A novel sequencing strategy that combines low-pass whole-genome sequencing with deep whole-exome sequencing in a single assay could significantly lower the cost of large-scale genomic studies without sacrificing analytical performance, according to a study published in Nature Genetics. The approach, known as blended genome–exome (BGE) sequencing, may help accelerate precision medicine initiatives by making comprehensive genomic profiling more accessible across diverse populations.
Researchers from Massachusetts General Hospital and the Broad Institute of MIT and Harvard developed BGE to overcome a persistent tradeoff in human genomics. High-coverage whole-genome sequencing offers the most comprehensive view of genetic variation but remains prohibitively expensive for many population studies. Genotyping arrays and exome sequencing are more affordable but either miss large portions of the genome or introduce bias by relying on variants selected primarily from European ancestry populations.
The BGE workflow integrates low-pass whole-genome sequencing at 1–4x coverage with deep exome sequencing at 30–40x coverage within a single library preparation and sequencing run on Illumina’s NovaSeqS4. The result is a unified dataset capable of supporting genome-wide association studies, rare variant discovery, copy number variant (CNV) detection, and polygenic analyses at approximately 28% of the cost of conventional 30x whole-genome sequencing.
The investigators validated the approach in more than 53,000 participants enrolled in the Populations Underrepresented in Mental Illness Associations Studies (PUMAS) Project, which includes African, African American, Hispanic/Latino, and Colombian cohorts. The scale and diversity of the study allowed the researchers to assess performance in populations that have historically been underrepresented in genomic research.
Imputed genotypes generated from BGE showed excellent agreement with Illumina Global Screening Array data, achieving concordance exceeding 95% for variants with minor allele frequencies above 1%. Importantly, performance remained consistent across multiple ancestry groups and local ancestry backgrounds, addressing one of the major limitations of conventional array-based genotyping.
The platform also demonstrated strong performance for clinically relevant structural variation. Using established computational pipelines, investigators achieved approximately 90% positive predictive value for protein-coding CNVs spanning three or more exons compared with deep whole-genome sequencing. In benchmarking studies, the method successfully detected all validated de novo coding CNVs in a reference autism cohort while maintaining low false-positive rates.
Beyond analytical performance, the study highlights potential operational advantages. By combining genome and exome sequencing into a single workflow, BGE simplifies laboratory processing, reduces the need for multiple assays, and minimizes sample attrition between sequencing platforms. These efficiencies could prove valuable for national biobanks, health system sequencing programs, and pharmaceutical research efforts that increasingly require genomic datasets from hundreds of thousands of participants.
The technology may also advance equity in precision medicine. Because low-pass genome sequencing does not depend on predefined variant content, it avoids many of the ascertainment biases associated with traditional genotyping arrays. The authors found that BGE captured substantially more coding and noncoding variants than array-based approaches while maintaining high-quality rare variant detection through deep exome coverage.
The researchers acknowledge that imputation performance remains influenced by the diversity of available reference panels, particularly for Indigenous American ancestry. However, as more globally representative reference datasets become available, they expect the accuracy of low-pass genome imputation to improve further.
As precision medicine increasingly depends on large, ancestrally diverse genomic datasets, technologies that balance cost, scalability, and comprehensive variant detection will be essential. BGE sequencing offers a practical alternative to deep whole-genome sequencing, enabling broader participation in genomic discovery while preserving much of the analytical power needed to identify clinically meaningful genetic variation.
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