Nonsense-mediated decay is highly stable across individuals and tissues

Aberrant gene expression prediction across human tissues

Despite the frequent implication of aberrant gene expression in diseases, algorithms predicting aberrantly expressed genes of an individual are lacking. To address this need, we compile an aberrant expression prediction benchmark covering 8.2 million rare variants from 633 individuals across 49 tissues. While not geared toward aberrant expression, the deleteriousness score CADD and the loss-of-function predictor LOFTEE show mild predictive ability (1-1.6% average precision). Leveraging these and further variant annotations, we next train AbExp, a model that yields 12% average precision by combining in a tissue-specific fashion expression variability with variant effects on isoforms and on aberrant splicing. Integrating expression measurements from clinically accessible tissues leads to another two-fold improvement. Furthermore, we show on UK Biobank blood traits that performing rare variant association testing using the continuous and tissue-specific AbExp variant scores instead of LOFTEE variant burden increases gene discovery sensitivity and enables improved phenotype predictions.

BCL11A intellectual developmental disorder: defining the clinical spectrum and genotype-phenotype correlations

An increasing number of individuals with intellectual developmental disorder (IDD) and heterozygous variants in BCL11A are identified, yet our knowledge of manifestations and mutational spectrum is lacking. To address this, we performed detailed analysis of 42 individuals with BCL11A-related IDD (BCL11A-IDD, a.k.a. Dias-Logan syndrome) ascertained through an international collaborative network, and reviewed 35 additional previously reported patients. Analysis of 77 affected individuals identified 60 unique disease-causing variants (30 frameshift, 7 missense, 6 splice-site, 17 stop-gain) and 8 unique BCL11A microdeletions. We define the most prevalent features of BCL11A-IDD: IDD, postnatal-onset microcephaly, hypotonia, behavioral abnormalities, autism spectrum disorder, and persistence of fetal hemoglobin (HbF), and identify autonomic dysregulation as new feature. BCL11A-IDD is distinguished from 2p16 microdeletion syndrome, which has a higher incidence of congenital anomalies. Our results underscore BCL11A as an important transcription factor in human hindbrain development, identifying a previously underrecognized phenotype of a small brainstem with a reduced pons/medulla ratio. Genotype-phenotype correlation revealed an isoform-dependent trend in severity of truncating variants: those affecting all isoforms are associated with higher frequency of hypotonia, and those affecting the long (BCL11A-L) and extra-long (-XL) isoforms, sparing the short (-S), are associated with higher frequency of postnatal microcephaly. With the largest international cohort to date, this study highlights persistence of fetal hemoglobin as a consistent biomarker and hindbrain abnormalities as a common feature. It contributes significantly to our understanding of BCL11A-IDD through an extensive unbiased multi-center assessment, providing valuable insights for diagnosis, management and counselling, and into BCL11A's role in brain development.

Direct and indirect effects of spliceosome disruption compromise gene regulation by Nonsense-Mediated mRNA Decay

Pre-mRNA splicing, carried out in the nucleus by a large ribonucleoprotein machine known as the spliceosome, is functionally and physically coupled to the mRNA surveillance pathway in the cytoplasm called nonsense mediated mRNA decay (NMD). The NMD pathway monitors for premature translation termination signals, which can result from alternative splicing, by relying on the exon junction complex (EJC) deposited on exon-exon junctions by the spliceosome. Recently, multiple genetic screens in human cell lines have identified numerous spliceosome components as putative NMD factors. Using publicly available RNA-seq datasets from K562 and HepG2 cells depleted of 18 different spliceosome components, we find that natural NMD targeted mRNA isoforms are upregulated when members of the catalytic spliceosome are reduced. While some of this increase could be due to widespread pleiotropic effects of spliceosome dysfunction (e.g., reduced expression of NMD factors due to mis-splicing of their mRNAs), we identify that AQR, SF3B1, SF3B4 and CDC40 may have a more direct role in NMD. We also test the hypothesis that increased production of novel NMD substrates may overwhelm the pathway to find a direct correlation between the amount of novel NMD substrates detected and the degree of NMD inhibition observed. Finally, similar transcriptome alterations and NMD substrate upregulation are also observed in cells treated with spliceosome inhibitors and in cells derived from retinitis pigmentosa patients with mutations in PRPF8 and PRPF31. Overall, our results show that regardless of the cause, spliceosome disruption upregulates a broad set of NMD targets, which could contribute to cellular dysfunction in spliceosomopathies.

