Ching‐Ti Liu

The paper analyzes expression and splicing quantitative trait loci (e/sQTL) across multiple tissues in the TOPMed dataset and finds that secondary e/sQTL signals are still discoverable, with many GWAS signals colocalizing with these e/sQTL.

The paper introduces a robust mixed model association test (RoM) for gene-environment interactions in related samples, showing improved type I error control compared to existing methods.

The paper identifies genes associated with obesity risk loci by integrating genetic and transcriptomic data from blood and brain tissues.

This study identifies novel genetic signals associated with severe obesity through GWAS and shows strong genetic overlap with BMI, highlighting shared comorbidities and lifestyle interactions.

This paper introduces a Bootstrap algorithm to infer Gaussian Graphical Models from correlated data, showing it controls Type I error better than conventional methods in simulations and a real family-based study.

The paper introduces MultiSTAAR, a statistical framework for multi-trait rare variant analysis in WGS studies, identifying new lipid trait associations in diverse populations.

This paper conducts a multi-ancestry time-to-event GWAS to identify genetic variants associated with incident cardiovascular disease in people with type 2 diabetes, finding three novel loci and confirming known CAD variants.

The paper identifies novel genetic loci associated with systolic blood pressure that interact with the DASH diet score through genome-wide interaction analysis.

The paper demonstrates that TOPMed imputation improves the identification of rare variants associated with type 2 diabetes in Latino populations, leading to novel associations and better polygenic scores.

The paper identifies a gene-diet interaction modifying the association between macronutrient intake and glycemic traits, highlighting challenges in detecting such interactions due to measurement error and sample size.

The paper performs whole genome sequence association analysis of fasting glucose and insulin levels in diverse cohorts, identifying 22 significant variants across 16 gene regions, including rare variants.

This study conducts a trans-ancestry GWAS using chronic ALT elevation as a proxy for NAFLD, identifies 77 significant loci, and validates 17 SNPs with histology and imaging data, revealing genetic links to metabolic and inflammatory traits.

The paper evaluates methods to handle missing lifestyle data in gene-by-lifestyle interaction meta-analyses and recommends the Safe Approach for accuracy.

The paper identifies rare non-coding variants from whole genome sequencing in the TOPMed program, showing they contribute significantly to type 2 diabetes heritability.

This study identifies DNA methylation sites associated with central adiposity traits in adults and adolescents using methylome-wide association analyses across multiple cohorts.

The paper introduces MAGEE, a method for efficient gene-environment interaction testing in large biobank-scale sequencing studies, reducing computational burden and enabling analysis of hundreds of thousands of individuals.

This paper introduces a unified method for detecting gene-environment interactions involving rare variants using a generalized linear mixed effect model, showing it maintains correct type I error and high power in simulations and replicates a known association.

This study identifies rare genetic variants associated with type 2 diabetes using exome sequencing of 20,791 cases and 24,440 controls, and provides a Bayesian framework to improve variant prioritization.

This study fine-maps type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps, identifying 73 variants with >80% posterior probability of association.