Transcriptomic signatures across human tissues identify functional rare genetic variation

The role of NUDT3 in lipid accumulation and its functional variants related to backfat thickness in pigs

NUDT3 is a leading candidate gene that strongly linked to pig fatness traits, however, its function in porcine adipocytes remains poorly understood. Here, the percentage of EdU+ cells was significantly reduced when NUDT3 was knocked down, as was the expression of cell cycle repressors. NUDT3 overexpression yielded the opposite outcome. Moreover, the knockdown of NUDT3 resulted in more lipid droplets in adipocytes, whereas its enforced expression had the reverse effect. In addition, exogenous expression of NUDT3 in adipose tissue significantly reduced fat expansion triggered by a high-fat diet in mice. At molecular level, integrative RIP-seq and RNA-seq analysis revealed that genes influenced by NUDT3 overexpression or knockdown were significantly enriched in the PI3K-AKT signaling pathway, and western blot confirmed that AKT phosphorylation was significantly increased by NUDT3 knockdown, while the phosphorylation levels of PI3K, AKT, and mTOR were significantly decreased by the enforced NUDT3 expression both ex vivo and in vivo. Notably, rs694899689 was identified as a potential genetic variant for modulates NUDT3 expression and impacting backfat thickness in pigs through analysis of multi-omics data, CRISPRi (CRISPR interference) and dual luciferase reporter assays. Overall, our work established NUDT3 as a novel negative regulator of adipogenesis and lipid deposition and revealed that rs694899689 might serve as a potential molecular marker for pig breeding.

Paired DNA and RNA sequencing uncovers common and rare genetic variants regulating gene expression in the human retina

Genetic disorders impacting vision affect millions of individuals worldwide, including age-related macular degeneration (common) and inherited retinal disorders (rare). There is incomplete understanding of the impact of genetic variation on gene expression in the human retina, and its role in genetic disorders. Through the generation of whole genome sequencing and bulk RNA-sequencing of neurosensory retina (NSR) and retinal pigment epithelium (RPE) from 201 post-mortem eyes, we uncovered common and rare genetic variants shaping retinal expression profiles. This includes 1,483,595 significant cis-expression quantitative trait loci (eQTLs) impacting 9,959 and 3,699 genes in NSR and RPE, respectively, with associated genetic variants enriched to cis-candidate regulatory elements and notable shared eGenes between NSR and RPE. We also detected 1051 expression outliers and prioritised 299 rare non-coding single-nucleotide, structural variants or copy number variants as plausible drivers for 28% of outlier events. This study increases understanding of gene expression regulation in the human retina.

Integration of transcriptomics and long-read genomics prioritizes structural variants in rare disease

Rare structural variants (SVs)-insertions, deletions, and complex rearrangements-can cause Mendelian disease, yet they remain difficult to accurately detect and interpret. We sequenced and analyzed Oxford Nanopore Technologies long-read genomes of 68 individuals from the undiagnosed disease network (UDN) with no previously identified diagnostic mutations from short-read sequencing. Using our optimized SV detection pipelines and 571 control long-read genomes, we detected 716 long-read rare (MAF < 0.01) SV alleles per genome on average, achieving a 2.4× increase from short reads. To characterize the functional effects of rare SVs, we assessed their relationship with gene expression from blood or fibroblasts from the same individuals and found that rare SVs overlapping enhancers were enriched (LOR = 0.46) near expression outliers. We also evaluated tandem repeat expansions (TREs) and found 14 rare TREs per genome; notably, these TREs were also enriched near overexpression outliers. To prioritize candidate functional SVs, we developed Watershed-SV, a probabilistic model that integrates expression data with SV-specific genomic annotations, which significantly outperforms baseline models that do not incorporate expression data. Watershed-SV identified a median of eight high-confidence functional SVs per UDN genome. Notably, this included compound heterozygous deletions in FAM177A1 shared by two siblings, which were likely causal for a rare neurodevelopmental disorder. Our observations demonstrate the promise of integrating long-read sequencing with gene expression toward improving the prioritization of functional SVs and TREs in rare disease patients.

3' untranslated region somatic variants connect alternative polyadenylation dysregulation in human cancers

Somatic variants in the cancer genome influence gene expression through diverse mechanisms depending on their specific locations. However, a systematic evaluation of the effects of somatic variants located in 3' untranslated regions (3' UTRs) on alternative polyadenylation (APA) of mRNA remains lacking. In this study, we analyze 10,199 tumor samples across 32 cancer types and identify 1333 somatic single nucleotide variants (SNVs) associated with abnormal 3' UTR APA. Mechanistically, these 3' UTR SNVs can alter cis-regulatory elements, such as the poly(A) signal and UGUA motif, leading to changes in APA. Minigene assays confirm that 3' UTR SNVs in multiple genes, including RPS23 and CHTOP, induce aberrant APA. Among affected genes, 62 exhibit differential stability between tandem 3' UTR isoforms, including HSPA4 and UCK2, validated by experimental assays. Finally, we establish that SNV-related abnormal APA usage serves as an additional layer of expression regulation for tumor-suppressor gene HMGN2 in breast cancer. Collectively, this study reveals 3' UTR APA as a critical mechanism mediating the functional impact of somatic noncoding variants in human cancers.

From Gene Discovery to Stroke Risk: C5orf24's Pivotal Role Uncovered

Stroke is a leading cause of death and disability worldwide. It is crucial to understand the influencing factors and potential mechanisms of stroke, as well as reducing its risk. This study identified the expression of the B230219D22Rik gene in mouse microglial cells, corresponding to the human gene C5orf24, using the NCBI database. We then validated the role of C5orf24 in stroke using quantitative real-time PCR, enzyme-linked immunosorbent assay, western blot and Mendelian randomization (MR) analysis. Additionally, we evaluated the causal association of C5orf24 with three other vascular diseases: coronary heart disease, myocardial infarction, and embolism. The gene B230219D22Rik and C5orf24 expressed in microglia was observed to have reduced expression in mouse and human cell stroke models, respectively. In MR analysis, we found a significant causal relationship between increased C5orf24 levels and reduced stroke risk (OR = 0.68, 95% CI 0.48-0.98, P = 4.07 × 10-2). However, this association was not observed in three other vascular diseases. To further explore the function of C5orf24 in stroke, we overexpressed C5orf24 in the oxygen-glucose deprivation/reperfusion (OGD/R) model of human microglial cell line clone 3 (HMC3) in vitro and found that C5orf24 inhibited the expression of inflammatory factors IL-1β and IL-6. In our study, we revealed a causal relationship between elevated levels of C5orf24 and a reduced risk of stroke through cell experiments and MR analysis, and found that inflammation might play a mediating role. This suggests that C5orf24 could be a promising drug target for stroke treatment.

