Christopher R. Gignoux

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.

This study identifies 51 genetic loci associated with thyroid cancer through a multi-ancestry GWAS and explores pleiotropic relationships to uncover mechanistic clusters and genetic subtypes.

The paper establishes a global consortium to discover rare genetic associations by integrating data from multiple biobanks, identifying 514 gene-trait associations, many detectable only through cross-ancestry meta-analysis.

CCAFE estimates case and control allele frequencies from GWAS summary statistics using odds ratios and sample sizes, improving reusability of genetic data.

This paper shows that using the mean or median of multiple observations improves GWAS power and polygenic prediction accuracy by reducing phenotypic noise.

SPC improves population structure adjustment in genomic analysis by capturing fine-scale patterns better than principal components, reducing p-value inflation and improving rare variant associations.

This study identifies novel genetic signals associated with severe obesity through GWAS and shows strong genetic overlap with BMI, highlighting shared comorbidities and lifestyle interactions.

The paper analyzes genome-wide data from Indigenous Patagonians to uncover genetic adaptations to cold, maritime diet, and pathogens, revealing prehistoric selection signals and admixture patterns.

This study models the genomic architecture of adiposity and anthropometrics across the lifespan, identifying shared and distinct genetic factors and their associations with health outcomes.

This paper develops a pipeline for translating type 1 diabetes polygenic risk scores into clinical use, showing they can distinguish type 1 from type 2 diabetes and identify high-risk individuals.

This paper introduces a rapid algorithm for pedigree inference in endogamous populations, accurately distinguishing relationships and handling reticulations.

STABIX improves GWAS data compression and query efficiency by enabling summary-statistic-based queries and achieving smaller file sizes and faster decompression speeds compared to tabix.

The paper develops gene expression prediction models for asthma-related tissues in African-ancestry populations and identifies 17 candidate causal asthma genes using transcriptome-wide association study.

Summix2 uses mixture modeling to estimate and adjust for substructure in genetic summary data, improving the robustness and equity of research using diverse datasets.

This study conducts an epigenome-wide association study (EWAS) meta-analysis of BMI in African American adults, identifying 208 significant CpGs and 54 DMRs associated with BMI, including novel and replicated findings.

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

GenoSiS dynamically finds patient-matched cohorts across populations using genotype similarity search to improve clinical analyses.

This paper improves GWAS performance in underrepresented groups by reassigning ambiguous ethnicity codes, using ancestry-matched imputation panels, and incorporating environmental covariates.

The paper benchmarks haplotype phasing methods on large datasets and introduces HAPTIC, an inter-chromosomal phasing method achieving low error rates for parent-of-origin classification.

MUSSEL improves polygenic risk prediction across populations by integrating GWAS summary statistics and using Bayesian hierarchical modeling with ensemble learning.

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

This study identifies ancestry-specific and sex-specific genetic loci for primary open-angle glaucoma through a meta-analysis of 15 biobanks, highlighting shared biology with vascular mechanisms and cell proliferation.

The paper describes the CCPM Biobank, a dual-purpose genomic resource linking health records and genomic data to support research and clinical applications.

The paper develops a multi-omic prediction framework to diagnose IBD using metagenomic, metatranscriptomic, metabolomic, and viromic data, finding that metabolomics and viromics provide stronger predictive power than other omics.

This paper introduces HAMSTA, a method to estimate heritability from admixture mapping summary statistics while adjusting for ancestral stratification bias, and applies it to African American datasets.

The paper identifies a novel risk locus for PAD on 2q35 in Dominicans using admixture mapping and health systems data.

This study shows that MASHR improves transcriptome prediction across populations, leading to more replicable discoveries in multi-ethnic transcriptome-wide association studies.

This study improves polygenic risk scores for colorectal cancer by combining Asian and European ancestry data, enhancing risk prediction across diverse racial and ethnic groups.