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Objectives: To determine the UK prevalence of behavioral problems in 5-year-old children born with isolated or syndromic cleft lip and/or palate (CL/P) compared to the general population and identify potentially associated factors. Design: Observational study using questionnaire data from the Cleft Collective 5-Year-Old Cohort study and three general population samples. Main Outcome Measure: The Strengths and Difficulties Questionnaire (SDQ). Participants: Mothers of children (age: 4.9-6.8 years) born with CL/P (n = 325). UK general population cohorts for SDQ scores were: Millennium Cohort Study (MCS) (n = 12 511), Office of National Statistics (ONS) normative school-age SDQ data (n = 5855), and Avon Longitudinal Study of Parents and Children (ALSPAC) (n = 9386). Results: By maternal report, 14.2% of children born with CL/P were above clinical cut-off for behavioral problems, which was more likely than in general population samples: 7.5% of MCS (OR = 2.05 [1.49-2.82], P < 0.001), 9.8% of ONS (OR = 1.52 [1.10-2.09], P = 0.008), and 6.6% of ALSPAC (OR = 2.34 [1.70-3.24], P < 0.001). Children in the Cleft Collective had higher odds for hyperactivity, emotional and peer problems, and less prosocial behaviors. Maternal stress, lower maternal health-related quality of life and family functioning, receiving government income support, and maternal smoking showed evidence of association (OR range: 4.41-10.13) with behavioral problems, along with maternal relationship status, younger age, and lower education (OR range: 2.34-3.73). Conclusions: Findings suggest elevated levels of behavioral problems in children born with CL/P compared to the general population with several associated maternal factors similar to the general population. [ABSTRACT FROM AUTHOR]

Background: Few studies have investigated associations between adiposity and reproductive factors using causal methods, both of which have a number of consequences on women's health. Here we assess whether adiposity at different points in the lifecourse affects reproductive factors differently and independently, and the plausibility of the impact of reproductive factors on adiposity. Methods: We used genetic data from UK Biobank (273,238 women) and other consortia (EGG, GIANT, ReproGen and SSGAC) for eight reproductive factors: age at menarche, age at menopause, age at first birth, age at last birth, number of births, being parous, age first had sexual intercourse and lifetime number of sexual partners, and two adiposity traits: childhood and adulthood body size. We applied multivariable Mendelian randomization to account for genetic correlation and to estimate the causal effects of childhood and adulthood adiposity, independently of each other, on reproductive factors. Additionally, we estimated the effects of reproductive factors, independently of other relevant reproductive factors, on adulthood adiposity. Results: We found a higher childhood body size leads to an earlier age at menarche, and an earlier age at menarche leads to a higher adulthood body size. Furthermore, we find contrasting and independent effects of childhood and adulthood body size on age at first birth (beta 0.22 SD (95% confidence interval: 0.14, 0.31) vs − 2.49 (− 2.93, − 2.06) per 1 SD increase), age at last birth (0.13 (0.06,0.21) vs − 1.86 (− 2.23, − 1.48) per 1 SD increase), age at menopause (0.17 (0.09, 0.25) vs − 0.99 (− 1.39, − 0.59) per 1 SD increase), and likelihood of having children (Odds ratio 0.97 (0.95, 1.00) vs 1.20 (1.06, 1.37) per 1 SD increase). Conclusions: Our findings demonstrate the importance of considering a lifecourse approach when investigating the inter-relationships between adiposity measures and reproductive events, as well as the use of 'age specific' genetic instruments when evaluating lifecourse hypotheses in a Mendelian randomization framework. [ABSTRACT FROM AUTHOR]

Objective: Juvenile idiopathic arthritis (JIA) is the most common pediatric rheumatic disease; however, little is known about its wider health impacts. This study explores health outcomes associated with JIA genetic liability. Methods: We used publicly available genetic data sets to interrogate the genetic correlation between JIA and 832 other health‐related traits using linkage disequilibrium score regression. Two‐sample Mendelian randomization (2SMR) was used to examine four genetic correlates for evidence of causality. Results: We found robust evidence (adjusted P [Padj] < 0.05) of genetic correlation between JIA and rheumatoid arthritis (genetic correlation [rg] = 0.63, Padj = 0.029), hypothyroidism/myxedema (rg = 0.61, Padj = 0.041), celiac disease (CD) (rg = 0.58, Padj = 0.032), systemic lupus erythematosus (rg = 0.40, Padj = 0.032), coronary artery disease (CAD) (rg = 0.42, Padj = 0.006), number of noncancer illnesses (rg = 0.42, Padj = 0.016), paternal health (rg = 0.57, Padj = 0.032), and strenuous sports (rg = −0.52, Padj = 0.032). 2SMR analyses found robust evidence that genetic liability to JIA was causally associated with the number of noncancer illnesses reported by UK Biobank (UKBB) participants (increase of 0.03 noncancer illnesses per doubling odds of JIA, 95% confidence interval 0.01‐0.05). Conclusion: This study illustrates genetic sharing between JIA and a diversity of health outcomes. The causal association between genetic liability to JIA and noncancer illnesses suggests a need for broader health assessments of patients with JIA to reduce their potential comorbid burden. The strength of genetic correlation with hypothyroidism and CD implies that patients with JIA may benefit from CD and thyroid function screening. Strong positive genetic correlation between JIA and CAD supports the need for cardiovascular risk assessment and risk factor modification. [ABSTRACT FROM AUTHOR]

