John K. Hewitt

This paper investigates heterogeneous genetic effects on smoking behaviors using UK Biobank data, finding evidence of sex- and psychiatric disorder-specific genetic associations and improved polygenic risk score prediction with sex-specific effects.

In 159,701 siblings from 17 cohorts, population GWAS overstate direct effects on height, education, smoking, cognition, depressive symptoms and age at first birth; clinical traits mostly agree across models.

This study finds genetic links between cannabis use and sleep deficits, including eveningness chronotype and insomnia, using LDSC and polygenic risk scores.

The paper compares genome-wide data from opioid use and dependence cases to identify genetic associations and differences between exposure and dependence.

The paper assesses the association of two gene sets with smoking behaviors using GWAS data and finds they contribute little to the heritability of these traits.

The paper identifies genetic associations between depression and CU traits in specific brain regions using twin data and neuroimaging.

The paper examines genetic and environmental influences on the human functional connectome using twin data, finding strong non-shared environmental effects and moderate genetic influence.

The paper describes neuroimaging processing methods for the ABCD Study to extract brain phenotypes from multimodal imaging data.

The paper imputes four VNTR polymorphisms and one modifying SNP into 486,551 UK Biobank individuals, enabling rigorous testing of their association with psychiatric phenotypes.

The paper develops a method to handle missing data in genetic meta-analysis, identifying novel low-frequency variants in nicotine dependence genes with improved accuracy.

The paper develops methods to analyze multi-allelic variants in large sequence datasets, showing improved power and unbiased estimates in association tests.