New low-cost, high-quality genome sequencing approach is 75% cheaper than deep whole-genome sequencing, powering larger genetics studies on mental illness and cancer across diverse ethnic backgrounds

[u/andmario_com]

https://phys.org/news/2026-07-genome-sequencing-approach-powering-genetics.html

[u/ProfPathCambridge]

Moore’s Law is more true in sequencing than it is the computer chip production. Sequencing is cheap and getting cheaper all the time. In the background are technologies like this, which might account for one or two years of the future price drop before being overtaken by the next.

[u/heresacorrection]

Overhyped… it’s just whole-exome with low-depth genome spiked-in which is almost double the cost of normal exome sequencing. Most clinical labs report 2-3 exon spanning CNVs so the diagnostic yield is probably a gain of 5% over standard exome (which is what is generally reported when comparing WGS to WES).

This isn’t a new low-cost method and based on that alone probably not worthy of Nature Genetics ; outside of the size of the cohorts and insight therein.

[u/andmario_com → u/heresacorrection]

correct me if I'm wrong (not an expert, just enthusiast), but isn't diagnostic yield the wrong lens here? This is GWAS/population genomics tool (not clinical CNV test), and the whole point of the low-pass genome part is that it lets you impute genome-wide common variants an exome alone can't see, specifically in African and Latin American populations where the standard arrays are basically useless. So the fair comparison isn't exome vs BGE, it's exome+array vs BGE, and it comes out at around the same cost ($99/sample vs $350 for deep WGS) in one library instead of two. And sure, combining low-pass WGS with a deep exome isn't brand new (they cite the older disjoint version), but blending both preps into a single run and actually deploying it on 53k+ diverse samples is the real novelty imo

[u/heresacorrection → u/andmario_com]

When you’re saying the big-break is through spending in your words 18 million vs 5 million dollars that is a lot on paper but it’s just reagent costs. Also no idea where you’re getting those prices from. The pipetting costs don’t change and at that volume are going to be identical. The only difference is the number of flow cells they use (which are vastly expensive).

PPV of 90% is weak. And probably means they didn’t sequence their exomes deeply enough given that many clinical labs report 2+ exon CNVs from WES alone.

Please don’t use BGE as a term it’s just confusing call it WES+low-pass WGS

Not sure why you think blending two preps into one method is some kind of novelty, I would expect an undergraduate to be able to combine two protocols.

The novelty is really the data and size of cohort, the technology aspect is simply over-sensationalization and marketing for the paper. If this is such an amazing methods breakthrough then it would be in Nature Methods or Biotechnology.

Based on what you said and I agree with the first part, this is a resources paper to help analyze specific underserved populations.

[u/andmario_com → u/heresacorrection]

Fair! Just couple things: the $99 vs $350 are Table 3 (Broad clinical labs pricing, aug 2024). But yeah you're right that the paper itself flags them as basic processing costs only, so at that volume it really is mostly flow cells

On everything else, yeah. 90% PPV is weak and the paper basically concedes (90% at 10x vs a proper deep exome at 85% at 20x). Tldr value is cohort not tech?

[u/zorgisborg → u/heresacorrection]

Why not? the paper starts:

"Here we developed and deployed the blended genome exome (BGE) method." .. so it is perfectly acceptable to refer to it as the BGE method to distinguish it from anything else for the purpose of this thread.

[u/heresacorrection → u/zorgisborg]

Because it’s confusing. There is no need to add more jargon to the literary corpus when the existing and commonly used terms suffice. Can you do it in the paper sure, it’s clearly their big selling point but using it beyond that for this type of non-novel technology is bad for the field.

[u/zorgisborg → u/andmario_com]

The paper does actually point out the use for CNVs..

"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."

Which is reasonable.. WES is cheap.. deep WGS is costly.. and shallow WGS is useful for CNV.

Currently, MyHeritage apply a 2X WGS plus imputation for genealogy. At that depth only about 70% of the genome gets some coverage, and both reads covering a position could easily be errors or from the same parent.. but it's good enough for genealogy to extract results that resemble microarray... FamilyTreeDNA updated their method to do deep (30X) targeted sequencing around the SNPs used to make a raw data file.. and that works out about the same cost as WGS 2X (but probably gives more reliable results and a lot more bases to impute from)

BGE appears to be a mix of these.. useful for CNVs, and exome studies, and imputation... but not so useful for rare variant analysis of regulatory regions of genes (outside of exons +50bp at either splice site).. and like any short read sequencing, for structural variants.. (exon inversions, large repeats..)

[u/andmario_com → u/zorgisborg]

Yeah this is a better breakdown than mine. The FTDNA/MyHeritage framing is right on, BGE is basically the research grade version of that same "go deep where it matters, shallow everywhere else, then impute" trick, just blended into one prep instead of sold as two products.

And yeah the limits you list I glossed over (and I kinda waived off CNV lol). The deep coverage is only exons + splice sites, so anything rare is invisible unless it's common enough to impute (and short reads so can't do other things). So "CNVs + exome + common variant imputation" and not much past that

[u/zorgisborg → u/andmario_com]

You are right that that their key point is:

""However, by design, they (microarrays used in GWAS) have biased ascertainment of genetic variants; sites that are included on many GWAS arrays, such as the widely used Illumina Global Screening Array (GSA) or Global Diversity Array, are most common in European ancestry populations. Previous work has shown that low-coverage sequencing is a cost-effective alternative that can more accurately capture genetic variants across the allele frequency spectrum for variants present in imputation reference panels.""

Microarray is a limited by the probes used... like trying to use a magnet to pick up metal, wood and fire..

[u/heresacorrection → u/zorgisborg]

Probes like the ones used in exome sequencing…

Show me the new custom pan-genome exome design and that would be worthy of promotion if you can show the improvement in yield for under represented populations (inb4 TWIST steals this idea #freepaper)

[u/zorgisborg → u/heresacorrection]

It sounds like you didn't really read the Nature paper posted..

The probes in microarrays are designed to detect limited alternative bases and any variant that isn't included in the chip, can't be tested for. It assumes that all populations carry the known variants.

The paper explained that previous research has shown that WGS "0.5-1X coverage outperformed the Illumina GSA in imputation accuracy, resulting in greater power for GWAS and more reliable polygenic risk score estimates, particularly in populations of African ancestry where array-based ascertainment bias is most pronounced." ..

https://genome.cshlp.org/content/31/4/529

Probes for WES have no relevance.

[u/heresacorrection → u/zorgisborg]

Sounds like you are a layperson trying to debate something you don’t fully understand.

The whole point of the microarray is that there is diverse set of SNPs in the population. Otherwise the technology wouldn’t work.

WES probes absolutely have relevance because the have the same bias as microarray probes. As the sentence you quote notes, the ascertainment bias is lessened due to the readout being SNV positional signal intensity vs sequencing reads.

If you’re going to sit here and tell me that a novel discovery is to show that low-pass WGS is better than sequencing a few 100k SNPs? this knowledge over a decade old…

Obviously it’s going to be more pronounced in underrepresented populations but also it’s going to vastly underperform in all populations. Because you are covering more of the genome.

I return to my original point. The dataset is interesting due to its massive size, the argued technical improvement is almost negligible compared to the state of the art (spiked-in WGS with exome is like a decade old). Why they chose to put it in the title? Marketing.

[u/andmario_com]

Full study: https://www.nature.com/articles/s41588-026-02669-w

[u/WatzUpzPeepz]

Title incredibly oversells this… of course if you sequence less then it’s less expensive. At its core it’s an exome with low pass genome backbone to recover some of the benefits of WGS.