Lifetime risk of autosomal recessive mitochondrial disorders calculated from genetic databases.
EBioMedicine. 2020 Apr 16;54:102730. doi: 10.1016/j.ebiom.2020.102730. [Epub ahead of print]
|Authors/Editors:||Tan J, Wagner M, Stenton SL, Strom TM, Wortmann SB, Prokisch H, Meitinger T, Oexle K, Klopstock T.|
Mitochondrial disorders are a group of rare diseases, caused by nuclear or mitochondrial DNA mutations. Their marked clinical and genetic heterogeneity as well as referral and ascertainment biases render phenotype-based prevalence estimations difficult. Here we calculated the lifetime risk of all known autosomal recessive mitochondrial disorders on basis of genetic data.
We queried the publicly available Genome Aggregation Database (gnomAD) and our in-house exome database to assess the allele frequency of disease-causing variants in genes associated with autosomal recessive mitochondrial disorders. Based on this, we estimated the lifetime risk of 249 autosomal recessive mitochondrial disorders. Three of these disorders and phenylketonuria (PKU) served as a proof of concept since calculations could be aligned with known birth prevalence data from newborn screening reports.
The estimated lifetime risks are very close to newborn screening data (where available), supporting the validity of the approach. For example, calculated lifetime risk of PKU (16·0/100,000) correlates well with known birth prevalence data (18·7/100,000). The combined estimated lifetime risk of 249 investigated mitochondrial disorders is 31·8 (20·9–50·6)/100,000 in our in-house database, 48·4 (40·3–58·5)/100,000 in the European gnomAD dataset, and 31·1 (26·7–36·3)/100,000 in the global gnomAD dataset. The disorders with the highest lifetime risk (> 3 per 100,000) were, in all datasets, those caused by mutations in the SPG7, ACADM, POLG and SLC22A5 genes.
We provide a population-genetic estimation on the lifetime risk of an entire class of monogenic disorders. Our findings reveal the substantial cumulative prevalence of autosomal recessive mitochondrial disorders, far above previous estimates. These data will be very important for assigning diagnostic a priori probabilities, and for resource allocation in therapy development, public health management and biomedical research.