Daniel Benjamin, Meike Bartels, Philipp Koellinger and colleagues report a genome-wide association meta-analysis of subjective well-being, depressive symptoms and neuroticism. The study leverages a large sample size together with genetic correlations between the phenotypes to identify, with high confidence, loci associated with each phenotype. Very few genetic variants have been associated with depression and neuroticism, likely because of limitations on sample size in previous studies. Subjective well-being, a phenotype that is genetically correlated with both of these traits, has not yet been studied with genome-wide data. We conducted genome-wide association studies of three phenotypes: subjective well-being (n = 298,420), depressive symptoms (n = 161,460), and neuroticism (n = 170,911). We identify 3 variants associated with subjective well-being, 2 variants associated with depressive symptoms, and 11 variants associated with neuroticism, including 2 inversion polymorphisms. The two loci associated with depressive symptoms replicate in an independent depression sample. Joint analyses that exploit the high genetic correlations between the phenotypes (|ρ^| ≈ 0.8) strengthen the overall credibility of the findings and allow us to identify additional variants. Across our phenotypes, loci regulating expression in central nervous system and adrenal or pancreas tissues are strongly enriched for association.

A large number of genetic loci are associated with adult body mass index. However, the genetics of childhood body mass index are largely unknown. We performed a meta-analysis of genome-wide association studies of childhood body mass index, using sex- and age-adjusted standard deviation scores. We included 35 668 children from 20 studies in the discovery phase and 11 873 children from 13 studies in the replication phase. In total, 15 loci reached genome-wide significance (P-value < 5 × 10⁻⁸) in the joint discovery and replication analysis, of which 12 are previously identified loci in or close to ADCY3, GNPDA2, TMEM18, SEC16B, FAIM2, FTO, TFAP2B, TNNI3K, MC4R, GPR61, LMX1B and OLFM4 associated with adult body mass index or childhood obesity. We identified three novel loci: rs13253111 near ELP3, rs8092503 near RAB27B and rs13387838 near ADAM23. Per additional risk allele, body mass index increased 0.04 Standard Deviation Score (SDS) [Standard Error (SE) 0.007], 0.05 SDS (SE 0.008) and 0.14 SDS (SE 0.025), for rs13253111, rs8092503 and rs13387838, respectively. A genetic risk score combining all 15 SNPs showed that each additional average risk allele was associated with a 0.073 SDS (SE 0.011, P-value = 3.12 × 10⁻¹⁰) increase in childhood body mass index in a population of 1955 children. This risk score explained 2% of the variance in childhood body mass index. This study highlights the shared genetic background between childhood and adult body mass index and adds three novel loci. These loci likely represent age-related differences in strength of the associations with body mass index.

Attention deficit hyperactivity disorder (ADHD) affects around 1–3% of children. There is a high level of comorbidity with developmental and learning problems as well as with a variety of psychiatric disorders. ADHD is highly heritable, although there is no single causal risk factor and non-inherited factors also contribute to its aetiology. The genetic and environmental risk factors that have been implicated appear to be associated with a range of neurodevelopmental and neuropsychiatric outcomes, not just ADHD. The evidence to date suggests that both rare and multiple common genetic variants likely contribute to ADHD and modify its phenotype. ADHD or a similar phenotype also appears to be more common in extreme low birth weight and premature children and those exposed to exceptional early adversity. In this review, the authors consider recent developments in the understanding of risk factors that influence ADHD.

Schizophrenia has a heritability of approximately 80%, and genome-wide association studies (GWASs) indicate that multiple independent loci contribute to its etiology.¹ The importance of studying the phenotypic manifestations of increased genetic liability for schizophrenia has been long recognized and originally involved small samples of individuals at high risk as indexed by having a family history.² Genetic advances now provide the opportunity to extend the power and generalizability of high-risk studies into the general population by examining individuals according to genetic risk. Although individual loci have small effects on risk, multiple-loci approaches show that, cumulatively, alleles on current GWAS platforms explain one-half to one-third of the genetic risk for schizophrenia.^3,4 Information from even moderately associated alleles can be collapsed into a single polygenic risk score (PRS) that can be used to explore shared genetic effects with other disorders and to examine how genetic risk is manifest early during development.⁵

It is often assumed that selection (including participation and dropout) does not represent an important source of bias in genetic studies. However, there is little evidence to date on the effect of genetic factors on participation. Using data on mothers (N = 7486) and children (N = 7508) from the Avon Longitudinal Study of Parents and Children, we: (i) examined the association of polygenic risk scores for a range of sociodemographic and lifestyle characteristics and health conditions related to continued participation; (ii) investigated whether associations of polygenic scores with body mass index (BMI; derived from self-reported weight and height) and self-reported smoking differed in the largest sample with genetic data and a subsample who participated in a recent follow-up; and (iii) determined the proportion of variation in participation explained by common genetic variants, using genome-wide data. We found evidence that polygenic scores for higher education, agreeableness and openness were associated with higher participation; and polygenic scores for smoking initiation, higher BMI, neuroticism, schizophrenia, attention-deficit hyperactivity disorder (ADHD) and depression were associated with lower participation. Associations between the polygenic score for education and self-reported smoking differed between the largest sample with genetic data [odds ratio (OR) for ever smoking per standard deviation (SD) increase in polygenic score: 0.85, 95% confidence interval (CI): 0.81, 0.89} and subsample (OR: 0.96, 95% CI: 0.89, 1.03). In genome-wide analysis, single nucleotide polymorphism based heritability explained 18–32% of variability in participation. Genetic association studies, including Mendelian randomization, can be biased by selection, including loss to follow-up. Genetic risk for dropout should be considered in all analyses of studies with selective participation.