Kate M. Tilling

Identification of Dynamic Genetic Influences on DNA Methylation from Birth to Adulthood

The paper identifies age-dependent methylation quantitative trait loci (longitudinal mQTLs) across the lifespan using blood DNAm data from two cohorts, finding consistent genotype-by-age effects and associations with multiple phenotypes.

An Evaluation of Age-varying Genetic Effects underlying Body-mass Index and Blood Pressure in the UK Biobank

This paper evaluates age-varying genetic effects on BMI and blood pressure traits using UK Biobank data, finding trait-dependent interactions that influence cardiovascular outcomes.

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

This study investigates how DNA methylation (DNAm) links racial discrimination, economic adversity, and air pollution to health inequities using epigenome-wide association analyses.

A framework for conducting time-varying genome-wide association studies: An application to body mass index across childhood in six multiethnic cohorts

The paper introduces a framework for time-varying GWAS to identify age-varying genetic effects on BMI across childhood, finding 28 genome-wide significant variants, including a novel one in DAOA.

Protein identification for stroke progression via Mendelian Randomization in Million Veteran Program and UK Biobank

This study uses Mendelian randomization to identify two proteins, CCL27 and TNFRSF14, that influence subsequent cardiovascular events after a stroke, suggesting inflammation as a contributing factor.

Two sample Mendelian Randomisation using an outcome from a multilevel model of disease progression

The paper applies a multivariate two-sample Mendelian Randomisation approach to estimate causal effects on disease progression intercept and slope using Parkinson's cohorts.

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

This study evaluates the reliability of DNA methylation measurements using the Illumina Infinium MethylationEPIC BeadChip at low DNA input levels, finding that 40ng DNA can be used with quality checks.

Strategies to investigate and mitigate collider bias in genetic and Mendelian randomization studies of disease progression

This paper reviews statistical methods to detect and adjust for collider bias in genetic and Mendelian randomization studies of disease progression using individual and summary-level data.

Hypothesis-free detection of gene-interaction effects on biomarker concentration in UK Biobank using variance prioritisation

The paper develops a variance prioritisation method to detect gene-interaction effects on blood biomarker concentrations using UK Biobank data, identifying novel gene-environment and gene-gene interactions.

A robust mean and variance test with application tohigh-dimensional phenotypes

The paper introduces a robust statistical test for detecting associations with both mean and variability in high-dimensional phenotypes, applied to DNA methylation data.

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

This study maps DNA methylation changes across the human life course using data from 7,037 individuals, showing increased variability and age-related mean changes at specific CpG sites.

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

This paper uses Mendelian randomization to investigate causal links between genetic liability to ADHD/ASD and educational attainment, finding distinct causal effects for ADHD and ASD.

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

This paper shows that using covariable-adjusted summary associations in two-sample Mendelian randomization can introduce bias, especially when there is residual confounding between the covariable and outcome.

Genetic predictors of participation in optional components of UK Biobank

The paper identifies genetic variants and phenotypes associated with participation in optional UK Biobank components and shows common participation bias across studies.

Smoking, DNA methylation and lung function: a Mendelian randomization analysis to investigate causal relationships

This study uses Mendelian randomization to investigate causal relationships between smoking-associated DNA methylation and lung function, identifying 18 CpGs with potential causal effects on FEV1.

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

Slope-Hunter corrects index-event bias in GWAS of subsequent traits by identifying and adjusting for genetic variants affecting only incidence, improving statistical performance in the presence of genetic correlation.

Using genetics to examine a general liability to childhood psychopathology

The paper examines whether genetic liability to schizophrenia and ADHD contributes to shared risks across childhood psychiatric diagnoses through a general psychopathology factor.

dmrff: identifying differentially methylated regions efficiently with power and control

Combining nearby CpG summary statistics by inverse-variance weighted meta-analysis holds false positives at the nominal level where comb-p and DMRcate inflate, and supports multi-cohort meta-analysis.

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

The paper investigates how genetic factors influence participation in a longitudinal study, finding polygenic associations with participation rates and highlighting potential biases in genetic studies due to selection.

Orienting The Causal Relationship Between Imprecisely Measured Traits Using Genetic Instruments

The paper introduces an extended Mendelian randomisation method to infer causal directions between traits, showing DNA methylation is more likely causal but sensitive to measurement error.

Genetic Epidemiology And Mendelian Randomization For Informing Disease Therapeutics: Conceptual And Methodological Challenges

The paper discusses challenges in using genetic epidemiology and Mendelian randomization to inform disease therapeutics, highlighting the gap between GWAS discoveries and therapeutic target identification.