Futao Zhang

OTTERS is a TWAS framework that uses summary-level eQTL reference data to estimate eQTL weights and perform omnibus TWAS, showing improved power and applicability.

The paper uses blood eQTL meta-analysis to explore the polygenic architecture of complex traits, finding that trans-eQTLs are more informative for identifying causal genes than cis-eQTLs.

The paper compares GLM and LMM approaches for mass-univariate vertex-wise analysis, showing LMMs reduce false positives and cluster size but increase computation.

This paper identifies 58 significant associations between grey matter structure and diverse human phenotypes using large neuroimaging datasets and shows body size factors explain part of these associations.

This study predicts promoter-anchored chromatin interactions using DNA methylation QTL data from a human cohort, finding overlaps with experimental data and associations with complex traits.

This study identifies DNA methylation loci associated with age-by-sex interactions in a cohort of 2,586 individuals, finding most significant loci are X-linked and linked to disease-related genes.

The paper identifies genetic variants associated with phenotypic variability and shows they exhibit genotype-by-environment interactions for obesity-related traits using UK Biobank data.

This paper introduces TWMR, a Mendelian Randomization method that identifies gene-trait associations by integrating GWAS and eQTL data, revealing 3,913 novel associations across 43 human phenotypes.

OSCA provides a tool for omic-data-based complex trait analysis using the MOMENT method, showing improved robustness and lower false positive rates in simulations.

GWAS in over one million individuals identify 611 loci across risk tolerance and risky behaviors, revealing shared genetic architecture and implicating glutamatergic and GABAergic neurotransmission.

This study identifies 139 common and 4 rare genetic variants associated with type 2 diabetes through GWAS and integrates gene expression and epigenomic data to highlight potential functional genes and regulatory mechanisms.

The paper identifies gene targets for brain-related traits using transcriptomic and methylomic data from blood, finding strong correlations between genetic effects in brain and blood tissues.

This study uses genetic data to identify causal relationships between risk factors and common diseases, finding protective and bidirectional effects.