Language

English

Publication Date

8-1-2026

Journal

Nature Genetics

DOI

10.1038/s41588-026-02669-w

PMID

42420521

PMCID

PMC13447102

PubMedCentral® Posted Date

7-8-2026

PubMedCentral® Full Text Version

Post-print

Abstract

Here we developed and deployed the blended genome exome (BGE) method, a DNA library approach that generates low-pass whole-genome (1–4× mean depth) and deep whole-exome (30–40× mean depth) data in a single sequencing run. BGE is cost-effective, empowers most genomic discoveries possible with deep whole-genome sequencing and captures global common single-nucleotide polymorphism diversity. We applied BGE to sequence >53,000 samples from the PUMAS Project (Populations Underrepresented in Mental Illness Associations Studies), including African, African American and Latin American populations. Imputed genotypes showed high concordance with Illumina Global Screening Array calls (R2 ≥ 95% for minor allele frequency ≥1%; ≥90% for minor allele frequency < 1%), with consistent performance across local ancestries in admixed cohorts. For protein-coding copy number variants, deletions and duplications spanning at least three exons had a positive predicted value of ~90% relative to deep whole-genome data. At ~28% of the cost of deep whole-genome sequencing, BGE provides a scalable, reliable platform to expand genomic discovery and equitable access to sequencing in underrepresented populations.

Keywords

Humans, Polymorphism, Single Nucleotide, Exome Sequencing, DNA Copy Number Variations, Genetic Variation, Cost-Benefit Analysis, Exome, Genome, Human, Whole Genome Sequencing, High-Throughput Nucleotide Sequencing, Gene Frequency, Sequence Analysis, DNA, Genotype, Genomics, Gene Library, DNA sequencing, Population genetics

Published Open-Access

yes

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