Biology subjects

Tilling, K.

Publications and source records attributed to Tilling, K..

At least 19 recordsLinked to original sources

bioRxiv2023

An epigenome-wide analysis of DNA methylation, racialized and economic inequities, and air pollution

ImportanceDNA methylation (DNAm) provides a plausible mechanism by which adverse exposures become embodied and contribute to health inequities, due to its role in genome regulation and responsiveness to social and biophysical exposures tied to societal context. However, scant epigenome-wide association studies (EWAS) have included structural and lifecourse measures of exposure, especially in relation to structural discrimination. ObjectiveOur study tests the hypothesis that DNAm is a mechanism by which racial discrimination, economic adversity, and air pollution become biologically embodied. DesignA series of cross-sectional EWAS, conducted in My Body My Story (MBMS, biological specimens collected 2008-2010, DNAm assayed in 2021); and the Multi Ethnic Study of Atherosclerosis (MESA; biological specimens collected 2010-2012, DNAm assayed in 2012-2013); using new georeferenced social exposure data for both studies (generated in 2022). SettingMBMS was recruited from four community health centers in Boston; MESA was recruited from four field sites in: Baltimore, MD; Forsyth County, NC; New York City, NY; and St. Paul, MN. ParticipantsTwo population-based samples of US-born Black non-Hispanic (Black NH), white non-Hispanic (white NH), and Hispanic individuals (MBMS; n=224 Black NH and 69 white NH) and (MESA; n=229 Black NH, n=555 white NH and n=191 Hispanic). ExposuresEight social exposures encompassing racial discrimination, economic adversity, and air pollution. Main outcomeGenome-wide changes in DNAm, as measured using the Illumina EPIC BeadChip (MBMS; using frozen blood spots) and Illumina 450k BeadChip (MESA; using purified monocytes). Our hypothesis was formulated after data collection. ResultsWe observed the strongest associations with traffic-related air pollution (measured via black carbon and nitrogen oxides exposure), with evidence from both studies suggesting that air pollution exposure may induce epigenetic changes related to inflammatory processes. We also found suggestive associations of DNAm variation with measures of structural racial discrimination (e.g., for Black NH participants, born in a Jim Crow state; adult exposure to racialized economic residential segregation) situated in genes with plausible links to effects on health. Conclusions and RelevanceOverall, this work suggests that DNAm is a biological mechanism through which structural racism and air pollution become embodied and may lead to health inequities. Key pointsO_ST_ABSQuestionC_ST_ABSCould DNAm be a mechanism by which adversity becomes embodied? FindingsTraffic-related air pollution exposure may induce epigenetic changes related to inflammatory processes and there are suggestive associations with measures of structural racism MeaningDNAm may be a biological mechanism through which structural racism and air pollution become biologically embodied

bioinformatics↗

bioRxiv2021

The impact of low input DNA on the reliability of DNA methylation as measured by the Illumina Infinium MethylationEPIC BeadChip

BackgroundDNA methylation (DNAm) is commonly assayed using the Illumina Infinium MethylationEPIC BeadChip, but there is currently little published evidence to define the lower limits of the amount of DNA that can be used whilst preserving data quality. Such evidence is valuable for analyses utilising precious or limited DNA sources. Materials and methodsWe use a single pooled sample of DNA in quadruplicate at three dilutions to define replicability and noise, and an independent population dataset of 328 individuals (from a community-based study including US-born non-Hispanic Black and white persons) to assess the impact of total DNA input on the quality of data generated using the Illumina Infinium MethylationEPIC BeadChip. ResultsData are less reliable and more noisy as DNA input decreases to 40ng, with clear reductions in data quality; however samples with a total input as low as 40ng pass standard quality control tests, and we observe little evidence that low input DNA obscures the associations between DNAm and two phenotypes, age and smoking status. ConclusionsDNA input as low as 40ng can be used with the Illumina Infinium MethylationEPIC BeadChip, provided quality checks and sensitivity analyses are undertaken.

bioinformatics↗

bioRxiv2021

Characterizing the human methylome across the life course: findings from eight UK-based studies