Mapping the regulatory effects of common and rare non-coding variants across cellular and developmental contexts in the brain and heart

Whole genome sequencing has identified over a billion non-coding variants in humans, while GWAS has revealed the non-coding genome as a significant contributor to disease. However, prioritizing causal common and rare non-coding variants in human disease, and understanding how selective pressures have shaped the non-coding genome, remains a significant challenge. Here, we predicted the effects of 15 million variants with deep learning models trained on single-cell ATAC-seq across 132 cellular contexts in adult and fetal brain and heart, producing nearly two billion context-specific predictions. Using these predictions, we distinguish candidate causal variants underlying human traits and diseases and their context-specific effects. While common variant effects are more cell-type-specific, rare variants exert more cell-type-shared regulatory effects, with selective pressures particularly targeting variants affecting fetal brain neurons. To prioritize de novo mutations with extreme regulatory effects, we developed FLARE, a context-specific functional genomic model of constraint. FLARE outperformed other methods in prioritizing case mutations from autism-affected families near syndromic autism-associated genes; for example, identifying mutation outliers near CNTNAP2 that would be missed by alternative approaches. Overall, our findings demonstrate the potential of integrating single-cell maps with population genetics and deep learning-based variant effect prediction to elucidate mechanisms of development and disease-ultimately, supporting the notion that genetic contributions to neurodevelopmental disorders are predominantly rare.

Statistical framework for calling allelic imbalance in high-throughput sequencing data

High-throughput sequencing facilitates large-scale studies of gene regulation and allows tracing the associations of individual genomic variants with changes in gene regulation and expression. Compared to classic association studies, the assessment of an allelic imbalance at heterozygous variants captures functional variant effects with smaller sample sizes, higher sensitivity, and better resolution. Yet, identification of allele-specific variants from allelic read counts remains challenging due to data-dependent biases and overdispersion arising from technical and biological variability. We present MIXALIME, a novel computational framework for calling allele-specific variants in diverse omics data with a repertoire of statistical models accounting for read mapping bias and copy number variation. We benchmark MIXALIME with DNase-Seq, ATAC-Seq, and CAGE-Seq data, and we demonstrate that the allelic imbalance highlights causal variants in GWAS results. Finally, as a showcase of the large-scale practical application of MIXALIME, we present an atlas of variants exhibiting allele-specific chromatin accessibility, built from thousands of available datasets obtained from diverse cell types.

Kidney multiome-based genetic scorecard reveals convergent coding and regulatory variants

Kidney dysfunction is a major cause of mortality, but its genetic architecture remains elusive. In this study, we conducted a multiancestry genome-wide association study in 2.2 million individuals and identified 1026 (97 previously unknown) independent loci. Ancestry-specific analysis indicated an attenuation of newly identified signals on common variants in European ancestry populations and the power of population diversity for further discoveries. We defined genotype effects on allele-specific gene expression and regulatory circuitries in more than 700 human kidneys and 237,000 cells. We found 1363 coding variants disrupting 782 genes, with 601 genes also targeted by regulatory variants and convergence in 161 genes. Integrating 32 types of genetic information, we present the "Kidney Disease Genetic Scorecard" for prioritizing potentially causal genes, cell types, and druggable targets for kidney disease.

Translating pharmacogenomic sequencing data into drug response predictions-How to interpret variants of unknown significance

The rapid development of sequencing technologies during the past 20 years has provided a variety of methods and tools to interrogate human genomic variations at the population level. Pharmacogenes are well known to be highly polymorphic and a plethora of pharmacogenomic variants has been identified in population sequencing data. However, so far only a small number of these variants have been functionally characterized regarding their impact on drug efficacy and toxicity and the significance of the vast majority remains unknown. It is therefore of high importance to develop tools and frameworks to accurately infer the effects of pharmacogenomic variants and, eventually, aggregate the effect of individual variations into personalized drug response predictions. To address this challenge, we here first describe the technological advances, including sequencing methods and accompanying bioinformatic processing pipelines that have enabled reliable variant identification. Subsequently, we highlight advances in computational algorithms for pharmacogenomic variant interpretation and discuss the added value of emerging strategies, such as machine learning and the integrative use of omics techniques that have the potential to further contribute to the refinement of personalized pharmacological response predictions. Lastly, we provide an overview of experimental and clinical approaches to validate in silico predictions. We conclude that the iterative feedback between computational predictions and experimental validations is likely to rapidly improve the accuracy of pharmacogenomic prediction models, which might soon allow for an incorporation of the entire pharmacogenetic profile into personalized response predictions.

Promoter Deletion Leading to Allele Specific Expression in a Genetically Unsolved Case of Primary Ciliary Dyskinesia

Variation in the non-coding genome represents an understudied mechanism of disease and it remains challenging to predict if single nucleotide variants, small insertions and deletions, or structural variants in non-coding genomic regions will be detrimental. Our approach using complementary RNA-seq and targeted long-read DNA sequencing can prioritize identification of non-coding variants that lead to disease via alteration of gene splicing or expression. We have identified a patient with primary ciliary dyskinesia with a pathogenic coding variant on one allele of the SPAG1 gene, while the second allele appears normal by whole exome sequencing despite an autosomal recessive inheritance pattern. RNA sequencing revealed reduced SPAG1 transcript levels and exclusive allele specific expression of the known pathogenic allele, suggesting the presence of a non-coding variant on the second allele that impacts transcription. Targeted long-read DNA sequencing identified a heterozygous 3 kilobase deletion of the 5' untranslated region of SPAG1, overlapping the promoter and first non-coding exon. This non-coding deletion was missed by whole exome sequencing and gene-specific deletion/duplication analysis, highlighting the importance of investigating the non-coding genome in patients with "missing" disease-causing variation. This paradigm demonstrates the utility of both RNA and long-read DNA sequencing in identifying pathogenic non-coding variants in patients with unexplained genetic disease.

Randomizing the human genome by engineering recombination between repeat elements

We lack tools to edit DNA sequences at scales necessary to study 99% of the human genome that is noncoding. To address this gap, we applied CRISPR prime editing to insert recombination handles into repetitive sequences, up to 1697 per cell line, which enables generating large-scale deletions, inversions, translocations, and circular DNA. Recombinase induction produced more than 100 stochastic megabase-sized rearrangements in each cell. We tracked these rearrangements over time to measure selection pressures, finding a preference for shorter variants that avoided essential genes. We characterized 29 clones with multiple rearrangements, finding an impact of deletions on expression of genes in the variant but not on nearby genes. This genome-scrambling strategy enables large deletions, sequence relocations, and the insertion of regulatory elements to explore genome dispensability and organization.

