An informatics approach to analyzing the incidentalome

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.

Ancestry-specific high-risk gene variant profiling unmasks diabetes-associated genes

How ancestry-associated genetic variance affects disparities in the risk of polygenic diseases and influences the identification of disease-associated genes warrants a deeper understanding. We hypothesized that the discovery of genes associated with polygenic diseases may be limited by the overreliance on single-nucleotide polymorphism (SNP)-based genomic investigation, as most significant variants identified in genome-wide SNP association studies map to introns and intergenic regions of the genome. To overcome such potential limitations, we developed a gene-constrained, function-based analytical method centered on high-risk variants (hrV) that encode frameshifts, stopgains or splice site disruption. We analyzed the total number of hrV per gene in populations of different ancestry, representing a total of 185 934 subjects. Using this analysis, we developed a quantitative index of hrV (hrVI) across 20 428 genes within each population. We then applied hrVI analysis to the discovery of genes associated with type 2 diabetes mellitus (T2DM), a polygenic disease with ancestry-related disparity. HrVI profiling and gene-to-gene comparisons of ancestry-specific hrV between the case (20 781 subjects) and control (24 440 subjects) populations in the T2DM national repository identified 57 genes associated with T2DM, 40 of which were discoverable only by ancestry-specific analysis. These results illustrate how a function-based, ancestry-specific analysis of genetic variations can accelerate the identification of genes associated with polygenic diseases. Besides T2DM, such analysis may facilitate our understanding of the genetic basis for other polygenic diseases that are also greatly influenced by environmental and behavioral factors, such as obesity, hypertension and Alzheimer's disease.

Regulatory features aid interpretation of 3'UTR variants

Our ability to determine the clinical impact of variants in 3' untranslated regions (UTRs) of genes remains poor. We provide a thorough analysis of 3' UTR variants from several datasets. Variants in putative regulatory elements, including RNA-binding protein motifs, eCLIP peaks, and microRNA sites, are up to 16 times more likely than variants not in these elements to have gene expression and phenotype associations. Variants in regulatory motifs result in allele-specific protein binding in cell lines and allele-specific gene expression differences in population studies. In addition, variants in shared regions of alternatively polyadenylated isoforms and those proximal to polyA sites are more likely to affect gene expression and phenotype. Finally, pathogenic 3' UTR variants in ClinVar are up to 20 times more likely than benign variants to fall in a regulatory site. We incorporated these findings into RegVar, a software tool that interprets regulatory elements and annotations for any 3' UTR variant and predicts whether the variant is likely to affect gene expression or phenotype. This tool will help prioritize variants for experimental studies and identify pathogenic variants in individuals.

Deep generative modeling of the human proteome reveals over a hundred novel genes involved in rare genetic disorders

Identifying causal mutations accelerates genetic disease diagnosis, and therapeutic development. Missense variants present a bottleneck in genetic diagnoses as their effects are less straightforward than truncations or nonsense mutations. While computational prediction methods are increasingly successful at prediction for variants in known disease genes, they do not generalize well to other genes as the scores are not calibrated across the proteome1-6. To address this, we developed a deep generative model, popEVE, that combines evolutionary information with population sequence data7 and achieves state-of-the-art performance at ranking variants by severity to distinguish patients with severe developmental disorders8 from potentially healthy individuals9. popEVE identifies 442 genes in patients this developmental disorder cohort, including evidence of 123 novel genetic disorders, many without the need for gene-level enrichment and without overestimating the prevalence of pathogenic variants in the population. A majority of these variants are close to interacting partners in 3D complexes. Preliminary analyses on child exomes indicate that popEVE can identify candidate variants without the need for inheritance labels. By placing variants on a unified scale, our model offers a comprehensive perspective on the distribution of fitness effects across the entire proteome and the broader human population. popEVE provides compelling evidence for genetic diagnoses even in exceptionally rare single-patient disorders where conventional techniques relying on repeated observations may not be applicable.

Quantifying negative selection in human 3' UTRs uncovers constrained targets of RNA-binding proteins

Many non-coding variants associated with phenotypes occur in 3' untranslated regions (3' UTRs), and may affect interactions with RNA-binding proteins (RBPs) to regulate gene expression post-transcriptionally. However, identifying functional 3' UTR variants has proven difficult. We use allele frequencies from the Genome Aggregation Database (gnomAD) to identify classes of 3' UTR variants under strong negative selection in humans. We develop intergenic mutability-adjusted proportion singleton (iMAPS), a generalized measure related to MAPS, to quantify negative selection in non-coding regions. This approach, in conjunction with in vitro and in vivo binding data, identifies precise RBP binding sites, miRNA target sites, and polyadenylation signals (PASs) under strong selection. For each class of sites, we identify thousands of gnomAD variants under selection comparable to missense coding variants, and find that sites in core 3' UTR regions upstream of the most-used PAS are under strongest selection. Together, this work improves our understanding of selection on human genes and validates approaches for interpreting genetic variants in human 3' UTRs.

Deep generative modeling of the human proteome reveals over a hundred novel genes involved in rare genetic disorders

Identifying causal mutations accelerates genetic disease diagnosis, and therapeutic development. Missense variants present a bottleneck in genetic diagnoses as their effects are less straightforward than truncations or nonsense mutations. While computational prediction methods are increasingly successful at prediction for variants in known disease genes, they do not generalize well to other genes as the scores are not calibrated across the proteome. To address this, we developed a deep generative model, popEVE, that combines evolutionary information with population sequence data and achieves state-of-the-art performance at ranking variants by severity to distinguish patients with severe developmental disorders from potentially healthy individuals. popEVE identifies 442 genes in a cohort of developmental disorder cases, including evidence of 119 novel genetic disorders without the need for gene-level enrichment and without overestimating the prevalence of pathogenic variants in the population. By placing variants on a unified scale, our model offers a comprehensive perspective on the distribution of fitness effects across the entire proteome and the broader human population. popEVE provides compelling evidence for genetic diagnoses even in exceptionally rare single-patient disorders where conventional techniques relying on repeated observations may not be applicable. Interactive web viewer and downloads available at pop.evemodel.org.

Accurate proteome-wide missense variant effect prediction with AlphaMissense

The vast majority of missense variants observed in the human genome are of unknown clinical significance. We present AlphaMissense, an adaptation of AlphaFold fine-tuned on human and primate variant population frequency databases to predict missense variant pathogenicity. By combining structural context and evolutionary conservation, our model achieves state-of-the-art results across a wide range of genetic and experimental benchmarks, all without explicitly training on such data. The average pathogenicity score of genes is also predictive for their cell essentiality, capable of identifying short essential genes that existing statistical approaches are underpowered to detect. As a resource to the community, we provide a database of predictions for all possible human single amino acid substitutions and classify 89% of missense variants as either likely benign or likely pathogenic.

Random allelic expression in the adult human body

Genes are typically assumed to express both parental alleles similarly, yet cell lines show random allelic expression (RAE) for many autosomal genes that could shape genetic effects. Thus, understanding RAE in human tissues could improve our understanding of phenotypic variation. Here, we develop a methodology to perform genome-wide profiling of RAE and biallelic expression in GTEx datasets for 832 people and 54 tissues. We report 2,762 autosomal genes with some RAE properties similar to randomly inactivated X-linked genes. We found that RAE is associated with rapidly evolving regions in the human genome, adaptive signaling processes, and genes linked to age-related diseases such as neurodegeneration and cancer. We define putative mechanistic subtypes of RAE distinguished by gene overlaps on sense and antisense DNA strands, aggregation in clusters near telomeres, and increased regulatory complexity and inputs compared with biallelic genes. We provide foundations to study RAE in human phenotypes, evolution, and disease.

Combining genetic constraint with predictions of alternative splicing to prioritize deleterious splicing in rare disease studies

BACKGROUND

Despite numerous molecular and computational advances, roughly half of patients with a rare disease remain undiagnosed after exome or genome sequencing. A particularly challenging barrier to diagnosis is identifying variants that cause deleterious alternative splicing at intronic or exonic loci outside of canonical donor or acceptor splice sites.

