Whole Exome Sequencing widens the genetic landscape of Hereditary Optic Neuropathies

Hereditary optic neuropathies (HON) are a genetic cause of visual impairment characterized by degeneration of retinal ganglion cells. The majority of HON are caused by pathogenic variants in mtDNA genes and in gene OPA1. However, several other genes can cause optic atrophy and can only be identified by high throughput genetic analysis. Whole Exome Sequencing (WES) is becoming the primary choice in rare disease molecular diagnosis, being both cost effective and informative. We performed WES on a cohort of 106 cases, of which 74 isolated ON patients (ON) and 32 syndromic ON patients (sON). The total diagnostic yield amounts to 27%, slightly higher for syndromic ON (31%) than for isolated ON (26%). The majority of genes found are related to mitochondrial function and already reported for harbouring HON pathogenic variants: ACO2, AFG3L2, C19orf12, DNAJC30, FDXR, MECR, MTFMT, NDUFAF2, NDUFB11, NDUFV2, OPA1, PDSS1, SDHA, SSBP1, and WFS1. Among these OPA1, ACO2, and WFS1 were confirmed as the most relevant genetic causes of ON. Moreover, several genes were identified, especially in sON patients, with direct impairment of non-mitochondrial molecular pathways: from autophagy and ubiquitin system (LYST, SNF8, WDR45, UCHL1), to neural cells development and function (KIF1A, GFAP, EPHB2, CACNA1A, CACNA1F), but also vitamin metabolism (SLC52A2, BTD), cilia structure (USH2A), and nuclear pore shuttling (NUTF2). Functional validation on yeast model was performed for pathogenic variants detected in MECR, MTFMT, SDHA, and UCHL1 genes. For SDHA and UCHL1 also muscle biopsy and fibroblast cell lines from patients were analysed, pointing to possible pathogenic mechanisms that will be investigated in further studies. In conclusion, WES proved to be an efficient tool when applied to our ON cohort, for both common disease-genes identification and novel genes discovery. It is therefore recommended to consider WES in ON molecular diagnostic pipeline, as for other rare genetic diseases.

A machine learning based method to detect genomic imbalances exploiting X chromosome exome reads

Whole Exome Sequencing (WES) is rapidly becoming the first-tier test in clinics, both thanks to its declining costs and the development of new platforms that help clinicians in the analysis and interpretation of SNV and InDels. However, we still know very little on how CNV detection could increase WES diagnostic yield. A plethora of exome CNV callers have been published over the years, all showing good performances towards specific CNV classes and sizes, suggesting that the combination of multiple tools is needed to obtain an overall good detection performance. Here we present TrainX, a ML-based method for calling heterozygous CNVs in WES data using EXCAVATOR2 Normalized Read Counts. We select males and females’ non pseudo-autosomal chromosome X alignments to construct our dataset and train our model, make predictions on autosomes target regions and use HMM to call CNVs. We compared TrainX against a set of CNV tools differing for the detection method (GATK4 gCNV, ExomeDepth, DECoN, CNVkit and EXCAVATOR2) and found that our algorithm outperformed them in terms of stability, as we identified both deletions and duplications with good scores (0.87 and 0.82 F1-scores respectively) and for sizes reaching the minimum resolution of 2 target regions. We also evaluated the method robustness using a set of WES and SNP array data (n=251), part of the Italian cohort of Epi25 collaborative, and were able to retrieve all clinical CNVs previously identified by the SNP array. TrainX showed good accuracy in detecting heterozygous CNVs of different sizes, making it a promising tool to use in a diagnostic setting.