GREGoR: Accelerating Genomics for Rare Diseases

Rare diseases are collectively common, affecting approximately one in twenty individuals worldwide. In recent years, rapid progress has been made in rare disease diagnostics due to advances in DNA sequencing, development of new computational and experimental approaches to prioritize genes and genetic variants, and increased global exchange of clinical and genetic data. However, more than half of individuals suspected to have a rare disease lack a genetic diagnosis. The Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium was initiated to study thousands of challenging rare disease cases and families and apply, standardize, and evaluate emerging genomics technologies and analytics to accelerate their adoption in clinical practice. Further, all data generated, currently representing ~7500 individuals from ~3000 families, is rapidly made available to researchers worldwide via the Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) to catalyze global efforts to develop approaches for genetic diagnoses in rare diseases (https://gregorconsortium.org/data). The majority of these families have undergone prior clinical genetic testing but remained unsolved, with most being exome-negative. Here, we describe the collaborative research framework, datasets, and discoveries comprising GREGoR that will provide foundational resources and substrates for the future of rare disease genomics.

Systematic analysis of nonsense variants uncovers peptide release rate as a novel modifier of nonsense-mediated mRNA decay

Nonsense variants underlie many genetic diseases. The phenotypic impact of nonsense variants is determined by nonsense-mediated mRNA decay (NMD), which degrades transcripts with premature termination codons (PTCs). Despite its clinical importance, the factors controlling transcript-specific and context-dependent variation in NMD activity remain poorly understood. Through analysis of human genetic datasets, we discovered that the amino acid preceding the PTC strongly influences NMD activity. Notably, glycine codons promote robust NMD efficiency and show striking enrichment before PTCs but depletion before normal termination codons (NTCs). This glycine-PTC enrichment is particularly pronounced in genes tolerant to loss-of-function variants, suggesting evolutionary selection or neutrality conferred by efficient elimination of truncated proteins from non-essential genes. Using biochemical assays and massively parallel reporter analysis, we demonstrated that the peptide release rate during translation termination varies substantially with the identity of the preceding amino acid and serves as the primary determinant of NMD activity. We propose a "window of opportunity" model where translation termination kinetics modulate NMD efficiency. By revealing how sequence context shapes NMD activity through translation termination dynamics, our findings provide a mechanistic framework for improved clinical interpretation of nonsense variants.

A spectrum of nonsense-mediated mRNA decay efficiency along the degree of mutational constraint

Despite its importance for regulating gene expression, nonsense-mediated mRNA decay (NMD) remains poorly understood. Here, we extend the findings of a previous landmark study that proposed several factors associated with NMD efficiency using matched genome and transcriptome data from The Cancer Genome Atlas Program (TCGA) by incorporating additional data including Genotype-Tissue Expression (GTEx), gnomAD, and metrics for mutational constraints. Factors affecting NMD efficiency are analyzed using an allele-specific expression (ASE)-based measure to reduce noise caused by random variations. Additionally, by combining our data with the updated allele frequency database of gnomAD, we demonstrate the spectrum of NMD efficiency according to the degree of gene-level mutational constraints (degree of a gene-tolerating loss-of-function variants). The NMD prediction model, trained on TCGA data, shows that gene-level mutational constraint is an important predictor of NMD efficiency. Findings of this study suggest the potential role of NMD on shaping the landscape of mutational constraints.

Computational methods for allele-specific expression in single cells

Allele-specific expression (ASE) is a powerful signal that can be used to investigate multiple molecular mechanisms, such as cis-regulatory effects and imprinting. Single-cell RNA-sequencing (scRNA-seq) enables ASE characterization at the resolution of individual cells. In this review, we highlight the computational methods for processing and analyzing single-cell ASE data. We first describe a bioinformatics pipeline to obtain ASE counts from raw reads synthesized from previous literature. We then discuss statistical methods for detecting allelic imbalance and its variability across conditions using scRNA-seq data. In addition, we describe other methods that use single-cell ASE to address specific biological questions. Finally, we discuss future directions and emphasize the need for an integrated, optimized bioinformatics pipeline, and further development of statistical methods for different technologies.

