Alicia R. Martin

The paper identifies a founder variant in ADAP1 associated with increased risk of schizophrenia and bipolar disorder in a Colombian genetic isolate.

This study analyzes breast cancer GWAS across multiple ancestries, finding similar heritability and genetic correlations, and identifies shared regulatory and cellular contexts.

The paper uses admixture mapping in the All of Us Research Program to identify genetic ancestry associations with 22 traits, finding 71 novel loci where African ancestry explains phenotypic variation.

FELIX improves genetic association analysis by accounting for local ancestry in admixed populations, increasing significant loci and improving polygenic score prediction.

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 performs large-scale GWAS and RVAS on 392,030 whole genomes from All of Us, identifying 49,863 associations and 193 novel pLoF gene-phenotype links through meta-analysis with UK Biobank.

This paper constructs phenome-derived polygenic scores and evaluates how they, along with social determinants of health, influence disease risk across different populations and diseases.

This paper improves genetic discovery and polygenic risk prediction for respiratory diseases by analyzing multiple traits and ancestries, identifying novel loci and developing a new polygenic risk score method.

This study conducts the first comprehensive genetic analysis of alcohol use disorder in a Nepalese population, identifying a novel genome-wide significant locus and highlighting the need for replication and improved polygenic prediction.

This study conducts GWAS in a large Korean cohort, identifying 301 novel loci and highlighting East Asian-specific genetic associations for 36 traits.

This study compares polygenic scores from genotyping arrays and whole-genome sequencing across traits and populations, finding that WGS generally improves prediction accuracy but with context-dependent advantages.

The paper introduces BGE, a sequencing method that combines low-pass whole genome and deep whole exome data, showing high accuracy and cost-effectiveness for genetic variant discovery.

The paper improves polygenic prediction accuracy by using diverse datasets, showing greater benefits for underrepresented populations.

The paper harmonizes diverse human genomes from multiple datasets to create a new reference panel with improved variant coverage and population structure analysis.

The paper introduces a cost-efficient sequencing method called Blended Genome Exome (BGE) that combines low-coverage whole genome and high-coverage exome data, achieving >99% concordance with 30x WGS for variant imputation and rare coding variant calling.

Cross-trait Elastic Net mixing of PGS Catalog scores (PRSmix+) lifted mean PRS accuracy 1.72-fold in Europeans and 1.42-fold in South Asians over the best single score.

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

Meta-analyzing per-ancestry GWAS of 7,266 UK Biobank traits across six genetic ancestry groups adds 14,676 loci missed by European-only analysis, including ancestry-enriched CAMK2D and G6PD signals.

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 analyzes genetic, ancestral, and geospatial data from a biobank to explore their relationships with health outcomes in a historically diverse population.

This study shows that polygenic risk scores from multi-ancestry GWAS perform better in understudied populations than single-ancestry scores, except when the understudied group is small.

HAPNEST generates and evaluates large-scale synthetic genotype and phenotype datasets for polygenic risk score analysis.

The paper presents phenotype and genetic analysis of 206 children with neurodevelopmental disorders from Kenya and South Africa, identifying pathogenic variants in 17% of South African and 38% of Kenyan cases.

The paper demonstrates that TOPMed imputation improves the identification of rare variants associated with type 2 diabetes in Latino populations, leading to novel associations and better polygenic scores.

This study identifies genetic overlaps between psychiatric disorders, language ability, and creativity through a GWAS of school grades.

This study examines genetic structure in African populations using ethnolinguistic data, finding that language classifications better capture genetic variation than geography alone.

This paper introduces PRS-CSx, a method that improves polygenic prediction across ancestrally diverse populations by integrating GWAS summary statistics and sharing genetic effects.

This paper introduces PolyPred and PolyPred+ to improve cross-population polygenic risk score accuracy by addressing LD, MAF, and causal effect size differences using fine-mapping and non-European training data.

This study evaluates the robustness of polygenic adaptation signals detected using GWAS summary statistics across different populations and cohorts.

This paper introduces MAMA, a method for multi-ancestry GWAS meta-analysis, which identifies novel genetic associations and improves power across populations.