RESULTS

Several existing tools predict the likelihood that a genetic variant causes alternative splicing. We sought to extend such methods by developing a new metric that aids in discerning whether a genetic variant leads to deleterious alternative splicing. Our metric combines genetic variation in the Genome Aggregate Database with alternative splicing predictions from SpliceAI to compare observed and expected levels of splice-altering genetic variation. We infer genic regions with significantly less splice-altering variation than expected to be constrained. The resulting model of regional splicing constraint captures differential splicing constraint across gene and exon categories, and the most constrained genic regions are enriched for pathogenic splice-altering variants. Building from this model, we developed ConSpliceML. This ensemble machine learning approach combines regional splicing constraint with multiple per-nucleotide alternative splicing scores to guide the prediction of deleterious splicing variants in protein-coding genes. ConSpliceML more accurately distinguishes deleterious and benign splicing variants than state-of-the-art splicing prediction methods, especially in "cryptic" splicing regions beyond canonical donor or acceptor splice sites.

CONCLUSION

Integrating a model of genetic constraint with annotations from existing alternative splicing tools allows ConSpliceML to prioritize potentially deleterious splice-altering variants in studies of rare human diseases.

Exploring the mutational landscape of genes associated with inherited retinal disease using large genomic datasets: identifying loss of function intolerance and outlying propensities for missense changes

Background

Large databases permit quantitative description of genes in terms of intolerance to loss of function (‘haploinsufficiency’) and prevalence of missense variants. We explored these parameters in inherited retinal disease (IRD) genes.

Methods

IRD genes (from the ‘RetNet’ resource) were classified by probability of loss of function intolerance (pLI) using online Genome Aggregation Database (gnomAD) and DatabasE of genomiC varIation and Phenotype in Humans using Ensembl Resources (DECIPHER) databases. Genes were identified having pLI ≥0.9 together with one or both of the following: upper bound of CI <0.35 for observed to expected (o/e) ratio of loss of function variants in the gnomAD resource; haploinsufficiency score <10 in the DECIPHER resource. IRD genes in which missense variants appeared under-represented or over-represented (Z score for o/e ratio of <−2.99 or >2.99, respectively) were also identified. The genes were evaluated in the gene ontology Protein Analysis THrough Evolutionary Relationships (PANTHER) resource.

Results

Of 280 analysed genes, 39 (13.9%) were predicted loss of function intolerant. A greater proportion of X-linked than autosomal IRD genes fulfilled these criteria, as expected. Most autosomal genes were associated with dominant disease. PANTHER analysis showed >100 fold enrichment of spliceosome tri-snRNP complex assembly. Most encoded proteins were longer than the median length in the UniProt database. Fourteen genes (11 of which were in the ‘haploinsufficient’ group) showed under-representation of missense variants. Six genes (SAMD11, ALMS1, WFS1, RP1L1, KCNV2, ADAMTS18) showed over-representation of missense variants.

Conclusion

A minority of IRD-associated genes appear to be ‘haploinsufficient’. Over-representation of spliceosome pathways was observed. When interpreting genetic tests, variants found in genes with over-representation of missense variants should be interpreted with caution.

Digital health-enabled genomics: Opportunities and challenges

Digital health solutions, with apps, virtual care, and electronic medical records, are gaining momentum across all medical disciplines, and their adoption has been accelerated, in part, by the COVID-19 pandemic. Personal wearables, sensors, and mobile technologies are increasingly being used to identify health risks and assist in diagnosis, treatment, and monitoring of health and disease. Genomics is a vanguard of digital healthcare as we witness a convergence of the fields of genomic and digital medicine. Spurred by the acute need to increase health literacy, empower patients' preference-sensitive decisions, or integrate vast amounts of complex genomic data into the clinical workflow, there has been an emergence of digital support tools in genomics-enabled care. We present three use cases that demonstrate the application of these converging technologies: digital genomics decision support tools, conversational chatbots to scale the genetic counseling process, and the digital delivery of comprehensive genetic services. These digital solutions are important to facilitate patient-centered care delivery, improve patient outcomes, and increase healthcare efficiencies in genomic medicine. Yet the development of these innovative digital genomic technologies also reveals strategic challenges that need to be addressed before genomic digital health can be broadly adopted. Alongside key evidentiary gaps in clinical and cost-effectiveness, there is a paucity of clinical guidelines, policy, and regulatory frameworks that incorporate digital health. We propose a research agenda, guided by learning healthcare systems, to realize the vision of digital health-enabled genomics to ensure its sustainable and equitable deployment in clinical care.

The 3D mutational constraint on amino acid sites in the human proteome

Quantification of the tolerance of protein sites to genetic variation has become a cornerstone of variant interpretation. We hypothesize that the constraint on missense variation at individual amino acid sites is largely shaped by direct interactions with 3D neighboring sites. To quantify this constraint, we introduce a framework called COntact Set MISsense tolerance (or COSMIS) and comprehensively map the landscape of 3D mutational constraint on 6.1 million amino acid sites covering 16,533 human proteins. We show that 3D mutational constraint is pervasive and that the level of constraint is strongly associated with disease relevance both at the site and the protein level. We demonstrate that COSMIS performs significantly better at variant interpretation tasks than other population-based constraint metrics while also providing structural insight into the functional roles of constrained sites. We anticipate that COSMIS will facilitate the interpretation of protein-coding variation in evolution and prioritization of sites for mechanistic investigation.

Frequencies of the MEFV Gene Mutations in Azerbaijan

The MEFV (familial Mediterranean fever gene) researches were performed in the population of the Republic of Azerbaijan in 2016–2021. Seven mutations of the MEFV gene were identified in heterozygous, homozygous and compound homozygous conditions: R761H, M694I, M694V, V726A, R202Q, M680I and E148Q. The E148Q and R202Q mutations were discovered in exon 2 and R761H M694I, M694V, V726A, M680I were found in exon 10 in the population of the Republic of Azerbaijan. The highest gene frequency of the MEFV gene examined in 42 patients was 42.85% in the M694V mutations. The second highest frequency was the R761H and the third most frequent mutation was V726A. According to world literature, five mutations, M694V, V726A, M694I, R202Q, M680I and E148Q, constitute 75.0% of all mutations found today. In our studies, these five mutations belong to the same group, and makes up 57.6% of the total mutations found. In order to prevent hereditary disease such as the familial Mediterranean fever (FMF) in the population of the Republic of Azerbaijan, it is planned to carry out prenatal diagnosis (PND) of the at-risk families.

CCR8-targeted specific depletion of clonally expanded Treg cells in tumor tissues evokes potent tumor immunity with long-lasting memory

Immunosuppressive Foxp3-expressing regulatory T cells (Tregs) in tumor tissues are assumed to be clonally expanding via recognizing tumor-associated antigens. By single-cell RNA sequencing, we have searched for the molecules that are specifically expressed by such multiclonal tumor Tregs, but not by tumor-infiltrating effector T cells or natural Tregs in other tissues. The search revealed the chemokine receptor CCR8 as a candidate. Treatment of tumor-bearing mice with cell-depleting anti-CCR8 antibody indeed selectively removed multiclonal tumor Tregs without affecting effector T cells or tissue Tregs, eradicating established tumors with induction of potent tumor-specific effector/memory T cells and without activating autoimmune T cells. Thus, specific depletion of clonally expanding tumor Tregs is clinically instrumental for evoking effective tumor immunity without autoimmune adverse effects.

Foxp3-expressing CD25⁺CD4⁺ regulatory T cells (Tregs) are abundant in tumor tissues. Here, hypothesizing that tumor Tregs would clonally expand after they are activated by tumor-associated antigens to suppress antitumor immune responses, we performed single-cell analysis on tumor Tregs to characterize them by T cell receptor clonotype and gene-expression profiles. We found that multiclonal Tregs present in tumor tissues predominantly expressed the chemokine receptor CCR8. In mice and humans, CCR8⁺ Tregs constituted 30 to 80% of tumor Tregs in various cancers and less than 10% of Tregs in other tissues, whereas most tumor-infiltrating conventional T cells (Tconvs) were CCR8^–. CCR8⁺ tumor Tregs were highly differentiated and functionally stable. Administration of cell-depleting anti-CCR8 monoclonal antibodies (mAbs) indeed selectively eliminated multiclonal tumor Tregs, leading to cure of established tumors in mice. The treatment resulted in the expansion of CD8⁺ effector Tconvs, including tumor antigen-specific ones, that were more activated and less exhausted than those induced by PD-1 immune checkpoint blockade. Anti-CCR8 mAb treatment also evoked strong secondary immune responses against the same tumor cell line inoculated several months after tumor eradication, indicating that elimination of tumor-reactive multiclonal Tregs was sufficient to induce memory-type tumor-specific effector Tconvs. Despite induction of such potent tumor immunity, anti-CCR8 mAb treatment elicited minimal autoimmunity in mice, contrasting with systemic Treg depletion, which eradicated tumors but induced severe autoimmune disease. Thus, specific removal of clonally expanding Tregs in tumor tissues for a limited period by cell-depleting anti-CCR8 mAb treatment can generate potent tumor immunity with long-lasting memory and without deleterious autoimmunity.