Variation in DNA methylation (DNAm) is associated with multiple biological processes that track growth and development, ageing and age-related diseases. However, there is little understanding of what constitutes typical patterns of DNAm variation and how these patterns change across the life course. In this study, we synthesised a map of the human methylome across the life course, focussing on changes in variability and mean DNAm. Harmonizing DNAm datasets across eight longitudinal and cross-sectional UK-based studies, we meta-analysed n=13,215 blood samples from n=7,037 unique individuals from birth to 98 years of age. Changes in CpG-specific variability and means were described across the life course using a meta-regression framework. CpG-specific associations of variability or mean DNAm in relation to the likelihood of association with 100 traits linked to environmental exposures, health and disease were tested within and across ten developmental age bins across the life course. Age was linked to DNAm variability at 29,212 CpG sites. On average, we observed a 1.26 fold increase in DNAm variability per year across the life course. 33,730 CpGs displayed changes in mean DNAm, with 64% of these loci showing decreases in DNAm over time. CpG sites linked to traits were in general more variable across the life course. Our study provides, for the first time, a map of the human methylome across the life course, which is publicly accessible through a searchable online database. This resource allows researchers to query CpG-specific trajectories from birth to old age and link these to health and disease.

genetics↗

bioRxiv2020

Is genetic liability to ADHD and ASD causally linked to educational attainment?

BackgroundIndividuals with Attention Deficit Hyperactivity Disorder (ADHD) or Autism Spectrum Disorder (ASD) are at risk of poor educational outcomes. Parental educational attainment has also been associated with risk of ADHD/ASD in the offspring. Despite evidence that ADHD and ASD show genetic links to educational attainment, less is known on the causal nature of the associations and the possible role of IQ. MethodsWe assessed the total causal effects of genetic liability to ADHD/ASD on educational attainment using two-sample Mendelian randomization (MR). We assessed the possible contribution of IQ to the identified causal effects by estimating the "direct" effects of ADHD/ASD on educational attainment, independent of IQ, using Multivariable MR (MVMR). Reverse direction analyses were performed. The latest GWAS meta-analyses of ADHD, ASD, educational attainment and IQ were used. Causal effect estimates were generated using inverse variance weighted models (IVW). Sensitivity analyses were performed to assess the robustness of the estimates and the presence of pleiotropy. ResultsGenetic liability to ADHD had a total (MRIVW:-3.3 months per doubling of liability to ADHD; 95%CI: -4.8 to -1.9; pval= 5*10-6) and direct negative causal effect on educational attainment (MVMRIVW:-1.6 months per doubling of liability to ADHD; 95%CI: -2.5 to -0.6; pval= 4*10-4). There was little evidence of a total causal effect of genetic liability to ASD on educational attainment (MRIVW: 4 days, per doubling of liability to ASD; 95%CI: -4.9 months to 5.6 months; pval= 0.9) but some evidence of a direct effect not via IQ (MVMRIVW:29 days per doubling the genetic liability to ASD; 95%CI: 2 to 48; pval= 0.03). Reverse direction analyses suggested that genetic liability to higher educational attainment was associated with lower risk of ADHD (MRIVWOR: 0.3 per standard deviation (SD) increase; 95%CI: 0.26 to 0.36; pval= 6*10-51), even after IQ was entered in the models (MVMRIVWOR: 0.33 per SD increase; 95%CI: 0.26 to 0.43; pval= 6*10-17). On the contrary, there was evidence consistent with a positive causal effect of genetic liability to higher educational attainment on risk of ASD (MRIVWOR: 1.51 per SD increase; 95%CI: 1.29 to 1.77; pval= 4*10-7), which was found to be largely explained by IQ (MVMRIVWOR per SD increase: 1.24; 95%CI: 0.96 to 1.60; pval= 0.09). ConclusionsOur findings suggest that despite the genetic and phenotypic overlap between ADHD and ASD, they present highly differentiated causal associations with educational attainment. This highlights the necessity for specialized educational interventions for children with ADHD and ASD. Further research is needed in order to decipher whether the identified causal effects reflect parentally transmitted effects, diagnostic masking, or selection bias.

genetics↗

bioRxiv2020

Genetic predictors of participation in optional components of UK Biobank

Large studies (e.g. UK Biobank) are increasingly used for GWAS and Mendelian randomization (MR) studies. Selection into and dropout from studies may bias genetic and phenotypic associations. We examine genetic factors affecting participation in four optional components in up to 451,306 UK Biobank participants. We used GWAS to identify genetic variants associated with participation, MR to estimate effects of phenotypes on participation, and genetic correlations to compare participation bias across different studies. 32 variants were associated with participation in one of the optional components (P<6x10-9), including loci with known links to intelligence and Alzheimers disease. Genetic correlations demonstrated that participation bias was common across studies. MR showed that longer educational duration, older menarche and taller stature increased participation, whilst higher levels of adiposity, dyslipidaemia, neuroticism, Alzheimers and schizophrenia reduced participation. Our effect estimates can be used for sensitivity analysis to account for selective participation biases in genetic or non-genetic analyses.

