Back

Estimated genetics prevalence of early-onset Parkinson's disease caused by PRKN gene mutations

DIOGO, D.; Wong, E. H.; DeBoever, C.; Qu, W.; Lee, J.; Crawford, S.; Hioki, T.; Padmanabhan, J.; Prilutsky, D.; Proetzel, G.

2024-01-23 epidemiology
10.1101/2024.01.22.24301610 medRxiv
Show abstract

BackgroundEstimating the prevalence of rare diseases is challenging due to very limited natural history studies, lack of studies in diverse populations, and frequent under or misdiagnosis. We leveraged human genetics to estimate the genetic prevalence (eGP) of familial Parkinsons disease (PD) caused by biallelic pathogenic variants in the Parkin (PRKN) gene (PRKN-PD). MethodsWe curated the reported PRKN-PD pathogenic variants and obtained the heterozygous carrier frequencies of these variants from gnomAD and the Japanese Multi-omics reference panel (jMorp). We used the carrier frequencies to estimate the eGP of PRKN-PD in eight genetic ancestries. ResultsNon-Japanese East Asians presented the highest eGP of PRKN-PD (24 per 100,000 individuals, 95% CI=4-165 per 100,000 individuals), followed by Non-Finnish Europeans (22 in 100,000 individuals, 95% CI = 11-64 per 100,000 individuals). Based on the proportions of races and ethnicities, we estimated the eGP in the USA and the world-wide eGP to be 18 per 100,000 individuals (95% CI=7-68 per 100,000 individuals). and 13 per 100,000 individuals (95% CI=3-70 per 100,000 individuals), respectively. These estimates were significantly reduced when excluding structural variants (world-wide eGP=2 per 100,000 individuals, 95% CI=1-5 per 100,000 individuals). ConclusionsThis is the first study estimating the PRKN-PD genetic prevalence. Our results suggest that the prevalence of the disease may be higher than previously reported, highlighting potential underdiagnosis. We also demonstrate the importance of carefully considering the known genetic epidemiology of each disease, and its limitations, when using the approach applied in this study to estimate the disease genetic prevalence.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.