NMDtxDB: data-driven identification and annotation of human NMD target transcripts

The nonsense-mediated RNA decay (NMD) pathway is a crucial mechanism of mRNA quality control. Current annotations of NMD substrate RNAs are rarely data-driven, but use generally established rules. We present a data set with four cell lines and combinations for SMG5, SMG6, and SMG7 knockdowns or SMG7 knockout. Based on this data set, we implemented a workflow that combines Nanopore and Illumina sequencing to assemble a transcriptome, which is enriched for NMD target transcripts. Moreover, we use coding sequence information (CDS) from Ensembl, Gencode consensus Ribo-seq ORFs, and OpenProt to enhance the CDS annotation of novel transcript isoforms. In summary, 302,889 transcripts were obtained from the transcriptome assembly process, out of which 24% are absent from Ensembl database annotations, 48,213 contain a premature stop codon, and 6433 are significantly upregulated in three or more comparisons of NMD active versus deficient cell lines. We present an in-depth view of these results through the NMDtxDB database, which is available at https://shiny.dieterichlab.org/app/NMDtxDB, and supports the study of NMD-sensitive transcripts. We open sourced our implementation of the respective web-application and analysis workflow at https://github.com/dieterich-lab/NMDtxDB and https://github.com/dieterich-lab/nmd-wf.

RNA Sequencing in Disease Diagnosis

RNA sequencing (RNA-seq) enables the accurate measurement of multiple transcriptomic phenotypes for modeling the impacts of disease variants. Advances in technologies, experimental protocols, and analysis strategies are rapidly expanding the application of RNA-seq to identify disease biomarkers, tissue- and cell-type-specific impacts, and the spatial localization of disease-associated mechanisms. Ongoing international efforts to construct biobank-scale transcriptomic repositories with matched genomic data across diverse population groups are further increasing the utility of RNA-seq approaches by providing large-scale normative reference resources. The availability of these resources, combined with improved computational analysis pipelines, has enabled the detection of aberrant transcriptomic phenotypes underlying rare diseases. Further expansion of these resources, across both somatic and developmental tissues, is expected to soon provide unprecedented insights to resolve disease origin, mechanism of action, and causal gene contributions, suggesting the continued high utility of RNA-seq in disease diagnosis.

ZSCAN10 deficiency causes a neurodevelopmental disorder with characteristic oto-facial malformations

Neurodevelopmental disorders are major indications for genetic referral and have been linked to more than 1500 loci including genes encoding transcriptional regulators. The dysfunction of transcription factors often results in characteristic syndromic presentations; however, at least half of these patients lack a genetic diagnosis. The implementation of machine learning approaches has the potential to aid in the identification of new disease genes and delineate associated phenotypes. Next generation sequencing was performed in seven affected individuals with neurodevelopmental delay and dysmorphic features. Clinical characterization included reanalysis of available neuroimaging datasets and 2D portrait image analysis with GestaltMatcher. The functional consequences of ZSCAN10 loss were modelled in mouse embryonic stem cells (mESCs), including a knockout and a representative ZSCAN10 protein truncating variant. These models were characterized by gene expression and western blot analyses, chromatin immunoprecipitation and quantitative PCR (ChIP-qPCR) and immunofluorescence staining. Zscan10 knockout mouse embryos were generated and phenotyped. We prioritized bi-allelic ZSCAN10 loss-of-function variants in seven affected individuals from five unrelated families as the underlying molecular cause. RNA-sequencing analyses in Zscan10-/- mESCs indicated dysregulation of genes related to stem cell pluripotency. In addition, we established in mESCs the loss-of-function mechanism for a representative human ZSCAN10 protein truncating variant by showing alteration of its expression levels and subcellular localization, interfering with its binding to DNA enhancer targets. Deep phenotyping revealed global developmental delay, facial asymmetry and malformations of the outer ear as consistent clinical features. Cerebral MRI showed dysplasia of the semicircular canals as an anatomical correlate of sensorineural hearing loss. Facial asymmetry was confirmed as a clinical feature by GestaltMatcher and was recapitulated in the Zscan10 mouse model along with inner and outer ear malformations. Our findings provide evidence of a novel syndromic neurodevelopmental disorder caused by bi-allelic loss-of-function variants in ZSCAN10.

