250 words). Background. Smoking prevalence is higher amongst individuals with schizophrenia and depression compared to the general population. Mendelian randomisation (MR) can examine whether this association is causal using genetic variants identified in genome-wide association studies (GWAS).Methods. We conducted a GWAS of lifetime smoking behaviour (capturing smoking duration, heaviness and cessation) in a sample of 462,690 individuals from the UK Biobank, and validated the findings via two-sample MR analyses of positive control outcomes (e.g., lung cancer). Having established the validity of our instrument, we used bi-directional twosample Mendelian randomisation to explore its effects on schizophrenia and depression.Outcomes. There was strong evidence to suggest smoking is a causal risk factor for both schizophrenia (OR = 2.27, 95% CI = 1.67 -3.08, P < 0.001) and depression (OR = 1.99, 95% CI = 1.71 -2.32, P < 0.001). We also found some evidence that genetic risk for both schizophrenia and depression cause increased lifetime smoking (β = 0.022, 95% CI = 0.005 -0.038, P = 0.009; β = 0.091, 95% CI = 0.027 -0.155, P = 0.005).Interpretation. These findings suggest that the association between smoking, schizophrenia and depression is due, at least in part, to a causal effect of smoking, providing further evidence for the detrimental consequences of smoking for mental health.Evidence before this study: The association between smoking and mental health (especially schizophrenia and depression) is often assumed to be the result of selfmedication (for example, to alleviate symptoms). However, more recent evidence has suggested that smoking might also be a risk factor for schizophrenia and depression. This alternative direction of effect is supported by meta-analyses and previous prospective observational evidence using related individuals to control for genetic and environmental confounding. However, observational evidence cannot completely account for confounding or the possibility of reverse causation. One way to get around these problems is Mendelian randomisation (MR). Previous MR studies of smoking and mental health have not shown an effect of smoking on depression and are inconclusive for the effects of smoking on schizophrenia. However, these studies have only looked at individual aspects of smoking behaviour and some studies required stratifying participants into smokers and non-smokers, reducing power. Added value of this study:We have developed a novel genetic instrument for lifetime smoking exposure which can be used within a two-sample MR framework, using publiclyavailable GWAS summary statistics. We were therefore able to test the bi-directional association between smoking with schizophrenia and depression to see if the effects are causal. We found strong evidence to suggest that smoking is a causal risk factor for both schizophrenia and depression. There was some evidence to suggest that risk of schizophrenia and depression increases lifetime smoking (consistent with the selfmedication hypothesis) but t...

Background It has been hypothesised that greater maternal adiposity before or during pregnancy causes greater offspring adiposity in childhood and adulthood, via causal intrauterine or periconceptional mechanisms. Previous Mendelian randomization (MR) estimates were imprecise, with wide confidence intervals that included potentially important protective or adverse effects, and may have been biased by collider effects or imperfect adjustment for genetic inheritance. Here we use an improved MR approach to investigate whether associations between maternal pre-/early pregnancy body mass index (BMI) and offspring adiposity from birth to adolescence are causal, or are instead due to confounding. Methods and findings We undertook confounder adjusted multivariable (MV) regression and Mendelian randomization (MR) using mother-offspring pairs from two UK cohorts: Avon Longitudinal Study of Parents and Children (ALSPAC) and Born in Bradford (BiB). In ALSPAC and BiB the outcomes were birthweight (BW; N = 9339) and BMI at age 1 (N = 8659) and 4 years (N = 7575), and in ALSPAC only we investigated BMI at 10 (N = 4476) and 15 years (N = 4112) and dual-energy X-ray absorptiometry (DXA) determined fat mass index (FMI) from age 10-18 years (N = 2659 to 3855). We compared MR results from several polygenic risk scores (PRS), calculated from maternal non-transmitted alleles at between 29 and 80,939 single nucleotide polymorphisms (SNPs). MV and MR showed a consistent positive association of maternal BMI with BW, but for adiposity at most older ages MR estimates were weaker than MV estimates. In MV regression a one standard deviation (SD) higher maternal BMI was associated with a 0.13 (95% confidence interval [CI]: 0.10, 0.16) SD increase in offspring BW. The corresponding MR estimate from the strongest PRS (including up to 80,939 SNPs) was 0.14 (95% CI: 0.05, 0.23), with no difference between the two estimates (Pdifference = 0.84). For 15 year BMI the MV and MR estimates (80,939 SNPs) were 0.32 (95% CI: 0.29, 0.36) and 0.13 (95% CI: 0.01, 0.24) respectively (Pdifference = 1.0e-3). Results for FMI were similar to those for adolescent BMI. As the number of SNPs included in the PRS increased, the MR confidence intervals narrowed and the effect estimates for adolescent adiposity became closer to the MV estimates. Sensitivity analyses suggested the stronger effects with more SNPs were explained by horizontal pleiotropic bias away from zero. Consequently, the unbiased difference between the MV and MR estimates is probably greater than shown in our main analyses. Furthermore, MR estimates from IVs with fewer SNPs provided no strong evidence for a causal effect on adolescent adiposity. Conclusions Our results suggest that higher maternal pre-/early-pregnancy BMI is not a key driver of higher adiposity in the next generation. Thus, they support interventions that target the whole population for reducing overweight and obesity, rather than a specific focus on women of reproductive age.

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In this study, we estimate (i) the SNP heritability of educational attainment at three time points throughout the compulsory educational lifecourse; (ii) the SNP heritability of value-added measures of educational progress built from test data; and (iii) the extent to which value-added measures built from teacher rated ability may be biased due to measurement error. We utilise a genome wide approach using generalized restricted maximum likelihood (GCTA-GREML) to determine the total phenotypic variance in educational attainment and value-added measures that is attributable to common genetic variation across the genome within a sample of unrelated individuals from a UK birth cohort, the Avon Longitudinal Study of Parents and Children. Our findings suggest that the heritability of educational attainment measured using point score test data increases with age from 47% at age 11 to 61% at age 16. We also find that genetic variation does not contribute towards value-added measures created only from educational attainment point score data, but it does contribute a small amount to measures that additionally control for background characteristics (up to 20.09% [95%CI: 6.06 to 35.71] from age 11 to 14). Finally, our results show that value-added measures built from teacher rated ability have higher heritability than those built from exam scores. Our findings suggest that the heritability of educational attainment increases through childhood and adolescence. Valueadded measures based upon fine grain point scores may be less prone to between-individual genomic differences than measures that control for students' backgrounds, or those built from more subjective measures such as teacher rated ability.