Biology subjects

Tilling, K.

Publications and source records attributed to Tilling, K..

13 recordsLinked to original sources

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↗

bioRxiv2017

The molecular genetics of participation in the Avon Longitudinal Study of Parents and Children

BackgroundIt 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.\n\nMethodsUsing data on mothers (N=7,486) and children (N=7,508) from the Avon Longitudinal Study of Parents and Children, we 1) examined the association of polygenic risk scores for a range of socio-demographic, lifestyle characteristics and health conditions related to continued participation, 2) 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 sub-sample who participated in a recent follow-up and 3) determined the proportion of variation in participation explained by common genetic variants using genome-wide data.\n\nResultsWe 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, 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 (OR for ever smoking per SD increase in polygenic score:0.85, 95% CI:0.81,0.89) and sub-sample (OR:0.95, 95% CI:0.88,1.02). In genome-wide analysis, single nucleotide polymorphism based heritability explained 17-31% of variability in participation.\n\nConclusionsGenetic 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.

genetics↗

bioRxiv2017

Selection bias in instrumental variable analyses

Participants in epidemiological and genetic studies are rarely true random samples of the populations they are intended to represent, and both known and unknown factors can influence participation in a study (known as selection into a study). The circumstances in which selection causes bias in an instrumental variable (IV) analysis are not widely understood by practitioners of IV analyses. We use directed acyclic graphs (DAGs) to depict assumptions about the selection mechanism (factors affecting selection) and show how DAGs can be used to determine when a two-stage least squares (2SLS) IV analysis is biased by different selection mechanisms. Via simulations, we show that selection can result in a biased IV estimate with substantial confidence interval undercoverage, and the level of bias can differ between instrument strengths, a linear and nonlinear exposure-instrument association, and a causal and noncausal exposure effect. We present an application from the UK Biobank study, which is known to be a selected sample of the general population. Of interest was the causal effect of education on the decision to smoke. The 2SLS exposure estimates were very different between the IV analysis ignoring selection and the IV analysis which adjusted for selection (e.g., 1.8 [95% confidence interval -1.5, 5.0] and -4.5 [-6.6, -2.4], respectively). We conclude that selection bias can have a major effect on an IV analysis and that statistical methods for estimating causal effects using data from nonrandom samples are needed.

epidemiology↗

bioRxiv2017

Associations Of Prenatal And Postnatal Growth With Insulin-Like Growth Factor-I Levels In Pre-Adolescence

BackgroundRapid pre - and postnatal growth have been associated with later life adverse health outcomes, which could implicate (as a mediator) circulating insulin-like-growth-factor I (IGF-I), an important regulator of growth. We investigated associations of prenatal (birth weight and length) and postnatal growth in infancy and childhood with circulating IGF-I measured at 11.5 years of age.\n\nMethodsWe analysed 11.5-year follow-up data from 17,046 Belarusian children who participated in the Promotion of Breastfeeding Intervention Trial (PROBIT) since birth.\n\nResultsComplete data were available for 5422 boys and 4743 girls (60%). We stratified the analyses by sex, as there was evidence of interaction between growth and sex in their associations with IGF-I. Weight and length/height velocity during childhood were positively associated with IGF-I at 11.5 years; associations increased with age at growth assessment and were stronger for length/height gain than for weight gain. The change in internal run-normalized IGF-I z-score at 11.5 years was 0.038 (95% CI -0.004,0.080) per standard deviation (SD) increase in length gain at 0-3 months amongst girls and 0.025 (95% CI - 0.011,0.060) amongst boys, increasing to 0.336 (95% CI 0.281,0.391;) and 0.211 (95% CI 0.165,0.256) for girls and boys, respectively, for growth during 6.5-11.5 years.\n\nConclusionPostnatal growth velocities in childhood are positively associated with levels of circulating IGF-I in pre-adolescents. Future studies should focus on assessing whether IGF-I is on the causal pathway between early growth and later health outcomes, such as cancer and diabetes.

epidemiology↗

bioRxiv2017

Epigenome-Wide Association Study Of Asthma And Wheeze In Childhood And Adolescence