Mutations causing premature termination codons discriminate and generate cellular and clinical variability in HHT

ABSTRACT

For monogenic diseases caused by pathogenic loss-of-function DNA variants, attention focuses on dysregulated gene-specific pathways, usually considering molecular subtypes together within causal genes. To better understand phenotypic variability in hereditary hemorrhagic telangiectasia (HHT), we subcategorized pathogenic DNA variants in ENG/endoglin, ACVRL1/ALK1, and SMAD4 if they generated premature termination codons (PTCs) subject to nonsense-mediated decay. In 3 patient cohorts, a PTC-based classification system explained some previously puzzling hemorrhage variability. In blood outgrowth endothelial cells (BOECs) derived from patients with ACVRL1+/PTC, ENG+/PTC, and SMAD4+/PTC genotypes, PTC-containing RNA transcripts persisted at low levels (8%-23% expected, varying between replicate cultures); genes differentially expressed to Bonferroni P < .05 in HHT+/PTC BOECs clustered significantly only to generic protein terms (isopeptide-bond/ubiquitin-like conjugation) and pulse-chase experiments detected subtle protein maturation differences but no evidence for PTC-truncated protein. BOECs displaying highest PTC persistence were discriminated in unsupervised hierarchical clustering of near-invariant housekeeper genes, with patterns compatible with higher cellular stress in BOECs with >11% PTC persistence. To test directionality, we used a HeLa reporter system to detect induction of activating transcription factor 4 (ATF4), which controls expression of stress-adaptive genes, and showed that ENG Q436X but not ENG R93X directly induced ATF4. AlphaFold accurately modeled relevant ENG domains, with AlphaMissense suggesting that readthrough substitutions would be benign for ENG R93X and other less rare ENG nonsense variants but more damaging for Q436X. We conclude that PTCs should be distinguished from other loss-of-function variants, PTC transcript levels increase in stressed cells, and readthrough proteins and mechanisms provide promising research avenues.

A Novel Frameshift Variant of the ELF4 Gene in a Patient with Autoinflammatory Disease: Clinical Features, Transcriptomic Profiling and Functional Studies

We described the diagnosis and treatment of a patient with autoinflammatory disease, named "Deficiency in ELF4, X-linked (DEX)". A novel ELF4 variant was discovered and its pathogenic mechanism was elucidated. The data about clinical, laboratory and endoscopic features, treatment, and follow-up of a patient with DEX were analyzed. Whole exome sequencing and Sanger sequencing were performed to identify potential pathogenic variants. The mRNA and protein levels of ELF4 were analyzed by qPCR and Western blotting, respectively. The association of ELF4 frameshift variant with nonsense-mediated mRNA decay (NMD) in the pathogenesis DEX was examined. Moreover, RNA-seq was performed to identify the key molecular events triggered by ELF4 variant. The relationship between ELF4 and IFN-β activity was validated using a dual-luciferase reporter assay and a ChIP-qPCR assay. An 11-year-old boy presented with a Behçet's-like phenotype. The laboratory abnormality was the most obvious in elevated inflammatory indicators. Endoscopy revealed multiple ileocecal ulcers. Intestinal histopathology showed inflammatory cell infiltrations. The patient was treated with long-term immunosuppressant and TNF-α blocker (adalimumab), which reaped an excellent response over 16 months of follow-up. Genetic analysis identified a maternal hemizygote frameshift variant (c.1022del, p.Q341Rfs*30) in ELF4 gene in the proband. The novel variant decreased the mRNA level of ELF4 via the NMD pathway. Mechanistically, insufficient expression of ELF4 disturbed the immune system, leading to immunological disorders and pathogen susceptibility, and disrupted ELF4-activating IFN-β responses. This analysis detailed the clinical characteristics of a Chinese patient with DEX who harbored a novel ELF4 frameshift variant. For the first time, we used patient-derived cells and carried out transcriptomic analysis to delve into the mechanism of ELF4 variant in DEX.