Diversity and consequences of structural variation in the human genome

The biomedical community is increasingly invested in capturing all genetic variants across human genomes, interpreting their functional consequences and translating these findings to the clinic. A crucial component of this endeavour is the discovery and characterization of structural variants (SVs), which are ubiquitous in the human population, heterogeneous in their mutational processes, key substrates for evolution and adaptation, and profound drivers of human disease. The recent emergence of new technologies and the remarkable scale of sequence-based population studies have begun to crystalize our understanding of SVs as a mutational class and their widespread influence across phenotypes. In this Review, we summarize recent discoveries and new insights into SVs in the human genome in terms of their mutational patterns, population genetics, functional consequences, and impact on human traits and disease. We conclude by outlining three frontiers to be explored by the field over the next decade.

Impact of rare non-coding variants on human diseases through alternative polyadenylation outliers

Although rare non-coding variants (RVs) play crucial roles in complex traits and diseases, understanding their mechanisms and identifying disease-associated RVs continue to be major challenges. Here we constructed a comprehensive atlas of alternative polyadenylation (APA) outliers (aOutliers), including 1334 3' UTR and 200 intronic aOutliers, from 15,201 samples across 49 human tissues. These aOutliers exhibit unique characteristics from transcription or splicing outliers, with a pronounced RV enrichment. Mechanistically, aOutlier-RVs alter poly(A) signals and splicing sites, and perturbation indeed triggers APA events. Furthermore, we developed a Bayesian-based APA RV prediction model, which successfully pinpointed a specific set of 1799 RVs impacting 278 genes with significantly large disease effect sizes. Notably, we observed a convergence effect between rare and common cancer variants, exemplified by regulation in the DDX18 gene. Together, this study introduced an APA-enhanced framework for genome annotation, underscoring APA's role in uncovering functional RVs linked to complex traits and diseases.

Transcriptome-wide outlier approach identifies individuals with minor spliceopathies

RNA-sequencing has improved the diagnostic yield of individuals with rare diseases. Current analyses predominantly focus on identifying outliers in single genes that can be attributed to cis-acting variants within the gene locus. This approach overlooks causal variants with trans-acting effects on splicing transcriptome-wide, such as variants impacting spliceosome function. We present a transcriptomics-first method to diagnose individuals with rare diseases by examining transcriptome-wide patterns of splicing outliers. Using splicing outlier detection methods (FRASER and FRASER2) we characterized splicing outliers from whole blood for 390 individuals from the Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) and Undiagnosed Diseases Network (UDN) consortia. We examined all samples for excess intron retention outliers in minor intron containing genes (MIGs). Minor introns, which make up about 0.5% of all introns in the human genome, are removed by small nuclear RNAs (snRNAs) in the minor spliceosome. This approach identified five individuals with excess intron retention outliers in MIGs, all of which were found to harbor rare, biallelic variants in minor spliceosome snRNAs. Four individuals had rare, compound heterozygous variants in RNU4ATAC, which aided the reclassification of four variants. Additionally, one individual had rare, highly conserved, compound heterozygous variants in RNU6ATAC that may disrupt the formation of the catalytic spliceosome, suggesting a novel gene-disease candidate. These results demonstrate that examining RNA-sequencing data for transcriptome-wide signatures can increase the diagnostic yield of individuals with rare diseases, provide variant-to-function interpretation of spliceopathies, and uncover novel disease gene associations.

The human and non-human primate developmental GTEx projects

Many human diseases are the result of early developmental defects. As most paediatric diseases and disorders are rare, children are critically underrepresented in research. Functional genomics studies primarily rely on adult tissues and lack critical cell states in specific developmental windows. In parallel, little is known about the conservation of developmental programmes across non-human primate (NHP) species, with implications for human evolution. Here we introduce the developmental Genotype-Tissue Expression (dGTEx) projects, which span humans and NHPs and aim to integrate gene expression, regulation and genetics data across development and species. The dGTEx cohort will consist of 74 tissue sites across 120 human donors from birth to adulthood, and developmentally matched NHP age groups, with additional prenatal and adult animals, with 126 rhesus macaques (Macaca mulatta) and 72 common marmosets (Callithrix jacchus). The data will comprise whole-genome sequencing, extensive bulk, single-cell and spatial gene expression profiles, and chromatin accessibility data across tissues and development. Through community engagement and donor diversity, the human dGTEx study seeks to address disparities in genomic research. Thus, dGTEx will provide a reference human and NHP dataset and tissue bank, enabling research into developmental changes in expression and gene regulation, childhood disorders and the effect of genetic variation on development.

Expanding Upon Genomics in Rare Diseases: Epigenomic Insights

DNA methylation is an essential epigenetic modification that plays a crucial role in regulating gene expression and maintaining genomic stability. With the advancement in sequencing technology, methylation studies have provided valuable insights into the diagnosis of rare diseases through the various identification of episignatures, epivariation, epioutliers, and allele-specific methylation. However, current methylation studies are not without limitations. This mini-review explores the current understanding of DNA methylation in rare diseases, highlighting the key mechanisms and diagnostic potential, and emphasizing the need for advanced methodologies and integrative approaches to enhance the understanding of disease progression and design more personable treatment for patients, given the nature of rare diseases.

Engineering structural variants to interrogate genome function

Structural variation, such as deletions, duplications, inversions and complex rearrangements, can have profound effects on gene expression, genome stability, phenotypic diversity and disease susceptibility. Structural variants can encompass up to millions of bases and have the potential to rearrange substantial segments of the genome. They contribute considerably more to genetic diversity in human populations and have larger effects on phenotypic traits than point mutations. Until recently, our understanding of the effects of structural variants was driven mainly by studying naturally occurring variation. New genome-engineering tools capable of generating deletions, insertions, inversions and translocations, together with the discovery of new recombinases and advances in creating synthetic DNA constructs, now enable the design and generation of an extended range of structural variation. Here, we discuss these tools and examples of their application and highlight existing challenges that will need to be overcome to fully harness their potential.