genetics↗

bioRxiv2020

A robust mean and variance test with application to epigenome-wide association studies

Most studies of high-dimensional phenotypes focus on assessing differences in mean levels (location) of the phenotype by exposure, e.g. epigenome-wide association studies of DNA methylation at CpG sites. However, identifying effects on the variability (scale) of these outcomes, and combining tests of mean and variability (location-and-scale), could provide additional insights into biological mechanisms. Here, we review variability tests, specifically an extended (for continuous exposures) version of the Brown-Forsythe test, and develop a novel joint location-and-scale score test for both categorical and continuous exposures (JLSsc). The Brown-Forsythe test and JLSsc performed well in comparison to alternative approaches in simulations. These approaches identified >7500 CpG sites that were associated with either mean or variability with gender or gestational age in cord blood methylation in ARIES (Accessible Resource for Integrated Studies). The Brown-Forsythe test and JLSsc are robust tests that can be used to detect associations not solely driven by a mean effect.

bioinformatics↗

bioRxiv2020

Slope-Hunter: A robust method for index-event bias correction in genome-wide association studies of subsequent traits

BackgroundStudying genetic associations with prognosis (e.g. survival, disability, subsequent disease events) is problematic due to selection bias - also termed index event bias or collider bias - whereby selection on disease status can induce associations between causes of incidence with prognosis. A current method for adjusting genetic associations for this bias assumes there is no genetic correlation between incidence and prognosis, which may not be a plausible assumption. MethodsWe propose an alternative, the Slope-Hunter approach, which is unbiased even when there is genetic correlation between incidence and prognosis. Our approach has two stages. First, we use cluster-based techniques to identify: variants affecting neither incidence nor prognosis (these should not suffer bias and only a random sub-sample of them are retained in the analysis); variants affecting prognosis only (excluded from the analysis). Second, we fit a cluster-based model to identify the class of variants only affecting incidence, and use this class to estimate the adjustment factor. ResultsSimulation studies showed that the Slope-Hunter method reduces type-1 error by between 49%-85%, increases power by 1%-36%, reduces bias by 17%-47% compared to other methods in the presence of genetic correlation and performs as well as previous methods when there is no genetic correlation. Slope-Hunter and the previous methods perform less well as the proportion of variation in incidence explained by genetic variants affecting only incidence decreases. ConclusionsThe key assumption of Slope-Hunter is that the contribution of the set of genetic variants affecting incidence only to the heritability of incidence is at least as large as the contribution of those affecting both incidence and prognosis. When this assumption holds, our approach is unbiased in the presence of genetic correlation between incidence and progression, and performs no worse than alternative approaches even when there is no correlation. Bias-adjusting methods should be used to carry out causal analyses when conditioning on incidence.

genetics↗

bioRxiv2019

Bias in two-sample Mendelian randomization by using covariable-adjusted summary associations

BackgroundTwo-sample Mendelian randomization (MR) allows the use of freely accessible summary association results from genome-wide association studies (GWAS) to estimate causal effects of modifiable exposures on outcomes. Some GWAS adjust for heritable covariables in an attempt to estimate direct effects of genetic variants on the trait of interest. One, both or neither of the exposure GWAS and outcome GWAS may have been adjusted for covariables. MethodsWe performed a simulation study comprising different scenarios that could motivate covariable adjustment in a GWAS and analysed real data to assess the influence of using covariable-adjusted summary association results in two-sample MR. ResultsIn the absence of residual confounding between exposure and covariable, between exposure and outcome, and between covariable and outcome, using covariable-adjusted summary associations for two-sample MR eliminated bias due to horizontal pleiotropy. However, covariable adjustment led to bias in the presence of residual confounding (especially between the covariable and the outcome), even in the absence of horizontal pleiotropy (when the genetic variants would be valid instruments without covariable adjustment). In an analysis using real data from the Genetic Investigation of ANthropometric Traits (GIANT) consortium and UK Biobank, the causal effect estimate of waist circumference on blood pressure changed direction upon adjustment of waist circumference for body mass index. ConclusionsOur findings indicate that using covariable-adjusted summary associations in MR should generally be avoided. When that is not possible, careful consideration of the causal relationships underlying the data (including potentially unmeasured confounders) is required to direct sensitivity analyses and interpret results with appropriate caution. Key messagesO_LISummary genetic associations from large genome-wide associations studies (GWAS) have been increasingly used in two-sample Mendelian randomization (MR) analyses. C_LIO_LIMany GWAS adjust for heritable covariates in an attempt to estimate direct genetic effects on the trait of interest. C_LIO_LIIn an extensive simulation study, we demonstrate that using covariable-adjusted summary associations may bias MR analyses. C_LIO_LIThe bias largely depends on the underlying causal structure, specially the presence of unmeasured common causes between the covariable and the outcome. C_LIO_LIOur findings indicate that using covariable-adjusted summary associations in MR should generally be avoided. C_LI