Asthma heritability has only been partially explained by genetic variants and is known to be sensitive to environmental factors, implicating epigenetic modifications such as DNA methylation in its pathogenesis.\n\nUsing data collected in the Avon Longitudinal Study of Parents and Children (ALSPAC), we assessed associations of asthma and wheeze with DNA methylation at 7.5 years and 16.5 years, at over 450,000 CpG sites in DNA from the peripheral blood of approx. 1000 participants. We used Mendelian randomization (MR), a method of causal inference that uses genetic variants as instrumental variables, to infer the direction of association between DNA methylation and asthma.\n\nWe identified 302 CpGs associated with current asthma status (FDR-adjusted P-value <0.05) and 445 with current wheeze status at 7.5 years, with substantial overlap between the two. Genes annotated to the 302 associated CpGs were enriched for pathways related to movement of cellular/subcellular components, locomotion, interleukin-4 production and eosinophil migration. All associations attenuated when adjusted for eosinophil and neutrophil cell count estimates. At 16.5 years, two sites were associated with current asthma after adjustment for cell counts. The CpGs mapped to the AP2A2 and IL5RA genes, with a -2.32 [95% CI -1.47,-3.18] and -2.49 [95% CI -1.56,-3.43] change in percentage methylation in asthma cases respectively. Two-sample bi-directional MR indicated a causal effect of asthma on DNA methylation at several CpG sites at 7.5 years. However, associations did not persist after adjustment for multiple testing. There was no evidence of a causal effect of asthma on DNA methylation at either of the two CpG sites at 16.5 years.\n\nThe majority of observed associations are driven by higher eosinophil cell counts in asthma cases, acting as an intermediate phenotype, with important implications for future studies of DNA methylation in atopic diseases.

epidemiology↗

bioRxiv2017

The effect of a lifestyle intervention in obese pregnant women on change in gestational metabolic profiles: findings form the UK Pregnancies Better Eating and Activity Trial (UPBEAT) RCT.

Background: Pregnancy metabolic disruption is believed to be enhanced in obese women and lead to adverse outcomes in them and their offspring. The UK Pregnancies Better Eating and Activity Trial (UPBEAT), a randomised controlled trial of a lifestyle intervention in obese pregnant women, has already been shown to improve diet and physical activity. We used UPBEAT to determine (a) the magnitude of change in metabolic profiles in obese pregnant women and (b) the impact of a lifestyle intervention that successfully improved diet and physical activity on these profiles. Methods: Detailed targeted metabolic profiling, with quantification of 158 metabolic features (129 lipid measures, 9 glycerides and phospholipids, and 20 low-molecular weight metabolites) was completed on three occasions (~17-, 28- and 35-weeks of gestation) in the UPBEAT participants using NMR. Random intercept and random slope models were used to quantify metabolite changes in obese women using the control (usual care) group only (N = 577). The effect of the intervention was determined by comparing rates of metabolite change between those randomised to intervention and usual care, using intention to treat analyses (N = 1158). Results: There were adverse changes across pregnancy in most lipoprotein subclasses, lipids, glycerides, phospholipids, several fatty acids and glucose. All extremely large, very large, large, medium, small and very small VLDL particles increased by 2 to 3 standard deviation units (SD), with IDL, and large, medium and small LDL particles increasing by 1 to 2SD, between 16- and 36-weeks. Triglycerides increased by 3 to 4SD and glucose increased by 2SD, with more modest changes in other metabolites. The intervention reduced the rate of increase in extremely large, very large, large and medium VLDL, in particular those containing triglycerides. Triglyceride to phosphoglyceride ratio was reduced and there were improvements in fatty acid profiles (increases in the proportion of all fatty acids that were linoleic, omega-6 and polyunsaturated and decreases in the proportion of saturated). Conclusion: Systemic metabolism is markedly disrupted in obese pregnant women, but a lifestyle intervention that improved their diet and physical activity has beneficial effects on some of these profiles; these effects might have long-term benefit. Clinical trial registration ID # ISRCTN89971375.

clinical trials↗

bioRxiv2017

Physical Activity Phenotyping With Activity Bigrams, And Their Association With BMI

