Miaoxin Li

GWAS Reveals Distinct Genetic Architecture of Schistosomiasis-Induced Hepatic Fibrosis with DGKG as a Key Mediator

This study identifies DGKG as a key mediator of schistosomiasis-induced hepatic fibrosis through GWAS and functional validation, revealing distinct genetic architecture compared to other fibrotic diseases.

SMECT: a framework for benchmarking post-GWAS methods for spatial mapping of cells associated with human complex traits

SMECT benchmarks post-GWAS methods for spatial mapping of cells associated with human complex traits, revealing trade-offs between sensitivity and specificity across methods.

Policy gradient-guided ensemble learning for enhanced polygenic risk prediction in ultra-high-dimensional genomics

This paper introduces ELAG, an ensemble learning framework for polygenic risk prediction that improves accuracy in high-dimensional genomics data by using policy gradient methods to guide variant selection.

Developmental expression of risk genes implicates the age of onset for neuropsychiatric disorders

This study links the developmental expression of risk genes to the age of onset of neuropsychiatric disorders, showing that disease peaks align with gene expression peaks in the prefrontal cortex.

Exploring Genetic Interactions with Rare Variants Reveals Gene Networks Susceptible to Complex Diseases

GGI-RUNNER identifies rare variant gene interactions linked to complex diseases and reveals interconnected gene networks.

Stratifying variant deleteriousness and trait-modulating effect under human recent adaptation

This study introduces a deep learning framework, FIND, to better classify genetic variants by their deleteriousness and trait-modulating effects, showing improved performance over existing methods.

Endogenous fine-mapping of functional regulatory elements in complex genetic loci

The paper identifies causal regulatory elements in complex genetic loci using CRISPR perturbations and shows that many causal variants lack classical epigenetic markers.

Phenome-wide analysis identifies parent-of-origin effects on the human methylome associated with changes in the rate of aging

The paper identifies parent-of-origin effects on the methylome linked to aging rates by analyzing CpG methylation in a large family cohort.

Deviation from baseline mutation burden provides powerful and robust rare-variants association test for complex diseases

This paper introduces a new statistical method, RUNNER, to detect rare variants associated with complex diseases by comparing observed mutation burdens to predicted baseline values, showing improved power over existing methods.

MCGA: a multi-strategy conditional gene-based association framework integrating with isoform-level expression profiles reveals new susceptible and druggable candidate genes of schizophrenia

MCGA identifies schizophrenia susceptibility genes using a multi-strategy framework integrating isoform-level eQTLs, revealing genes enriched in neuronal signaling and drug targets.

A global overview of single-cell type selectivity and pleiotropy in complex diseases and traits

The paper constructs a cell-type-phenotype association landscape using scRNA-seq and GWAS data to identify cell-type selectivity and pleiotropy in complex diseases.

Systematic comparative analysis of Mendelian randomization methods for inferring causal genes of complex phenotypes and the application to psychiatric diseases

This study compares multiple Mendelian randomization methods for causal gene inference in complex phenotypes, identifies two robust methods, and applies them to psychiatric diseases to discover known and novel causal genes.

Estimating driver-tissues by robust selective expression of genes associated with complex diseases or traits

This paper introduces a framework to estimate driver tissues of complex diseases by analyzing selective gene expression from GWAS data, finding lung as a driver tissue for rheumatoid arthritis.

WITER: A powerful method for the estimation of cancer-driver genes using a weighted iterative regression accurately modelling background mutation rate

WITER improves cancer-driver gene detection by accurately modeling background mutations, identifying 178 driver genes across 26 cancers with high validation rates.