genetics↗

bioRxiv2019

ADHD genetic liability and physical health outcomes - A two-sample Mendelian randomization study

ObjectiveAttention-deficit/hyperactivity disorder (ADHD) has been associated with a broad range of physical health problems, including cardiometabolic, neurological and immunological conditions. Determining whether ADHD plays a causal role in these associations is of great importance for treatment and prevention but also because comorbid health problems further increase the serious social and economic impacts of ADHD on individuals and their families.\n\nMethodsWe used a two-sample Mendelian randomization (MR) approach to examine the causal relationships between genetic liability for ADHD and previously implicated physical health conditions. 11 genetic variants associated with ADHD were obtained from the latest summary statistics. Consistent effects obtained from IVW, weighted median and MR Egger methods were taken forward for sensitivity analysis, including bidirectional MR and multivariable MR (MVMR).\n\nResultsWe found evidence of a causal effect of genetic liability for ADHD on childhood obesity (OR:1.29 (95% CI:1.02,1.63)) and coronary artery disease (CAD) (OR:1.11 (95% CI:1.03,1.19)) with consistent results across different MR approaches. There was further evidence for a bidirectional relationship between genetic liability for ADHD and childhood obesity. The effect of genetic liability for ADHD on CAD was independent of smoking heaviness but was attenuated when simultaneously controlling for childhood obesity. There was little evidence for a causal effect on other cardiometabolic, immunological, neurological disorders and lung cancer.\n\nConclusionOur findings strengthen the argument for early treatment and support for children with ADHD and their families and especially promoting physical activity and providing them with dietary advice to reduce future risk for developing CAD.\n\nKey Message O_LIEpidemiological studies have reported observational associations between ADHD and adult onset physical health outcomes.\nC_LIO_LIMendelian Randomization can be used to assess causal associations for ADHD on health outcomes that would traditionally require long term follow-up and may suffer confounding\nC_LIO_LIWe found that genetic liability for ADHD was associated with coronary artery disease and there was evidence for a bidirectional association between genetic liability for ADHD and childhood obesity\nC_LIO_LIMultivariable mendelian randomization suggests that the link between genetic liability and coronary artery disease might partially act through childhood obesity but was independent of smoking heaviness\nC_LIO_LIThere was little evidence of a causal of ADHD on other cardiometabolic and immunological diseases.\nC_LI

