Biology subjects

Tilling, K.

Publications and source records attributed to Tilling, K..

26 records · Page 2Linked to original sources

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↗