Chris Haley

This study examines genetic nurturing effects using polygenic scores in parent-offspring trios and identifies biases when parental phenotypes are included.

This paper uses Mendelian randomisation to identify potential drug targets for cardiovascular disease by analysing protein levels in plasma and PBMCs from large cohorts.

The paper presents PigGTEx, a resource linking genetic variants to transcriptomic phenotypes in pigs, showing pigs' similarity to humans in gene regulation and complex traits.

This study simulates Atlantic salmon breeding programs to show that mixing between parallel year groups improves genomic prediction accuracy by reducing genetic distance and increasing genetic variation.

The paper identifies parent-of-origin effects on the methylome linked to aging rates by analyzing CpG methylation in a large family cohort.

This study examines genome-by-trauma interaction effects on depression using whole genome data and finds significant variance explained by these interactions, particularly in males.

The paper develops eleven omics-based biological age clocks with strong correlations to chronological age and shows their association with disease risk.

The paper identifies KRAB-ZFP genes' influence on CpG-site methylation and links these methylation patterns to disease phenotypes like rheumatoid arthritis and HIV infection.

SNHap-RHM jointly maps common and rare genetic variation affecting complex traits by combining SNP and haplotype-based genomic relationship matrices, identifying significant genomic regions for height and MDD.

This study identifies modifiable parent-of-origin effects on DNA methylation by analyzing genetic and environmental factors in a large cohort.

This study uses genome-wide methylation data to assess the impact of smoking and gene-smoking interactions on obesity-related traits, showing improved variance explanation compared to traditional methods.

This study identifies epigenome-wide DNA methylation differences between APOE ε4 and ε2 carriers in whole blood, implicating lipid-related genes.

The paper identifies pQTLs for cardiovascular proteins and uses them to evaluate drug targets and suggest new candidates for precision medicine.

The paper analyzes whole genome sequencing data from an isolated Shetland population, finding increased ultra-rare variants in exonic and regulatory regions compared to mainland Scottish populations.

This paper develops a multi-trait framework for discovering, replicating, and interpreting pleiotropic loci using GWAS summary statistics, identifying novel genetic associations without additional genotyping.

This paper introduces a Bayesian method to estimate epigenetic associations with complex traits, finding that methylation data captures significant variance in BMI and smoking behavior.

The paper improves age prediction from DNA methylation using larger datasets, showing reduced error and limitations in capturing biological ageing variation.

The paper develops DNA methylation predictors for BMI, smoking, alcohol, and education, finding they explain some phenotypic variance and predict mortality for some traits.

This study conducts a GWAS on depression phenotypes in UK Biobank, identifying genetic variants enriched in excitatory synaptic pathways.

This study examines the association between polygenic risk scores for Major Depressive Disorder derived from the NETRIN1 pathway and whole-genome variants with white matter microstructure in the UK Biobank.

The paper identifies shared expression patterns of genetic variants across tissues and cell types, enabling fine mapping of disease-associated variants and revealing key cell types involved in disease pathogenesis.

The paper investigates the genetic and environmental contributions to stressful life events and their overlap with depression and neuroticism using a large family-based sample.

The paper uses family data to estimate genetic effects on intelligence and personality, finding that rare variants explain more variance than previously reported using unrelated individuals.

This study performs genome-wide meta-analyses of depression in two large cohorts, finding one significant locus and suggesting larger sample sizes may be more informative than phenotypic stratification.

This study performs a genome-wide haplotype-based association analysis for major depressive disorder using two cohorts, identifying significant haplotypes potentially linked to the disorder.

This study identifies shared genetic and couple-associated environmental factors contributing to both clinical and self-declared depression risks.