Genetic diseases are conditions caused by abnormalities in an individual’s DNA. They can be inherited from one or both parents, or can occur as a result of spontaneous genetic mutations. They may be caused by a mutation in a single gene (monogenic); by a chromosomal change where there are more or fewer copies than usual; or complex disorders caused by simultaneous effects of many genes (polygenic).
While some genetic conditions are well known, e.g. cystic fibrosis, Huntington’s disease, and Down syndrome, there are 1000s more rare genetic conditions that are much less researched and potentially under diagnosed, as we do not know how common they are. Further, the rarity of many conditions means that many healthcare professionals may never encounter the condition, or not recognise the condition, so it goes undiagnosed or misdiagnosed for years. This can lead to underestimation of the prevalence as well as delays in diagnosis and treatment.
Another challenge is that since rare genetic diseases affect only a small number of people, it is difficult to collect enough data to accurately estimate how many people are carriers. Traditionally, the number of people with the disease is estimated from the recorded data of patients already diagnosed and extrapolating this number across the population to get a rough estimate of population prevalence. This may result in a large underestimate of the disease since carriers of the disease not yet symptomatic or diagnosed will be registered.
However, with the expansion of freely available, large genetic datasets such as gnomAD and TOPMed, prevalence can be calculated using a more granular ‘bottom up’ approach as illustrated in these papers by researchers investigating prion disease, polycystic kidney and liver disease and iron-refractory iron deficiency anaemia. The methods outlined in these papers are, in principle, applicable to any genetic condition.
HealthLumen is assessing these genetic databases to determine how the data can be applied to produce accurate estimates of the prevalence of rare genetic mutations causing disease. These estimates can then be used in our predictive modelling analysis to quantify the future burden of specific rare conditions and determine the impact of proposed interventions.
Why it matters
Accurately calculating the prevalence of rare genetic diseases is a crucial input for estimating the future health and economic burden of these conditions. This then informs R&D investment decisions for the development of new drugs, setting correct treatment price points, and to identify carriers of the mutation before they become symptomatic to potentially diagnose and treat earlier.
Estimating the burden accurately is also imperative for public health policy-making, and healthcare planning, as well as for patient advocacy organisations, providing the hard evidential research needed to raise awareness and support the case for why new research should be carried out, and new treatments developed.
In conclusion, while challenges remain, advances in technology and data collection are providing new opportunities to improve our understanding of these conditions in order to make more informed investment decisions, with the ultimate goal of improving outcomes for patients.