The Newborn Screening Programme Revisited: An Expert Opinion on the Challenges of Rett Syndrome

Abstract

Genomic sequencing has the potential to revolutionise newborn screening (NBS) programmes. In 2024, Genomics England began to recruit for the Generation Study (GS), which uses whole genome sequencing (WGS) to detect genetic changes in 500 genes in more than 200 rare conditions. Ultimately, its purpose is to facilitate the earlier identification of rare conditions and thereby improve health-related outcomes for individuals. The adoption of rare conditions into the GS was guided by four criteria: (1) the gene causing the condition can be reliably detected; (2) if undiagnosed, the rare condition would have a serious impact; (3) early or presymptomatic testing would substantially improve outcomes; and (4) interventions for conditions screened are accessible to all. Rett syndrome (RTT, OMIM 312750), a paediatric neurodevelopment disorder, was not included in the list of rare conditions in the GS. In this opinion article, we revisit the GS and discuss RTT from the perspective of these four criteria. We begin with an introduction to the GS and then summarise key points about the four principles, presenting challenges and opportunities for individuals with RTT. We provide insight into how data could be collected during the presymptomatic phase, which could facilitate early diagnosis and improve our understanding of the prodromal stage of RTT. Although many features of RTT present a departure from criteria adopted by the GS, advances in RTT research, combined with advocacy from parent-based organisations, could facilitate its entry into future newborn screening programmes.

Keywords: Generation Study; Rett syndrome; digital phenotyping; newborn screening; presymptomatic testing.

Figure 1

Digital phenotyping of individuals with Rett syndrome. Abbreviations: RTT, Rett syndrome. Notes: Digital phenotyping in individuals with Rett syndrome (RTT) could facilitate diagnosis before the child shows signs of illness (in the presymptomatic stage). Interrogating a digital repository of images/videos from birth for hand movements, saccades, facial gestures, and speech combined with the predicted power of artificial intelligence could lead to quasi-causal inferences from data. This would improve our understanding of RTT neurobiology, improve diagnostic accuracy, and allow for early intervention that could modify or delay symptoms from emerging.