Genes to therapy: a comprehensive literature review of whole-exome sequencing in neurology and neurosurgery

Whole-exome sequencing (WES), a ground-breaking technology, has emerged as a linchpin in neurology and neurosurgery, offering a comprehensive elucidation of the genetic landscape of various neurological disorders. This transformative methodology concentrates on the exonic portions of DNA, which constitute approximately 1% of the human genome, thus facilitating an expedited and efficient sequencing process. WES has been instrumental in advancing our understanding of neurodegenerative diseases, neuro-oncology, cerebrovascular disorders, and epilepsy by revealing rare variants and novel mutations and providing intricate insights into their genetic complexities. This has been achieved while maintaining a substantial diagnostic yield, thereby offering novel perspectives on the pathophysiology and personalized management of these conditions. The utilization of WES boasts several advantages over alternative genetic sequencing methodologies, including cost-effectiveness, reduced incidental findings, simplified analysis and interpretation process, and reduced computational demands. However, despite its benefits, there are challenges, such as the interpretation of variants of unknown significance, cost considerations, and limited accessibility in resource-constrained settings. Additionally, ethical, legal, and social concerns are raised, particularly in the context of incidental findings and patient consent. As we look to the future, the integration of WES with other omics-based approaches could help revolutionize the field of personalized medicine through its implications in predictive models and the development of targeted therapeutic strategies, marking a significant stride toward more effective and clinically oriented solutions.

SCAR-6 elncRNA locus epigenetically regulates PROZ and modulates coagulation and vascular function

In this study, we characterize a novel lncRNA-producing gene locus that we name Syntenic Cardiovascular Conserved Region-Associated lncRNA-6 (scar-6) and functionally validate its role in coagulation and cardiovascular function. A 12-bp deletion of the scar-6 locus in zebrafish (scar-6gib007Δ12/Δ12) results in cranial hemorrhage and vascular permeability. Overexpression, knockdown and rescue with the scar-6 lncRNA modulates hemostasis in zebrafish. Molecular investigation reveals that the scar-6 lncRNA acts as an enhancer lncRNA (elncRNA), and controls the expression of prozb, an inhibitor of factor Xa, through an enhancer element in the scar-6 locus. The scar-6 locus suppresses loop formation between prozb and scar-6 sequences, which might be facilitated by the methylation of CpG islands via the prdm14-PRC2 complex whose binding to the locus might be stabilized by the scar-6 elncRNA transcript. Binding of prdm14 to the scar-6 locus is impaired in scar-6gib007Δ12/Δ12 zebrafish. Finally, activation of the PAR2 receptor in scar-6gib007Δ12/Δ12 zebrafish triggers NF-κB-mediated endothelial cell activation, leading to vascular dysfunction and hemorrhage. We present evidence that the scar-6 locus plays a role in regulating the expression of the coagulation cascade gene prozb and maintains vascular homeostasis.

saseR: Juggling offsets unlocks RNA-seq tools for fast and Scalable differential usage, Aberrant Splicing and Expression Retrieval

RNA-seq data analysis relies on many different tools, each tailored to specific applications and coming with unique assumptions and restrictions. Indeed, tools for differential transcript usage, or diagnosing patients with rare diseases through splicing and expression outliers, either lack in performance, discard information, or do not scale to massive data compendia. Here, we show that replacing the normalisation offsets unlocks bulk RNA-seq workflows for scalable differential usage, aberrant splicing and expression analyses. Our method, saseR, is much faster than state-of-the-art methods, dramatically outperforms these to detect aberrant splicing, and provides a single workflow for various short- and long-read RNA-seq applications.

Genetic Regulatory Perturbation of Gene Expression Impacted by Genomic Introgression in Fiber Development of Allotetraploid Cotton

Interspecific genomic introgression is an important evolutionary process with respect to the generation of novel phenotypic diversity and adaptation. A key question is how gene flow perturbs gene expression networks and regulatory interactions. Here, an introgression population of two species of allopolyploid cotton (Gossypium) to delineate the regulatory perturbations of gene expression regarding fiber development accompanying fiber quality change is utilized. De novo assembly of the recipient parent (G. hirsutum Emian22) genome allowed the identification of genomic variation and introgression segments (ISs) in 323 introgression lines (ILs) from the donor parent (G. barbadense 3-79). It documented gene expression dynamics by sequencing 1,284 transcriptomes of developing fibers and characterized genetic regulatory perturbations mediated by genomic introgression using a multi-locus model. Introgression of individual homoeologous genes exhibiting extreme low or high expression bias can lead to a parallel expression bias in their non-introgressed duplicates, implying a shared yet divergent regulatory fate of duplicated genes following allopolyploidy. Additionally, the IL N182 with improved fiber quality is characterized, and the candidate gene GhFLAP1 related to fiber length is validated. This study outlines a framework for understanding introgression-mediated regulatory perturbations in polyploids, and provides insights for targeted breeding of superior upland cotton fiber.

Multi-omic characterization of air pollution effects: Applications of AirSigOmniTWP Hub

Air pollution, particularly fine particulate matter and gaseous pollutants including NO2 and NOx, presents significant public health challenges. While the harmful effects of these pollutants are well-documented, the molecular mechanisms underlying their impact on health remain incompletely understood. In this study, we utilized genome-wide association study (GWAS) data from the UK Biobank, expression quantitative trait loci (eQTL) data from the Genotype-Tissue Expression (GTEx) project, and protein quantitative trait loci (pQTL) data from the Atherosclerosis Risk in Communities (ARIC) study to conduct comprehensive analyses using the Unified Test for Molecular Signatures (UTMOST), Transcriptome-wide Association Studies (TWAS), and Proteome-wide Association Studies (PWAS). To integrate and synthesize these analyses, we developed the AirSigOmniTWP Hub, a specialized platform designed to consolidate and interpret the results from UTMOST, TWAS, and PWAS. TWAS analysis identified a significant association between PM10 exposure and the gene INO80E in females (P = 4.37×10⁻⁵, FDR = 0.0383), suggesting a potential role in chromatin remodeling. PWAS analysis revealed a significant association between NOx exposure and the gene PIP in females (P = 2.28×10⁻⁵, FDR = 0.0299), implicating its involvement in inflammatory pathways. Additionally, UTMOST analyses uncovered significant associations between various pollutants and genes including NCOA4P3 and SPATS2L with PM2.5 exposure, indicating potential mechanisms related to transcriptional regulation and gene-environment interactions.