Background: Women's reproductive factors include their age at menarche and menopause, the age at which they start and stop having children and the number of children they have. Studies that have linked these factors with disease risk have largely investigated individual reproductive factors and have not considered the genetic correlation and total interplay that may occur between them. This study aimed to investigate the nature of the relationships between eight female reproductive factors.Methods: We used data from the UK Biobank and genetic consortia with data available for the following reproductive factors: age at menarche, age at menopause, age at first birth, age at last birth, number of births, being parous, age first had sexual intercourse and lifetime number of sexual partners. Linkage disequilibrium score regression (LDSC) was performed to investigate the genetic correlation between reproductive factors. We then applied Mendelian randomisation (MR) methods to estimate the causal relationships between these factors. Sensitivity analyses were used to investigate directionality of the effects, test for evidence of pleiotropy and account for sample overlap.Results: LDSC indicated that most reproductive factors are genetically correlated (rg range: |0.06-0.94|), though there was little evidence for genetic correlations between lifetime number of sexual partners and age at last birth, number of births and ever being parous (rg < 0.01). MR revealed potential causal relationships between many reproductive factors, including later age at menarche (1 SD increase) leading to a later age at first sexual intercourse (beta (B) = 0.09 SD, 95% confidence intervals (CI) = 0.06,0.11), age at first birth (B = 0.07 SD, CI = 0.04,0.10), age at last birth (B = 0.06 SD, CI = 0.04,0.09) and age at menopause (B = 0.06 SD, CI = 0.03,0.10). Later age at first birth was found to lead to a later age at menopause (B = 0.21 SD, CI = 0.13,0.29), age at last birth (B = 0.72 SD, CI = 0.67, 0.77) and a lower number of births (B = -0.38 SD, CI = -0.44, -0.32).Conclusion: This study presents evidence that women's reproductive factors are genetically correlated and causally related. Future studies examining the health sequelae of reproductive factors should consider a woman's entire reproductive history, including the causal interplay between reproductive factors. [ABSTRACT FROM AUTHOR]

Background: High levels of prenatal alcohol exposure are known to cause an array of adverse outcomes including fetal alcohol syndrome (FAS); however, the effects of low to moderate exposure are less-well characterized. Previous findings suggest that differences in normal-range facial morphology may be a marker for alcohol exposure and related adverse effects.Methods: In the Avon Longitudinal Study of Parents and Children, we tested for an association between maternal alcohol consumption and six FAS-related facial phenotypes in their offspring, using both self-report questionnaires and the maternal genotype at rs1229984 in ADH1B as measures of maternal alcohol consumption.Results: In both self-reported alcohol consumption (N = 4233) and rs1229984 genotype (N = 3139) analyses, we found no strong statistical evidence for an association between maternal alcohol consumption and facial phenotypes tested. The directions of effect estimates were compatible with the known effects of heavy alcohol exposure, but confidence intervals were largely centered around zero.Conclusions: There is no strong evidence, in a sample representative of the general population, for an effect of prenatal alcohol exposure on normal-range variation in facial morphology. [ABSTRACT FROM AUTHOR]

