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Educational and occupational success, jointly referred to as socioeconomic status, are associated with many important life outcomes, including physical and mental health, life satisfaction, happiness, and longevity (1–4). Individual differences in socioeconomic outcomes (combined socioeconomic status, or employment status, educational attainment and income separately) are often assumed to be environmental in origin, however, twin and family-based research has shown that both genetic and environmental factors play an important role in explaining individual variation (5–8). The heritability (the proportion of individual differences explained by inherited DNA differences) of educational attainment as well as occupational status is around 50%, with the rest of the variation explained by environmental factors (5,8,9). Heritability has traditionally been calculated by comparing the similarity of identical and non-identical twin pairs. It is also possible to estimate heritability directly from DNA, by comparing the phenotypic, or trait, similarity between people with their genetic similarity, calculated using hundreds of thousands of DNA variants, or SNPs (single nucleotide polymorphisms). These SNP-based heritabilities have also been shown to be substantial for socioeconomic outcomes, around 20% on average (7,10–12). SNP heritability for complex traits, like educational and occupational outcomes, is roughly half of that of family-based heritability (13). The reasons for this are that SNP heritability is calculated using genotyped genetic markers, so can only index the variation captured by genetic markers genotyped on a SNP array (SNP chip). As these arrays only uses common SNPs, this excludes rare and structural variants, and it only estimates additive genetic effects (14). The missing heritability, the gap between the family-based heritability and SNP heritability, has been shown to be almost recovered for height and body-mass-index (BMI) by including rare variants in a model using whole-genome sequence data (15). It remains to be seen if the missing heritability in other complex traits, for example for educational status and occupational status, is also explained by the rare variants, which would indicate that the heritability of complex traits is explained mainly by additive genetic effects. Alternatively, it has been argued that twin heritability overestimates the true heritability (16), and this explains (or, rather, explains away) the missing heritability. Another way to estimate genetic influence on complex traits is to use genome-wide polygenic scores (GPS). GPS use summary statistics of GWA (Genome-wide Association) studies that aim to identify specific genetic markers associated with a phenotype of interest, by aggregating thousands of SNP associations across the genome to predict the trait of interest (17). This prediction using GPS serves as an index that summarizes the current prediction of additive genetic influences of the trait of interest (18). These GPS heritabilities for socioeconomic variables have also been shown to be substantial, around 11% on average (19,20). However, all these heritability estimates may be different in varied environments and may change with major changes in environment, for example with societal changes such as changes in policy (5,21). Paradoxically, heritability for educational outcomes tends to be higher when environmental variation is attenuated. That is, with less variation in the environment, a larger proportion of individual differences is explained by genetic factors (22). Thus heritability could be used as an index of equal opportunities (18,23). In addition, research has shown that individual differences in socioeconomic outcomes are explained both by inherited DNA differences but also by non-transmitted alleles operating via parental genotypes. That is, part of the variance in educational attainment is explained by so-called genetic nurture; that is, parents’ genetic variants not transmitted to their children still predict educational attainment in offspring (24,25). If parents’ non-transmitted genetics predict their offspring’s achievement, this link must happen through parent-driven environmental processes, independent of child’s own biology. The environmental experiences that parents create for their children are associated with their offspring educational outcomes, are a clear example of passive gene-environment correlation. The gene-environment correlation is a well-studied area in quantitative genetic research (26,27), but often limited to between family variance. However, DNA data is allowing us to get closer to this phenomenon and quantify the genetic nurture component in several social outcomes. Genetic nurture has been shown to be substantial for educational outcomes (25) and to a smaller degree for depressive symptoms (28). It is a plausible hypothesis meriting major research interest to study how genetic nurture is influenced by changes in the larger social environment. A related issue here concerns the locus of the heritability of socioeconomic outcomes. It is well-established, for example, that educational achievement is explained by a package of heritable cognitive and non-cognitive traits (29). However, it is not known what proportion of the putative change in heritability of socioeconomic outcomes is explained by cognitive and non-cognitive factors following a major societal change in the environment. Furthermore, it is not known how the change in social environment reflects genetic nurture, that is the proportion of variance in offspring social outcomes that is explained by parental genotypes, independent of child’s biology, and the proportion of this genetic nurture that is explained by cognitive or non-cognitive genetic variances in parental genotypes. A newly developed genomic method, GWAS-by-subtraction, allows researchers to investigate these outstanding research questions by partitioning the SNP associations of educational attainment into cognitive and non-cognitive parts of variance (30). Educational attainment has also been shown to be associated with fecundity (the ability to produce many offspring): a more educated people tend to have children later in life and as a result, or in addition to that, tends to have fewer offspring. The relationship between educational attainment and fecundity could result in a decline of educationally relevant genetic variants in the population. Although this effect in one generation is small, it could lead to a significant decline over many generations (31). We have previously shown a massive shift in the aetiology of social outcomes in Estonia following an abrupt social change - the shift from communism to capitalism following the collapse of the Soviet Union. DNA differences explained twice as much variance in social outcomes (occupational status, educational attainment) independent Estonia compared to when Estonia was occupied by the Soviet Union (32). We also reported interesting sex differences, where the shift was much larger for females compared to males, but we were cautious with our conclusions due to limited power in the sex-stratified analyses. We also tested the decline of educationally relevant genetic variants in the population, but did not find evidence for the hypothesis. In addition, we did not see a decrease in average EduYears GPS across birth cohorts, but we did not account for potential differences in fecundity (32). The Estonian Biobank sample, used in our previous study, has grown from 50,000 DNA donors, making up 5% of the adult population, to 205,000 DNA donors, making up 20% of the adult population in Estonia, thus offering an opportunity to replicate and extend our previous research.

This study aims to use the phenome-wide association study (PheWAS) method to investigate associations between ADHD PRS, as well as diagnosis of ADHD and ICD-10 diagnoses codes based on electronic health records (EHR) to explore the associations of ADHD in population-based biobank participants.

The aim of this study is to conduct a GWAS of ADHD total symptoms score, as well as for inattention and hyperactivity-impulsivity symptoms to explore whether there are unique genetic effects of these symptom domains

Additional file 1. Supplementary information about BMI data cleaning, Dementia data in the Swedish Twin Registry, and PGS calculation in the Swedish Twin Registry. Table S1. Age group characteristics for the analysis sample. Table S2. ICD codes used to identify dementia. Table S3. ATC-codes for identification of dementia medication. Table S4. Risk of incident dementia in relation to 5 units higher body mass index measured at different age categories in the Swedish Twin Registry and the Health and Retirement Study, stratified by sex. Table S5. Risk of incident dementia in relation to 5 units higher body mass index measured at different age categories in the Health and Retirement Study, stratified by ethnicity. Table S6. Risk of incident dementia in relation to being underweight, overweight, or obese at different age categories in the Swedish Twin Registry and the Health and Retirement Study. Table S7. Cause specific hazard rate ratios of dementia in relation to 5 units higher body mass index measured at different age categories in the Swedish Twin Registry and the Health and Retirement Study. Figure S1. Flow chart of the sample. Figure S2. Collections of BMI information in the Swedish Twin Registry.