Perceptions of uncertainties about carrier results identified by exome sequencing in a randomized controlled trial

How individuals perceive uncertainties in sequencing results may affect their clinical utility. The purpose of this study was to explore perceptions of uncertainties in carrier results and how they relate to psychological well-being and health behavior. Post-reproductive adults (N = 462) were randomized to receive carrier results from sequencing through either a web platform or a genetic counselor. On average, participants received two results. Group differences in affective, evaluative, and clinical uncertainties were assessed from baseline to 1 and 6 months; associations with test-specific distress and communication of results were assessed at 6 months. Reductions in affective uncertainty (∆x̅ = 0.78, 95% CI: 0.53, 1.02) and evaluative uncertainty (∆x̅ = 0.69, 95% CI: 0.51, 0.87) followed receipt of results regardless of randomization arm at 1 month. Participants in the web platform arm reported greater clinical uncertainty than those in the genetic counselor arm at 1 and 6 months; this was corroborated by the 1,230 questions asked of the genetic counselor and residual questions reported by those randomized to the web platform. Evaluative uncertainty was associated with a lower likelihood of communicating results to health care providers. Clinical uncertainty was associated with a lower likelihood of communicating results to children. Learning one's carrier results may reduce perceptions of uncertainties, though web-based return may lead to less reduction in clinical uncertainty in the short term. These findings warrant reinforcement of clinical implications to minimize residual questions and promote appropriate health behavior (communicating results to at-risk relatives in the case of carrier results), especially when testing alternative delivery models.

Integrative analysis of genomic variants reveals new associations of candidate haploinsufficient genes with congenital heart disease

Numerous genetic studies have established a role for rare genomic variants in Congenital Heart Disease (CHD) at the copy number variation (CNV) and de novo variant (DNV) level. To identify novel haploinsufficient CHD disease genes, we performed an integrative analysis of CNVs and DNVs identified in probands with CHD including cases with sporadic thoracic aortic aneurysm. We assembled CNV data from 7,958 cases and 14,082 controls and performed a gene-wise analysis of the burden of rare genomic deletions in cases versus controls. In addition, we performed variation rate testing for DNVs identified in 2,489 parent-offspring trios. Our analysis revealed 21 genes which were significantly affected by rare CNVs and/or DNVs in probands. Fourteen of these genes have previously been associated with CHD while the remaining genes (FEZ1, MYO16, ARID1B, NALCN, WAC, KDM5B and WHSC1) have only been associated in small cases series or show new associations with CHD. In addition, a systems level analysis revealed affected protein-protein interaction networks involved in Notch signaling pathway, heart morphogenesis, DNA repair and cilia/centrosome function. Taken together, this approach highlights the importance of re-analyzing existing datasets to strengthen disease association and identify novel disease genes and pathways.

Evaluating the clinical utility of early exome sequencing in diverse pediatric outpatient populations in the North Carolina Clinical Genomic Evaluation of Next-generation Exome Sequencing (NCGENES) 2 study: a randomized controlled trial

BACKGROUND

Exome sequencing (ES) has probable utility for shortening the diagnostic odyssey of children with suspected genetic disorders. This report describes the design and methods of a study evaluating the potential of ES as a routine clinical tool for pediatric patients who have suspected genetic conditions and who are in the early stages of the diagnostic odyssey.

METHODS

The North Carolina Clinical Genomic Evaluation by Next-generation Exome Sequencing (NCGENES) 2 study is an interdisciplinary, multi-site Phase III randomized controlled trial of two interventions: educational pre-visit preparation (PVP) and offer of first-line ES. In this full-factorial design, parent-child dyads are randomly assigned to one of four study arms (PVP + usual care, ES + usual care, PVP + ES + usual care, or usual care alone) in equal proportions. Participants are recruited from Pediatric Genetics or Neurology outpatient clinics in three North Carolina healthcare facilities. Eligible pediatric participants are < 16 years old and have a first visit to a participating clinic, a suspected genetic condition, and an eligible parent/guardian to attend the clinic visit and complete study measures. The study oversamples participants from underserved and under-represented populations. Participants assigned to the PVP arms receive an educational booklet and question prompt list before clinical interactions. Randomization to offer of first-line ES is revealed after a child's clinic visit. Parents complete measures at baseline, pre-clinic, post-clinic, and two follow-up timepoints. Study clinicians provide phenotypic data and complete measures after the clinic visit and after returning results. Reportable study-related research ES results are confirmed in a CLIA-certified clinical laboratory. Results are disclosed to the parent by the clinical team. A community consultation team contributed to the development of study materials and study implementation methods and remains engaged in the project.

DISCUSSION

NCGENES 2 will contribute valuable knowledge concerning technical, clinical, psychosocial, and health economic issues associated with using early diagnostic ES to shorten the diagnostic odyssey of pediatric patients with likely genetic conditions. Results will inform efforts to engage diverse populations in genomic medicine research and generate evidence that can inform policy, practice, and future research related to the utility of first-line diagnostic ES in health care.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03548779 . Registered on June 07, 2018.

Expectations and blind spots for structural variation detection from long-read assemblies and short-read genome sequencing technologies

Virtually all genome sequencing efforts in national biobanks, complex and Mendelian disease programs, and medical genetic initiatives are reliant upon short-read whole-genome sequencing (srWGS), which presents challenges for the detection of structural variants (SVs) relative to emerging long-read WGS (lrWGS) technologies. Given this ubiquity of srWGS in large-scale genomics initiatives, we sought to establish expectations for routine SV detection from this data type by comparison with lrWGS assembly, as well as to quantify the genomic properties and added value of SVs uniquely accessible to each technology. Analyses from the Human Genome Structural Variation Consortium (HGSVC) of three families captured ~11,000 SVs per genome from srWGS and ~25,000 SVs per genome from lrWGS assembly. Detection power and precision for SV discovery varied dramatically by genomic context and variant class: 9.7% of the current GRCh38 reference is defined by segmental duplication (SD) and simple repeat (SR), yet 91.4% of deletions that were specifically discovered by lrWGS localized to these regions. Across the remaining 90.3% of reference sequence, we observed extremely high (93.8%) concordance between technologies for deletions in these datasets. In contrast, lrWGS was superior for detection of insertions across all genomic contexts. Given that non-SD/SR sequences encompass 95.9% of currently annotated disease-associated exons, improved sensitivity from lrWGS to discover novel pathogenic deletions in these currently interpretable genomic regions is likely to be incremental. However, these analyses highlight the considerable added value of assembly-based lrWGS to create new catalogs of insertions and transposable elements, as well as disease-associated repeat expansions in genomic sequences that were previously recalcitrant to routine assessment.

Unique Variant Spectrum in a Jordanian Cohort with Inherited Retinal Dystrophies

Whole Exome Sequencing (WES) is a powerful approach for detecting sequence variations in the human genome. The aim of this study was to investigate the genetic defects in Jordanian patients with inherited retinal dystrophies (IRDs) using WES. WES was performed on proband patients' DNA samples from 55 Jordanian families. Sanger sequencing was used for validation and segregation analysis of the detected, potential disease-causing variants (DCVs). Thirty-five putatively causative variants (6 novel and 29 known) in 21 IRD-associated genes were identified in 71% of probands (39 of the 55 families). Three families showed phenotypes different from the typically reported clinical findings associated with the causative genes. To our knowledge, this is the largest genetic analysis of IRDs in the Jordanian population to date. Our study also confirms that WES is a powerful tool for the molecular diagnosis of IRDs in large patient cohorts.

