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Understanding how parents’ cognitive and non-cognitive skills influence offspring education is essential for educational, family and economic policy. We use genetics (GWAS-by-subtraction) to assess a latent, broad non-cognitive skills dimension. To index parental effects controlling for genetic transmission, we estimate indirect parental genetic effects of polygenic scores on childhood and adulthood educational outcomes, using siblings ( N  = 47,459), adoptees ( N  = 6407), and parent-offspring trios ( N  = 2534) in three UK and Dutch cohorts. We find that parental cognitive and non-cognitive skills affect offspring education through their environment: on average across cohorts and designs, indirect genetic effects explain 36–40% of population polygenic score associations. However, indirect genetic effects are lower for achievement in the Dutch cohort, and for the adoption design. We identify potential causes of higher sibling- and trio-based estimates: prenatal indirect genetic effects, population stratification, and assortative mating. Our phenotype-agnostic, genetically sensitive approach has established overall environmental effects of parents’ skills, facilitating future mechanistic work. Understanding how parents’ cognitive and non-cognitive skills influence their children’s educational trajectories is important for educational, family and economic policy. Here, the authors investigate parental influence on children’s education using genetic approaches.

Partners resemble each other in health and education, but studies usually examine one trait at a time in established couples. Using data from all Norwegian first-time parents ( N  = 187,926) between 2016–2020, we analyse grade point average at age 16, educational attainment, and medical records of 10 mental and 10 somatic health conditions measured 10 to 5 years before childbirth. We find stronger partner similarity in mental (median r  = 0.14) than in somatic health conditions (median r  = 0.04), with ubiquitous cross-trait correlations in mental health (median r  = 0.13). High grade point average or education is associated with better partner mental (median r  = −0.16) and somatic (median r  = −0.08) health. Elevated mental health correlations (median r  = 0.25) in established couples indicate convergence. Analyses of siblings and in-laws suggest that health similarity is influenced by indirect assortment based on related traits. Adjusting for grade point average or education reduces partner health correlations by 30–40%. These findings have implications for the distribution of risk factors among children, genetic studies, and intergenerational transmission. By analyzing 187,926 Norwegian first-time parents, researchers found that partners are more similar in mental than physical health, with mental health similarity increasing over time. Educational similarity partially explained health similarity.

Assortative mating – the non-random mating of individuals with similar traits – is known to increase trait-specific genetic variance and genetic similarity between relatives. However, empirical evidence is limited for many traits, and the implications hinge on whether assortative mating has started recently or many generations ago. Here we show theoretically and empirically that genetic similarity between relatives can provide evidence on the presence and history of assortative mating. First, we employed path analysis to understand how assortative mating affects genetic similarity between family members across generations, finding that similarity between distant relatives is more affected than close relatives. Next, we correlated polygenic indices of 47,135 co-parents from the Norwegian Mother, Father, and Child Cohort Study (MoBa) and found genetic evidence of assortative mating in nine out of sixteen examined traits. The same traits showed elevated similarity between relatives, especially distant relatives. Six of the nine traits, including educational attainment, showed greater genetic variance among offspring, which is inconsistent with stable assortative mating over many generations. These results suggest an ongoing increase in familial similarity for these traits. The implications of this research extend to genetic methodology and the understanding of social and economic disparities. Non-random mating can complicate genetic studies, but implications hinge on its history in prior generations. Here, the authors use genetic similarity between relatives to investigate which traits show evidence of recent changes in mating behavior.

