Paola Sebastiani B

hypeR-GEM connects metabolite signatures to enzyme-coding genes using genome-scale metabolic models, showing concordance with proteomic and transcriptomic data in real-world applications.

This study shows that structural and molecular lung damage occurs before functional decline in mice exposed to smoke, highlighting the need for early biomarkers in firefighters.

The paper identifies 44 SNPs associated with steroid-induced ocular hypertension using a pharmacogenomic approach.

This study shows that wildfire smoke exposure in hypercholesterolemic mice causes vascular inflammation, oxidative stress, and fibrosis, leading to aortic stiffening and hypertension.

The paper identifies metabolomic signatures associated with extreme old age using data from the New England Centenarian Study and links them to biological age and mortality risk.

This study identifies novel plasma proteomic biomarkers for Dupuytren Disease, showing impaired Collagen I degradation and developing diagnostic risk scores with moderate accuracy.

This study identifies age and survival-related transcriptomic signatures in longevity-enriched cohorts using RNAseq data from blood samples.

This study identifies genetic variants associated with cognitive domains like memory and attention using GWAS in the Long Life Family Study, finding domain-specific genetic architecture.

The paper uses iPSC-derived neurons from centenarians to identify resilience signatures associated with longevity, showing enhanced stress response and specific transcriptional programs.

This study constructs a polygenic protective score for Alzheimer's disease and finds stronger genetic protection against AD in centenarians compared to non-longevity individuals.

This paper introduces a Bootstrap algorithm to infer Gaussian Graphical Models from correlated data, showing it controls Type I error better than conventional methods in simulations and a real family-based study.

The paper validates and expands a serum protein signature associated with APOE genotypes, identifying new protein associations and potential targets for Alzheimer's disease prevention.

This study compares two metagenomic classifiers, finds classifier-specific results, and recommends integrating multiple classifiers to better capture age-associated taxa.

The paper identifies APOE genotype-related metabolite associations with biological age and bioenergetics in humans, showing counterintuitive patterns in {varepsilon}2 and {varepsilon}4 carriers.

The paper establishes a resource of iPSCs from centenarians and their offspring to study human longevity and resilience.

The paper validates and expands a serum protein signature of extreme old age using LC-MS/MS, identifying 80 conserved proteins across platforms associated with aging pathways.

The paper introduces yQTL Pipeline, a tool for QTL discovery and visualization, applied to metabolomics data from NECS, identifying 14,983 mQTLs with improved efficiency.

This paper introduces a method to identify conserved molecular features across non-independent omics studies by adjusting p-values to account for inter-study dependencies.

This paper introduces a Bayesian multinomial regression method for analyzing cell type proportions without requiring a reference cell type, using the rjags package in R.

A GWAS identified 20 rare genetic variants associated with better processing speed in the Long Life Family Study, replicated in Danish twin cohorts.

The paper evaluates 54 polygenic risk scores in long-lived individuals and their offspring, finding lower genetic risk for Alzheimer's, CAD, and lupus, but not for dementia in long-lived individuals.

This study examines how polygenic risk scores for cancer and longevity-associated variants relate to cancer risk and finds that longevity scores do not significantly improve cancer risk prediction beyond cancer-specific scores.

The paper develops a DNA methylation-based risk score for cardiovascular disease and shows it predicts risk independently of traditional factors.

The paper develops a gene-diet interaction score to predict dietary fat response for cardiometabolic risk factors using the Women's Health Initiative cohort.

The paper identifies DNA methylation modules and regions associated with incident cardiovascular disease and cumulative risk factor exposure using data from the Women's Health Initiative and Framingham Heart Study Offspring Cohort.

iEDGE integrates epi-DNA and gene expression data to identify cis and trans driver genes in cancer, showing enrichment for known oncogenes and tumor suppressors.

CaDrA identifies functionally relevant subsets of binary genomic features associated with specific outcomes, showing sensitivity and specificity in cancer datasets.

The paper identifies cis- and trans-acting eQTLs influencing HBG2 and HBG1 expression and suggests BCL2L1 as a potential trans-acting HbF activator.