MPRAVarDB: an online database and web server for exploring regulatory effects of genetic variants

SUMMARY

Massively parallel reporter assay (MPRA) is an important technology for evaluating the impact of genetic variants on gene regulation. Here, we present MPRAVarDB, an online database and web server for exploring regulatory effects of genetic variants. MPRAVarDB harbors 18 MPRA experiments designed to assess the regulatory effects of genetic variants associated with GWAS loci, eQTLs, and genomic features, totaling 242 818 variants tested more than 30 cell lines and 30 human diseases or traits. MPRAVarDB enables users to query MPRA variants by genomic region, disease and cell line, or any combination of these parameters. Notably, MPRAVarDB offers a suite of pretrained machine-learning models tailored to the specific disease and cell line, facilitating the prediction of regulatory variants. The user-friendly interface allows users to receive query and prediction results with just a few clicks.

AVAILABILITY AND IMPLEMENTATION

https://mpravardb.rc.ufl.edu.

Addressing Technical Pitfalls in Pursuit of Molecular Factors That Mediate Immunoglobulin Gene Regulation

The expressed Ab repertoire is a critical determinant of immune-related phenotypes. Ab-encoding transcripts are distinct from other expressed genes because they are transcribed from somatically rearranged gene segments. Human Abs are composed of two identical H and L chain polypeptides derived from genes in IGH locus and one of two L chain loci. The combinatorial diversity that results from Ab gene rearrangement and the pairing of different H and L chains contributes to the immense diversity of the baseline Ab repertoire. During rearrangement, Ab gene selection is mediated by factors that influence chromatin architecture, promoter/enhancer activity, and V(D)J recombination. Interindividual variation in the composition of the Ab repertoire associates with germline variation in IGH, implicating polymorphism in Ab gene regulation. Determining how IGH variants directly mediate gene regulation will require integration of these variants with other functional genomic datasets. In this study, we argue that standard approaches using short reads have limited utility for characterizing regulatory regions in IGH at haplotype resolution. Using simulated and chromatin immunoprecipitation sequencing reads, we define features of IGH that limit use of short reads and a single reference genome, namely 1) the highly duplicated nature of the DNA sequence in IGH and 2) structural polymorphisms that are frequent in the population. We demonstrate that personalized diploid references enhance performance of short-read data for characterizing mappable portions of the locus, while also showing that long-read profiling tools will ultimately be needed to fully resolve functional impacts of IGH germline variation on expressed Ab repertoires.

Misexpression of inactive genes in whole blood is associated with nearby rare structural variants

Gene misexpression is the aberrant transcription of a gene in a context where it is usually inactive. Despite its known pathological consequences in specific rare diseases, we have a limited understanding of its wider prevalence and mechanisms in humans. To address this, we analyzed gene misexpression in 4,568 whole-blood bulk RNA sequencing samples from INTERVAL study blood donors. We found that while individual misexpression events occur rarely, in aggregate they were found in almost all samples and a third of inactive protein-coding genes. Using 2,821 paired whole-genome and RNA sequencing samples, we identified that misexpression events are enriched in cis for rare structural variants. We established putative mechanisms through which a subset of SVs lead to gene misexpression, including transcriptional readthrough, transcript fusions, and gene inversion. Overall, we develop misexpression as a type of transcriptomic outlier analysis and extend our understanding of the variety of mechanisms by which genetic variants can influence gene expression.

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.

Emerging Multi-omic Approaches to the Molecular Diagnosis of Mitochondrial Disease and Available Strategies for Treatment and Prevention

Mitochondria are semi-autonomous organelles present in several copies within most cells in the human body that are controlled by the precise collaboration of mitochondrial DNA (mtDNA) and nuclear DNA (nDNA) encoding mitochondrial proteins. They play important roles in numerous metabolic pathways, such as the synthesis of adenosine triphosphate (ATP), the predominant energy substrate of the cell generated through oxidative phosphorylation (OXPHOS), intracellular calcium homeostasis, metabolite biosynthesis, aging, cell cycles, and so forth. Previous studies revealed that dysfunction of these multi-functional organelles, which may arise due to mutations in either the nuclear or mitochondrial genome, leads to a diverse group of clinically and genetically heterogeneous disorders. These diseases include neurodegenerative and metabolic disorders as well as cardiac and skeletal myopathies in both adults and newborns. The plethora of phenotypes and defects displayed leads to challenges in the diagnosis and treatment of mitochondrial diseases. In this regard, the related literature proposed several diagnostic options, such as high throughput mitochondrial genomics and omics technologies, as well as numerous therapeutic options, such as pharmacological approaches, manipulating the mitochondrial genome, increasing the mitochondria content of the affected cells, and recently mitochondrial diseases transmission prevention. Therefore, the present article attempted to review the latest advances and challenges in diagnostic and therapeutic options for mitochondrial diseases.

Target genes regulated by CLEC16A intronic region associated with common variable immunodeficiency

BACKGROUND

CLEC16A intron 19 has been identified as a candidate locus for common variable immunodeficiency (CVID).

OBJECTIVES

This study sought to elucidate the molecular mechanism by which variants at the CLEC16A intronic locus may contribute to the pathogenesis of CVID.

METHODS

The investigators performed fine-mapping of the CLEC16A locus in a CVID cohort, then deleted the candidate functional SNP in T-cell lines by the CRISPR-Cas9 technique and conducted RNA-sequencing to identify target gene(s). The interactions between the CLEC16A locus and its target genes were identified using circular chromosome conformation capture. The transcription factor complexes mediating the chromatin interactions were determined by proteomic approach. The molecular pathways regulated by the CLEC16A locus were examined by RNA-sequencing and reverse phase protein array.

RESULTS

This study showed that the CLEC16A locus is an enhancer regulating expression of multiple target genes including a distant gene ATF7IP2 through chromatin interactions. Distinct transcription factor complexes mediate the chromatin interactions in an allele-specific manner. Disruption of the CLEC16A locus affects the AKT signaling pathway, as well as the molecular response of CD4+ T cells to immune stimulation.

CONCLUSIONS

Through multiomics and targeted experimental approaches, this study elucidated the underlying target genes and signaling pathways involved in the genetic association of CLEC16A with CVID, and highlighted plausible molecular targets for developing novel therapeutics.

