North Carolina macular dystrophy: phenotypic variability and computational analysis of disease-implicated non-coding variants

Abstract

Purpose: North Carolina macular dystrophy (NCMD) is an autosomal dominant, congenital disorder affecting the central retina. Here, we report clinical and genetic findings in three families segregating NCMD and use epigenomic datasets from human tissues to gain insights into the effect of NCMD-implicated variants. Methods: Clinical assessment and genetic testing were performed. Publicly-available transcriptomic and epigenomic datasets were analyzed and the 'Activity-by-Contact' (ABC) method for scoring enhancer elements and linking them to target genes was used. Results: A previously-described, heterozygous, non-coding variant upstream of the PRDM13 gene was detected in all six affected study participants (chr6:100,040,987G>C [GRCh37/hg19]). Inter- and intra-familial variability were observed; the visual acuity ranged from 0.0 to 1.6 LogMAR and fundoscopic findings ranged from visually insignificant, confluent, drusen-like macular deposits to coloboma-like macular lesions. Variable degrees of peripheral retinal spots (which were easily detected on widefield retinal imaging) were observed in all study subjects. Notably, a 6-year-old patient developed choroidal neovascularization and required treatment with intravitreal bevacizumab injections. Computational analysis of the five single nucleotide variants that have been implicated in NCMD revealed that these non-coding changes lie within two putative enhancer elements; these elements are predicted to interact with PRDM13 in the developing human retina. PRDM13 was found to be expressed in the fetal retina, with highest expression in the amacrine precursor cell population. Conclusions: We provide further evidence supporting the role of PRDM13 dysregulation in the pathogenesis of NCMD and highlight the utility of widefield retinal imaging in individuals suspected to have this condition.

Strategies to Uplift Novel Mendelian Gene Discovery for Improved Clinical Outcomes

“…However, this necessitates improved methods for interpreting the spectrum of functional variation across all genes and particularly in the interpretation of non-coding variation, an area of investigation still in its infancy but beginning to make headway. Indeed, disruption of non-coding topologically associated domains have been associated with limb malformations (Lupiáñez et al, 2015;Spielmann et al, 2018), and non-coding variants upstream of PRDM13 and CCNC have been linked to North Carolina macular dystrophy (Small et al, 2016;Green et al, 2021). While efforts like the Atlas of Variant Effect Alliance are working toward achieving the mammoth goal of interpreting the impact of all genomic variation, there is still a long way to go (Matreyek et al, 2018;Jepsen et al, 2020).…”

EyeG2P: an automated variant filtering approach improves efficiency of diagnostic genomic testing for inherited ophthalmic disorders

“…Our ability to detect pathogenic genomic variants from high-throughput sequencing data sets has expanded in recent years to include a wide range of mechanisms, including large structural genomic variants,29 30 deletions and duplications within single genes (‘exonic deletions),31 32 variants deep within introns that may cause aberrant mRNA splicing,33–36 variants in regulatory regions37–39 and complex alleles that comprised combinations of genomic variants 40 41. This requires a high level of specialist knowledge.…”

EyeG2P: an automated variant filtering approach improves efficiency of diagnostic genomic testing for inherited ophthalmic disorders

“…For example, the recent elucidation of DYNC2H1 as a cause of inherited ophthalmic conditions was not captured in our initial curation process but can be subsequently included in EyeG2P analysis through addition of a single data line to the released EyeG2P datafile. 25 Our ability to detect disease-causing genomic variants from high-throughput sequencing datasets has expanded in recent years to include complex structural variants, 26,27 exonic deletions and duplications, 28,29 deeply intronic variants causing aberrant splicing, [30][31][32][33] variants in regulatory regions [34][35][36] and complex alleles comprised of combinations of genomic variants common in the general population. 37,38 Here we found that, in addition to characterizing novel exonic variants, EyeG2P is capable of prioritizing these diverse types of disease-causing variation, achieving 99.5% sensitivity in comparison to routine analytical approaches (Figure 1).…”