Milton Pividori

Common cis-eQTLs do not explain chromosome 21 expression deviations from the ploidy expectation in Down syndrome

The paper investigates whether common cis-eQTLs explain gene expression deviations from ploidy expectations in Down syndrome using transcriptomic data from the Human Trisome Project and GTEx.

The landscape of polygenic score prediction accuracy and transportability across the phenome in ancestrally diverse populations

This study evaluates polygenic score prediction accuracy and cross-ancestry portability across 3,994 PGSs in diverse populations, identifying factors affecting their performance and equity.

An AI-Powered Trisomy 21 Research Assistant

The paper introduces a section-aware RAG system for Down syndrome research that prioritizes experimental results to generate accurate, cited responses.

CCC-GPU: A graphics processing unit (GPU)-accelerated nonlinear correlation coefficient for large-scale transcriptomic analyses

CCC-GPU provides a GPU-accelerated nonlinear correlation coefficient for large-scale transcriptomic data, improving speed and detecting nonlinear relationships.

Mechanistic insights into Down syndrome comorbidities via convergent RNA-seq and TWAS signals

This study integrates transcriptomic and genetic data to identify molecular signatures and potential biomarkers for Down syndrome comorbidities.

A genetically informed atlas of transcriptomic programs from massive human RNA-seq compendia

CLAMP integrates genetic associations and drug signatures with large-scale RNA-seq data to identify interpretable gene modules linked to complex diseases.

Uncovering hidden gene-trait patterns through biclustering analysis of the UK Biobank

The paper applies biclustering to GWAS data from the UK Biobank to uncover local gene-trait associations that traditional methods miss.

An efficient not-only-linear correlation coefficient based on machine learning

This paper introduces CCC, a machine learning-based correlation coefficient that detects both linear and nonlinear patterns in gene expression data, outperforming linear-only methods in identifying biologically relevant relationships.

Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms

This paper introduces PhenoPLIER, a method that maps gene-trait and drug associations into gene modules to reveal disease mechanisms and drug targets.

Multi-ancestry gene-trait connection landscape using electronic health record (EHR) linked biobank data

The paper uses multi-ancestry biobank data and integrative methods to identify gene-disease associations across different ancestry groups, finding shared and ancestry-specific genetic links.

Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

The paper uses GTEx data to analyze GWAS loci, showing dose-dependent RNA effects and proposing target genes for 47% of loci using combined methods.

Probabilistic Colocalization of Genetic Variants from Complex and Molecular Traits: Promise and Limitations

The paper evaluates colocalization methods for genetic variants, identifying limitations and recommending best practices for accurate analysis of complex traits and molecular QTLs.

Polygenic transcriptome risk scores improve portability of polygenic risk scores across ancestries

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.

PhenomeXcan: Mapping the genome to the phenome through the transcriptome

PhenomeXcan integrates GWAS and transcriptomic data to identify gene-trait associations, providing a resource for translational research with biological context.

Shared and distinct genetic risk factors for childhood onset and adult onset asthma: genome- and transcriptome-wide Studies

This study identifies shared and distinct genetic risk factors for childhood and adult onset asthma using GWAS and transcriptome-wide analysis, finding 61 loci with 23 childhood-specific, 1 adult-specific, and 37 shared.

Integrating Predicted Transcriptome From Multiple Tissues Improves Association Detection

The paper introduces MulTiXcan, a method that improves association detection by integrating eQTL data across tissues, showing it identifies more significant genes than single-tissue analyses.