Dyslexia, ADHD, Autism, and Hyperneuroplasticity: What a New Genetics Paper Adds

By Dr. Patty Gently on September 29, 2025

ADHD & Dyslexia

Bright Insight Support Network founder and president Dr. Patricia Gently supports gifted and twice-exceptional adults in their own autopsychotherapy through identity exploration, structured reflection, and alignment with inner values. A writer, educator, and 2e adult, Dr. Patty centers depth, integrity, and complexity in all aspects of her work.

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Dyslexia, ADHD, Autism, and Hyperneuroplasticity: What a New Genetics Paper Adds

I am dyslexic and have related auditory processing differences, I am gifted/galvanic, and I am probably also diagnosable with several other “disorders,” often identifying myself as a liminal type who exists between labels. This is probably part of why the hyperneuroplasticity work I am doing is so important to me.

It is personal.

So imagine when I saw a cheesy Facebook post about a supposed 2025 genetics study that modeled how the DNA signals for dyslexia relate to other conditions and found that dyslexia aligns most closely with ADHD on a shared, attention-and-learning genetic pattern. Whoa. Too good to be true? Not when it's believable and supports a framework I’ve worked to develop.

I looked it up (or rather, my indispensable right-hand Youssef Sleiman located it and sent it to me on an airplane), and it was real. Scholarly, peer-reviewed, and REAL!

And guess what else? Per this research, autism aligned with a broader neurodevelopmental pattern also identified largely in ADHD and dyslexia, and the authors also flagged 49 DNA sites that influence both dyslexia and ADHD (many not reported before). These results fit cleanly with a hyperneuroplasticity (HNP) view, acknowledging with scientific support, that some nervous systems show a high-gain, fast-adapting learning orientation in language-attention systems that is distinct from other neurodevelopmental profiles (Ciulkinyte et al., 2025).

SO let’s break this down (and why I am so excited about this study). 

What the 2025 Study Actually Did

Within this study, completed by researchers at the University of Edinburgh in collaboration with 23andMe and international partners, large genome-wide datasets were combined and analyzed with Genomic Structural Equation Modeling (GenomicSEM) to see which traits “travel together” genetically. They grouped conditions by shared genomic signals and then tested how well different models fit the data. Dyslexia and ADHD clustered on an attention-and-learning dimension, while autism aligned with a broader neurodevelopmental dimension; ADHD showed ties to both, and the dyslexia-autism genetic link, though not as strong, was present (Ciulkinyte et al., 2025; Grotzinger et al., 2019).

Beyond the ADHD-dyslexia pairing, the modeling also revealed other groupings of conditions that shared meaningful genomic connections, even if not as strongly as the attention-and-learning dimension. For example, some traits clustered together on a broader neurodevelopmental factor, while others aligned with cognitive or psychiatric dimensions already observed in earlier GenomicSEM work. Dyslexia and ADHD remained the most tightly connected pair, yet the existence of multiple overlapping clusters reinforces the view that these types are not isolated entities, but part of a wider genetic and developmental framework.

The team also scanned for pleiotropic DNA variants, single sites that relate to both dyslexia and ADHD, finding 49 shared loci, the majority newly highlighted by this joint analysis. Pleiotropic variants are genetic changes that influence more than one trait, and shared loci are DNA regions that jointly contribute to multiple conditions. This work builds on a 2022 study with 1.1 million participants that first mapped 42 dyslexia-associated regions, enabling more powerful multivariate modeling (Ciulkinyte et al., 2025; Doust et al., 2022).

Why This Matters for the Hyperneuroplasticity (HNP) Conceptualization

HNP is a whole-system orientation toward rapid, deep, and flexible neural reconfiguration. The genetics here suggest an attention-and-learning substrate (a foundational set of genetic influences that support attention and learning processes) that helps explain why many dyslexic and ADHD profiles show fast pattern-formation and strong imprinting in reading-related and attentional networks, often with uneven skill profiles across domains.

ADHD also showed overlap with autism on a broader neurodevelopmental dimension, underscoring that these conditions do not sit in rigid or exclusive categories, instead sharing adaptable, variable genetic influences. The authors emphasized that the genetic architecture influencing ADHD shows greater overlap with dyslexia than with autism. This finding may help explain why dyslexia and ADHD so often co-occur, while autism may follow a somewhat different developmental trajectory with significant dimensionality that also co-occurs with ADHD.

Taken together, these findings underscore not only the particular closeness of dyslexia and ADHD, but also the broader web of genetic relationships across neurodevelopmental conditions. The fact that multiple groupings emerged highlights adaptability and variation across the system as a whole. And this is precisely the kind of systemic, high-gain orientation that the hyperneuroplasticity (HNP) framework seeks to articulate. Dyslexia and ADHD may represent the most vivid example of shared biology, yet the presence of additional clusters reminds us that all ten conditions modeled in the study express overlapping qualities of neurodevelopmental plasticity.

