior problems. The mean of GCSE core subjects (English, mathematics, science) is more heritable (62%) than the nine predictor domains (35–58%). Each of the domains correlates significantly with GCSE results, and these correlations are largely mediated genetically. The main finding is that, although intelligence accounts for more of the heritability of GCSE than any other single domain, the other domains collectively account for about as much GCSE heritability as intelligence. Together with intelligence, these domains account for 75% of the heritability of GCSE. We conclude that the high heritability of educational achievement reflects many genetically influenced traits, not just intelligence.
It has recently been proposed that a single dimension, called the p factor, can capture a person's liability to mental disorder. Relevant to the p hypothesis, recent genetic research has found ...surprisingly high genetic correlations between pairs of psychiatric disorders. Here, for the first time, we compare genetic correlations from different methods and examine their support for a genetic p factor. We tested the hypothesis of a genetic p factor by applying principal component analysis to matrices of genetic correlations between major psychiatric disorders estimated by three methods-family study, genome-wide complex trait analysis, and linkage-disequilibrium score regression-and on a matrix of polygenic score correlations constructed for each individual in a UK-representative sample of 7 026 unrelated individuals. All disorders loaded positively on a first unrotated principal component, which accounted for 57, 43, 35, and 22% of the variance respectively for the four methods. Our results showed that all four methods provided strong support for a genetic p factor that represents the pinnacle of the hierarchical genetic architecture of psychopathology.
BACKGROUND: Body mass index (BMI) has been shown to be highly heritable, but most studies were carried out in cohorts born before the onset of the "obesity epidemic." OBJECTIVE: We aimed to quantify ...genetic and environmental influences on BMI and central adiposity in children growing up during a time of dramatic rises in pediatric obesity. DESIGN: We carried out twin analyses of BMI and waist circumference (WC) in a UK sample of 5092 twin pairs aged 8-11 y. Quantitative genetic model-fitting was used for the univariate analyses, and bivariate quantitative genetic model-fitting was used for the analysis of covariance between BMI and WC. RESULTS: Quantitative genetic model-fitting confirmed substantial heritability for BMI and WC (77% for both). Bivariate genetic analyses showed that, although the genetic influence on WC was largely common to BMI (60%), there was also a significant independent genetic effect (40%). For both BMI and WC, there was a very modest shared-environment effect, and the remaining environmental variance was unshared. CONCLUSIONS: Genetic influences on BMI and abdominal adiposity are high in children born since the onset of the pediatric obesity epidemic. Most of the genetic effect on abdominal adiposity is common to BMI, but 40% is attributable to independent genetic influences. Environmental effects are small and are divided approximately equally between shared and nonshared effects. Targeting the family may be vital for obesity prevention in the earliest years, but longer-term weight control will require a combination of individual engagement and society-wide efforts to modify the environment, especially for children at high genetic risk.
The predictive utility of polygenic scores is increasing, and many polygenic scoring methods are available, but it is unclear which method performs best. This study evaluates the predictive utility ...of polygenic scoring methods within a reference-standardized framework, which uses a common set of variants and reference-based estimates of linkage disequilibrium and allele frequencies to construct scores. Eight polygenic score methods were tested: p-value thresholding and clumping (pT+clump), SBLUP, lassosum, LDpred1, LDpred2, PRScs, DBSLMM and SBayesR, evaluating their performance to predict outcomes in UK Biobank and the Twins Early Development Study (TEDS). Strategies to identify optimal p-value thresholds and shrinkage parameters were compared, including 10-fold cross validation, pseudovalidation and infinitesimal models (with no validation sample), and multi-polygenic score elastic net models. LDpred2, lassosum and PRScs performed strongly using 10-fold cross-validation to identify the most predictive p-value threshold or shrinkage parameter, giving a relative improvement of 16-18% over pT+clump in the correlation between observed and predicted outcome values. Using pseudovalidation, the best methods were PRScs, DBSLMM and SBayesR. PRScs pseudovalidation was only 3% worse than the best polygenic score identified by 10-fold cross validation. Elastic net models containing polygenic scores based on a range of parameters consistently improved prediction over any single polygenic score. Within a reference-standardized framework, the best polygenic prediction was achieved using LDpred2, lassosum and PRScs, modeling multiple polygenic scores derived using multiple parameters. This study will help researchers performing polygenic score studies to select the most powerful and predictive analysis methods.
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