epidemiology↗

bioRxiv2019

A cross-disorder MR-pheWAS of 5 major psychiatric disorders in UK Biobank

Psychiatric disorders are highly heritable and associated with a wide variety of social adversity and physical health problems. Using genetic liability (rather than phenotypic measures of disease) as a proxy for psychiatric disease risk can be a useful alternative for research questions that would traditionally require large cohort studies with long-term follow up.\n\nHere we conducted a hypothesis-free phenome-wide association study in about 300,000 participants from the UK Biobank to examine associations of polygenic risk scores (PRS) for five psychiatric disorders (major depression (MDD), bipolar disorder (BP), schizophrenia (SCZ), attention-deficit/ hyperactivity disorder (ADHD) and autism spectrum disorder (ASD)) with 23,004 outcomes in UK Biobank, using the open-source PHESANT software package.\n\nThere was evidence after multiple testing (p<2.55x10-06) for associations of PRSs with 226 outcomes, most of them attributed to associations of PRSMDD (n=120) with mental health factors and PRSADHD (n=77) with socio-demographic factors. Among others, we found strong evidence of associations between a 1 standard deviation increase in PRSADHD with 1.1 months younger age at first sexual intercourse [95% confidence interval [CI]: -1.26,-0.94]; PRSASD with 0.01% reduced lower erythrocyte distribution width [95%CI: -0.013,-0.007]; PRSSCZ with 0.98 odds of playing computer games [95%CI:0.976,0.989]; PRSMDD with a 0.11 points higher neuroticism score [95%CI:0.094,0.118] and PRSBP with 1.04 higher odds of having a university degree [95%CI:1.033,1.048].\n\nWe were able to show that genetic liabilities for five major psychiatric disorders associate with long-term aspects of adult life, including socio-demographic factors, mental and physical health. This is evident even in individuals from the general population who do not necessarily present with a psychiatric disorder diagnosis.\n\nAUTHOR SUMMARYPsychiatric disorders are associated with a wide range of adverse health, social and economic problems. Our study investigates the association of genetic risk for five common psychiatric disorders with socio-demographics, lifestyle and health of about 330,000 participants in the UK Biobank using a systematic, hypothesis-free approach. We found that genetic risk for attention deficit/hyperactivity disorder (ADHD) and bipolar disorder were most strongly associated with lifestyle factors, such as time of first sexual intercourse and educational attainment. Genetic risks for autism spectrum disorder and schizophrenia were associated with altered blood cell counts and time playing computer games, respectively. Increased genetic risk for depression was associated with other mental health outcomes such as neuroticism and irritability. In general, our results suggest that genetic risk for psychiatric disorders associates with a range of health and lifestyle traits that were measured in adulthood, in individuals from the general population who do not necessarily present with a psychiatric disorder diagnosis. However, it is important to note that these associations arent necessary causal but can themselves be influenced by other factors, like socio-economic factors and selection into the cohort. The findings inform future hypotheses to be tested using causally informative designs.

epidemiology↗

bioRxiv2019

Using genetic instruments to estimate interactions in Mendelian Randomization studies

BACKGROUNDThe interactive effect of two exposures on an outcome can be confounded. We demonstrate the use of Mendelian Randomization (MR) to estimate unconfounded additive interactions. METHODSUsing simulation, we test an extension to multivariable MR using two-stage least squares to estimate the additive interaction between two continuous exposures on a continuous outcome, including scenarios where one exposure has a causal effect on the other (mediation). The interaction parameters were set to be one third of the main effects parameters to impose a limit on the variance explained by interaction terms. We compare the performance of the two-stage least squares estimator to a Factorial MR design, in which genetic risk scores for each exposure are dichotomised to create four groups, akin to a factorial randomized controlled trial. As an illustrative example, we apply factorial MR and the 2SLS estimator to the interactive effect of education and BMI on systolic blood pressure in UK Biobank. RESULTSOur simulations demonstrate that factorial MR has very low statistical power; 5-7% at N=50,000 and 8-23% at N=500,000 across the range of parameters tested. The two-stage least squares estimator had higher power to detect interactions than factorial MR and a lower type I error. For N=500,000, the two-stage least squares estimator had a power ranging from 29.7-92.9% and a type I error ranging from 4-6%. 95% Monte Carlo confidence intervals suggested that the estimator was unbiased to a reasonable degree of accuracy at this sample size. In comparison, the power at N=50,000 was 7-55%. CONCLUSIONSA two-stage least squares estimator using genetic risk scores for each exposure is a more powerful alternative for detecting an unconfounded additive interaction of two exposures on an outcome than existing approaches that rely on dichotomising genetic risk scores, but it requires a large sample size and instruments of adequate strength.

epidemiology↗

bioRxiv2018

dmrff: identifying differentially methylated regions efficiently with power and control

A differentially methylated region (DMR) is a genomic region in which DNA methylation is consistently positively or negatively associated with a phenotype or exposure. We demonstrate that existing algorithms for identifying DMRs either fail to consistently control false positive rates (comb-p and DMRcate), suffer from low power (bumphunter) or lack modeling flexibility (seqlm). We introduce a new method, dmrff, that overcomes these shortcomings and can additionally be used to meta-analyze multiple datasets. When applied to investigate associations of age in multiple datasets, dmrff identifies novel DMRs near genes previously linked to age. An R implementation is available on Github (http://github.com/perishky/dmrff).

