Nora Franceschini

The paper identifies 16 genetic loci interacting with sleep duration to influence glycemic traits in over 480,000 individuals, revealing distinct biological mechanisms for short and long sleep.

TorchGWAS2 improves genome-wide association testing in related samples by using a deterministic variance-correction algorithm, achieving faster and more powerful results across large-scale omics data.

This paper performs a local ancestry-aware GWAS on SCD cohorts to identify novel genetic loci associated with eGFR, uncovering regions linked to inflammation, ECM integrity, and other pathways relevant to SCD nephropathy.

This study integrates polygenic risk scores and proteomic risk scores to improve prediction of complex traits across diverse populations.

The paper introduces the expected polygenic risk score (ePRS) framework to address population stratification in polygenic risk score analysis by modeling ancestral makeup, showing it provides equitable risk quantification across diverse populations.

This paper improves polygenic prediction of height in an admixed Latino cohort by incorporating genetic ancestry, achieving a 0.1 increase in R² compared to using polygenic scores alone.

The paper improves risk prediction for cardiometabolic traits in American Indians by integrating polygenic scores with lifestyle and clinical context variables, enhancing model discrimination for coronary heart disease.

The paper investigates unexplained admixture mapping signals in the Hispanic Community Health Study/Study of Latinos by examining rare variants and expanded common variant regions, finding that common variants explain most signals.

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

LDscore provides a cloud-based, Python 3 web platform for LDSC analysis, improving accessibility and scalability for heritability and genetic correlation studies.

The paper analyzes circulating metabolites using genome-wide association data from the TOPMed program, identifying 1,778 loci and highlighting sex-specific genetic contributions.

This study compares old and new local ancestry inference methods in Hispanic/Latino populations using HCHS/SOL data, finding high similarity but some regional discrepancies.

The paper performs GWAS on cardiovascular outcomes in CKD patients, identifying stroke-associated SNVs but not replicating CHD findings, and finding genetic correlations with published GWAS.

The paper introduces MultiSTAAR, a statistical framework for multi-trait rare variant analysis in WGS studies, identifying new lipid trait associations in diverse populations.

This study identifies genetic signals associated with age at menarche, highlights genes involved in pubertal development, and links reproductive timing to later life disease risk.

The paper estimates mCA fitness in the NHLBI TOPMed cohort and identifies genetic variants associated with clonal expansion rates.

Admix-kit provides tools for genetic analysis of admixed populations, enabling simulation, association testing, and polygenic scoring.

The paper identifies novel genetic loci associated with systolic blood pressure that interact with the DASH diet score through genome-wide interaction analysis.

FORGEdb integrates genomic annotations to prioritize functional variants and target genes for complex diseases.

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.

This paper constructs a multi-ethnic polygenic risk score for hypertension and shows it is associated with hypertension prevalence, progression, and related clinical outcomes.

The paper identifies ancestry-derived loci associated with CKD traits and risk factors in U.S. Hispanic/Latino individuals of Central American origin using admixture mapping.

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 identifies novel genetic associations for biomarker traits in non-European UK Biobank populations, highlighting missed signals in European-centric analyses.

This study uses admixture mapping to identify ancestry-specific loci associated with eGFR and CKD in U.S. Hispanics/Latinos and validates findings in African Americans.

The paper identifies novel genetic loci associated with blood pressure that interact with sleep duration across multiple ancestries.

The paper describes a system for harmonizing phenotype data across multiple studies in the TOPMed program, enabling reproducible and shared phenotype data for over 80 studies.

This study conducts the first GWAS of eGFR in Africans, identifies two genome-wide significant loci, and highlights the need for more African-specific genetic research on kidney function.

Over 400 million variants, 97% below 1% frequency, emerge from deep whole-genome sequencing of 53,831 diverse TOPMed participants; TOPMed haplotypes enable imputation down to ~0.01% frequency.

The paper identifies 49 novel blood lipid loci associated with gene-sleep interactions in 126,926 individuals across 5 ancestry groups, with gene-short-sleep interactions explaining 4.25% of triglyceride variance.