Discovery of a polymorphic gene fusion via bottom-up chimeric RNA prediction

Gene fusions and their chimeric products are commonly linked with cancer. However, recent studies have found chimeric transcripts in non-cancer tissues and cell lines. Large-scale efforts to annotate structural variations have identified gene fusions capable of generating chimeric transcripts even in normal tissues. In this study, we present a bottom-up approach targeting population-specific chimeric RNAs, identifying 58 such instances in the GTEx cohort, including notable cases such as SUZ12P1-CRLF3, TFG-ADGRG7 and TRPM4-PPFIA3, which possess distinct patterns across different ancestry groups. We provide direct evidence for an additional 29 polymorphic chimeric RNAs with associated structural variants, revealing 13 novel rare structural variants. Additionally, we utilize the All of Us dataset and a large cohort of clinical samples to characterize the association of the SUZ12P1-CRLF3-causing variant with patient phenotypes. Our study showcases SUZ12P1-CRLF3 as a representative example, illustrating the identification of elusive structural variants by focusing on those producing population-specific fusion transcripts.

Massively parallel screen uncovers many rare 3' UTR variants regulating mRNA abundance of cancer driver genes

Understanding the function of rare non-coding variants represents a significant challenge. Using MapUTR, a screening method, we studied the function of rare 3' UTR variants affecting mRNA abundance post-transcriptionally. Among 17,301 rare gnomAD variants, an average of 24.5% were functional, with 70% in cancer-related genes, many in critical cancer pathways. This observation motivated an interrogation of 11,929 somatic mutations, uncovering 3928 (33%) functional mutations in 155 cancer driver genes. Functional MapUTR variants were enriched in microRNA- or protein-binding sites and may underlie outlier gene expression in tumors. Further, we introduce untranslated tumor mutational burden (uTMB), a metric reflecting the amount of somatic functional MapUTR variants of a tumor and show its potential in predicting patient survival. Through prime editing, we characterized three variants in cancer-relevant genes (MFN2, FOSL2, and IRAK1), demonstrating their cancer-driving potential. Our study elucidates the function of tens of thousands of non-coding variants, nominates non-coding cancer driver mutations, and demonstrates their potential contributions to cancer.

MPRAVarDB: an online database and web server for exploring regulatory effects of genetic variants

Massively parallel reporter assay (MPRA) is an important technology to evaluate the impact of genetic variants on gene regulation. Here, we present MPRAVarDB, an online database and web server, for exploring regulatory effects of genetic variants. MPRAVarDB harbors 18 MPRA experiments designed to assess the regulatory effects of genetic variants associated with GWAS loci, eQTLs and various genomic features, resulting in a total of 242,818 variants tested across more than 30 cell lines and 30 human diseases or traits. MPRAVarDB empowers the query of MPRA variants by genomic region, disease and cell line or by any combination of these query terms. Notably, MPRAVarDB offers a suite of pretrained machine learning models tailored to the specific disease and cell line, facilitating the genome-wide prediction of regulatory variants. MPRAVarDB is friendly to use, and users only need a few clicks to receive query and prediction results.

Diallel panel reveals a significant impact of low-frequency genetic variants on gene expression variation in yeast

Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level.

The impact of genetically controlled splicing on exon inclusion and protein structure

Common variants affecting mRNA splicing are typically identified though splicing quantitative trait locus (sQTL) mapping and have been shown to be enriched for GWAS signals by a similar degree to eQTLs. However, the specific splicing changes induced by these variants have been difficult to characterize, making it more complicated to analyze the effect size and direction of sQTLs, and to determine downstream splicing effects on protein structure. In this study, we catalogue sQTLs using exon percent spliced in (PSI) scores as a quantitative phenotype. PSI is an interpretable metric for identifying exon skipping events and has some advantages over other methods for quantifying splicing from short read RNA sequencing. In our set of sQTL variants, we find evidence of selective effects based on splicing effect size and effect direction, as well as exon symmetry. Additionally, we utilize AlphaFold2 to predict changes in protein structure associated with sQTLs overlapping GWAS traits, highlighting a potential new use-case for this technology for interpreting genetic effects on traits and disorders.

Improved detection of aberrant splicing with FRASER 2.0 and the intron Jaccard index

Detection of aberrantly spliced genes is an important step in RNA-seq-based rare-disease diagnostics. We recently developed FRASER, a denoising autoencoder-based method that outperformed alternative methods of detecting aberrant splicing. However, because FRASER's three splice metrics are partially redundant and tend to be sensitive to sequencing depth, we introduce here a more robust intron-excision metric, the intron Jaccard index, that combines the alternative donor, alternative acceptor, and intron-retention signal into a single value. Moreover, we optimized model parameters and filter cutoffs by using candidate rare-splice-disrupting variants as independent evidence. On 16,213 GTEx samples, our improved algorithm, FRASER 2.0, called typically 10 times fewer splicing outliers while increasing the proportion of candidate rare-splice-disrupting variants by 10-fold and substantially decreasing the effect of sequencing depth on the number of reported outliers. To lower the multiple-testing correction burden, we introduce an option to select the genes to be tested for each sample instead of a transcriptome-wide approach. This option can be particularly useful when prior information, such as candidate variants or genes, is available. Application on 303 rare-disease samples confirmed the relative reduction in the number of outlier calls for a slight loss of sensitivity; FRASER 2.0 recovered 22 out of 26 previously identified pathogenic splicing cases with default cutoffs and 24 when multiple-testing correction was limited to OMIM genes containing rare variants. Altogether, these methodological improvements contribute to more effective RNA-seq-based rare diagnostics by drastically reducing the amount of splicing outlier calls per sample at minimal loss of sensitivity.

The functional impact of rare variation across the regulatory cascade

Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis, which included several hundred individuals, with whole-genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, 10 years apart. We evaluated each multi-omics phenotype's ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62 times and rare frameshift variants 216 times as frequently as controls, compared to 13-27 times as frequently for expression or protein effects alone. We extended a Bayesian hierarchical model, "Watershed," to prioritize specific rare variants underlying multi-omics signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimer's disease.