bioinformatics↗

bioRxiv2018

GLU: A tool for analysing continuously measured glucose in epidemiology

MotivationContinuous glucose monitors (CGM) record interstitial glucose continuously, producing a sequence of measurements for each participant (e.g. the average glucose every 5 minutes over several days, both day and night). To analyze these data, researchers tend to derive summary variables such as the Area Under the Curve (AUC), to then use in subsequent analyses. To date, a lack of consistency and transparency of precise definitions used for these summary variables has hindered interpretation, replication and comparison of results across studies. We present GLU, an open-source software package for deriving a consistent set of summary variables from CGM data.\n\nGeneral featuresGLU performs quality control of each CGM sample (e.g. addressing missing data), derives a diverse set of summary variables (e.g. AUC, and proportion of time spent in hypo-, normo- and hyper-glycaemic levels) covering six broad domains, and outputs these (with quality control information) to the user.\n\nImplementationGLU is implemented in R.\n\nAVAILABILITYGLU is available on GitHub at [https://github.com/MRCIEU/GLU]. Git tag v0.1 corresponds to the version presented here.

epidemiology↗

bioRxiv2018

MR-pheWAS with stratification and interaction: Searching for the causal effects of smoking heaviness identified an effect on facial aging

Mendelian randomization (MR) is an established approach for estimating the causal effect of an environmental exposure on a downstream outcome. The gene x environment (GxE) study design can be used within an MR framework to determine whether MR estimates may be biased if the genetic instrument affects the outcome through pathways other than via the exposure of interest (known as horizontal pleiotropy). MR phenome-wide association studies (MR-pheWAS) search for the effects of an exposure, and a recently published tool (PHESANT) means that it is now possible to do this comprehensively, across thousands of traits in UK Biobank. In this study, we introduce the GxE MR-pheWAS approach, and search for the causal effects of smoking heaviness - stratifying on smoking status (ever versus never) - as an exemplar. If a genetic variant is associated with smoking heaviness (but not smoking initiation), and this variant affects an outcome (at least partially) via tobacco intake, we would expect the effect of the variant on the outcome to differ in ever versus never smokers. If this effect is entirely mediated by tobacco intake, we would expect to see an effect in ever smokers but not never smokers. We used PHESANT to search for the causal effects of smoking heaviness, instrumented by genetic variant rs16969968, among never and ever smokers respectively, in UK Biobank. We ranked results by: 1) strength of effect of rs16969968 among ever smokers, and 2) strength of interaction between ever and never smokers. We replicated previously established causal effects of smoking heaviness, including a detrimental effect on lung function and pulse rate. Novel results included a detrimental effect of heavier smoking on facial aging. We have demonstrated how GxE MR-pheWAS can be used to identify causal effects of an exposure, while simultaneously assessing the extent that results may be biased by horizontal pleiotropy.\n\nAuthor summaryMendelian randomization uses genetic variants associated with an exposure to investigate causality. For instance, a genetic variant that relates to how heavily a person smokes has been used to test whether smoking causally affects health outcomes. Mendelian randomization is biased if the genetic variant also affects the outcome via other pathways. We exploit additional information - that the effect of heavy smoking only occurs in people who actually smoke - to overcome this problem. By testing associations in ever and never smokers separately we can assess whether the genetic variant affects an outcome via smoking or another pathway. If the effect is entirely via smoking heaviness, we would expect to see an effect in ever but not never smokers, and this would suggest that smoking causally influences the outcome. Previous Mendelian randomization studies of smoking heaviness focused on specific outcomes - here we searched for the causal effects of smoking heaviness across over 18,000 traits. We identified previously established effects (e.g. a detrimental effect on lung function) and novel results including a detrimental effect of heavier smoking on facial aging. Our approach can be used to search for the causal effects of other exposures, where the exposure only occurs in known subsets of the population.

epidemiology↗

bioRxiv2018

Identifying novel subtypes of irritability using a developmental genetic approach