Diagnosis of Schaaf-Yang syndrome in Korean children with developmental delay and hypotonia

Schaaf-Yang syndrome (SYS) is a recently identified disorder caused by a loss-of-function mutation in a maternally imprinted gene, MAGEL2, at 15q11.2q13. Due to its extreme rarity and wide range of clinical severity, clinical suspicion is difficult for a physician. In the current study, its frequency among the Korean pediatric patients with developmental delay (DD) or intellectual disability (ID) was assessed. As the first report of Korean patients with SYS, our study aims to increase the awareness of this condition among the physicians taking care of the pediatric patients with DD/ID and hypotonia.The patients diagnosed with SYS by whole-exome sequencing (WES) among the 460 Korean pediatric patients with DD/ID were included, and their clinical and molecular features were reviewed.Four patients (0.9%) were diagnosed with SYS. Profound DD (4 patients), multiple anomalies including joint contractures and facial dysmorphism (4 patients), generalized hypotonia (3 patients), and severe respiratory difficulty requiring mechanical ventilation (3 patients) were noted in most cases, similar to those in previous reports. Sleep apnea (2 patients), autistic features (2 patients), a high grade of gastroesophageal reflux (1 patient), and seizures (1 patient) were found as well. A total of 3 different truncating MAGEL2 mutations were identified. A previously-reported mutation, to be the most common one, c.1996dupC, was found in 2 patients. The other 2 mutations, c.2217delC and c.3449_3450delTT were novel mutations. As MAGEL2 is maternally imprinted, 2 patients had inherited the MAGEL2 mutation from their respective healthy fathers.SYS is an extremely rare cause of DD/ID. However, hypotonia, joint contractures, profound DD/ID and facial dysmorphism are the suggestive clinical features for SYS. As a maternally imprinted disorder, it should be reminded that SYS may be inherited in form of a mutation from a healthy father.

Diagnostic yield and clinical utility of whole exome sequencing using an automated variant prioritization system, EVIDENCE

EVIDENCE, an automated variant prioritization system, has been developed to facilitate whole exome sequencing analyses. This study investigated the diagnostic yield of EVIDENCE in patients with suspected genetic disorders. DNA from 330 probands (age range, 0‐68 years) with suspected genetic disorders were subjected to whole exome sequencing. Candidate variants were identified by EVIDENCE and confirmed by testing family members and/or clinical reassessments. EVIDENCE reported a total 228 variants in 200 (60.6%) of the 330 probands. The average number of organs involved per patient was 4.5 ± 5.0. After clinical reassessment and/or family member testing, 167 variants were identified in 141 probands (42.7%), including 105 novel variants. These variants were confirmed as being responsible for 121 genetic disorders. A total of 103 (61.7%) of the 167 variants in 95 patients were classified as pathogenic or probably to be pathogenic before, and 161 (96.4%) variants in 137 patients (41.5%) after, clinical assessment and/or family member testing. Factor associated with a variant being regarded as causative includes similar symptom scores of a gene variant to the phenotype of the patient. This new, automated variant interpretation system facilitated the diagnosis of various genetic diseases with a 42.7% diagnostic yield.

Beyond medically actionable results: an analytical pipeline for decreasing the burden of returning all clinically significant secondary findings

Genomic sequencing advances have increased the potential to identify secondary findings (SFs). Current guidelines recommend the analysis of 59 medically actionable genes; however, patient preferences indicate interest in learning a broader group of SFs. We aimed to develop an analytical pipeline for the efficient analysis and return of all clinically significant SFs. We developed a pipeline consisting of comprehensive gene lists for five categories of SFs and filtration parameters for prioritization of variants in each category. We applied the pipeline to 42 exomes to assess feasibility and efficiency. Comprehensive lists of clinically significant SF genes were curated for each category: (1) 90 medically actionable genes and 28 pharmacogenomic variants; (2) 17 common disease risk variants; (3) 3166 Mendelian disease genes, (4) 7 early onset neurodegenerative disorder genes; (5) 688 carrier status results. Analysis of 42 exomes using our pipeline resulted in a significant decrease (> 98%) in variants compared to the raw analysis (13,036.56 ± 59.72 raw variants/exome vs 161.32 ± 7.68 filtered variants/exome), and aided in time and costs savings for the overall analysis process. Our pipeline represents a critical step in overcoming the analytic challenge associated with returning all clinically relevant SFs to allow for its routine implementation in clinical practice.

Unified inference of missense variant effects and gene constraints in the human genome

A challenge in medical genomics is to identify variants and genes associated with severe genetic disorders. Based on the premise that severe, early-onset disorders often result in a reduction of evolutionary fitness, several statistical methods have been developed to predict pathogenic variants or constrained genes based on the signatures of negative selection in human populations. However, we currently lack a statistical framework to jointly predict deleterious variants and constrained genes from both variant-level features and gene-level selective constraints. Here we present such a unified approach, UNEECON, based on deep learning and population genetics. UNEECON treats the contributions of variant-level features and gene-level constraints as a variant-level fixed effect and a gene-level random effect, respectively. The sum of the fixed and random effects is then combined with an evolutionary model to infer the strength of negative selection at both variant and gene levels. Compared with previously published methods, UNEECON shows improved performance in predicting missense variants and protein-coding genes associated with autosomal dominant disorders, and feature importance analysis suggests that both gene-level selective constraints and variant-level predictors are important for accurate variant prioritization. Furthermore, based on UNEECON, we observe a low correlation between gene-level intolerance to missense mutations and that to loss-of-function mutations, which can be partially explained by the prevalence of disordered protein regions that are highly tolerant to missense mutations. Finally, we show that genes intolerant to both missense and loss-of-function mutations play key roles in the central nervous system and the autism spectrum disorders. Overall, UNEECON is a promising framework for both variant and gene prioritization.

Numerous statistical methods have been developed to predict deleterious missense variants or constrained genes in the human genome, but unified prioritization methods that utilize both variant- and gene-level information are underdeveloped. Here we present UNEECON, an evolution-based deep learning framework for unified variant and gene prioritization. By integrating variant-level predictors and gene-level selective constraints, UNEECON outperforms existing methods in predicting missense variants and protein-coding genes associated with dominant disorders. Based on UNEECON, we show that disordered proteins are tolerant to missense mutations but not to loss-of-function mutations. In addition, we find that genes under strong selective constraints at both missense and loss-of-function levels are strongly associated with the central nervous system and the autism spectrum disorders, highlighting the need to investigate the function of these highly constrained genes in future studies.

An approach to integrating exome sequencing for fetal structural anomalies into clinical practice

PURPOSE

We investigated the diagnostic and clinical performance of trio exome sequencing (ES) in parent-fetus trios where the fetus had sonographic abnormalities but normal karyotype, microarray and, in some cases, normal gene-specific sequencing.

METHODS

ES was performed from DNA of 102 anomalous fetuses and from peripheral blood from their parents. Parents provided consent for the return of diagnostic results in the fetus, medically actionable findings in the parents, and identification as carrier couple for significant autosomal recessive conditions.

RESULTS

In 21/102 (20.6%) fetuses, ES provided a positive-definitive or positive-probable diagnosis. In 10/102 (9.8%), ES provided an inconclusive-possible result. At least 2/102 (2.0%) had a repeat pregnancy during the study period and used the information from the study for prenatal diagnosis in the next pregnancy. Six of 204 (2.9%) parents received medically actionable results that affected their own health and 3/102 (2.9%) of couples received results that they were carriers for the same autosomal recessive condition.

CONCLUSION

ES has diagnostic utility in a select population of fetuses where a genetic diagnosis was highly suspected. Challenges related to genetics literacy, variant interpretation, and various types of diagnostic results affecting both fetal and parental health must be addressed by highly tailored pre- and post-test genetic counseling.

Transcript expression-aware annotation improves rare variant interpretation

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

Age dependent association of inbreeding with risk for schizophrenia in Egypt

BACKGROUND

Self-reported consanguinity is associated with risk for schizophrenia (SZ) in several inbred populations, but estimates using DNA-based coefficients of inbreeding are unavailable. Further, it is not known whether recessively inherited risk mutations can be identified through homozygosity by descent (HBD) mapping.

