Maya Ghoussaini

A genomic-led strategy to anticipate drug safety effects

The paper uses Mendelian Randomization to predict adverse drug reactions by linking genetic variants to gene expression and protein abundance across large datasets.

Genetic dose-response modelling predicts drug mechanisms, dosing, and adverse events

VIDRA models genetic dose-response relationships to predict drug mechanisms, therapeutic potential, and adverse events using phenotypically associated variants.

Impact of proteogenomic evidence on clinical success

This study shows that plasma protein quantitative trait loci (pQTL) support therapeutic hypotheses more effectively than other genetic evidence, increasing the likelihood of drug development success.

Genomic ascertainment of CHEK2-related cancer predisposition

This study quantifies cancer risk and penetrance in CHEK2 heterozygotes using genomic data from two large biobanks, finding increased risks for several cancers and younger age of onset.

Leveraging Large-Scale Biobanks for Therapeutic Target Discovery

This study uses large biobanks to identify gene-trait pairs with causal evidence, rediscover approved drug targets, and develop a predictive model for drug development.

Computationally efficient meta-analysis of gene-based tests using summary statistics in large-scale genetic studies

The paper introduces REMETA, a tool for efficiently performing meta-analysis of gene-based tests using summary statistics, demonstrating its application on UK Biobank data.

Variant Classification Using Proteomics-Informed Large Language Models Increases Power of Rare Variant Association Studies and Enhances Target Discovery

The paper improves rare variant association studies by using proteomics-informed large language models, identifying more gene-trait associations than existing methods.

Mapping interindividual dynamics of innate immune response at single-cell resolution

The paper develops GASPACHO to identify dynamic eQTLs in antiviral responses using single-cell RNA-seq, uncovering thousands of eQTLs and linking them to disease risk loci.

Network expansion of genetic associations defines a pleiotropy map of human cell biology

The paper uses network-based expansion of GWAS results to identify pleiotropic gene modules linked to multiple traits and demonstrates their application in studying inflammatory bowel disease.

Multi-ancestry omic Mendelian randomization revealing putative drug targets of COVID-19 severity

This study uses multi-ancestry Mendelian randomization to identify six protein targets for COVID-19 severity, showing ancestry-specific effects.

A map of transcriptional heterogeneity and regulatory variation in human microglia

The paper profiles gene expression in human microglia from 141 patients, identifies population shifts in acute brain injury, maps eQTLs, and fine-maps Alzheimer's risk variants using hIPSC-derived macrophages.

Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation

The paper uses single-cell RNA sequencing to study genetic variant effects on dopaminergic neuron differentiation from iPSCs, identifying eQTL and cell fate markers.

Mapping the human genetic architecture of COVID-19 by worldwide meta-analysis

This study identifies 13 genome-wide significant loci associated with SARS-CoV-2 infection or severe COVID-19 through a global meta-analysis of 49,562 patients.

Open Targets Genetics: An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci

The paper presents an open resource for fine-mapping and gene prioritization at GWAS loci, identifying causal variants and genes and improving drug target identification.

Fine-mapping of 150 breast cancer risk regions identifies 178 high confidence target genes

This study fine-maps 150 breast cancer risk regions to identify 178 high confidence target genes using genomic and functional annotations.