ObjectiveIrritability is a common reason for referral to services, strongly associated with impairment and negative outcomes, but is a nosological and treatment challenge. A major issue is how irritability should be conceptualized. This study used a developmental approach to test the hypothesis that there are several forms of irritability, including a neurodevelopmental/ADHD-like subtype with onset in childhood and a depression/mood subtype with onset in adolescence.\n\nMethodData were analyzed in the Avon Longitudinal Study of Parents and Children, a prospective UK population-based cohort. Irritability trajectory-classes were estimated for 7924 individuals with data at multiple time-points across childhood and adolescence (4 possible time-points from approximately ages 7 to 15 years). Psychiatric diagnoses were assessed at approximately ages 7 and 15 years. Psychiatric genetic risk was indexed by polygenic risk scores (PRS) for attention-deficit/hyperactivity disorder (ADHD) and major depressive disorder (MDD) derived using large genome-wide association study results.\n\nResultsFive irritability trajectory classes were identified: low (81.2%), decreasing (5.6%), increasing (5.5%), late-childhood limited (5.2%) and high-persistent (2.4%). The early-onset, high-persistent trajectory was associated with male preponderance, childhood ADHD (OR=108.64 (57.45-204.41), p<0.001) and ADHD PRS (OR=1.31 (1.09-1.58), p=0.005); the adolescent-onset, increasing trajectory was associated with female preponderance, adolescent MDD (OR=5.14 (2.47-10.73), p<0.001) and MDD PRS (OR=1.20, (1.05-1.38), p=0.009). Both trajectory classes were associated with MDD diagnosis and ADHD genetic risk.\n\nConclusionsThe developmental context of irritability may be important in its conceptualization: early-onset persistent irritability maybe more neurodevelopmental/ADHD-like and later-onset irritability more depression/mood-like. This has implications for treatment as well as nosology.

genetics↗

bioRxiv2018

The contribution of psychiatric risk alleles to a general liability to psychopathology in early life

BackgroundPsychiatric disorders show phenotypic as well as genetic overlaps. Factor analyses of child and adult psychopathology have found that phenotypic overlaps largely can be explained by a latent general \"p\" factor that reflects general liability to psychopathology. We investigated whether shared genetic liability across disorders would be reflected in associations between multiple different psychiatric polygenic risk scores (PRS) and a general psychopathology factor in childhood.\n\nMethodsThe sample was a UK, prospective, population-based cohort (ALSPAC), including data on psychopathology at age 7 (N=8161) years. PRS were generated from large published genome-wide association studies.\n\nOutcomesThe general psychopathology factor was associated with both schizophrenia PRS and attention-deficit/hyperactivity disorder (ADHD) PRS, whereas there was no strong evidence of association with major depressive disorder and autism spectrum disorder PRS. Schizophrenia PRS was also associated with a specific \"emotional\" problems factor.\n\nInterpretationOur findings suggest that genetic liability to schizophrenia and ADHD may contribute to shared genetic risks across childhood psychiatric diagnoses at least partly via the general psychopathology factor. However, the pattern of observations could not be explained by a general \"p\" factor on its own.\n\nFundingThis work was supported by the Wellcome Trust (204895/Z/16/Z).Introduction

genetics↗

bioRxiv2017

Searching for the causal effects of BMI in over 300 000 individuals, using Mendelian randomization

Mendelian randomization (MR) has been used to estimate the causal effect of body mass index (BMI) on particular traits thought to be affected by BMI. However, BMI may also be a modifiable, causal risk factor for outcomes where there is no prior reason to suggest that a causal effect exists. We perform a MR phenome-wide association study (MR-pheWAS) to search for the causal effects of BMI in UK Biobank (n=334 968), using the PHESANT open-source phenome scan tool. Of the 20 461 tests performed, our MR-pheWAS identified 519 associations below a stringent P value threshold corresponding to a 5% estimated false discovery rate, including many previously identified causal effects. We also identified several novel effects, including protective effects of higher BMI on a set of psychosocial traits, identified initially in our preliminary MR-pheWAS and replicated in an independent subset of UK Biobank. Such associations need replicating in an independent sample.

epidemiology↗

bioRxiv2017

A systematic review of sample size and power in leading neuroscience journals

Adequate sample size is key to reproducible research findings: low statistical power can increase the probability that a statistically significant result is a false positive. Journals are increasingly adopting methods to tackle issues of reproducibility, such as by introducing reporting checklists. We conducted a systematic review comparing articles submitted to Nature Neuroscience in the 3 months prior to checklists (n=36) that were subsequently published with articles submitted to Nature Neuroscience in the 3 months immediately after checklists (n=45), along with a comparison journal Neuroscience in this same 3-month period (n=123). We found that although the proportion of studies commenting on sample sizes increased after checklists (22% vs 53%), the proportion reporting formal power calculations decreased (14% vs 9%). Using sample size calculations for 80% power and a significance level of 5%, we found little evidence that sample sizes were adequate to achieve this level of statistical power, even for large effect sizes. Our analysis suggests that reporting checklists may not improve the use and reporting of formal power calculations.

scientific communication and education↗