Qiongshi Lu

SARDINE uses EWAS summary statistics to estimate epigenetic clock-phenotype associations without individual-level data, revealing heterogeneity in biomarker associations with aging and diseases.

This paper introduces GENJI, a method to estimate genetic correlation using both individual-level and summary-level GWAS data, improving accuracy and efficiency over existing methods.

This study investigates challenges in case-only analysis using polygenic risk scores for gene-environment interaction detection, showing false positives due to model misspecification and collider bias in large biobanks.

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 paper introduces a statistical framework for regularized ensemble polygenic risk prediction using GWAS summary statistics, showing improved performance across traits and populations.

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

This paper introduces a statistical framework to optimize and benchmark polygenic risk scores using GWAS summary statistics, showing improved performance over existing methods.

This study identifies risks of false positives in ML-assisted GWAS and introduces POP-GWAS, a framework that provides valid inference for GWAS on ML-imputed phenotypes, applied to BMD data to find 89 novel loci.

The paper identifies variance quantitative trait loci (vQTLs) for vitamin D levels and characterizes gene-environment interactions involving modifiable factors and sex.

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 genetic variants associated with income and shows they correlate with health outcomes and educational attainment.

The paper uses BADGERS to identify risk factors and endophenotypes for late-onset Alzheimer's disease by analyzing biobank-wide data.

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 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.

The paper develops an APOE genetic risk score based on neuropathology that better explains CSF biomarker variance than traditional APOE ε4 metrics.

This paper introduces X-Wing, a framework that improves genetic prediction across populations using GWAS summary statistics by identifying portable genetic effects.

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

The paper investigates the association between polygenic risk score and gut microbiome in MS patients, finding distinct microbiota profiles in clusters with higher genetic risk.

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.

This paper introduces EncoreDNM, a statistical framework to quantify shared genetic effects of de novo mutations between disorders using exome-wide summary data, revealing abundant pairwise enrichment correlations.

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 study introduces SUPERGNOVA, a framework for estimating local genetic correlations, and identifies 150 trait pairs with significant local genetic correlations, including autism spectrum disorder and cognitive performance.

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.

This paper introduces LOGODetect, a statistical method to identify local genetic correlation signals across multiple phenotypes using GWAS summary statistics, finding 49 non-overlapping genome regions associated with psychiatric disorders.

The paper identifies novel risk genes for autism spectrum disorder using transcriptome-wide transmission disequilibrium analysis across 7,805 ASD proband-parent trios.

The paper performs a metabolome-wide association study to identify CSF metabolite-phenotype associations in neurological and psychiatric disorders.

The paper identifies seven metabolites associated with executive function decline in early Alzheimer’s disease using longitudinal plasma samples from the WRAP cohort.

The paper identifies CSF metabolites associated with tau levels and shows they improve prediction of Alzheimer's disease status.