A blended genome and exome sequencing method captures genetic variation in an unbiased and cost-effective manner

Authors

• Toni Boltz, Broad Institute of MIT and Harvard, USA
• Benjamin Chu, Stanford University, USA
• Matthew DeFelice, Broad Institute of MIT and Harvard, USA
• Calwing Liao, Broad Institute of MIT and Harvard, USA
• Julia Sealock, Broad Institute of MIT and Harvard, USA
• Robert Ye, Broad Institute of MIT and Harvard, USA
• Jacqueline Goldstein, Broad Institute of MIT and Harvard, USA
• Lerato Majara, Broad Institute of MIT and Harvard, USA
• Jack Fu, Broad Institute of MIT and Harvard, USA
• Lukoye Atwoli, Aga Khan UniversityFollow

Document Type

Article

Department

Internal Medicine (East Africa); Brain and Mind Institute

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.

Publication (Name of Journal)

Nature genetics

DOI

https://doi.org/10.1038/s41588-026-02669-w

Recommended Citation

Boltz, T., Chu, B., DeFelice, M., Liao, C., Sealock, J., Ye, R., Goldstein, J., Majara, L., Fu, J., Atwoli, L. (2026). A blended genome and exome sequencing method captures genetic variation in an unbiased and cost-effective manner. Nature genetics, 1-18.
Available at: https://ecommons.aku.edu/eastafrica_fhs_mc_intern_med/619

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.