Guanghao Qi

Building an Interoperable Rare Disease Multi-omic Resource: The GREGoR Data Model and Dataset

The paper introduces a data model and dataset for rare disease research, enabling interoperable multi-omic data integration across clinical sites.

DAESC+: High-performance, integrated software for single-cell allele-specific expression data

DAESC+ is a software package for single-cell allele-specific expression analysis, offering improved accuracy and speed compared to existing methods.

CausalGRN: deciphering causal gene regulatory networks from single-cell CRISPR screens

CausalGRN infers causal gene regulatory networks from single-cell CRISPR screens and predicts responses to unseen perturbations with improved accuracy.

Transcriptome-wide association studies at cell state level using single-cell eQTL data

The paper introduces TWiST, a method for transcriptome-wide association studies at cell state resolution using single-cell eQTL data, showing improved power in detecting dynamic gene-disease associations.

Genome-Wide Large-Scale Multi-Trait Analysis Characterizes Global Patterns of Pleiotropy and Unique Trait-Specific Variants

This study identifies pleiotropic variants and trait-specific susceptibility variants using GWAS data from 116 traits, revealing widespread pleiotropy and unique genetic contributions to specific traits.

Mendelian Randomization Analysis Using Multiple Biomarkers of an Underlying Common Exposure

This paper introduces MRLE, a method for Mendelian Randomization analysis of latent exposures using multiple biomarkers, showing improved power and evidence for inflammation's causal role in diseases.

Single-cell allele-specific expression analysis reveals dynamic and cell-type-specific regulatory effects

DAESC is a statistical method for differential allele-specific expression analysis in scRNA-seq data, identifying genes with context-specific regulatory effects in endoderm differentiation and type 2 diabetes.

Uncovering context-specific genetic-regulation of gene expression from single-cell RNA-sequencing using latent-factor models

This paper introduces SURGE, a method to discover context-specific eQTLs from single-cell RNA-seq data, showing its relevance in disease through colocalization analysis.

Single-Cell Sequencing Reveals Lineage-Specific Dynamic Genetic Regulation of Gene Expression During Human Cardiomyocyte Differentiation

The paper identifies lineage-specific dynamic eQTLs during human cardiomyocyte differentiation using single-cell RNA-seq and deconvolves bulk RNA-seq data to attribute dynamic effects to specific lineages.

Genome-wide association studies of 27 accelerometry-derived physical activity measurements identifies novel loci and genetic mechanisms

This study identifies 5 novel genetic loci associated with physical activity using 27 accelerometry-derived phenotypes from 88,411 UK Biobank participants.

A Comprehensive Evaluation of Methods for Mendelian Randomization Using Realistic Simulations and an Analysis of 38 Biomarkers for Risk of Type-2 Diabetes

This paper evaluates Mendelian randomization methods using realistic simulations and real GWAS data, finding that performance varies with sample size, instrument validity, and InSIDE assumption.

Genome-wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype-specific analyses

This study identifies 32 new breast cancer susceptibility loci through GWAS, with associations varying by tumor subtype and genetic correlations among subtypes.

Kidney Function and Blood Pressure: A Bi-directional Mendelian Randomisation Study

This study uses Mendelian randomisation to show higher kidney function causally lowers blood pressure, but blood pressure does not significantly affect kidney function.

Mendelian Randomization Analysis Using Mixture Models (MRMix) for Genetic Effect-Size-Distribution Leads to Robust Estimation of Causal Effects

The paper introduces MRMix, a mixture model-based method for Mendelian randomization that provides robust causal effect estimates, showing improved performance in simulations and identifying several genetic risk factors for diseases.

Heritability Informed Power Optimization (HIPO) Leads to Enhanced Detection of Genetic Associations Across Multiple Traits

HIPO enhances genetic association detection by optimizing pleiotropic analysis using heritability and trait overlaps, increasing significant loci across multiple traits.

Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits and implications for the future

The paper estimates effect-size distributions of SNPs using GWAS summary statistics across 32 traits, revealing polygenic diversity and sample size requirements for heritability explanation.

Effect Size and Power in fMRI Group Analysis

Subsampling 491 Human Connectome Project subjects puts numbers on fMRI group analysis: roughly 40 subjects catch large effects (d > 0.8), near 80 are needed for medium ones.