Elizabeth G. Atkinson

The paper analyzes genetic variation, ancestry, and DNA methylation in Hispanic/Latino participants using long-read sequencing, identifying novel variants and phenotype associations.

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.

The paper introduces Tractor-Mix, a mixed model method for GWAS in admixed cohorts with high relatedness, improving ancestry-specific association detection and controlling false positives.

Tractor-Burden improves gene discovery in admixed populations by incorporating local ancestry in rare variant burden testing, identifying additional genes and pathways for type 2 diabetes.

The paper introduces a Nextflow workflow for local ancestry-aware GWAS, demonstrating its utility in analyzing admixed individuals and identifying ancestry-specific genetic associations.

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.

MosaicSim simulates mosaic variants in sequencing data to evaluate detection methods, showing that higher coverage improves detection rates without false positives.

The paper analyzes over 800,000 human genomes to assess penetrance of clinically relevant variants, finding that 95% of pLoF variants in severe diseases have explanations for lack of disease manifestation.

This paper improves allele frequency estimates in gnomAD by applying local ancestry inference to admixed populations, revealing significant frequency differences that could affect clinical interpretation.

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.

This study evaluates LAI performance in admixed populations, finding reduced accuracy for Amerindigenous tracts and highlighting the importance of reference panel matching.

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

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.

Rare-variant depletion in 1kb windows of 76,156 gnomAD genomes, versus feature-adjusted mutation expectations, marks regulatory elements and outperforms seven alternative scores at flagging functional non-coding variants.

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.

The paper identifies discordant genotype calls across sequencing methods in gnomAD, provides a blacklist of problematic variants, and introduces a classifier to detect such variants in other datasets.

The paper infers demographic models of African population history using genomic data, finding a weakly structured stem model explains polymorphism better than archaic introgression.

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

A genome-wide association study of 69,369 schizophrenia cases and 236,642 controls identifies 270 risk loci, prioritizes 130 genes, and links common and rare risk variants to neuronal synaptic biology.

This study applies an improved GWAS strategy to age-related diseases, identifying new genetic loci by using multiple reference panels, analyzing the X chromosome, and testing non-additive models.

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

This study shows low-coverage sequencing is more accurate and cost-effective than GWAS arrays for detecting genetic variation in diverse African populations.

Tractor improves GWAS power and resolution by including admixed individuals through local ancestry-aware analysis.

This study evaluates the generalizability of polygenic scores in African populations, finding that PRS accuracy varies significantly across African ancestries and is improved by using ancestrally diverse genetic studies.

This paper conducts a GWAS using longitudinal reading performance data to identify GARRE1 as a novel gene associated with reading disability, showing persistent effects on reading development.

This study conducts the largest GWAS for PTSD, identifying 6 genome-wide significant loci in European and African ancestries and highlighting novel biological pathways.

The paper shows that skin pigmentation in African populations, particularly the KhoeSan, is highly heritable and polygenic, with many new genetic loci identified beyond previously known ones.