Reclassification of variants of tumor suppressor genes based on Sanger RNA sequencing without NMD inhibition

Introduction: RNA sequence analysis can be effectively used to identify aberrant splicing, and tumor suppressor genes are adequate targets considering their loss-of-function mechanisms. Sanger sequencing is the simplest method for RNA sequence analysis; however, because of its insufficient sensitivity in cases with nonsense-mediated mRNA decay (NMD), the use of cultured specimens with NMD inhibition has been recommended, hindering its wide adoption. Method: The results of Sanger sequencing of peripheral blood RNA without NMD inhibition performed on potential splicing variants of tumor suppressor genes were retrospectively reviewed. For negative cases, in which no change was identified in the transcript, the possibility of false negativity caused by NMD was assessed through a review of the up-to-date literature. Results: Eleven potential splice variants of various tumor suppressor genes were reviewed. Six variants were classified as pathogenic or likely pathogenic based on the nullifying effect identified by Sanger RNA sequencing. Four variants remained as variants of uncertain significance because of identified in-frame changes or normal expression of both alleles. The result of one variant was suspected to be a false negative caused by NMD after reviewing a recent study that reported the same variant as causing a nullifying effect on the affected transcript. Conclusion: Although RNA changes found in the majority of cases were expected to undergo NMD by canonical rules, most cases (10/11) were interpretable by Sanger RNA sequencing without NMD inhibition due to incomplete NMD efficiency or allele-specific expression despite highly efficient NMD.

aenmd: annotating escape from nonsense-mediated decay for transcripts with protein-truncating variants

SUMMARY

DNA changes that cause premature termination codons (PTCs) represent a large fraction of clinically relevant pathogenic genomic variation. Typically, PTCs induce transcript degradation by nonsense-mediated mRNA decay (NMD) and render such changes loss-of-function alleles. However, certain PTC-containing transcripts escape NMD and can exert dominant-negative or gain-of-function (DN/GOF) effects. Therefore, systematic identification of human PTC-causing variants and their susceptibility to NMD contributes to the investigation of the role of DN/GOF alleles in human disease. Here we present aenmd, a software for annotating PTC-containing transcript-variant pairs for predicted escape from NMD. aenmd is user-friendly and self-contained. It offers functionality not currently available in other methods and is based on established and experimentally validated rules for NMD escape; the software is designed to work at scale, and to integrate seamlessly with existing analysis workflows. We applied aenmd to variants in the gnomAD, Clinvar, and GWAS catalog databases and report the prevalence of human PTC-causing variants in these databases, and the subset of these variants that could exert DN/GOF effects via NMD escape.

AVAILABILITY AND IMPLEMENTATION

aenmd is implemented in the R programming language. Code is available on GitHub as an R-package (github.com/kostkalab/aenmd.git), and as a containerized command-line interface (github.com/kostkalab/aenmd_cli.git).

Mapping genetic variants for nonsense-mediated mRNA decay regulation across human tissues

BACKGROUND

Nonsense-mediated mRNA decay (NMD) was originally conceived as an mRNA surveillance mechanism to prevent the production of potentially deleterious truncated proteins. Research also shows NMD is an important post-transcriptional gene regulation mechanism selectively targeting many non-aberrant mRNAs. However, how natural genetic variants affect NMD and modulate gene expression remains elusive.

RESULTS

Here we elucidate NMD regulation of individual genes across human tissues through genetical genomics. Genetic variants corresponding to NMD regulation are identified based on GTEx data through unique and robust transcript expression modeling. We identify genetic variants that influence the percentage of NMD-targeted transcripts (pNMD-QTLs), as well as genetic variants regulating the decay efficiency of NMD-targeted transcripts (dNMD-QTLs). Many such variants are missed in traditional expression quantitative trait locus (eQTL) mapping. NMD-QTLs show strong tissue specificity especially in the brain. They are more likely to overlap with disease single-nucleotide polymorphisms (SNPs). Compared to eQTLs, NMD-QTLs are more likely to be located within gene bodies and exons, especially the penultimate exons from the 3' end. Furthermore, NMD-QTLs are more likely to be found in the binding sites of miRNAs and RNA binding proteins.