Background: Evidence suggests that in utero exposure to undernutrition and overnutrition might affect adiposity in later life. Epigenetic modification is suggested as a plausible mediating mechanism.Methods: We used multivariable linear regression and a negative control design to examine offspring epigenome-wide DNA methylation in relation to maternal and offspring adiposity in 1018 participants.Results: Compared with neonatal offspring of normal weight mothers, 28 and 1621 CpG sites were differentially methylated in offspring of obese and underweight mothers, respectively [false discovert rate (FDR)-corrected P-value < 0.05), with no overlap in the sites that maternal obesity and underweight relate to. A positive association, where higher methylation is associated with a body mass index (BMI) outside the normal range, was seen at 78.6% of the sites associated with obesity and 87.9% of the sites associated with underweight. Associations of maternal obesity with offspring methylation were stronger than associations of paternal obesity, supporting an intrauterine mechanism. There were no consistent associations of gestational weight gain with offspring DNA methylation. In general, sites that were hypermethylated in association with maternal obesity or hypomethylated in association with maternal underweight tended to be positively associated with offspring adiposity, and sites hypomethylated in association with maternal obesity or hypermethylated in association with maternal underweight tended to be inversely associated with offspring adiposity.Conclusions: Our data suggest that both maternal obesity and, to a larger degree, underweight affect the neonatal epigenome via an intrauterine mechanism, but weight gain during pregnancy has little effect. We found some evidence that associations of maternal underweight with lower offspring adiposity and maternal obesity with greater offspring adiposity may be mediated via increased DNA methylation. [ABSTRACT FROM AUTHOR]

Numerous observational studies have attempted to identify risk factors for infection with SARS-CoV-2 and COVID-19 disease outcomes. Studies have used datasets sampled from patients admitted to hospital, people tested for active infection, or people who volunteered to participate. Here, we highlight the challenge of interpreting observational evidence from such non-representative samples. Collider bias can induce associations between two or more variables which affect the likelihood of an individual being sampled, distorting associations between these variables in the sample. Analysing UK Biobank data, compared to the wider cohort the participants tested for COVID-19 were highly selected for a range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. We discuss the mechanisms inducing these problems, and approaches that could help mitigate them. While collider bias should be explored in existing studies, the optimal way to mitigate the problem is to use appropriate sampling strategies at the study design stage. Many published studies of the current SARS-CoV-2 pandemic have analysed data from non-representative samples from populations. Here, using UK BioBank samples, Gibran Hemani and colleagues discuss the potential for such studies to suffer from collider bias, and provide suggestions for optimising study design to account for this. [ABSTRACT FROM AUTHOR]

Paternal exposures (and other non-maternal factors) around pregnancy could have important effects on offspring health. One challenge is that data on partners are usually from a subgroup of mothers with data, potentially introducing selection bias, limiting generalisability of findings. We aimed to investigate the potential for selection bias in studies using partner data.We characterise availability of data on father/partner and mother health behaviours (smoking, alcohol, caffeine and physical activity) around pregnancy from three UK cohort studies: the Avon Longitudinal Study of Parents and Children (ALSPAC), Born in Bradford and the Millennium Cohort Study. We assess the extent of sample selection by comparing characteristics of families where fathers/partners do and do not participate. Using the association of parental smoking during pregnancy and child birthweight as an example, we perform simulations to investigate the extent to which missing father/partner data may induce bias in analyses conducted only in families with participating fathers/partners.In all cohorts, father/partner data were less detailed and collected at fewer timepoints than mothers. Partners with a lower socio-economic position were less likely to participate. In simulations based on ALSPAC data, there was little evidence of selection bias in associations of maternal smoking with birthweight, and bias for father/partner smoking was relatively small. Missing partner data can induce selection bias. In our example analyses of the effect of parental smoking on offspring birthweight, the bias had a relatively small impact. In practice, the impact of selection bias will depend on both the analysis model and the selection mechanism.

Background: There is mounting evidence that our environment and lifestyle has an impact on epigenetic regulatory mechanisms, such as DNA methylation. It has been suggested that these molecular processes may mediate the effect of risk factors on disease susceptibility, although evidence in this regard has been challenging to uncover. Using genetic variants as surrogate variables, we have used two-sample Mendelian randomization (2SMR) to investigate the potential implications of putative changes to DNA methylation levels on disease susceptibility., Methods: To illustrate our approach, we identified 412 CpG sites where DNA methylation was associated with prenatal smoking. We then applied 2SMR to investigate potential downstream effects of these putative changes on 643 complex traits using findings from large-scale genome-wide association studies. To strengthen evidence of mediatory mechanisms, we used multiple-trait colocalization to assess whether DNA methylation, nearby gene expression and complex trait variation were all influenced by the same causal genetic variant., Results: We identified 22 associations that survived multiple testing (P < 1.89 × 10-7). In-depth follow-up analyses of particular note suggested that the associations between DNA methylation at the ASPSCR1 and REST/POL2RB gene regions, both linked with reduced lung function, may be mediated by changes in gene expression. We validated associations between DNA methylation and traits using independent samples from different stages across the life course., Conclusion: Our approach should prove valuable in prioritizing CpG sites that may mediate the effect of causal risk factors on disease. In-depth evaluations of findings are necessary to robustly disentangle causality from alternative explanations such as horizontal pleiotropy., (© The Author(s) 2019. Published by Oxford University Press on behalf of the International Epidemiological Association.)