Conor V. Dolan

The paper investigates how parental polygenic scores for complex traits are associated with offspring DNA methylation, finding significant links for schizophrenia, educational attainment, BMI, and height.

This study uses twin-based Mendelian randomisation to find bidirectional causation between smoking and blood DNA methylation at multiple genomic sites.

This paper introduces MRDoC2, a model for detecting bidirectional causation using instrumental variables and relative data, without requiring distinct genetic or environmental variance sources.

This study controls for geographic confounding in GWASs of 56 traits, showing that regional differences in SES reduce genetic signals for traits like education and income.

This paper applies a 3-level multilevel model to twin data, showing that regional clustering explains 1.8% of children's height variance, which is ancestry-related and disappears after genetic principal components correction.

This study compares the power of two designs to detect cultural transmission, finding the Nuclear Twin Family Design is well-powered and polygenic risk scores do not significantly enhance it.

This study identifies traits associated with polygenic risk for depression using UK Biobank data and finds causal links between depression risk and brain structure, as well as environmental interactions.

The paper examines how parental genotypes influence offspring education and ADHD, finding genetic nurturing effects for education but not ADHD.

This study identifies 73 genome-wide significant loci for mood disorders, revealing shared and distinct genetic factors between major depressive disorder and bipolar disorder.

This study investigates whether genetic variation in the Major Histocompatibility Complex (MHC) is associated with depression risk, finding no significant associations at a conservative threshold.

This paper explores estimating genotype-environment covariance in twin models by incorporating polygenic scores, showing increased statistical power when fixing variance explained by polygenic scores.

The paper examines how genetic and environmental factors influence DNA methylation at variably methylated regions in neonatal blood, finding that combined effects best explain methylation variability.

This study evaluates multivariate association tests for genome-wide analysis, comparing their power and Type I error rates under various conditions.

GWAS in over one million individuals identify 611 loci across risk tolerance and risky behaviors, revealing shared genetic architecture and implicating glutamatergic and GABAergic neurotransmission.

The paper applies polygenic risk scoring to a large family with bipolar and depressive disorders to study genetic risk patterns and anticipation.

This paper introduces MR-DoC, a twin-based model integrating Mendelian Randomization and Direction of Causation to test causal relationships while accounting for pleiotropy and improving statistical power.

Genome-wide association meta-analysis of 130,664 major depression cases and 330,470 controls identifies 44 independent risk loci enriched in cortical neurons, conserved mammalian regions, splicing networks, and antidepressant targets.

The paper evaluates the performance of likelihood ratio and score tests under weight misspecification in rare variant association studies and finds LRT more robust and powerful.