CONCLUSIONS

We reveal the genome-wide landscape of genetic variants associated with NMD regulation across human tissues. Our analysis results indicate important roles of NMD in the brain. The preferential genomic positions of NMD-QTLs suggest key attributes for NMD regulation. Furthermore, the overlap with disease-associated SNPs and post-transcriptional regulatory elements implicates regulatory roles of NMD-QTLs in disease manifestation and their interactions with other post-transcriptional regulators.

Aberrant splicing prediction across human tissues

Aberrant splicing is a major cause of genetic disorders but its direct detection in transcriptomes is limited to clinically accessible tissues such as skin or body fluids. While DNA-based machine learning models can prioritize rare variants for affecting splicing, their performance in predicting tissue-specific aberrant splicing remains unassessed. Here we generated an aberrant splicing benchmark dataset, spanning over 8.8 million rare variants in 49 human tissues from the Genotype-Tissue Expression (GTEx) dataset. At 20% recall, state-of-the-art DNA-based models achieve maximum 12% precision. By mapping and quantifying tissue-specific splice site usage transcriptome-wide and modeling isoform competition, we increased precision by threefold at the same recall. Integrating RNA-sequencing data of clinically accessible tissues into our model, AbSplice, brought precision to 60%. These results, replicated in two independent cohorts, substantially contribute to noncoding loss-of-function variant identification and to genetic diagnostics design and analytics.

Nonsense Mutations in Eukaryotes

Nonsense mutations are a type of mutations which results in a premature termination codon occurrence. In general, these mutations have been considered to be among the most harmful ones which lead to premature protein translation termination and result in shortened nonfunctional polypeptide. However, there is evidence that not all nonsense mutations are harmful as well as some molecular mechanisms exist which allow to avoid pathogenic effects of these mutations. This review addresses relevant information on nonsense mutations in eukaryotic genomes, characteristics of these mutations, and different molecular mechanisms preventing or mitigating harmful effects thereof.

Molecular Quantitative Trait Locus Mapping in Human Complex Diseases

Mapping quantitative trait loci (QTLs) for molecular traits from chromatin to metabolites (i.e., xQTLs) provides insight into the locations and effect modes of genetic variants that influence these molecular phenotypes and the propagation of functional consequences of each variant. xQTL studies indirectly interrogate the functional landscape of the molecular basis of complex diseases, including the impact of non-coding regulatory variants, the tissue specificity of regulatory elements, and their contribution to disease by integrating with genome-wide association studies (GWAS). We summarize a variety of molecular xQTL studies in human tissues and cells. In addition, using the Alzheimer's Disease Sequencing Project (ADSP) as an example, we describe the ADSP xQTL project, a collaborative effort across the ADSP Functional Genomics Consortium (ADSP-FGC). The project's ultimate goal is a reference map of Alzheimer's-related QTLs using existing datasets from multiple omics layers to help us study the consequences of genetic variants identified in the ADSP. xQTL studies enable the identification of the causal genes and pathways in GWAS loci, which will likely aid in the discovery of novel biomarkers and therapeutic targets for complex diseases. © 2022 Wiley Periodicals LLC.

Clinical implementation of RNA sequencing for Mendelian disease diagnostics

BACKGROUND

Lack of functional evidence hampers variant interpretation, leaving a large proportion of individuals with a suspected Mendelian disorder without genetic diagnosis after whole genome or whole exome sequencing (WES). Research studies advocate to further sequence transcriptomes to directly and systematically probe gene expression defects. However, collection of additional biopsies and establishment of lab workflows, analytical pipelines, and defined concepts in clinical interpretation of aberrant gene expression are still needed for adopting RNA sequencing (RNA-seq) in routine diagnostics.

METHODS

We implemented an automated RNA-seq protocol and a computational workflow with which we analyzed skin fibroblasts of 303 individuals with a suspected mitochondrial disease that previously underwent WES. We also assessed through simulations how aberrant expression and mono-allelic expression tests depend on RNA-seq coverage.