Phenotypic variance partitioning by transcriptomic gene expression levels and environmental variables for anthropometric traits using GTEx data

Phenotypic variation in human is the results of genetic variation and environmental influences. Understanding the contribution of genetic and environmental components to phenotypic variation is of great interest. The variance explained by genome-wide single nucleotide polymorphisms (SNPs) typically represents a small proportion of the phenotypic variance for complex traits, which may be because the genome is only a part of the whole biological process to shape the phenotypes. In this study, we propose to partition the phenotypic variance of three anthropometric traits, using gene expression levels and environmental variables from GTEx data. We use the gene expression of four tissues that are deemed relevant for the anthropometric traits (two adipose tissues, skeletal muscle tissue and blood tissue). Additionally, we estimate the transcriptome-environment correlation that partly underlies the phenotypes of the anthropometric traits. We found that genetic factors play a significant role in determining body mass index (BMI), with the proportion of phenotypic variance explained by gene expression levels of visceral adipose tissue being 0.68 (SE = 0.06). However, we also observed that environmental factors such as age, sex, ancestry, smoking status, and drinking alcohol status have a small but significant impact (0.005, SE = 0.001). Interestingly, we found a significant negative correlation between the transcriptomic and environmental effects on BMI (transcriptome-environment correlation = -0.54, SE = 0.14), suggesting an antagonistic relationship. This implies that individuals with lower genetic profiles may be more susceptible to the effects of environmental factors on BMI, while those with higher genetic profiles may be less susceptible. We also show that the estimated transcriptomic variance varies across tissues, e.g., the gene expression levels of whole blood tissue and environmental variables explain a lower proportion of BMI phenotypic variance (0.16, SE = 0.05 and 0.04, SE = 0.004 respectively). We observed a significant positive correlation between transcriptomic and environmental effects (1.21, SE = 0.23) for this tissue. In conclusion, phenotypic variance partitioning can be done using gene expression and environmental data even with a small sample size (n = 838 from GTEx data), which can provide insights into how the transcriptomic and environmental effects contribute to the phenotypes of the anthropometric traits.

Splicing defects in rare diseases: transcriptomics and machine learning strategies towards genetic diagnosis

Genomic variants affecting pre-messenger RNA splicing and its regulation are known to underlie many rare genetic diseases. However, common workflows for genetic diagnosis and clinical variant interpretation frequently overlook splice-altering variants. To better serve patient populations and advance biomedical knowledge, it has become increasingly important to develop and refine approaches for detecting and interpreting pathogenic splicing variants. In this review, we will summarize a few recent developments and challenges in using RNA sequencing technologies for rare disease investigation. Moreover, we will discuss how recent computational splicing prediction tools have emerged as complementary approaches for revealing disease-causing variants underlying splicing defects. We speculate that continuous improvements to sequencing technologies and predictive modeling will not only expand our understanding of splicing regulation but also bring us closer to filling the diagnostic gap for rare disease patients.

An RNA-informed dosage sensitivity map reflects the intrinsic functional nature of genes

Understanding dosage sensitivity or why Mendelian diseases have dominant vs. recessive modes of inheritance is crucial for uncovering the etiology of human disease. Previous knowledge of dosage sensitivity is mainly based on observations of rare loss-of-function mutations or copy number changes, which are underpowered due to ultra rareness of such variants. Thus, the functional underpinnings of dosage constraint remain elusive. In this study, we aim to systematically quantify dosage perturbations from cis-regulatory variants in the general population to yield a tissue-specific dosage constraint map of genes and further explore their underlying functional logic. We reveal an inherent divergence of dosage constraints in genes by functional categories with signaling genes (transcription factors, protein kinases, ion channels, and cellular machinery) being dosage sensitive, while effector genes (transporters, metabolic enzymes, cytokines, and receptors) are generally dosage resilient. Instead of being a metric of functional dispensability, we show that dosage constraint reflects underlying homeostatic constraints arising from negative feedback. Finally, we employ machine learning to integrate DNA and RNA metrics to generate a comprehensive, tissue-specific map of dosage sensitivity (MoDs) for autosomal genes.

Integrative omics approaches to advance rare disease diagnostics

Over the past decade high-throughput DNA sequencing approaches, namely whole exome and whole genome sequencing became a standard procedure in Mendelian disease diagnostics. Implementation of these technologies greatly facilitated diagnostics and shifted the analysis paradigm from variant identification to prioritisation and evaluation. The diagnostic rates vary widely depending on the cohort size, heterogeneity and disease and range from around 30% to 50% leaving the majority of patients undiagnosed. Advances in omics technologies and computational analysis provide an opportunity to increase these unfavourable rates by providing evidence for disease-causing variant validation and prioritisation. This review aims to provide an overview of the current application of several omics technologies including RNA-sequencing, proteomics, metabolomics and DNA-methylation profiling for diagnostics of rare genetic diseases in general and inborn errors of metabolism in particular.

Synthetic whole-slide image tile generation with gene expression profile-infused deep generative models

In this work, we propose an approach to generate whole-slide image (WSI) tiles by using deep generative models infused with matched gene expression profiles. First, we train a variational autoencoder (VAE) that learns a latent, lower-dimensional representation of multi-tissue gene expression profiles. Then, we use this representation to infuse generative adversarial networks (GANs) that generate lung and brain cortex tissue tiles, resulting in a new model that we call RNA-GAN. Tiles generated by RNA-GAN were preferred by expert pathologists compared with tiles generated using traditional GANs, and in addition, RNA-GAN needs fewer training epochs to generate high-quality tiles. Finally, RNA-GAN was able to generalize to gene expression profiles outside of the training set, showing imputation capabilities. A web-based quiz is available for users to play a game distinguishing real and synthetic tiles: https://rna-gan.stanford.edu/, and the code for RNA-GAN is available here: https://github.com/gevaertlab/RNA-GAN.

Beyond the exome: What's next in diagnostic testing for Mendelian conditions

Despite advances in clinical genetic testing, including the introduction of exome sequencing (ES), more than 50% of individuals with a suspected Mendelian condition lack a precise molecular diagnosis. Clinical evaluation is increasingly undertaken by specialists outside of clinical genetics, often occurring in a tiered fashion and typically ending after ES. The current diagnostic rate reflects multiple factors, including technical limitations, incomplete understanding of variant pathogenicity, missing genotype-phenotype associations, complex gene-environment interactions, and reporting differences between clinical labs. Maintaining a clear understanding of the rapidly evolving landscape of diagnostic tests beyond ES, and their limitations, presents a challenge for non-genetics professionals. Newer tests, such as short-read genome or RNA sequencing, can be challenging to order, and emerging technologies, such as optical genome mapping and long-read DNA sequencing, are not available clinically. Furthermore, there is no clear guidance on the next best steps after inconclusive evaluation. Here, we review why a clinical genetic evaluation may be negative, discuss questions to be asked in this setting, and provide a framework for further investigation, including the advantages and disadvantages of new approaches that are nascent in the clinical sphere. We present a guide for the next best steps after inconclusive molecular testing based upon phenotype and prior evaluation, including when to consider referral to research consortia focused on elucidating the underlying cause of rare unsolved genetic disorders.

