Biology subjects

Millard, L. A.

Publications and source records attributed to Millard, L. A..

4 recordsLinked to original sources

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↗

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↗

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↗