Patrick Turley

The paper introduces GRMA, a method that improves genetic association analyses by reducing bias and expanding population coverage compared to GWAS.

This study evaluates the portability of polygenic indexes for behavioral traits across genetic ancestries, finding reduced accuracy in non-European populations and factors contributing to this loss.

The paper updates a polygenic index repository with more phenotypes, datasets, and improved causal inference methods, enhancing predictive performance and interpretability.

This study performs the first GWAS on a latent partner choice index using 206,617 individuals, identifying genetic correlates and solutions to reduce assortative mating biases in genetic studies.

The paper evaluates the impact of non-random mating and environmental transmission on genetic methods and introduces a simulation tool to improve robustness.

Family-based GWAS meta-analysis of 34 phenotypes shows standard GWAS effects carry sizeable confounding uncorrelated with direct genetic effects, while assortative mating likely inflates SNP heritability and genetic correlations.

This paper introduces SBayesRC, a method that improves polygenic prediction of complex traits by integrating GWAS summary statistics with functional genomic annotations, showing significant accuracy gains across ancestries.

Meta-analyzing per-ancestry GWAS of 7,266 UK Biobank traits across six genetic ancestry groups adds 14,676 loci missed by European-only analysis, including ancestry-enriched CAMK2D and G6PD signals.

This study shows that polygenic risk scores from multi-ancestry GWAS perform better in understudied populations than single-ancestry scores, except when the understudied group is small.

This paper introduces a method to adjust genetic studies for assortative mating using polygenic indexes, finding that assortative mating inflates genetic correlation estimates between health traits and education by 14% on average.

This study identifies genetic overlaps between psychiatric disorders, language ability, and creativity through a GWAS of school grades.

The paper develops a method to impute parental genotypes from sibling data to estimate direct and indirect genetic effects, finding that standard GWAS underestimates direct effects for several traits.

The paper constructs polygenic indexes for 47 phenotypes across 11 datasets and provides a tool to correct errors-in-variables bias when using PGIs as proxies for the additive SNP factor.

This paper introduces MAMA, a method for multi-ancestry GWAS meta-analysis, which identifies novel genetic associations and improves power across populations.

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 identifies genetic loci associated with macronutrient intake and links them to health outcomes using GWAS in over 235,000 individuals.

This study identifies five genome-wide significant loci and seven shared with other traits in a large ASD GWAS, highlighting polygenic heterogeneity and the value of large-scale GWAS in ASD.

The paper investigates polygenic associations between ADHD and literacy/language abilities, finding that genetic confounding with educational attainment masks ADHD-specific reading impairments.

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.

Genome-wide association meta-analysis of 20,183 ADHD cases and 35,191 controls finds 12 significant loci; signal concentrates in conserved regions, constrained genes, and CNS regulatory elements.

The paper quantifies assortative mating on complex traits by analyzing SNP data from 400,000 individuals, finding significant evidence for height and educational attainment.

Genetic correlations across 25 brain disorders (215,683 cases) reveal broad psychiatric common-variant sharing, largely distinct neurological disorders, and migraine as the only significant cross-category link.

MTAG improves GWAS results by combining summary statistics across traits, increasing significant loci and enhancing polygenic score variance.

The paper identifies genetic variants associated with subjective well-being, depressive symptoms, and neuroticism using genome-wide association studies and proxy-phenotype analyses.