METHODS

We studied self-reported and DNA-based estimates of inbreeding among Egyptian patients with SZ (n = 421, DSM IV criteria) and adult controls without psychosis (n = 301), who were evaluated using semi-structured diagnostic interview schedules and genotyped using the Illumina Infinium PsychArray. Following quality control checks, coefficients of inbreeding (F) and regions of homozygosity (ROH) were estimated using PLINK software for HBD analysis. Exome sequencing was conducted in selected cases.

RESULTS

Inbreeding was associated with schizophrenia based on self-reported consanguinity (χ2 = 4.506, 1 df, p = 0.034) and DNA-based estimates for inbreeding (F); the latter with a significant F × age interaction (β = 32.34, p = 0.0047). The association was most notable among patients older than age 40 years. Eleven ROH were over-represented in cases on chromosomes 1, 3, 6, 11, and 14; all but one region is novel for schizophrenia risk. Exome sequencing identified six recessively-acting genes in ROH with loss-of-function variants; one of which causes primary hereditary microcephaly.

CONCLUSIONS

We propose consanguinity as an age-dependent risk factor for SZ in Egypt. HBD mapping is feasible for SZ in adequately powered samples.

Effectiveness of the Genomics ADvISER decision aid for the selection of secondary findings from genomic sequencing: a randomized clinical trial

PURPOSE

To evaluate the effectiveness of the Genomics ADvISER (www.genomicsadviser.com) decision aid (DA) for selection of secondary findings (SF), compared with genetic counseling alone.

METHODS

A randomized controlled trial (RCT) was conducted to evaluate whether the Genomics ADvISER is superior to genetic counseling when hypothetically selecting SF. Participants were randomized to use the DA followed by discussion with a genetic counselor, or to genetic counseling alone. Surveys were administered at baseline and post-intervention. Primary outcome was decisional conflict. Secondary outcomes were knowledge, preparation for, and satisfaction with decision-making, anxiety, and length of counseling session.

RESULTS

Participants (n = 133) were predominantly White/European (74%), female (90%), and ≥50 years old (60%). Decisional conflict (mean difference 0.05; P = 0.60), preparation for decision-making (0.17; P = 0.95), satisfaction with decision (-2.18; P = 0.06), anxiety (0.72; P = 0.56), and knowledge of sequencing limitations (0.14; P = 0.70) did not significantly differ between groups. However, intervention participants had significantly higher knowledge of SF (0.39; P < 0.001) and sequencing benefits (0.97; P = 0.01), and significantly shorter counseling time (24.40 minutes less; P < 0.001) CONCLUSIONS: The Genomics ADvISER did not decrease decisional conflict but reduced counseling time and improved knowledge. This decision aid could serve as an educational tool, reducing in-clinic time and potentially health care costs.

Ranking of non-coding pathogenic variants and putative essential regions of the human genome

A gene is considered essential if loss of function results in loss of viability, fitness or in disease. This concept is well established for coding genes; however, non-coding regions are thought less likely to be determinants of critical functions. Here we train a machine learning model using functional, mutational and structural features, including new genome essentiality metrics, 3D genome organization and enhancer reporter data to identify deleterious variants in non-coding regions. We assess the model for functional correlates by using data from tiling-deletion-based and CRISPR interference screens of activity of cis-regulatory elements in over 3 Mb of genome sequence. Finally, we explore two user cases that involve indels and the disruption of enhancers associated with a developmental disease. We rank variants in the non-coding genome according to their predicted deleteriousness. The model prioritizes non-coding regions associated with regulation of important genes and with cell viability, an in vitro surrogate of essentiality.

Whole genome sequencing (WGS) holds promise to solve a subset of Mendelian disease cases for which exome sequencing did not provide a genetic diagnosis. Here, Wells et al. report a supervised machine learning model trained on functional, mutational and structural features for rank-scoring and interpreting variants in non-coding regions from WGS.

Genes with High Network Connectivity Are Enriched for Disease Heritability

Recent studies have highlighted the role of gene networks in disease biology. To formally assess this, we constructed a broad set of pathway, network, and pathway+network annotations and applied stratified LD score regression to 42 diseases and complex traits (average N = 323K) to identify enriched annotations. First, we analyzed 18,119 biological pathways. We identified 156 pathway-trait pairs whose disease enrichment was statistically significant (FDR < 5%) after conditioning on all genes and 75 known functional annotations (from the baseline-LD model), a stringent step that greatly reduced the number of pathways detected; most significant pathway-trait pairs were previously unreported. Next, for each of four published gene networks, we constructed probabilistic annotations based on network connectivity. For each gene network, the network connectivity annotation was strongly significantly enriched. Surprisingly, the enrichments were fully explained by excess overlap between network annotations and regulatory annotations from the baseline-LD model, validating the informativeness of the baseline-LD model and emphasizing the importance of accounting for regulatory annotations in gene network analyses. Finally, for each of the 156 enriched pathway-trait pairs, for each of the four gene networks, we constructed pathway+network annotations by annotating genes with high network connectivity to the input pathway. For each gene network, these pathway+network annotations were strongly significantly enriched for the corresponding traits. Once again, the enrichments were largely explained by the baseline-LD model. In conclusion, gene network connectivity is highly informative for disease architectures, but the information in gene networks may be subsumed by regulatory annotations, emphasizing the importance of accounting for known annotations.

Allele frequency analysis of variants reported to cause autosomal dominant inherited retinal diseases question the involvement of 19% of genes and 10% of reported pathogenic variants

BACKGROUND

Next generation sequencing (NGS) generates a large amount of genetic data that can be used to better characterise disease-causing variants. Our aim was to examine allele frequencies of sequence variants reported to cause autosomal dominant inherited retinal diseases (AD-IRDs).

METHODS

Genetic information was collected from various databases, including PubMed, the Human Genome Mutation Database, RETNET and gnomAD.

RESULTS

We generated a database of 1223 variants reported in 58 genes, including their allele frequency in gnomAD that contains NGS data of over 138 000 individuals. While the majority of variants are not represented in gnomAD, 138 had an allele count of >1 and were examined carefully for various aspects including cosegregation and functional analyses. The analysis revealed 122 variants that were reported pathogenic but unlikely to cause AD-IRDs. Interestingly, in some cases, these unlikely pathogenic variants were the only ones reported to cause disease in AD inheritance pattern for a particular gene, therefore raising doubt regarding the involvement of 11 (19%) of the genes in AD-IRDs.

CONCLUSION

We predict that these data are not limited to a specific disease or inheritance pattern since non-pathogenic variants were mistakenly reported as pathogenic in various diseases. Our results should serve as a warning sign for geneticists, variant database curators and sequencing panels' developers not to automatically accept reported variants as pathogenic but cross-reference the information with large databases.

National Rare Diseases Registry System of China and Related Cohort Studies: Vision and Roadmap

Rare diseases are major challenges in healthcare and medical research and are the basis of national development strategies in many countries. However, inadequate definition of rare diseases and lags in orphan drug development in China hinder rare disease research. In response, the first National Rare Diseases Registry System of China (NRDRS) was established, and various cohort studies have been launched since 2016. More than 20 top academic institutions in China are currently participating in this joint effort to carry out nationwide registration of rare diseases. The primary objectives are to establish standardization for the registration platform, build biobanks of genomic data, and create partnerships for data sharing and research collaboration. Innovative informatics technologies have been implemented to develop the NRDRS, including employment of ontological and knowledge bases to render standardization and support standard of care. Development of informatics analysis tools will facilitate accurate and more efficient diagnoses for rare diseases. Long-term research collaboration is encouraged to create additional national rare disease networks for research translation and to benefit patients with rare diseases. The NRDRS of China and related cohort studies are anticipated to enlighten rare disease research significantly in China.

Reconciling Opportunistic and Population Screening in Clinical Genomics

Opportunistic genomic screening is becoming increasingly common as laboratories adopt recommendations to report secondary genomic findings. In parallel, interest in using genome sequencing as a population screening test has grown rapidly. We consider here 3 potential applications of genome sequencing for preventive medicine: (1) provider-ordered predispositional testing in healthy adults, (2) indication-based testing with opportunistic screening of secondary results, and (3) population screening in the public health context. We conclude that despite superficial similarities, there are important and fundamental differences in the way medical risks and benefits can be addressed in these 3 contexts. Recommendations to report secondary genomic findings should not be interpreted as an endorsement of population genomic screening. Ongoing work is developing the evidence that will be needed to fully justify current and future initiatives in population genomic screening. Ongoing work is developing the evidence that will be needed to fully justify current and future initiatives in population genomic screening.

