Claudia Giambartolomei

This study expands the human plasma proteome analysis using an enhanced SomaScan platform, identifying new genetic associations and disease mechanisms beyond the classical secretome.

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

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.

SAFE-LD estimates linkage disequilibrium from GWAS summary statistics using pseudo-genotypes that preserve in-sample LD while protecting privacy.

The paper investigates how AR-bound CREs influence gene expression through epigenetic changes and chromatin looping, finding that contact frequency with promoters drives expression.

This study identifies novel genetic risk variants and potential drug targets for heart failure using GWAS and Mendelian randomization proteomics.

This study develops snpSTARRseq, a high-throughput method to test non-coding variants from GWAS, showing 37% of prostate cancer risk loci alter enhancer activity.

The paper uses machine learning to analyze genotyping data and identifies interactions between SNVs in late onset Alzheimer's disease, highlighting specific genomic profiles and interactions involving APOE and other genes.

The paper uses HiChIP to link prostate cancer GWAS risk variants to target genes, finding new associations not detected by eQTL methods and validating them with CRISPRi.

This paper introduces a statistical method to identify tissue-specific genetic contributions to complex traits at the individual level using eQTLs and applies it to BMI and WHRadjBMI in the UK Biobank.

The paper examines common genetic variants' contribution to bipolar disorder risk in multiplex families and finds higher polygenic risk scores in affected individuals compared to controls and unaffected family members.

This study identifies 30 genome-wide significant loci associated with bipolar disorder, including novel genes involved in ion channels, neurotransmitters, and synaptic components.

This paper introduces a Bayesian framework to identify regulatory effects at GWAS risk loci by integrating GWAS summary data with molecular QTL data.

The paper integrates GWAS and conditional eQTL data to identify schizophrenia risk loci with co-localization, finding 40 loci and six with conditional eQTL co-localization.

The paper uses ATAC-seq to profile open chromatin in human brain cells, identifying cell-type-specific regulatory elements and linking them to schizophrenia risk variants.