Ani W Manichaikul

The paper identifies genetic drivers of protein change over time using longitudinal proteomics data from MESA, UK Biobank, and CARDIA, finding that baseline adjustment improves replication but may introduce biases.

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

MetaboXcan predicts plasma metabolites from genetic data and associates them with complex traits, identifying disease risk genes and metabolites in chronic kidney disease.

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.

This study improves proteome-wide association study results by using multi-ancestry data, finding more protein-phenotype associations and better prediction models.

This study investigates the association between mosaic loss of Y chromosome (mLOY) and lung function, emphysema, and epigenetic aging in men across multiple cohorts.

The paper integrates genomics and transcriptomics data from human heart samples to identify regulatory mechanisms linking genetic variation to dilated cardiomyopathy.

cellSTAAR integrates single-cell ATAC-seq with whole-genome sequencing to enhance power and interpretation of rare variant association tests in non-coding regions.

The paper introduces SECRET, a method to develop polygenic scores for traits with limited data, showing improved prediction power over existing methods.

This paper develops a method to improve Mendelian randomization analysis by integrating GWAS results across ancestries, enhancing accuracy and power in discovering causal genes for autoimmune diseases.

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 identifies proteomic networks and genetic variants associated with smoking and COPD phenotypes using multi-ethnic proteomic data and network-based GWAS.

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.

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

This study identifies FHL5 as a causal gene for vascular disease risk through eQTL colocalization and functional analyses, showing its role in vascular remodeling and calcification.

This study performs a multi-ancestry GWAS on lung function and COPD, identifying 559 genes and 29 pathways, and showing genetic risk scores are associated with COPD across ancestries.

This study identifies 118 candidate causal genes and 21 novel lipid loci on the X chromosome through a multi-ancestry meta-analysis of blood lipid levels, revealing sex-biased effects and pleiotropy with cardiovascular and hormonal traits.

The paper introduces a framework for detecting noncoding rare variant associations using whole-genome sequencing data, applying it to lipid traits in the TOPMed program.

The paper performs whole genome sequence analysis of plasma lipid levels in 66,329 individuals, identifying new lipid-associated alleles, including rare non-coding variants.

The paper quantifies similarity in causal effects across local ancestries in admixed individuals and finds high genetic correlation (radmix=0.95) within individuals.

This paper integrates GWAS data with functional genomic datasets to identify regulatory mechanisms and causal genes underlying lipid-related traits.

This study shows that proteomic data can be linked to specific genomes using pQTL knowledge, highlighting privacy risks and the need for protections similar to genomic data.

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.

The paper estimates genetic and environmental correlations between complex phenotypes across different racial/ethnic groups and sexes using data from the TOPMed program.

The paper shows Amerind ancestry is linked to lower LC-PUFA levels and increased cardiometabolic risk in Hispanic populations due to FADS genetic variation.

This study improves transcriptome-wide association studies by integrating pleiotropy-guided methods with GWAS data to identify new cancer susceptibility genes.

This paper introduces RUTH, a new HWE test that accounts for population structure and genotype uncertainty, showing it reduces false positives compared to traditional methods.

This study develops polygenic transcriptome risk scores (PTRS) to improve the portability of polygenic risk scores across ancestries, showing better performance in African populations compared to traditional PRS.

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.