RESULTS

We detected on average 12,500 genes per sample including around 60% of all disease genes-a coverage substantially higher than with whole blood, supporting the use of skin biopsies. We prioritized genes demonstrating aberrant expression, aberrant splicing, or mono-allelic expression. The pipeline required less than 1 week from sample preparation to result reporting and provided a median of eight disease-associated genes per patient for inspection. A genetic diagnosis was established for 16% of the 205 WES-inconclusive cases. Detection of aberrant expression was a major contributor to diagnosis including instances of 50% reduction, which, together with mono-allelic expression, allowed for the diagnosis of dominant disorders caused by haploinsufficiency. Moreover, calling aberrant splicing and variants from RNA-seq data enabled detecting and validating splice-disrupting variants, of which the majority fell outside WES-covered regions.

CONCLUSION

Together, these results show that streamlined experimental and computational processes can accelerate the implementation of RNA-seq in routine diagnostics.

A 1-bp deletion in bovine QRICH2 causes low sperm count and immotile sperm with multiple morphological abnormalities

BACKGROUND

Semen quality and insemination success are monitored in artificial insemination bulls to ensure high male fertility rates. Only ejaculates that fulfill minimum quality requirements are processed and eventually used for artificial inseminations. We examined 70,990 ejaculates from 1343 Brown Swiss bulls to identify bulls from which all ejaculates were rejected due to low semen quality. This procedure identified a bull that produced 12 ejaculates with an aberrantly small number of sperm (0.2 ± 0.2 × 109 sperm per mL) which were mostly immotile due to multiple morphological abnormalities.

RESULTS

The genome of this bull was sequenced at a 12× coverage to investigate a possible genetic cause. Comparing the sequence variant genotypes of this bull with those from 397 fertile bulls revealed a 1-bp deletion in the coding sequence of the QRICH2 gene which encodes the glutamine rich 2 protein, as a compelling candidate causal variant. This 1-bp deletion causes a frameshift in translation and a premature termination codon (ENSBTAP00000018337.1:p.Cys1644AlafsTer52). The analysis of testis transcriptomes from 76 bulls showed that the transcript with the premature termination codon is subject to nonsense-mediated mRNA decay. The 1-bp deletion resides in a 675-kb haplotype that includes 181 single nucleotide polymorphisms (SNPs) from the Illumina BovineHD Bead chip. This haplotype segregates at a frequency of 5% in the Brown Swiss cattle population. Our analysis also identified another bull that carried the 1-bp deletion in the homozygous state. Semen analyses from the second bull confirmed low sperm concentration and immotile sperm with multiple morphological abnormalities that primarily affect the sperm flagellum and, to a lesser extent, the sperm head.

CONCLUSIONS

A recessive loss-of-function allele of the bovine QRICH2 gene likely causes low sperm concentration and immotile sperm with multiple morphological abnormalities. Routine sperm analyses unambiguously identify homozygous bulls for this allele. A direct gene test can be implemented to monitor the frequency of the undesired allele in cattle populations.

Toward transcriptomics as a primary tool for rare disease investigation

In the past 5 years transcriptome or RNA-sequencing (RNA-seq) has steadily emerged as a complementary assay for rare disease diagnosis and discovery. In this perspective, we summarize several recent developments and challenges in the use of RNA-seq for rare disease investigation. Using an accessible patient sample, such as blood, skin, or muscle, RNA-seq enables the assay of expressed RNA transcripts. Analysis of RNA-seq allows the identification of aberrant or outlier gene expression and alternative splicing as functional evidence to support rare disease study and diagnosis. Further, many types of variant effects can be profiled beyond coding variants, as the consequences of noncoding variants that impact gene expression and splicing can be directly observed. This is particularly apparent for structural variants that disproportionately underlie outlier gene expression and for splicing variants in which RNA-seq can both measure aberrant canonical splicing and detect deep intronic effects. However, a major potential limitation of RNA-seq in rare disease investigation is the developmental and cell type specificity of gene expression as a pathogenic variant's effect may be limited to a specific spatiotemporal context and access to a patient's tissue sample from the relevant tissue and timing of disease expression may not be possible. We speculate that as advances in computational methods and emerging experimental techniques overcome both developmental and cell type specificity, there will be broadening use of RNA sequencing and multiomics in rare disease diagnosis and delivery of precision health.