Understanding Blended Genome and Exome (BGE)

Genomic testing in clinical practice often involves a compromise between breadth and depth. Whole genome sequencing (WGS) offers the most comprehensive view of genetic variation but remains expensive and data-intensive for routine use. Whole exome sequencing (WES), on the other hand, provides deep coverage of coding regions where many disease-causing variants lie, but it misses important information outside exons, including copy number changes, mitochondrial variants, and broader genomic context. Blended Genome Exome (BGE) sequencing has emerged as a pragmatic middle ground, designed to balance clinical utility, cost, and scalability.

BGE combines two sequencing strategies into a single experiment. It integrates low-pass whole genome sequencing, typically at around 5× coverage, with deep exome sequencing, usually achieving 100× coverage over coding regions. Importantly, both components are generated from a single sequencing library, producing one unified dataset rather than two separate tests. This blended approach allows clinicians and researchers to interrogate both rare, high-impact coding variants and genome-wide variation within the same assay.

Blended Genome Exome (BGE) integrates low-pass whole genome sequencing with deep exome sequencing in a single assay, generating one unified dataset.

How BGE Works in Practice

The strength of BGE lies in its dual coverage profile. The deep exome component provides reliable detection of single nucleotide variants and small insertions or deletions in protein-coding genes, which remain the most common causes of monogenic disorders. For clinicians familiar with WES, the diagnostic yield and interpretability of coding variants in BGE are largely comparable.

The low-pass genome component, although shallow, spans the entire genome. While it is not sufficient for confident detection of all single nucleotide variants on its own, it provides valuable quantitative information across chromosomes. When combined with statistical imputation using large reference panels, this genome-wide data allows inference of common variants with accuracy approaching that of genotyping arrays or even higher, while avoiding the need for a separate array-based test.

Crucially for clinical use, the genome-wide signal also enables detection of certain classes of structural variation that are either missed or inconsistently captured by standard exome sequencing.

Clinical Strengths: Beyond Single-Gene Diagnosis

One of the most clinically relevant advantages of BGE is its improved ability to identify copy number variants (CNVs). Exome sequencing alone infers CNVs indirectly based on read depth across captured exons, which can be noisy and unreliable, particularly for smaller deletions or duplications and for regions with uneven capture. The addition of low-pass genome coverage provides a more uniform baseline across the genome, improving confidence in detecting both exon-level and multi-exonic CNVs.

The low-pass genome layer adds uniform genome-wide coverage, improving confidence in detecting copy number variants compared with exome-only approaches.

This has direct implications for clinical diagnostics. Many neurodevelopmental disorders, congenital anomalies, and multisystem syndromes are caused by deletions or duplications rather than single nucleotide changes. BGE improves the likelihood of identifying these events in a single test, reducing the need for reflex testing such as chromosomal microarray.

BGE enhances identification of both exon-level and multi-exonic deletions and duplications, reducing reliance on separate chromosomal microarray testing.

Another important clinical application is the detection of mitochondrial DNA variants. Standard exome sequencing often provides inconsistent and incomplete coverage of the mitochondrial genome, making reliable interpretation difficult. Because mitochondrial DNA is present in high copy numbers, even low-pass genome sequencing can achieve sufficient depth to detect mitochondrial variants and heteroplasmy. This makes BGE particularly attractive in the evaluation of suspected mitochondrial disorders, where both nuclear and mitochondrial variants may contribute to disease.

Mitochondrial DNA variants can be detected more consistently due to genome-wide sequencing, supporting evaluation of suspected mitochondrial disorders.

The genome-wide component of BGE also provides broader genomic context for variant interpretation. While most pathogenic variants lie within coding regions, regulatory elements and structural features outside exons can influence gene expression and disease severity. Although BGE is not designed to comprehensively interrogate non-coding regions at single-base resolution, the added context can assist in interpreting variants of uncertain significance, particularly when combined with family studies and phenotypic data.

Use in Large-Scale and Translational Clinical Studies

BGE was originally developed for large population and biobank studies, where cost constraints make deep WGS impractical. Its ability to support genome-wide association analyses through imputation, while simultaneously capturing rare coding variants, makes it well suited for studies exploring genotype–phenotype correlations across large cohorts.

From a clinical perspective, this scalability is increasingly relevant. Health systems and academic centers are moving toward integrated genomic programs that serve both research and clinical care. BGE offers a single platform that can support discovery, validation, and clinical interpretation without requiring multiple parallel assays.

In reproductive and prenatal contexts, BGE has potential value as well. The ability to detect pathogenic coding variants, copy number changes, and mitochondrial variants in one assay could support carrier screening and, in the future, selected prenatal or preconception applications. However, widespread clinical adoption in these settings will require further validation and careful consideration of reporting standards.

Understanding the Limitations of BGE

Despite its advantages, BGE is not equivalent to deep whole genome sequencing, and it is important for clinicians to understand what it does not do well.

Certain variant types are not accurately or consistently identified using BGE. Pharmacogenomic variants, particularly those involving complex haplotypes, gene–gene interactions, or structural variation in highly polymorphic loci, are not reliably captured. This is due to both the shallow genome coverage and limitations of short-read sequencing in resolving these regions.

Repeat expansions, such as those responsible for conditions like Huntington disease or certain ataxias, are also poorly detected. These variants require specialized methods or long-read sequencing to accurately determine repeat length, which is beyond the capability of BGE.

Copy-neutral structural variants, including inversions and balanced translocations, are generally not detectable with confidence using BGE. Similarly, complex structural variants involving multiple breakpoints or rearrangements are difficult to resolve with short-read, low-coverage genome data. For patients in whom such variants are suspected based on phenotype or family history, alternative technologies such as long-read sequencing or cytogenetic approaches remain necessary.

Deep whole genome sequencing or long-read technologies remain necessary when comprehensive structural or non-coding variant analysis is required.

It is also important to note that while imputation performs well for common variants at a population level, it is less reliable for rare non-coding variants at the individual patient level. Therefore, BGE should not be viewed as a substitute for deep WGS when comprehensive non-coding variant analysis is clinically required.

Positioning BGE in Clinical Practice

Blended Genome Exome sequencing represents an evolution rather than a replacement of existing genomic tests. It is best viewed as a flexible, cost-effective option that expands what can be achieved with a single assay. For many clinical scenarios—particularly rare disease evaluation, suspected CNV-related disorders, and conditions where mitochondrial involvement is possible—BGE can offer meaningful advantages over standard exome sequencing.

At the same time, careful test selection remains essential. Understanding the strengths and blind spots of BGE allows clinicians to choose it appropriately, set realistic expectations, and avoid missed diagnoses due to technical limitations.

BGE is best positioned as a complementary clinical tool, expanding the diagnostic scope of exome sequencing rather than replacing whole genome sequencing

As genomic medicine continues to mature, approaches like BGE highlight a broader shift toward smarter, more integrated testing strategies. Rather than asking whether one technology is superior to another, the more relevant question is how to deploy each tool in the right clinical context. In that regard, BGE offers a compelling balance of depth, breadth, and affordability, with clear and growing relevance to everyday clinical genomics.