Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes¹. Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.

Multiple genes underlying focal segmental glomerulosclerosis (FSGS) and/or steroid-resistant nephrotic syndrome (SRNS) have been identified, with the recent inclusion of collagen IV mutations responsible for Alport disease (AD) or thin basement membrane nephropathy (TBMN). We aimed to investigate the distribution of gene mutations in adult patients with primary FSGS/SRNS by targeted next generation sequencing (NGS). Eighty-one adults from 76 families were recruited; 24 families had a history of renal disease. A targeted NGS panel was designed and applied, covering 39 genes implicated in FSGS/SRNS including COL4A3-5. Confirmed pathogenic mutations were found in 10 patients (6 with family history) from 9 families (diagnostic rate 12%). Probably pathogenic mutations were identified in an additional six patients (combined diagnostic rate 20%). Definitely pathogenic mutations were identified in 22% of patients with family history and 10% without. Mutations in COL4A3-5 were present in eight patients from six families, representing 56% of definitely pathogenic mutations, and establishing a diagnosis of AD in six patients and TBMN in two patients. Collagen mutations were identified in 38% of families with familial FSGS, and 3% with sporadic FSGS, with over half the mutations occurring in COL4A5. Patients with collagen mutations were younger at presentation and more likely to have family history, haematuria and glomerular basement membrane abnormalities. We show that collagen IV mutations, including COL4A5, frequently underlie FSGS and should be considered, particularly with a positive family history. Targeted NGS improves diagnostic efficiency by investigating many candidate genes in parallel.

The acceleration of DNA sequencing in samples from patients and population studies has resulted in extensive catalogues of human genetic variation, but the interpretation of rare genetic variants remains problematic. A notable example of this challenge is the existence of disruptive variants in dosage-sensitive disease genes, even in apparently healthy individuals. Here, by manual curation of putative loss-of-function (pLoF) variants in haploinsufficient disease genes in the Genome Aggregation Database (gnomAD)¹, we show that one explanation for this paradox involves alternative splicing of mRNA, which allows exons of a gene to be expressed at varying levels across different cell types. Currently, no existing annotation tool systematically incorporates information about exon expression into the interpretation of variants. We develop a transcript-level annotation metric known as the ‘proportion expressed across transcripts’, which quantifies isoform expression for variants. We calculate this metric using 11,706 tissue samples from the Genotype Tissue Expression (GTEx) project² and show that it can differentiate between weakly and highly evolutionarily conserved exons, a proxy for functional importance. We demonstrate that expression-based annotation selectively filters 22.8% of falsely annotated pLoF variants found in haploinsufficient disease genes in gnomAD, while removing less than 4% of high-confidence pathogenic variants in the same genes. Finally, we apply our expression filter to the analysis of de novo variants in patients with autism spectrum disorder and intellectual disability or developmental disorders to show that pLoF variants in weakly expressed regions have similar effect sizes to those of synonymous variants, whereas pLoF variants in highly expressed exons are most strongly enriched among cases. Our annotation is fast, flexible and generalizable, making it possible for any variant file to be annotated with any isoform expression dataset, and will be valuable for the genetic diagnosis of rare diseases, the analysis of rare variant burden in complex disorders, and the curation and prioritization of variants in recall-by-genotype studies.

Diagnosis of genetic disorders is hampered by large numbers of variants of uncertain significance (VUSs) identified through next-generation sequencing. Many such variants may disrupt normal RNA splicing. We examined effects on splicing of a large cohort of clinically identified variants and compared performance of bioinformatic splicing prediction tools commonly used in diagnostic laboratories. Two hundred fifty-seven variants (coding and noncoding) were referred for analysis across three laboratories. Blood RNA samples underwent targeted reverse transcription polymerase chain reaction (RT-PCR) analysis with Sanger sequencing of PCR products and agarose gel electrophoresis. Seventeen samples also underwent transcriptome-wide RNA sequencing with targeted splicing analysis based on Sashimi plot visualization. Bioinformatic splicing predictions were obtained using Alamut, HSF 3.1, and SpliceAI software. Eighty-five variants (33%) were associated with abnormal splicing. The most frequent abnormality was upstream exon skipping (39/85 variants), which was most often associated with splice donor region variants. SpliceAI had greatest accuracy in predicting splicing abnormalities (0.91) and outperformed other tools in sensitivity and specificity. Splicing analysis of blood RNA identifies diagnostically important splicing abnormalities and clarifies functional effects of a significant proportion of VUSs. Bioinformatic predictions are improving but still make significant errors. RNA analysis should therefore be routinely considered in genetic disease diagnostics.

Next-generation sequencing has catapulted healthcare into a revolutionary genomics era. One such technology, whole-exome sequencing, which targets the protein-coding regions of the genome, has proven success in identifying new causal mutations for diseases of previously unknown etiology. With a successful diagnostic rate approaching 25% for rare disease in recent studies, its clinical utility is becoming increasingly popular. However, the interpretation of whole-exome sequencing data requires expertise in genomic informatics and clinical medicine to ensure the accurate and safe reporting of findings back to the bedside. This is challenged by vast amounts of sequencing data harbouring approximately 25 000 variants per sequenced individual. Computational strategies and fastidious filtering frameworks are thus required to extricate candidate variants in a sea of common polymorphisms. Once prioritized, identified variants require intensive scrutiny at a biological level, and require judicious assessment alongside the clinical phenotype. In the final step, all evidence is collated and documented alongside pathogenicity guidelines to produce an exome report that returns to the clinic. This review provides a practical guide for clinicians and genomic informaticians on the clinical application of whole-exome sequencing. We address sequencing capture and methodology, quality control parameters at different stages of sequencing analysis and propose an exome data filtering strategy that includes primary filtering (for the removal of probable benign variants) and secondary filtering for the prioritization of remaining candidates.