Yongmei Liu

This study integrates CAD GWAS with transcriptomic data to identify genes and networks associated with CAD and subclinical atherosclerosis.

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 identifies genetic and phenotypic correlates of mosaic loss of chromosome Y in blood using whole genome sequencing data from the TOPMed program.

The paper conducts proteome-wide and transcriptome-wide association studies for blood lipids, finding high-level consistency between proteome and transcriptome-lipid associations across tissues.

The paper maps interaction QTLs (iQTLs) for cell type, age, and other variables in blood multi-omic data to identify context-specific genetic regulatory effects.

The paper identifies impactful rare variants by integrating multi-omic data from MESA, showing that combining expression and protein data increases the detection of rare stop and frameshift variants.

This paper introduces MCFA, an unsupervised integration method for multi-omic data, and applies it to five omics types across 614 samples to infer shared and private factors.

This study shows that MASHR improves transcriptome prediction across populations, leading to more replicable discoveries in multi-ethnic transcriptome-wide association studies.

This paper improves multi-omics integration by enhancing orthogonality in canonical vectors and demonstrates transferability across cohorts for proteomics and methylomics data.

The paper tests for genetic interactions affecting causal variant effect sizes in gene expression and LDL-C across European and African American populations, finding significant evidence in gene expression but not LDL-C.

The paper develops genetic predictors of plasma protein abundance in diverse populations and demonstrates their utility in proteome-wide association studies (PWAS) for complex traits.

This study examines how Western and Mediterranean diets affect monocyte gene expression and social behavior in macaques, finding proinflammatory gene changes and behavioral differences.

The paper benchmarks RNA-seq association analyses for continuous exposures using DESeq2, edgeR, limma, and linear regression, finding linear regression methods are faster and better at controlling false detections.

The paper identifies novel genetic loci associated with blood pressure that interact with sleep duration across multiple ancestries.

This study identifies 5,552 blood-cell trait-variant associations across 746,667 individuals from 5 global populations, including 71 novel loci and ancestry-specific variants not found in European ancestry populations.

This study identifies 5,106 new genetic variants associated with blood cell traits using large-scale GWAS, highlighting the polygenic and monogenic basis of hematopoiesis.

The paper examines gene expression in blood cells linked to sleep apnea traits, identifying CD1D and RAB20 expression changes reversed by CPAP treatment.

The paper identifies genetic variants affecting HbA1c levels in multi-ancestry cohorts, showing ancestry-specific impacts on diabetes diagnosis via HbA1c.

The paper identifies DNA methylation sites associated with daytime sleepiness in African Americans using epigenome-wide association analysis in the Multi-Ethnic Study of Atherosclerosis.

The paper identifies 49 novel blood lipid loci associated with gene-sleep interactions in 126,926 individuals across 5 ancestry groups, with gene-short-sleep interactions explaining 4.25% of triglyceride variance.

The paper investigates genetic architecture of gene expression across diverse populations, finding that predictive performance varies due to allele frequency differences and highlighting the need for diverse population sampling.

This study identifies over 50 new genetic loci associated with pulmonary function through a multiethnic meta-analysis of 90,715 individuals.

The paper analyzes the proteomic architecture of human coronary and aortic atherosclerosis using mass spectrometry data from 200 specimens, identifying key protein networks and regulatory features associated with early disease.