Estimating the contributions of genetic and environmental factors is key to understanding differences in socioeconomic status (SES). However, the heritability of SES varies by measure, method, and context. Here, we estimate genetic and environmental sources of variance and commonality in the ‘big four’ SES indicators. We use high-quality administrative data on educational attainment, occupational prestige, income, and wealth, and employ four family-based and unrelated genotype-based heritability methods, all drawn from the same population-wide cohort of >170,000 Norwegians aged 35-45. By drawing subsamples from a consistent sample and using registry-based data, we reduce differences in estimates due to population characteristics and measurement error. Our results show that genetic variation consistently explains more for educational attainment and occupational prestige. Family-shared environmental contributions explained more for educational attainment and wealth. Our results highlight considerable common influences on the four SES indicators among genetic and shared environmental factors, but not among non-shared environmental factors. Overall, we show how the relative importance of genetic and environmental factors to SES differences in Norway varies by method and type of socioeconomic attainment. This study is a reliable source for comparing heritability methods, and for comparing SES indicators and their genetic and environmental commonality in a social-democratic welfare state. The authors examine the genetic and environmental contributions to socioeconomic status (SES) in Norway using a large, population-wide dataset and four different heritability estimation methods. They find that genetic factors explain more variance in education and occupational prestige, while shared environmental factors are more influential for education and wealth, highlighting the complexity and context-dependence of SES determinants.

Assortative mating on heritable traits can have implications for the genetic resemblance between siblings and in-laws in succeeding generations. We studied polygenic scores and phenotypic data from pairs of partners ( n  = 26,681), siblings ( n  = 2,170), siblings-in-law ( n  = 3,905), and co-siblings-in-law ( n  = 1,763) in the Norwegian Mother, Father and Child Cohort Study. Using structural equation models, we estimated associations between measurement error-free latent genetic and phenotypic variables. We found evidence of genetic similarity between partners for educational attainment ( r g  = 0.37), height ( r g  = 0.13), and depression ( r g  = 0.08). Common genetic variants associated with educational attainment correlated between siblings above 0.50 (r g  = 0.68) and between siblings-in-law ( r g  = 0.25) and co-siblings-in-law ( r g  = 0.09). Indirect assortment on secondary traits accounted for partner similarity in education and depression, but not in height. Comparisons between the genetic similarities of partners and siblings indicated that genetic variances were in intergenerational equilibrium. This study shows genetic similarities between extended family members and that assortative mating has taken place for several generations. Assortative mating could violate the assumption of random mating used in many genetic studies. Here, the authors study more than 25,000 Norwegian families to find genetic similarity between partners, siblings, and in-laws in genetic factors related to educational attainment, height, and depression.

Background Many studies detect associations between parent behaviour and child symptoms of anxiety and depression. Despite knowledge that anxiety and depression are influenced by a complex interplay of genetic and environmental risk factors, most studies do not account for shared familial genetic risk. Quantitative genetic designs provide a means of controlling for shared genetics, but rely on observed putative exposure variables, and require data from highly specific family structures. Methods The intergenerational genomic method, Relatedness Disequilibrium Regression (RDR), indexes environmental effects of parents on child traits using measured genotypes. RDR estimates how much the parent genome influences the child indirectly via the environment, over and above effects of genetic factors acting directly in the child. This ‘genetic nurture’ effect is agnostic to parent phenotype and captures unmeasured heritable parent behaviours. We applied RDR in a sample of 11,598 parent-offspring trios from the Norwegian Mother, Father and Child Cohort Study (MoBa) to estimate parental genetic nurture separately from direct child genetic effects on anxiety and depression symptoms at age 8. We tested for mediation of genetic nurture via maternal anxiety and depression symptoms. Results were compared to a complementary non-genomic pedigree model. Results Parental genetic nurture explained 14% of the variance in depression symptoms at age 8. Subsequent analyses suggested that maternal anxiety and depression partially mediated this effect. The genetic nurture effect was mirrored by the finding of family environmental influence in our pedigree model. In contrast, variance in anxiety symptoms was not significantly influenced by common genetic variation in children or parents, despite a moderate pedigree heritability. Conclusions Genomic methods like RDR represent new opportunities for genetically sensitive family research on complex human traits, which until now has been largely confined to adoption, twin and other pedigree designs. Our results are relevant to debates about the role of parents in the development of anxiety and depression in children, and possibly where to intervene to reduce problems.

