Low Sperm Count and Hidden DNA Changes: What Standard Genetic Tests Can Miss

Rare DNA structural variants absent from the comparison group were found in 25% of 88 men with infertility.

Optical genome mapping offers a new investigative route, but a rare finding is not automatically a diagnosis.

Low sperm counts or an absence of sperm production can remain unexplained even after chromosome and genetic testing. A study published in Scientific Reports in October 2026 suggests that a closer look at DNA structure may reveal changes those tests missed.

Among 88 men whose previous genetic investigations had not identified a clear explanation for infertility, the new analysis detected rare DNA structural variants in 25% that were not found in the healthy comparison group.

The researchers considered some of these changes potentially relevant to impaired sperm production. Detecting alterations that conventional methods struggle to identify could broaden the investigation of unexplained male infertility.

Why can sperm production fail when standard tests are normal?

Male infertility can involve low sperm numbers, poor movement or abnormal shape. Azoospermia, in which no sperm are found in semen, is another important presentation.

Alongside semen and hormone testing, clinicians may order chromosome or genetic investigations when appropriate. Examples include karyotyping, which detects chromosome-number abnormalities and large structural changes, and Y-chromosome microdeletion testing, which checks for missing regions important to sperm production.

A substantial number of patients still have no identified genetic explanation after these tests. Conventional methods cannot detect every complex structural change in DNA.

The study addressed that gap: a normal result on an existing test does not necessarily mean that DNA structure is entirely unaffected.

Comparing 88 men with infertility and 132 controls

A team including clinicians from the University of Ljubljana in Slovenia analyzed DNA from 220 men: 88 with infertility and 132 comparison participants.

The infertility group had no explanatory abnormality on previous chromosome testing, Y-chromosome microdeletion testing and related investigations.

The researchers applied optical genome mapping, or OGM.

This technique labels long DNA molecules with fluorescent markers and analyzes their arrangement to identify deletions, duplications, inversions and other structural changes.

If conventional genetic testing can be compared with checking individual letters or particular pages in a book, optical genome mapping is more like looking for missing or duplicated pages and pages placed in the wrong order.

Even when individual DNA letters are unchanged, loss or rearrangement of an entire segment can affect gene function.

Rare structural variants in one in four participants with infertility

The total number of DNA structural variants was not substantially different between men with infertility and healthy controls.

A different pattern emerged when the researchers focused on genes and DNA regions relevant to sperm production and male reproductive function.

Rare variants absent from the comparison participants were identified in 25% of the infertility group. There were 22 rare variants in total; five were assessed as likely candidates for explaining male infertility.

Two of these more strongly implicated variants would probably have been difficult to detect using conventional methods.

The findings included deletions, duplications, inversions, insertions and repeat expansions.

Examples included a deletion involving SPAG1, a gene associated with sperm function, and an insertion or expansion involving DMPK.

Finding a rare variant does not establish that it causes infertility. The researchers noted that most detected variants could not be classified as definite direct causes under current genetic interpretation criteria.

Looking beyond sperm counts to the structure of DNA

The study matters because it may extend the way causes of male infertility are investigated.

Semen analysis is essential for evaluating sperm count, motility and morphology, but it cannot explain every reason for low production or every genetic influence on that process.

For men with repeatedly confirmed, severe and unexplained impairment of sperm production, previously undetected structural variants could provide additional diagnostic clues.

An identified genetic explanation may eventually support more appropriate additional testing and genetic counseling, and more precise information when planning IVF or ICSI.

However, this study does not show that undergoing optical genome mapping improves pregnancy or birth rates. Its purpose was to investigate whether the technique could identify genetic changes that earlier testing had missed.

Does every man with infertility need this test?

These findings do not mean that every patient should immediately undergo a new genetic test.

Optical genome mapping cannot identify every genetic abnormality. Some chromosome regions and types of structural change remain difficult to assess, and further work is needed to determine whether individual findings actually impair sperm production.

The infertility group was relatively small, at 88 men. The 25% figure should not be generalized to all men with infertility or interpreted as a definitive diagnosis in one out of four patients.

Nevertheless, the study demonstrates that men with normal conventional results may still carry genetic changes relevant to sperm production.

Investigation is expanding beyond sperm numbers and movement to the complex DNA structures involved in producing sperm.

The researchers emphasized the need for larger validation studies and functional investigation of the variants before the technology can be widely adopted as a clinical test.

Study source

This article is based on a 2026 Scientific Reports study using optical genome mapping to investigate structural genomic variation in male infertility. It analyzed DNA from 88 men with infertility and 132 comparison participants, 220 in total. DOI: 10.1038/s41598-026-70834-6.

The illustration was generated using ChatGPT (OpenAI). Any people depicted are fictional.