Jason M. Fletcher

This paper introduces a statistical framework to identify genetically driven heterogeneous treatment effects using causal transfer learning, applied to RCTs to find key determinants of treatment efficacy.

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.

This study introduces PENGUIN, a framework to control genetic confounding in epidemiologic studies, showing it effectively removes confounding in educational attainment associations.

This paper shows sibling models fail to accurately uncover direct genetic effects and can introduce biases when genetic nurture effects are present.

This study identifies biases in proxy GWAS using parental Alzheimer's history, showing misleading genetic correlations and highlighting design issues in biobank cohorts.

The paper investigates how social environments interact with genetic factors related to educational attainment, finding that higher social mobility reduces genetic influence on educational outcomes.

The paper uses polygenic scores from sibling pairs in the UK Biobank to show compensatory processes in educational attainment, reducing genetic associations by 20% for higher-ranked siblings.

This study re-examines genetic selective migration in the UK by analyzing migration patterns rather than place types, finding evidence of genetic selection in migration behavior.

This study introduces PIGEON, a statistical framework for gene-environment interaction analysis using summary statistics, showing its superiority in multiple analytic settings.

This paper introduces QUAIL, a model for detecting genetic variants affecting trait variability, and finds 11 novel BMI vQTL with interactions with physical activity and sedentary behavior.

The paper uses GWAS to link infant mortality rates with genetic variants, finding genome-wide significant loci associated with mortality exposure and related traits.

This paper estimates direct and indirect genetic effects using summary statistics from multi-generational GWAS, showing distinct patterns for traits like educational attainment and autism spectrum disorder.

ORIGAMI simulates offspring genomes from parental data to improve polygenic transmission disequilibrium analysis, revealing significant within-family risk variation for complex traits.

The paper uses multi-generational data to separate the genetic and environmental effects of parental polygenic scores on child educational outcomes.

This paper introduces PUMAS, a method to fine-tune polygenic risk scores using GWAS summary statistics, improving predictive performance and statistical power in downstream analyses.

The paper tests gene-environment interactions between polygenic scores for mental health and schooling attainment using two U.S. longitudinal datasets.

This study identifies sex-specific genetic variants associated with facial attractiveness using a genome-wide association approach in 3,928 samples.

The paper examines genetic similarities between friends and schoolmates using longitudinal health data, finding that social forces contribute to genetic correlation rather than individual selection.