BackgroundAnalysis of physical activity usually focuses on a small number of summary statistics derived from accelerometer recordings: average counts per minute, and the proportion of time spent in moderate-vigorous physical activity or in sedentary behaviour. We show how bigrams, a concept from the field of text mining, can be used to describe how a persons activity levels change across (brief) time points. These variables can, for instance, differentiate between two people with the same time in moderate activity, where one person often stays in moderate activity from one moment to the next and the other does not.\n\nMethodsWe use data on 4810 participants of the Avon Longitudinal Study of Parents and Children (ALSPAC). We generate a profile of bigram frequencies for each participant and test the association of each frequency with body mass index (BMI), as an exemplar.\n\nResultsWe found several associations between changes in bigram frequencies and BMI. For instance, a 1 standard deviation decrease in the number of adjacent minutes in sedentary then moderate activity (or vice versa), with a corresponding increase in the number of adjacent minutes in moderate then vigorous activity (or vice versa), was associated with a 2.36 kg/m2 lower BMI [95% CI: -3.47, -1.26], after accounting for the time spent at sedentary, low, moderate and vigorous activity.\n\nConclusionsActivity bigrams are novel variables that capture how a persons activity changes from one moment to the next. These variables can be used to investigate how sequential activity patterns associate with other traits.\n\nKey MessagesO_LIEpidemiologists typically use only a small number of variables to analyse the association of physical activity with other traits, such as the average counts per minute and the proportion of time spent in moderate-vigorous physical activity or being sedentary.\nC_LIO_LIWe demonstrate how activity bigrams can be used as a set of interpretable variables describing how a persons activity levels change from one moment to the next.\nC_LIO_LITesting the association of activity bigrams with exposures or outcomes can help us gain further understanding of how physical activity is associated with other traits; with further research they might provide evidence for more refined public health advice.\nC_LI

epidemiology↗

bioRxiv2017

Orienting The Causal Relationship Between Imprecisely Measured Traits Using Genetic Instruments

Inference of the causal structure that induces correlations between two traits can be achieved by combining genetic associations with a mediation-based approach, as is done in the causal inference test (CIT) and others. However, we show that measurement error in the phenotypes can lead to mediation-based approaches inferring the wrong causal direction, and that increasing sample sizes has the adverse effect of increasing confidence in the wrong answer. Here we introduce an extension to Mendelian randomisation, a method that uses genetic associations in an instrumentation framework, that enables inference of the causal direction between traits, with some advantages. First, it is less susceptible to bias in the presence of measurement error; second, it is more statistically efficient; third, it can be performed using only summary level data from genome-wide association studies; and fourth, its sensitivity to measurement error can be evaluated. We apply the method to infer the causal direction between DNA methylation and gene expression levels. Our results demonstrate that, in general, DNA methylation is more likely to be the causal factor, but this result is highly susceptible to bias induced by systematic differences in measurement error between the platforms. We emphasise that, where possible, implementing MR and appropriate sensitivity analyses alongside other approaches such as CIT is important to triangulate reliable conclusions about causality.

systems biology↗

bioRxiv2017

PHESANT: a tool for performing automated phenome scans in UK Biobank

MotivationEpidemiological cohorts typically contain a diverse set of phenotypes such that automation of phenome scans is non-trivial, because they require highly heterogeneous models. For this reason, phenome scans have to date tended to use a smaller homogeneous set of phenotypes that can be analysed in a consistent fashion. We present PHESANT (PHEnome Scan ANalysis Tool), a software package for performing comprehensive phenome scans in UK Biobank.\n\nGeneral featuresPHESANT tests the association of a specified trait with all continuous, integer and categorical variables in UK Biobank, or a specified subset. PHESANT uses a novel rule-based algorithm to determine how to appropriately test each trait, then performs the analyses and produces plots and summary tables.\n\nImplementationThe PHESANT phenome scan is implemented in R. PHESANT includes a novel Javascript D3.js visualization, and accompanying Java code that converts the phenome scan results to the required JavaScript Object Notation (JSON) format.\n\nAVAILABILITYPHESANT is available on GitHub at [https://github.com/MRCIEU/PHESANT]. Git tag v0.2 corresponds to the version presented here.

epidemiology↗