Ethics in genetic counselling

Difficult ethical issues arise for patients and professionals in medical genetics, and often relate to the patient's family or their social context. Tackling these issues requires sensitivity to nuances of communication and a commitment to clarity and consistency. It also benefits from an awareness of different approaches to ethical theory. Many of the ethical problems encountered in genetics relate to tensions between the wishes or interests of different people, sometimes even people who do not (yet) exist or exist as embryos, either in an established pregnancy or in vitro. Concern for the long-term welfare of a child or young person, or possible future children, or for other members of the family, may lead to tensions felt by the patient (client) in genetic counselling. Differences in perspective may also arise between the patient and professional when the latter recommends disclosure of information to relatives and the patient finds that too difficult, or when the professional considers the genetic testing of a child, sought by parents, to be inappropriate. The expectations of a patient's community may also lead to the differences in perspective between patient and counsellor. Recent developments of genetic technology permit genome-wide investigations. These have generated additional and more complex data that amplify and exacerbate some pre-existing ethical problems, including those presented by incidental (additional sought and secondary) findings and the recognition of variants currently of uncertain significance, so that reports of genomic investigations may often be provisional rather than definitive. Experience is being gained with these problems but substantial challenges are likely to persist in the long term.

A map of constrained coding regions in the human genome

Deep catalogs of genetic variation from thousands of humans enable the detection of intraspecies constraint by identifying coding regions with a scarcity of variation. While existing techniques summarize constraint for entire genes, single gene-wide metrics conceal regional constraint variability within each gene. Therefore, we have created a detailed map of constrained coding regions (CCRs) by leveraging variation observed among 123,136 humans from the Genome Aggregation Database. The most constrained CCRs are enriched for pathogenic variants in ClinVar and mutations underlying developmental disorders. CCRs highlight protein domain families under high constraint and suggest unannotated or incomplete protein domains. The highest-percentile CCRs complement existing variant prioritization methods when evaluating de novo mutations in studies of autosomal dominant disease. Finally, we identify highly constrained CCRs within genes lacking known disease associations. This observation suggests that CCRs may identify regions under strong purifying selection that, when mutated, cause severe developmental phenotypes or embryonic lethality.

The Genetic Landscape of Diamond-Blackfan Anemia

Diamond-Blackfan anemia (DBA) is a rare bone marrow failure disorder that affects 7 out of 1,000,000 live births and has been associated with mutations in components of the ribosome. In order to characterize the genetic landscape of this heterogeneous disorder, we recruited a cohort of 472 individuals with a clinical diagnosis of DBA and performed whole-exome sequencing (WES). We identified relevant rare and predicted damaging mutations for 78% of individuals. The majority of mutations were singletons, absent from population databases, predicted to cause loss of function, and located in 1 of 19 previously reported ribosomal protein (RP)-encoding genes. Using exon coverage estimates, we identified and validated 31 deletions in RP genes. We also observed an enrichment for extended splice site mutations and validated their diverse effects using RNA sequencing in cell lines obtained from individuals with DBA. Leveraging the size of our cohort, we observed robust genotype-phenotype associations with congenital abnormalities and treatment outcomes. We further identified rare mutations in seven previously unreported RP genes that may cause DBA, as well as several distinct disorders that appear to phenocopy DBA, including nine individuals with biallelic CECR1 mutations that result in deficiency of ADA2. However, no new genes were identified at exome-wide significance, suggesting that there are no unidentified genes containing mutations readily identified by WES that explain >5% of DBA-affected case subjects. Overall, this report should inform not only clinical practice for DBA-affected individuals, but also the design and analysis of rare variant studies for heterogeneous Mendelian disorders.

The Return of Actionable Variants Empirical (RAVE) Study, a Mayo Clinic Genomic Medicine Implementation Study: Design and Initial Results

OBJECTIVES

To identify clinically actionable genetic variants from targeted sequencing of 68 disease-related genes, estimate their penetrance, and assess the impact of disclosing results to participants and providers.

PATIENTS AND METHODS

The Return of Actionable Variants Empirical (RAVE) Study investigates outcomes following the return of pathogenic/likely pathogenic (P/LP) variants in 68 disease-related genes. The study was initiated in December 2016 and is ongoing. Targeted sequencing was performed in 2533 individuals with hyperlipidemia or colon polyps. The electronic health records (EHRs) of participants carrying P/LP variants in 36 cardiovascular disease (CVD) genes were manually reviewed to ascertain the presence of relevant traits. Clinical outcomes, health care utilization, family communication, and ethical and psychosocial implications of disclosure of genomic results are being assessed by surveys, telephone interviews, and EHR review.

RESULTS

Of 29,208 variants in the 68 genes, 1915 were rare (frequency <1%) and putatively functional, and 102 of these (60 in 36 CVD genes) were labeled P/LP based on the American College of Medical Genetics and Genomics framework. Manual review of the EHRs of participants (n=73 with P/LP variants in CVD genes) revealed that 33 had the expected trait(s); however, only 6 of 45 participants with non-familial hypercholesterolemia (FH) P/LP variants had the expected traits.

CONCLUSION

Expected traits were present in 13% of participants with P/LP variants in non-FH CVD genes, suggesting low penetrance; this estimate may change with additional testing performed as part of the clinical evaluation. Ongoing analyses of the RAVE Study will inform best practices for genomic medicine.

Reconceptualizing harms and benefits in the genomic age

As new, high-powered sequencing technologies are increasingly incorporated into genomics research, we believe that there has been a break point in how risks and benefits associated with genetic information are being characterized and understood. Genomic sequencing provides the potential benefit of a wealth of information, but also has the potential to alter how we conceptualize risks of sequencing. Until now, our conceptions of risks and benefits have been generally static, arising out of the early ethical, legal and social implications studies conducted in the context of targeted genetics. This paper investigates how the increasing availability of genetic information is changing views about risks and benefits, particularly examining our evolving understanding of psychosocial harms and our expanding conception of benefit. We argue that the lack of robust empirical evidence of psychosocial harms and the expanding view that benefits of genomic research include indirect familial benefit necessitate continued ethical, legal and social implications research.

Assessment of DNA repair susceptibility genes identified by whole exome sequencing in head and neck cancer

Head and neck cancer (HNC), the sixth most common cancer globally, stands second in India. In Northeast (NE) India, it is the sixth most common cause of death in males and seventh in females. Prolonged tobacco and alcohol consumption constitute the major etiological factors for HNC development, which induce DNA damage. Therefore, DNA repair pathway is a crucial system in maintaining genomic integrity and preventing carcinogenesis. The present work was aimed to predict the consequence of significant germline variants of the DNA repair genes in disease predisposition. Whole exome sequencing was performed in Ion Proton™ platform on 15 case-control samples from the HNC-prevalent states of Manipur, Mizoram, and Nagaland. Variant annotation was done in Ion Reporter™ as well as wANNOVAR. Subsequent statistical and bioinformatics analysis identified significant exonic and intronic variants associated with HNC. Amongst our observed variants, 78.6% occurred in ExAC, 94% reported in dbSNP and 5.8% & 9.3% variants were present in ClinVar and HGMD, respectively. The total variants were dispersed among 199 genes with DSBR and FA pathway being the most mutated pathways. The allelic association test suggested that the intronic variants in HLTF and RAD52 gene significantly associated (P < 0.05) with the risk (OR > 5), while intronic variants in PARP4, RECQL5, EXO1 and PER1 genes and exonic variant in TDP2 gene showed protection (OR < 1) for HNC. MDR analysis proposed the exonic variants in MSH6, BRCA2, PALB2 and TP53 genes and intronic variant in RECQL5 genetic region working together during certain phase of DNA repair mechanism for HNC causation. In addition, other intronic and 3'UTR variations caused modifications in the transcription factor binding sites and miRNA target sites associated with HNC. Large-scale validation in NE Indian population, in-depth structure prediction and subsequent simulation of our recognized polymorphisms is necessary to identify true causal variants related to HNC.

