Oculocutaneous albinism (OCA) and ocular albinism (OA) are inherited disorders of melanin biosynthesis, resulting in loss of pigment and severe visual deficits. OCA encompasses a range of subtypes with overlapping, often hypomorphic phenotypes. OCA1 is the most common cause of albinism in European populations and is inherited through autosomal recessive mutations in the Tyrosinase (TYR) gene. However, there is a high level of reported missing heritability, where only a single heterozygous mutation is found in TYR. This is also the case for other OCA subtypes including OCA2 caused by mutations in the OCA2 gene. Here we have interrogated the genetic cause of albinism in a well phenotyped, hypomorphic albinism population by sequencing a broad gene panel and performing segregation studies on phenotyped family members. Of eighteen probands we can confidently diagnose three with OA and OCA2, and one with a PAX6 mutation. Of six probands with only a single heterozygous mutation in TYR, all were found to have the two common variants S192Y and R402Q. Our results suggest that a combination of R402Q and S192Y with a deleterious mutation in a ‘tri-allelic genotype’ can account for missing heritability in some hypomorphic OCA1 albinism phenotypes.

In a subset of pediatric cancers, a germline cancer predisposition is highly suspected based on clinical and pathological findings, but genetic evidence is lacking, which hampers genetic counseling and predictive testing in the families involved. We describe a family with two siblings born from healthy parents who were both neonatally diagnosed with atypical teratoid rhabdoid tumor (ATRT). This rare and aggressive pediatric tumor is associated with biallelic inactivation of SMARCB1, and in 30% of the cases, a predisposing germline mutation is involved. Whereas the tumors of both siblings showed loss of expression of SMARCB1 and acquired homozygosity of the locus, whole exome and whole genome sequencing failed to identify germline or somatic SMARCB1 pathogenic mutations. We therefore hypothesized that the insertion of a pathogenic repeat‐rich structure might hamper its detection, and we performed optical genome mapping (OGM) as an alternative strategy to identify structural variation in this locus. Using this approach, an insertion of ~2.8 kb within intron 2 of SMARCB1 was detected. Long‐range PCR covering this region remained unsuccessful, but PacBio HiFi genome sequencing identified this insertion to be a SINE‐VNTR‐Alu, subfamily E (SVA‐E) retrotransposon element, which was present in a mosaic state in the mother. This SVA‐E insertion disrupts correct splicing of the gene, resulting in loss of a functional allele. This case demonstrates the power of OGM and long‐read sequencing to identify genomic variations in high‐risk cancer‐predisposing genes that are refractory to detection with standard techniques, thereby completing the clinical and molecular diagnosis of such complex cases and greatly improving counseling and surveillance of the families involved. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

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Background Next-generation sequencing is revolutionising diagnosis and treatment of rare diseases, however its application to understanding common disease aetiology is limited. Rare disease applications binarily attribute genetic change(s) at a single locus to a specific phenotype. In common diseases, where multiple genetic variants within and across genes contribute to disease, binary modelling cannot capture the burden of pathogenicity harboured by an individual across a given gene/pathway. We present GenePy, a novel gene-level scoring system for integration and analysis of next-generation sequencing data on a per-individual basis that transforms NGS data interpretation from variant-level to gene-level. This simple and flexible scoring system is intuitive and amenable to integration for machine learning, network and topological approaches, facilitating the investigation of complex phenotypes. Results Whole-exome sequencing data from 508 individuals were used to generate GenePy scores. For each variant a score is calculated incorporating: i) population allele frequency estimates; ii) individual zygosity, determined through standard variant calling pipelines and; iii) any user defined deleteriousness metric to inform on functional impact. GenePy then combines scores generated for all variants observed into a single gene score for each individual. We generated a matrix of ~ 14,000 GenePy scores for all individuals for each of sixteen popular deleteriousness metrics. All per-gene scores are corrected for gene length. The majority of genes generate GenePy scores < 0.01 although individuals harbouring multiple rare highly deleterious mutations can accumulate extremely high GenePy scores. In the absence of a comparator metric, we examine GenePy performance in discriminating genes known to be associated with three common, complex diseases. A Mann-Whitney U test conducted on GenePy scores for this positive control gene in cases versus controls demonstrates markedly more significant results ( p = 1.37 × 10 − 4 ) compared to the most commonly applied association tool that combines common and rare variation ( p = 0.003). Conclusions Per-gene per-individual GenePy scores are intuitive when assessing genetic variation in individual patients or comparing scores between groups. GenePy outperforms the currently accepted best practice tools for combining common and rare variation. GenePy scores are suitable for downstream data integration with transcriptomic and proteomic data that also report at the gene level. Electronic supplementary material The online version of this article (10.1186/s12859-019-2877-3) contains supplementary material, which is available to authorized users.