Clark, W. T. | Search bioRxiv

bioRxiv2020

Identifying therapeutic drug targets for rare and common forms of short stature

While GWAS of common diseases has delivered thousands of novel genetic findings, prioritizing genes for translation to therapeutics has been challenging. Here, we propose an approach to resolve that issue by identifying genes that have both gain of function (GoF) and loss of function (LoF) mutations associated with opposing effects on phenotype (Bidirectional Effect Selected Targets, BEST). Bidirectionality is a desirable feature of the best targets because it implies both a causal role on the phenotype in one direction and that modulating the target activity might be safe and therapeutically beneficial in the other. We used height, a highly heritable trait and a model of complex diseases, to test our approach. Using 34,231 individuals with exome sequence data and height, we identified five genes (IGF1R, NPPC, NPR2, FGFR3, and SHOX) with evidence for bidirectional effects on stature. Rare protein-altering variants significantly increased risk for idiopathic short stature (ISS) (OR=2.75, p= 3.99x10-8). These genes are key members of the only two pathways successfully targeted for short stature: Growth Hormone/Insulin-like growth factor 1 axis and C-type Natriuretic peptide (CNP) for Achondroplasia, a monogenic form of dwarfism. We assayed a subset of NPR2 mutations and identified those with elevated (GoF) and diminished (LoF) activity and found that a polygenic score for height modulates the penetrance of pathogenic variants. We also demonstrated that adding exogenous CNP (encoded by NPPC) rescues the NPR2 haploinsufficiency molecular phenotype in a CRISPR-engineered cell line, thus validating its potential therapeutic treatment for inherited forms of short stature. Finally, we found that these BEST targets increase the probability of success in clinical trials above and beyond targets with other genetic evidence. Our results show the value of looking for bidirectional effects to identify and validate drug targets.

genetics↗

bioRxiv2018

Utilizing ExAC to Assess the Hidden Contribution of Variants of Unknown Significance to Sanfilippo Type B Incidence

Given the large and expanding quantity of publicly available sequencing data, it should be possible to extract incidence information for monogenic diseases from allele frequencies, provided one knows which mutations are causal. We tested this idea on a rare, monogenic, lysosomal storage disorder, Sanfilippo Type B (Mucopolysaccharidosis type IIIB).\n\nSanfilippo Type B is caused by mutations in the gene encoding -N-acetylglucosaminidase (NAGLU). There were 189 NAGLU missense variants found in the ExAC dataset that comprises roughly 60,000 individual exomes. Only 24 of the 189 missense variants were known to be pathogenic; the remaining 165 variants were of unknown significance (VUS), and their potential contribution to disease is unknown.\n\nTo address this problem, we measured enzymatic activities of 164 NAGLU missense VUS in the ExAC dataset and developed a statistical framework for estimating disease incidence with associated confidence intervals. We found that 25% of VUS decreased the activity of NAGLU to levels consistent with Sanfilippo Type B pathogenic alleles. We found that a substantial fraction of Sanfilippo Type B incidence (67%) could be accounted for by novel mutations not previously identified in patients, illustrating the utility of combining functional activity data for VUS with population-wide allele frequency data in estimating disease incidence.

bioinformatics↗