Genomic sequencing identifies secondary findings in a cohort of parent study participants

PURPOSE

Clinically relevant secondary variants were identified in parents enrolled with a child with developmental delay and intellectual disability.

METHODS

Exome/genome sequencing and analysis of 789 "unaffected" parents was performed.

RESULTS

Pathogenic/likely pathogenic variants were identified in 21 genes within 25 individuals (3.2%), with 11 (1.4%) participants harboring variation in a gene defined as clinically actionable by the American College of Medical Genetics and Genomics. These 25 individuals self-reported either relevant clinical diagnoses (5); relevant family history or symptoms (13); or no relevant family history, symptoms, or clinical diagnoses (7). A limited carrier screen was performed yielding 15 variants in 48 (6.1%) parents. Parents were also analyzed as mate pairs (n = 365) to identify cases in which both parents were carriers for the same recessive disease, yielding three such cases (0.8%), two of which had children with the relevant recessive disease. Four participants had two findings (one carrier and one noncarrier variant). In total, 71 of the 789 enrolled parents (9.0%) received secondary findings.

CONCLUSION

We provide an overview of the rates and types of clinically relevant secondary findings, which may be useful in the design and implementation of research and clinical sequencing efforts to identify such findings.

Relationship between Deleterious Variation, Genomic Autozygosity, and Disease Risk: Insights from The 1000 Genomes Project

Genomic regions of autozygosity (ROAs) represent segments of individual genomes that are homozygous for haplotypes inherited identical-by-descent (IBD) from a common ancestor. ROAs are nonuniformly distributed across the genome, and increased ROA levels are a reported risk factor for numerous complex diseases. Previously, we hypothesized that long ROAs are enriched for deleterious homozygotes as a result of young haplotypes with recent deleterious mutations-relatively untouched by purifying selection-being paired IBD as a consequence of recent parental relatedness, a pattern supported by ROA and whole-exome sequence data on 27 individuals. Here, we significantly bolster support for our hypothesis and expand upon our original analyses using ROA and whole-genome sequence data on 2,436 individuals from The 1000 Genomes Project. Considering CADD deleteriousness scores, we reaffirm our previous observation that long ROAs are enriched for damaging homozygotes worldwide. We show that strongly damaging homozygotes experience greater enrichment than weaker damaging homozygotes, while overall enrichment varies appreciably among populations. Mendelian disease genes and those encoding FDA-approved drug targets have significantly increased rates of gain in damaging homozygotes with increasing ROA coverage relative to all other genes. In genes implicated in eight complex phenotypes for which ROA levels have been identified as a risk factor, rates of gain in damaging homozygotes vary across phenotypes and populations but frequently differ significantly from non-disease genes. These findings highlight the potential confounding effects of population background in the assessment of associations between ROA levels and complex disease risk, which might underlie reported inconsistencies in ROA-phenotype associations.

Web Platform vs In-Person Genetic Counselor for Return of Carrier Results From Exome Sequencing: A Randomized Clinical Trial

IMPORTANCE

A critical bottleneck in clinical genomics is the mismatch between large volumes of results and the availability of knowledgeable professionals to return them.

OBJECTIVE

To test whether a web-based platform is noninferior to a genetic counselor for educating patients about their carrier results from exome sequencing.

DESIGN, SETTING, AND PARTICIPANTS

A randomized noninferiority trial conducted in a longitudinal sequencing cohort at the National Institutes of Health from February 5, 2014, to December 16, 2016, was used to compare the web-based platform with a genetic counselor. Among the 571 eligible participants, 1 to 7 heterozygous variants were identified in genes that cause a phenotype that is recessively inherited. Surveys were administered after cohort enrollment, immediately following trial education, and 1 month and 6 months later to primarily healthy postreproductive participants who expressed interest in learning their carrier results. Both intention-to-treat and per-protocol analyses were applied.

INTERVENTIONS

A web-based platform that integrated education on carrier results with personal test results was designed to directly parallel disclosure education by a genetic counselor. The sessions took a mean (SD) time of 21 (10.6), and 27 (9.3) minutes, respectively.

MAIN OUTCOMES AND MEASURES

The primary outcomes and noninferiority margins (δNI) were knowledge (0 to 8, δNI = -1), test-specific distress (0 to 30, δNI = +1), and decisional conflict (15 to 75, δNI = +6).

RESULTS

After 462 participants (80.9%) provided consent and were randomized, all but 3 participants (n = 459) completed surveys following education and counseling; 398 (86.1%) completed 1-month surveys and 392 (84.8%) completed 6-month surveys. Participants were predominantly well-educated, non-Hispanic white, married parents; mean (SD) age was 63 (63.1) years and 246 (53.6%) were men. The web platform was noninferior to the genetic counselor on outcomes assessed at 1 and 6 months: knowledge (mean group difference, -0.18; lower limit of 97.5% CI, -0.63; δNI = -1), test-specific distress (median group difference, 0; upper limit of 97.5% CI, 0; δNI = +1), and decisional conflict about choosing to learn results (mean group difference, 1.18; upper limit of 97.5% CI, 2.66; δNI = +6). There were no significant differences between the genetic counselors and web-based platform detected between modes of education delivery in disclosure rates to spouses (151 vs 159; relative risk [RR], 1.04; 95% CI, 0.64-1.69; P > .99), children (103 vs 117; RR, 1.07; 95% CI, 0.85-1.36; P = .59), or siblings (91 vs 78; RR, 1.17; 95% CI, 0.94-1.46; P = .18).

CONCLUSIONS AND RELEVANCE

This trial demonstrates noninferiority of web-based return of carrier results among postreproductive, mostly healthy adults. Replication studies among younger and more diverse populations are needed to establish generalizability. Yet return of results via a web-based platform may be sufficient for subsets of test results, reserving genetic counselors for return of results with a greater health threat.

TRIAL REGISTRATION

clinicaltrials.gov Identifier: NCT00410241.

Improved ethical guidance for the return of results from psychiatric genomics research

There is an emerging consensus that genomic researchers should, at a minimum, offer to return to individual participants clinically valid, medically important and medically actionable genomic findings (for example, pathogenic variants in BRCA1) identified in the course of research. However, this is not a common practice in psychiatric genetics research. Furthermore, psychiatry researchers often generate findings that do not meet all of these criteria, yet there may be ethically compelling arguments to offer selected results. Here, we review the return of results debate in genomics research and propose that, as for genomic studies of other medical conditions, psychiatric genomics researchers should offer findings that meet the minimum criteria stated above. Additionally, if resources allow, psychiatry researchers could consider offering to return pre-specified 'clinically valuable' findings even if not medically actionable-for instance, findings that help corroborate a psychiatric diagnosis, and findings that indicate important health risks. Similarly, we propose offering 'likely clinically valuable' findings, specifically, variants of uncertain significance potentially related to a participant's symptoms. The goal of this Perspective is to initiate a discussion that can help identify optimal ways of managing the return of results from psychiatric genomics research.

Weighted likelihood inference of genomic autozygosity patterns in dense genotype data

Background

Genomic regions of autozygosity (ROA) arise when an individual is homozygous for haplotypes inherited identical-by-descent from ancestors shared by both parents. Over the past decade, they have gained importance for understanding evolutionary history and the genetic basis of complex diseases and traits. However, methods to infer ROA in dense genotype data have not evolved in step with advances in genome technology that now enable us to rapidly create large high-resolution genotype datasets, limiting our ability to investigate their constituent ROA patterns.

Methods

We report a weighted likelihood approach for inferring ROA in dense genotype data that accounts for autocorrelation among genotyped positions and the possibilities of unobserved mutation and recombination events, and variability in the confidence of individual genotype calls in whole genome sequence (WGS) data.

Results

Forward-time genetic simulations under two demographic scenarios that reflect situations where inbreeding and its effect on fitness are of interest suggest this approach is better powered than existing state-of-the-art methods to infer ROA at marker densities consistent with WGS and popular microarray genotyping platforms used in human and non-human studies. Moreover, we present evidence that suggests this approach is able to distinguish ROA arising via consanguinity from ROA arising via endogamy. Using subsets of The 1000 Genomes Project Phase 3 data we show that, relative to WGS, intermediate and long ROA are captured robustly with popular microarray platforms, while detection of short ROA is more variable and improves with marker density. Worldwide ROA patterns inferred from WGS data are found to accord well with those previously reported on the basis of microarray genotype data. Finally, we highlight the potential of this approach to detect genomic regions enriched for autozygosity signals in one group relative to another based upon comparisons of per-individual autozygosity likelihoods instead of inferred ROA frequencies.