Single-cell genomics meets human genetics

Single-cell genomic technologies are revealing the cellular composition, identities and states in tissues at unprecedented resolution. They have now scaled to the point that it is possible to query samples at the population level, across thousands of individuals. Combining single-cell information with genotype data at this scale provides opportunities to link genetic variation to the cellular processes underpinning key aspects of human biology and disease. This strategy has potential implications for disease diagnosis, risk prediction and development of therapeutic solutions. But, effectively integrating large-scale single-cell genomic data, genetic variation and additional phenotypic data will require advances in data generation and analysis methods. As single-cell genetics begins to emerge as a field in its own right, we review its current state and the challenges and opportunities ahead.

Diallel panel reveals a significant impact of low-frequency genetic variants on gene expression variation in yeast

Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level.

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.

Improved detection of aberrant splicing using the Intron Jaccard Index

Detection of aberrantly spliced genes is an important step in RNA-seq-based rare disease diagnostics. We recently developed FRASER, a denoising autoencoder-based method for aberrant splicing detection that outperformed alternative approaches. However, as FRASER's three splice metrics are partially redundant and tend to be sensitive to sequencing depth, we introduce here a more robust intron excision metric, the Intron Jaccard Index, that combines alternative donor, alternative acceptor, and intron retention signal into a single value. Moreover, we optimized model parameters and filter cutoffs using candidate rare splice-disrupting variants as independent evidence. On 16,213 GTEx samples, our improved algorithm called typically 10 times fewer splicing outliers while increasing the proportion of candidate rare splice-disrupting variants by 10 fold and substantially decreasing the effect of sequencing depth on the number of reported outliers. Application on 303 rare disease samples confirmed the reduction fold-change of the number of outlier calls for a slight loss of sensitivity (only 2 out of 22 previously identified pathogenic splicing cases not recovered). Altogether, these methodological improvements contribute to more effective RNA-seq-based rare diagnostics by a drastic reduction of the amount of splicing outlier calls per sample at minimal loss of sensitivity.

Current sequence-based models capture gene expression determinants in promoters but mostly ignore distal enhancers

BACKGROUND

The largest sequence-based models of transcription control to date are obtained by predicting genome-wide gene regulatory assays across the human genome. This setting is fundamentally correlative, as those models are exposed during training solely to the sequence variation between human genes that arose through evolution, questioning the extent to which those models capture genuine causal signals.

RESULTS

Here we confront predictions of state-of-the-art models of transcription regulation against data from two large-scale observational studies and five deep perturbation assays. The most advanced of these sequence-based models, Enformer, by and large, captures causal determinants of human promoters. However, models fail to capture the causal effects of enhancers on expression, notably in medium to long distances and particularly for highly expressed promoters. More generally, the predicted impact of distal elements on gene expression predictions is small and the ability to correctly integrate long-range information is significantly more limited than the receptive fields of the models suggest. This is likely caused by the escalating class imbalance between actual and candidate regulatory elements as distance increases.

CONCLUSIONS

Our results suggest that sequence-based models have advanced to the point that in silico study of promoter regions and promoter variants can provide meaningful insights and we provide practical guidance on how to use them. Moreover, we foresee that it will require significantly more and particularly new kinds of data to train models accurately accounting for distal elements.

Łabaj P, Mangul S. RNA-seq data science: From raw data to effective interpretation

RNA sequencing (RNA-seq) has become an exemplary technology in modern biology and clinical science. Its immense popularity is due in large part to the continuous efforts of the bioinformatics community to develop accurate and scalable computational tools to analyze the enormous amounts of transcriptomic data that it produces. RNA-seq analysis enables genes and their corresponding transcripts to be probed for a variety of purposes, such as detecting novel exons or whole transcripts, assessing expression of genes and alternative transcripts, and studying alternative splicing structure. It can be a challenge, however, to obtain meaningful biological signals from raw RNA-seq data because of the enormous scale of the data as well as the inherent limitations of different sequencing technologies, such as amplification bias or biases of library preparation. The need to overcome these technical challenges has pushed the rapid development of novel computational tools, which have evolved and diversified in accordance with technological advancements, leading to the current myriad of RNA-seq tools. These tools, combined with the diverse computational skill sets of biomedical researchers, help to unlock the full potential of RNA-seq. The purpose of this review is to explain basic concepts in the computational analysis of RNA-seq data and define discipline-specific jargon.

Genetics of mitochondrial diseases: Current approaches for the molecular diagnosis

Mitochondrial diseases are a genetically and phenotypically variable set of monogenic disorders. The main characteristic of mitochondrial diseases is a defective oxidative phosphorylation. Both nuclear and mitochondrial DNA encode the approximately 1500 mitochondrial proteins. Since identification of the first mitochondrial disease gene in 1988 a total of 425 genes have been associated with mitochondrial diseases. Mitochondrial dysfunctions can be caused both by pathogenic variants in the mitochondrial DNA or the nuclear DNA. Hence, besides maternal inheritance, mitochondrial diseases can follow all modes of Mendelian inheritance. The maternal inheritance and tissue specificity distinguish molecular diagnostics of mitochondrial disorders from other rare disorders. With the advances made in the next-generation sequencing technology, whole exome sequencing and even whole-genome sequencing are now the established methods of choice for molecular diagnostics of mitochondrial diseases. They reach a diagnostic rate of more than 50% in clinically suspected mitochondrial disease patients. Moreover, next-generation sequencing is delivering a constantly growing number of novel mitochondrial disease genes. This chapter reviews mitochondrial and nuclear causes of mitochondrial diseases, molecular diagnostic methodologies, and their current challenges and perspectives.

Rare diseases of epigenetic origin: Challenges and opportunities

Rare diseases (RDs), more than 80% of which have a genetic origin, collectively affect approximately 350 million people worldwide. Progress in next-generation sequencing technology has both greatly accelerated the pace of discovery of novel RDs and provided more accurate means for their diagnosis. RDs that are driven by altered epigenetic regulation with an underlying genetic basis are referred to as rare diseases of epigenetic origin (RDEOs). These diseases pose unique challenges in research, as they often show complex genetic and clinical heterogeneity arising from unknown gene-disease mechanisms. Furthermore, multiple other factors, including cell type and developmental time point, can confound attempts to deconvolute the pathophysiology of these disorders. These challenges are further exacerbated by factors that contribute to epigenetic variability and the difficulty of collecting sufficient participant numbers in human studies. However, new molecular and bioinformatics techniques will provide insight into how these disorders manifest over time. This review highlights recent studies addressing these challenges with innovative solutions. Further research will elucidate the mechanisms of action underlying unique RDEOs and facilitate the discovery of treatments and diagnostic biomarkers for screening, thereby improving health trajectories and clinical outcomes of affected patients.