Biology subjects

Smart, M.

Publications and source records attributed to Smart, M..

3 recordsLinked to original sources

bioRxiv2019

Body mass index, earnings and partnership: genetic instrumental variable analysis in two nationally representative UK samples

In high-income countries there is an established link between high body mass index (BMI) and low income, but the direction of this association is unclear. Recent analyses in a large UK population using genetically-instrumented BMI supported a causal influence of BMI on household income, educational attainment and job class. Since analyses were based on an age-restricted and relatively wealthy population, it is unclear whether results are generalizable, and limited income data precluded decomposition of household income effects into own-income and partnership effects. Investigation is therefore warranted in more representative UK populations where associations may differ, and where individual and partner-based mechanisms can be studied separately.\n\nData came from two nationally-representative samples, the UK Household Longitudinal Survey (UKHLS) and the English Longitudinal Study of Ageing (ELSA). Analysis was conducted in each sample, with results then pooled by meta-analysis. We used externally-weighted polygenic scores based on the latest genome-wide association study for BMI to examine the influence of genetically-instrumented BMI on earnings, probability of employment, job class conditional on working, likelihood of partnership, and partners earnings.\n\nA one-unit (kg/m2) increase in genetically-instrumented BMI was associated with a roughly 9% decrease in own monthly earnings (pooled coefficient: 0.91, CI:0.86, 0.97) and lower probability of employment (OR: 0.89, CI:0.83, 0.96) or having a university degree (OR: 0.95, CI:0.90, 0.99). Employed individuals with higher genetically-instrumented BMI were less likely to have professional or managerial occupations (OR: 0.91, CI:0.86, 0.96). No associations were seen with partnership. A one-unit increase in BMI was associated with a 5% decrease in partners earnings, but estimates were imprecise (pooled coefficient: 0.95, CI:0.88,1.01).\n\nResults are consistent with a negative influence of body mass index on a range of labour market and educational outcomes for both men and women.\n\nKey MessagesO_LIHigher genetically-instrumented BMI was associated with lower earnings and odds of working\nC_LIO_LIAlso with lower odds of holding a managerial/professional occupation or a degree\nC_LIO_LINo associations were seen with probability of cohabiting partnership\nC_LI

epidemiology↗

bioRxiv2018

Properties of the epigenetic clock and age acceleration

BackgroundThe methylation status of numerous CpG sites in the human genome varies with age. The Horvath epigenetic clock used a wide variety of published DNA methylation data to produce an age prediction that has been widely used to, predict age in unknown samples, and draw conclusions about speed of ageing in various tissues, environments, and diseases. Despite its utility, there are a number of assumptions in the model that require examination. We explore the characteristics of the model in whole blood and multiple brain regions from older people, who are not well represented in the original training data, and in blood from a cross-sectional population study.\n\nResultsWe find that the model systematically underestimates age in tissues from older people. A decrease in slope of the predicted ages were observed at approximately 60 years, indicating that some loci in the model may change differently with age, and that age acceleration measures will themselves be age-dependent. This is seen most strongly in the cerebellum but is also present in other examined tissues, and is consistently observed in multiple datasets. An apparent association of Alzheimers disease with age acceleration disappears when age is used as a covariate. Association tests in the literature use a variety of methods for calculating age acceleration and often do not use age as a covariate. This is a potential cause of misleading findings.\n\nConclusionsAssociations of phenotypes with age acceleration should be evaluated cautiously, and chronological age should be included as a covariate in all analyses.

genomics↗

bioRxiv2017

Multivariate Genome-Wide and Integrated Transcriptome and Epigenome-Wide Analyses of the Well-being Spectrum.

Phenotypes related to well-being (life satisfaction, positive affect, neuroticism, and depressive symptoms), are genetically highly correlated (| rg | > .75). Multivariate genome-wide analyses (Nobs = 958,149) of these traits, collectively referred to as the well-being spectrum, reveals 63 significant independent signals, of which 29 were not previously identified. Transcriptome and epigenome analyses implicate variation in gene expression at 8 additional loci and CpG methylation at 6 additional loci in the etiology of well-being. We leverage an anatomically comprehensive survey of gene expression in the brain to annotate our findings, showing that SNPs within genes excessively expressed in the cortex and part of the hippocampal formation are enriched in their effect on well-being.

genetics↗