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. 2022 Nov 9;10(11):2865.
doi: 10.3390/biomedicines10112865.

Estimating the Prevalence of De Novo Monogenic Neurodevelopmental Disorders from Large Cohort Studies

Madelyn A Gillentine  1 , Tianyun Wang  2   3 , Evan E Eichler  4   5
Affiliations

Estimating the Prevalence of De Novo Monogenic Neurodevelopmental Disorders from Large Cohort Studies

Madelyn A Gillentine et al. Biomedicines. .

Abstract

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.

Keywords: de novo; monogenic; neurodevelopmental disorders; prevalence; rare disease.

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Conflict of interest statement

E.E.E. is a scientific advisory board (SAB) member of Variant Bio, Inc. (Seattle, WA, USA).

Figures

Figure 1
Figure 1
Prevalence of DNVs by gene extrapolated from percent of cases in total NDD cohort. Genes are indicated along the x-axis, with prevalence of each variant type on the y-axis. (A) NDD DNV cases, (B) NDD dnLGD cases, (C) NDD dnMIS cases, and (D) NDD dnMIS30 cases. The proportion of each gene and mutation type in our cohort was multiplied by the estimated prevalence of NDDs (DD/ID, ASD, and epilepsy) from Zablotsky and Black, 2020.
Figure 2
Figure 2
Prevalence of DNVs by gene extrapolated from number of cases in DD/ID cohort. Genes are indicated along the x-axis, with prevalence of each variant type on the y-axis. (A) DD/ID DNV cases, (B) DD/ID dnLGD cases, (C) DD/ID dnMIS cases, and (D) DD/ID dnMIS30 cases. The proportion of each gene and mutation type in our cohort was multiplied by the estimated prevalence of DD (DD/ID) from Zablotsky and Black, 2020.
Figure 3
Figure 3
Prevalence of DNVs by gene extrapolated from number of cases in ASD cohort. Genes are indicated along the x-axis, with prevalence of each variant type on the y-axis. (A) ASD DNV cases, (B) ASD dnLGD cases, (C) ASD dnMIS cases, and (D) ASD dnMIS30 cases. The proportion of each gene and mutation type in our cohort was multiplied by the estimated prevalence of ASD from Zablotsky and Black, 2020.
Figure 4
Figure 4
Prevalence of DNVs by gene versus incidence estimates from [14]. (A) NDD DNV cases (p < 0.0001 with Bonferroni correction), (B) NDD dnLGD cases (p < 0.0001), and (C) NDD dnMIS cases (p < 0.0001). All variant types had a positive correlation with previous incidence estimates, shown with Pearson’s correlation coefficients (PCC). Notably, some genes without clinical relevance, such as TTN, are also shown. Corrected p values and confidence intervals are shown in Table S6.
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