Granted, it is worth considering the possibility of misdiagnosis contributing to these findings, and this is something that future research will need to examine more closely. I relate personally to this, having once thought I might be ADHD, only later realizing that many of those traits were more accurately explained by my dyslexia (though the jury is still out on this one, LOL).

These findings clarify dyslexia’s relationship to ADHD and autism, showing overlap and divergence without reducing dyslexia to attention differences. Within an HNP view, this points to a shared learning-system architecture that allows for dyslexia-specific biology while also showing how ADHD, autism, and dyslexia illustrate variations on overlapping developmental themes. The overlap between ADHD and autism reflects a shared neurodevelopmental structure, where common genetic influences shape traits of attention, regulation, and social communication, while ADHD and dyslexia share learning system dynamics. Taken together, these results highlight adaptability and variation across conditions even without invoking the HNP framework (Ciulkinyte et al., 2025), reinforcing the idea of overlapping qualities across many HNP types.

Mechanisms that Connect the Dots Into Plasticity Pathways

It is helpful for those who want more of the nitty-gritty-nerdy stuff to consider the domains of genetic evidence that bridge dyslexia and ADHD, and why they matter for hyperneuroplasticity. These mechanisms are present in three layers: key shared genes (such as SORCS3, AMT, TRAIP, and TCTA) that directly influence synaptic and developmental pathways; broader pleiotropic pathways that regulate plasticity windows and systemic adaptability; and somatic links like chronic pain and joint conditions that reveal how these same mechanisms extend beyond cognition into body regulation. Together, these findings show that what begins as a genetic overlap in language-attention networks scales outward into a whole-system orientation of responsiveness.

Key Shared Genes

Genes like SORCS3, AMT, TRAIP, and TCTA illustrate this. SORCS3 is a postsynaptic sorting receptor that modulates long-term synaptic depression (LTD) and learning, with Sorcs3-deficient mice showing altered LTD and learning changes (Breiderhoff et al., 2013). AMT is part of the glycine cleavage system, where glycine serves as an NMDA receptor co-agonist shaping developmental plasticity (Ichinohe et al., 2004). TRAIP participates in DNA damage response and protein ubiquitination, and TCTA, first described in bone absorption, has been associated with cognitive ability and educational outcomes. Together, these loci point toward synaptic reweighting, thresholds for plasticity, genome integrity, and regulatory processes that shape learning systems.

Broader Pleiotropic Pathways and Somatic Links

Other pleiotropic genes identified in the study are involved in protein modification, metabolism, and developmental regulation. These processes collectively act as tuning knobs for brain adaptability, influencing timing, stability, and responsiveness of plasticity windows, whether systems recalibrate rapidly or require sustained repetition. By clustering around pathways that regulate when neurons update and how signals are sorted, these shared variants highlight a deeper biological structuring of learning differences.

A related and striking finding was that the study also noted correlations between the dyslexia-ADHD genetic factor and chronic pain and joint disorders. This suggests that the same heightened plasticity systems influencing learning may also extend to body regulation and pain perception. From an HNP perspective, a system tuned for rapid adaptation in cognitive domains may also be more sensitive in somatic domains, making chronic pain another expression of whole‑system responsiveness. Importantly, this correlation suggests that hyperneuroplastic systems are not only tuned for learning but are also broadly sensitive across brain–body pathways. Just as rapid encoding and reconfiguration can shape reading and attention, heightened sensitivity may amplify how pain signals are processed and maintained. This means the lived reality of dyslexia or ADHD may include somatic challenges alongside cognitive ones, underscoring the need for supports that integrate physical and psychological care. Future research should examine whether the same pleiotropic variants tied to learning also shape vulnerability to persistent pain states, reinforcing the view of HNP as a whole‑system orientation that crosses traditional diagnostic boundaries.

What This Study Does Not Say (Limitations)

It is important to be clear about what this study does not claim, since its results are already being widely cited. First, it does not identify a single “dyslexia gene.” Instead, the architecture is polygenic and varied, meaning many genetic variants each contribute small effects that add up to influence risk (Doust et al., 2022). Second, it does not frame autism and dyslexia as opposites. The current datasets simply showed less shared genomic signal between them than between dyslexia and ADHD. This difference reflects sampling, measurement, and the present limits of genetic power, not a sharp separation between conditions. Broader ancestry coverage and developmental sampling could refine these relationships (Ciulkinyte et al., 2025).

Another limitation is the nature of the data itself. Most source datasets involved adults of European ancestry and included some self-reported diagnoses. These features create boundaries around how far the findings can be generalized. Ascertainment bias, vertical pleiotropy (where one trait indirectly influences another along a causal pathway), and sample overlap can all shape observed correlations. Replication across ancestries and life stages was therefore identified as essential (Ciulkinyte et al., 2025). In short, these results highlight promising directions, yet they are not the final word. Larger, more diverse, and developmentally sensitive datasets are needed to confirm how dyslexia, ADHD, and autism connect across populations and across time.

