Bringing attention to quirk of genetics
Our researchers are calling for discussion among transplant scientists on an important quirk that could affect how some genetic data is interpreted. It could potentially lead to improved accuracy when matching patients and donors for transplants.
The team from Anthony Nolan, led by Dr Thomas Turner, published a commentary in the prestigious HLA journal calling attention to the genetic quirk, called ‘repeat regions,’ which can be misinterpreted or overlooked by current genetic technologies.
Transplant genetics explained
When patients and donors are matched for a stem cell transplant, they are tested for their tissue type, which is determined by a collection of genes known as the HLA genes. These genes are extremely varied in humans, with potentially millions of different versions scattered among our population.
Anthony Nolan’s team of bioinformaticians maintain and manage a crucial global database that contains the genetic sequences of all the discovered versions of the HLA genes: the IPD-IMGT/HLA Database. Read more about this database and how it is used in transplants around the world.
As genetic technologies have improved, our researchers have been able to analyse these HLA gene variants more and more accurately, which can help transplant providers find closer matches between patients and donors.
However, the more you zoom in to a gene, the more complex it can become!
The problem with repetition
Most genes have what are called ‘repeat regions,’ where the DNA that forms the ‘code’ of the gene creates repeating patterns. It’s sort of like reading a book and seeing a page of the same word repeated again and again. It’s easy to forget how many times you saw the same word!
These repeat regions can be crucial in how a gene works, but they can also cause scientists a headache. Because of the way genetic typing works, the miniscule machinery that analyses genes can get confused by repeat regions, and accidentally miss a repeat or count one twice. This can mean that the genetic sequence we think we have is actually just a little bit off.
If an HLA gene with a repeat region is sequenced twice, the genetic sequencing technology may get two slightly different results. It can mean that we count the same gene twice in our database, or that we don't know which sequence is the correct one.
This could mean that scientists could potentially improve the accuracy of their genetic data by being more aware of these so-called ‘repeat region ambiguities’ (RRAs), which could also mean more accurate matches for some patients in very specific cases. But we’re still at the earliest stages of this conversation, says lead author of the commentary Dr Thomas Turner:
The first step is to make scientists working on this kind of data aware of the existence of RRAs, and then begin a discussion on how we can best address them. This might involve an improvement in our sequencing technology, or changes in how we analyse the data. What we do know is that a better accounting for RRAs in our data could potentially produce more matches for patients waiting for a stem cell transplant.
Starting the conversation
Dr Turner estimates that around 1-2% of discovered HLA gene variants could have differences in those repeat regions, where it can be harder to tell what the exact genetic sequence is.
If scientists can start to discuss this issue, and ultimately decide on a course of action to look at RRAs in more detail, it could make a considerable difference in the way we find matches for transplant patients.
Anthony Nolan’s researchers will be attending several scientific conferences in 2025, where they’ll be hoping to spark conversation and bring this issue to the forefront of the transplant science community.
Read the full commentary here.
Learn more about the work of our Immunogenetics and Bioinformatics teams here.
Full reference
Turner TR, Natarajan RHL, Robinson J, Marsh SGE, Mayor NP. Commentary: Identification of Repeat Region Ambiguities in HLA Typing and the Implications for Immunogenetics Research. HLA. 2024 Dec;104(6):e15768. doi: 10.1111/tan.15768. PMID: 39616521; PMCID: PMC11608571.