In 2003, scientists with the Human Genome Project reached a milestone that transformed biology by creating the first reference human genome—essentially a book of instructions for building and operating the human body. It wasn’t the genome of a single person, but a carefully assembled reference that gave researchers a common starting point for studying DNA. For the first time, scientists had a reliable guide they could use to compare newly sequenced genomes and search for the genetic differences that make each of us unique.

Some of these differences involve a change in just a single DNA letter. Called single nucleotide polymorphisms (SNPs), these changes help explain everything from eye color and blood type to why some people have a higher risk of certain diseases. Because SNPs are used to study disease, evolution, and genetic diversity, accurately identifying them is one of the most important steps in modern genomics.

However, finding SNPs isn’t as simple as comparing two DNA sequences letter by letter. Modern sequencing technologies generate millions of short DNA fragments that must be pieced back together and compared to the reference genome using sophisticated computer software. Different programs can analyze the same sequencing data in different ways, sometimes disagreeing about whether an SNP is actually present. In a new study published in GENETICS, researchers from the Jackson Laboratory set out to determine which of these commonly used tools most accurately identifies SNPs in laboratory mice, helping scientists make more confident genetic discoveries.

Comparing different variant-calling programs presents an unusual challenge because there is no easy way to know which one is right. If researchers analyze DNA from a real mouse, they don’t know the exact location of every true SNP ahead of time. Without that answer key, it’s impossible to fairly compare one program against another.

To overcome this problem, the researchers created simulated mouse genomes. They began with the genomes of 10 well-studied inbred laboratory mouse strains and inserted known SNPs throughout them. Because they knew exactly where every genetic difference had been placed, they could objectively measure how accurately each software program identified those variants while avoiding false positives. 

The team wanted to know whether software designed for genetically diverse populations would perform just as well in these much more uniform genomes. Many of today’s most popular variant-calling programs were originally developed and tested using human genomes, where individuals inherit different DNA from each parent. Laboratory mouse strains are different. After generations of inbreeding, individuals within a strain are nearly genetically identical. 

The results showed that no single variant-calling program consistently outperformed the others. 

Each tool had pros and cons. Some programs identified truer SNPs but also reported more false positives, while others were more conservative and missed genuine variants. Surprisingly, the most reliable results came from combining multiple programs rather than relying on a single one. By looking for SNPs identified by more than one tool, the researchers were able to improve confidence that those variants were real. They also found that variant calling became increasingly difficult as mouse strains became more genetically different from the reference genome, highlighting the importance of continuing to improve reference genomes and the computational tools used to analyze them.

While this study focused on laboratory mice, its implications extend far beyond a single model organism. Every genomic discovery begins with accurately identifying genetic variation. Before researchers can uncover genes linked to disease, reconstruct evolutionary history, or understand the genetic basis of complex traits, they first need to know that the variants they’re studying are real. As sequencing technologies and reference genomes continue to improve, studies like this help ensure that the computational tools used to interpret genomic data keep pace. By providing practical guidance for benchmarking and combining variant-calling approaches, this work strengthens the foundation on which future discoveries in genetics are built.

References

  • Garretson, A., Blanco-Berdugo, L., M G Roberts, A., et al. “Benchmarking genomic variant calling tools in inbred mouse strains: recommendations and considerations, GENETICS, (2026): 223-3. https://doi.org/10.1093/genetics/iyag131.

Jenny Montooth is a science communicator and co-founder of The Science Underground. Jenny is passionate about using creative and innovative strategies to make complex topics engaging and accessible to wider audiences. You can follow Jenny on LinkedIn.

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