And on a personal, neurodiversity-affirming note, I would like to draw attention to how some of the substrates are named. The learning-and-attention substrate, especially, is used to suggest the presence of deficit for ADHD and dyslexic individuals. I find it meaningful to suggest here that the type of learning and dynamic attentiveness experienced by these and other persons is less deficit and more difference. It’s the environment where such labeling takes place that often expects linearity and a more neuronormative process (or product!).

Practical Takeaways for Readers, Parents, and Practitioners

For those living with dyslexia, ADHD, autism, or a combination of types, for those in a diagnostically liminal space like me, and for the parents, educators, and practitioners who support them (and benefit from them), these findings carry practical implications. They show that genetic overlap is not a matter of effort or choice. We know this in some ways, however, this scientific reinforcement of shared biology and this recognition can shape how we design supports across settings.

Dyslexia may often overlap with ADHD for genetic reasons. Co-occurrence is common, and it can now be more fully understood as a shared learning-system biology rather than personal effort or teaching quality (Ciulkinyte et al., 2025; Doust et al., 2022). The genetic clustering work shows that ADHD and dyslexia share a learning-oriented substrate that helps explain why difficulties in both reading and attention appear together so frequently. This reinforces the importance of seeing dyslexia as part of a broader neurodevelopmental context.

And intervention timing and context continue to matter. Systems that update quickly can learn efficiently with precise, structured, multi-channel input, and can also encode stress or mismatch just as quickly. HNP-sensitive supports aim to amplify the former and limit the latter (Grotzinger et al., 2019). In practice, this means that supportive environments should be designed to provide rich, patterned input without overwhelming the system. Small adjustments, such as using multimodal teaching approaches, integrating rest, and carefully paced challenges can make a disproportionate difference for students whose systems are highly plastic and sensitive.

It is also important to expect uneven profiles. An attention-and-learning orientation can yield exceptional strengths alongside reading or processing-specific difficulty. Screening for both dyslexia and ADHD reduces missed support (Ciulkinyte et al., 2025). These uneven or “spikey" profiles often include strong problem-solving, creativity, or verbal reasoning coupled with sometimes slower reading accuracy or fluency. Recognizing this unevenness as a natural outcome of shared neurodevelopmental structuring helps avoid deficit framing and instead directs support toward balancing challenge with opportunity.

Finally, think developmentally and across the lifespan. Because these genetic influences shape learning systems from early childhood through adulthood, interventions should not be confined to school years alone. Adults with dyslexia and ADHD often continue to benefit from adaptive strategies, workplace accommodations, and environments that recognize both strengths and challenges.

Taken together, these points highlight that dyslexia and ADHD (and autism) are best understood as expressions of shared, dynamic learning systems rather than isolated diagnoses. This perspective calls for more integrated support across education, work, and community life. In this light, the attention‑and‑learning signal can be seen as a genetic scaffold for learning‑oriented plasticity. Pleiotropic variants tune synaptic and developmental pathways so that language‑attention systems update at high gain, consistent with the hyperneuroplastic orientation. Viewed this way, the study not only begins clarifying dyslexia’s relationship to ADHD and autism. It also connects these findings to a broader HNP framework, showing that what may appear as isolated difficulties are better understood as expressions of whole‑system adaptability with implications for learning and identity development that extend beyond childhood.

References

Breiderhoff, T., Christiansen, G. B., Pallesen, L. T., Vaegter, C., Nykjaer, A., Holm, M. M., Glerup, S., Willnow, T. E., & Hermey, G. (2013). Sortilin-Related Receptor SORCS3 is a postsynaptic modulator of synaptic depression and fear extinction. PLOS ONE, 8(9), e75006. https://doi.org/10.1371/journal.pone.0075006

Ciulkinyte, A., Mountford, H. S., Fontanillas, P., 23andMe Research Team, Bates, T. C., Martin, N. G., Fisher, S. E., & Luciano, M. (2025). Genetic neurodevelopmental clustering and dyslexia. Molecular Psychiatry, 30, 140–150. https://doi.org/10.1038/s41380-024-02649-8

Doust, C., Fontanillas, P., Eising, E., Gordon, S. D., Wang, Z., Alagöz, G., … Cox, S. R. (2022). Discovery of 42 genome-wide significant loci associated with dyslexia. Nature Genetics, 54, 1621–1629. https://doi.org/10.1038/s41588-022-01192-y

Grotzinger, A. D., Rhemtulla, M., de Vlaming, R., Ritchie, S. J., Mallard, T. T., Hill, W. D., … Tucker-Drob, E. M. (2019). Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nature Human Behaviour, 3(5), 513–525. https://doi.org/10.1038/s41562-019-0566-x

Ichinohe, A., Kure, S., Mikawa, S., Ueki, T., Kojima, K., Fujiwara, K., … Takahashi, T. (2004). Glycine cleavage system in neurogenic regions. European Journal of Neuroscience, 19(9), 2365–2370. https://doi.org/10.1111/j.0953-816X.2004.03345.x