Gillentine Madelyn A, Wang Tianyun, Eichler Evan E
Department of Laboratories, Seattle Children's Hospital, Seattle, WA 98105, USA.
Department of Medical Genetics, Center for Medical Genetics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China.
Biomedicines. 2022 Nov 9;10(11):2865. doi: 10.3390/biomedicines10112865.
Rare diseases impact up to 400 million individuals globally. Of the thousands of known rare diseases, many are rare neurodevelopmental disorders (RNDDs) impacting children. RNDDs have proven to be difficult to assess epidemiologically for several reasons. The rarity of them makes it difficult to observe them in the population, there is clinical overlap among many disorders, making it difficult to assess the prevalence without genetic testing, and data have yet to be available to have accurate counts of cases. Here, we utilized large sequencing cohorts of individuals with rare, de novo monogenic disorders to estimate the prevalence of variation in over 11,000 genes among cohorts with developmental delay, autism spectrum disorder, and/or epilepsy. We found that the prevalence of many RNDDs is positively correlated to the previously estimated incidence. We identified the most often mutated genes among neurodevelopmental disorders broadly, as well as developmental delay and autism spectrum disorder independently. Finally, we assessed if social media group member numbers may be a valuable way to estimate prevalence. These data are critical for individuals and families impacted by these RNDDs, clinicians and geneticists in their understanding of how common diseases are, and for researchers to potentially prioritize research into particular genes or gene sets.
罕见病影响着全球多达4亿人。在数千种已知的罕见病中，许多是影响儿童的罕见神经发育障碍（RNDDs）。由于多种原因，RNDDs在流行病学评估方面已被证明具有难度。其罕见性使得在人群中观察它们变得困难，许多病症之间存在临床重叠，这使得在没有基因检测的情况下难以评估患病率，而且目前尚无数据可进行准确的病例计数。在此，我们利用患有罕见的、新发单基因疾病的个体的大型测序队列，来估计发育迟缓、自闭症谱系障碍和/或癫痫队列中超过11000个基因的变异患病率。我们发现，许多RNDDs的患病率与先前估计的发病率呈正相关。我们广泛地确定了神经发育障碍中最常发生突变的基因，以及发育迟缓与自闭症谱系障碍各自最常发生突变的基因。最后，我们评估了社交媒体群组的成员数量是否可能是估计患病率的一种有价值的方法。这些数据对于受这些RNDDs影响的个人和家庭、临床医生和遗传学家了解疾病的常见程度至关重要，对于研究人员潜在地优先开展对特定基因或基因集的研究也至关重要。