Emerging evidence suggests that ADHD is associated with increased risk for metabolic and cardiovascular (cardiometabolic) diseases. However, an understanding of the mechanisms underlying these associations is still limited. In this study we estimated the associations of polygenic scores (PGS) for ADHD with several cardiometabolic diseases and biomarkers. Furthermore, we investigated to what extent the PGS effect was influenced by direct and indirect genetic effects (i.e., shared familial effects). We derived ADHD-PGS in 50,768 individuals aged 18–90 years from the Dutch Lifelines Cohort study. Using generalised estimating equations, we estimated the association of PGS with cardiometabolic diseases, derived from self-report and several biomarkers measured during a physical examination. We additionally ran within-sibling PGS analyses, using fixed effects models, to disentangle direct effects of individuals’ own ADHD genetic risk from confounding due to indirect genetic effects of relatives, as well as population stratification. We found that higher ADHD-PGS were statistically significantly associated with several cardiometabolic diseases (R-squared [R 2 ] range = 0.03–0.50%) and biomarkers (related to inflammation, blood pressure, lipid metabolism, amongst others) (R 2 range = 0.01–0.16%) ( P  < 0.05). Adjustment for shared familial factors attenuated the associations between ADHD-PGS and cardiometabolic outcomes (on average 56% effect size reduction), and significant associations only remained for metabolic disease. Overall our findings suggest that increased genetic liability for ADHD confers a small but significant risk increase for cardiometabolic health outcomes in adulthood. These associations were observable in the general population, even in individuals without ADHD diagnosis, and were partly explained by familial factors shared among siblings.

Background Fundamental questions about the roles of genes, environments, and their interplay in developmental psychopathology have traditionally been the domain of twin and family studies. More recently, the rapidly growing availability of large genomic datasets, composed of unrelated individuals, has generated novel insights. However, there are major stumbling blocks. Only a small fraction of the total genetic influence on childhood psychopathology estimated from family data is captured with measured DNA. Moreover, genetic influence identified using DNA is often confounded with indirect genetic effects of relatives, population stratification and assortative mating. Methods The goal of this paper is to review how combining DNA‐based genomic research with family‐based quantitative genetics helps to address key issues in genomics and push knowledge further. Results We focus on three approaches to obtaining more accurate and novel genomic findings on the developmental aetiology of psychopathology: (a) using knowledge from twin and family studies, (b) triangulating with twin and family studies, and (c) integrating data and methods with twin and family studies. Conclusion We support the movement towards family‐based genomic research, and show that developmental psychologists are particularly well‐placed to contribute hypotheses, analysis tools, and data. There are major stumbling blocks in the genomics of psychopathology. This review shows how more accurate and novel genomic findings on the developmental aetiology of psychopathology can be obtained by (a) using knowledge from twin and family studies, (b) triangulating with twin and family studies, and (c) integrating data and methods with twin and family studies.

The widespread comorbidity observed across psychiatric disorders may be the result of processes such as assortative mating, gene-environment correlation, or selection into population studies. Between-family analyses of comorbidity are subject to these sources of bias, whereas within-family analyses are not. Because of Mendelian inheritance, alleles are randomly assigned within families, conditional on parental alleles. We exploit this variation to compare the structure of comorbidity across broad psychiatric polygenic scores when calculated either between-family (child polygenic scores) or within-family (child polygenic scores regressed on parental polygenic scores) in over 25,000 genotyped parent-offspring trios from the Norwegian Mother Father and Child Cohort study (MoBa). We fitted a series of factor models to the between- and within-family data, which consisted of a single genetic p-factor and a varying number of uncorrelated subfactors. The best-fitting model was identical for between- and within-family analyses and included three subfactors capturing variants associated with neurodevelopment, psychosis, and constraint, in addition to the genetic p-factor. Partner genetic correlations, indicating assortative mating, were not present for the genetic p-factor, but were substantial for the psychosis ( b  = 0.081;95% CI [0.038,0.124]) and constraint ( b  = 0.257;95% CI [0.075,0.439]) subfactors. When average factor levels for MoBa mothers and fathers were compared to a population mean of zero we found evidence of sex-specific participation bias, which has implications for the generalizability of findings from cohort studies. Our results demonstrate the power of the within-family design for better understanding the mechanisms driving psychiatric comorbidity and their consequences on population health.