Conclusions

This weighted likelihood ROA inference approach can assist population- and disease-geneticists working with a wide variety of data types and species to explore ROA patterns and to identify genomic regions with differential ROA signals among groups, thereby advancing our understanding of evolutionary history and the role of recessive variation in phenotypic variation and disease.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-4312-3) contains supplementary material, which is available to authorized users.

Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines

BACKGROUND

The American College of Medical Genetics and American College of Pathologists (ACMG/AMP) variant classification guidelines for clinical reporting are widely used in diagnostic laboratories for variant interpretation. The ACMG/AMP guidelines recommend complete concordance of predictions among all in silico algorithms used without specifying the number or types of algorithms. The subjective nature of this recommendation contributes to discordance of variant classification among clinical laboratories and prevents definitive classification of variants.

RESULTS

Using 14,819 benign or pathogenic missense variants from the ClinVar database, we compared performance of 25 algorithms across datasets differing in distinct biological and technical variables. There was wide variability in concordance among different combinations of algorithms with particularly low concordance for benign variants. We also identify a previously unreported source of error in variant interpretation (false concordance) where concordant in silico predictions are opposite to the evidence provided by other sources. We identified recently developed algorithms with high predictive power and robust to variables such as disease mechanism, gene constraint, and mode of inheritance, although poorer performing algorithms are more frequently used based on review of the clinical genetics literature (2011-2017).

CONCLUSIONS

Our analyses identify algorithms with high performance characteristics independent of underlying disease mechanisms. We describe combinations of algorithms with increased concordance that should improve in silico algorithm usage during assessment of clinically relevant variants using the ACMG/AMP guidelines.

Prenatal exome sequencing in anomalous fetuses: new opportunities and challenges

PurposeWe investigated the diagnostic and clinical performance of exome sequencing in fetuses with sonographic abnormalities with normal karyotype and microarray and, in some cases, normal gene-specific sequencing.MethodsExome sequencing was performed on DNA from 15 anomalous fetuses and from the peripheral blood of their parents. Parents provided consent to be informed of diagnostic results in the fetus, medically actionable findings in the parents, and their identification as carrier couples for significant autosomal recessive conditions. We assessed the perceptions and understanding of exome sequencing using mixed methods in 15 mother-father dyads.ResultsIn seven (47%) of 15 fetuses, exome sequencing provided a diagnosis or possible diagnosis with identification of variants in the following genes: COL1A1, MUSK, KCTD1, RTTN, TMEM67, PIEZO1 and DYNC2H1. One additional case revealed a de novo nonsense mutation in a novel candidate gene (MAP4K4). The perceived likelihood that exome sequencing would explain the results (5.2 on a 10-point scale) was higher than the approximately 30% diagnostic yield discussed in pretest counseling.ConclusionExome sequencing had diagnostic utility in a highly select population of fetuses where a genetic diagnosis was highly suspected. Challenges related to genetics literacy and variant interpretation must be addressed by highly tailored pre- and posttest genetic counseling.

MAPPIN: a method for annotating, predicting pathogenicity and mode of inheritance for nonsynonymous variants

Nonsynonymous single nucleotide variants (nsSNVs) constitute about 50% of known disease-causing mutations and understanding their functional impact is an area of active research. Existing algorithms predict pathogenicity of nsSNVs; however, they are unable to differentiate heterozygous, dominant disease-causing variants from heterozygous carrier variants that lead to disease only in the homozygous state. Here, we present MAPPIN (Method for Annotating, Predicting Pathogenicity, and mode of Inheritance for Nonsynonymous variants), a prediction method which utilizes a random forest algorithm to distinguish between nsSNVs with dominant, recessive, and benign effects. We apply MAPPIN to a set of Mendelian disease-causing mutations and accurately predict pathogenicity for all mutations. Furthermore, MAPPIN predicts mode of inheritance correctly for 70.3% of nsSNVs. MAPPIN also correctly predicts pathogenicity for 87.3% of mutations from the Deciphering Developmental Disorders Study with a 78.5% accuracy for mode of inheritance. When tested on a larger collection of mutations from the Human Gene Mutation Database, MAPPIN is able to significantly discriminate between mutations in known dominant and recessive genes. Finally, we demonstrate that MAPPIN outperforms CADD and Eigen in predicting disease inheritance modes for all validation datasets. To our knowledge, MAPPIN is the first nsSNV pathogenicity prediction algorithm that provides mode of inheritance predictions, adding another layer of information for variant prioritization.

Identification of novel candidate disease genes from de novo exonic copy number variants

BACKGROUND

Exon-targeted microarrays can detect small (<1000 bp) intragenic copy number variants (CNVs), including those that affect only a single exon. This genome-wide high-sensitivity approach increases the molecular diagnosis for conditions with known disease-associated genes, enables better genotype-phenotype correlations, and facilitates variant allele detection allowing novel disease gene discovery.

METHODS

We retrospectively analyzed data from 63,127 patients referred for clinical chromosomal microarray analysis (CMA) at Baylor Genetics laboratories, including 46,755 individuals tested using exon-targeted arrays, from 2007 to 2017. Small CNVs harboring a single gene or two to five non-disease-associated genes were identified; the genes involved were evaluated for a potential disease association.

RESULTS

In this clinical population, among rare CNVs involving any single gene reported in 7200 patients (11%), we identified 145 de novo autosomal CNVs (117 losses and 28 intragenic gains), 257 X-linked deletion CNVs in males, and 1049 inherited autosomal CNVs (878 losses and 171 intragenic gains); 111 known disease genes were potentially disrupted by de novo autosomal or X-linked (in males) single-gene CNVs. Ninety-one genes, either recently proposed as candidate disease genes or not yet associated with diseases, were disrupted by 147 single-gene CNVs, including 37 de novo deletions and ten de novo intragenic duplications on autosomes and 100 X-linked CNVs in males. Clinical features in individuals with de novo or X-linked CNVs encompassing at most five genes (224 bp to 1.6 Mb in size) were compared to those in individuals with larger-sized deletions (up to 5 Mb in size) in the internal CMA database or loss-of-function single nucleotide variants (SNVs) detected by clinical or research whole-exome sequencing (WES). This enabled the identification of recently published genes (BPTF, NONO, PSMD12, TANGO2, and TRIP12), novel candidate disease genes (ARGLU1 and STK3), and further confirmation of disease association for two recently proposed disease genes (MEIS2 and PTCHD1). Notably, exon-targeted CMA detected several pathogenic single-exon CNVs missed by clinical WES analyses.

CONCLUSIONS

Together, these data document the efficacy of exon-targeted CMA for detection of genic and exonic CNVs, complementing and extending WES in clinical diagnostics, and the potential for discovery of novel disease genes by genome-wide assay.

Using ALoFT to determine the impact of putative loss-of-function variants in protein-coding genes

Variants predicted to result in the loss of function of human genes have attracted interest because of their clinical impact and surprising prevalence in healthy individuals. Here, we present ALoFT (annotation of loss-of-function transcripts), a method to annotate and predict the disease-causing potential of loss-of-function variants. Using data from Mendelian disease-gene discovery projects, we show that ALoFT can distinguish between loss-of-function variants that are deleterious as heterozygotes and those causing disease only in the homozygous state. Investigation of variants discovered in healthy populations suggests that each individual carries at least two heterozygous premature stop alleles that could potentially lead to disease if present as homozygotes. When applied to de novo putative loss-of-function variants in autism-affected families, ALoFT distinguishes between deleterious variants in patients and benign variants in unaffected siblings. Finally, analysis of somatic variants in >6500 cancer exomes shows that putative loss-of-function variants predicted to be deleterious by ALoFT are enriched in known driver genes.Variants causing loss of function (LoF) of human genes have clinical implications. Here, the authors present a method to predict disease-causing potential of LoF variants, ALoFT (annotation of Loss-of-Function Transcripts) and show its application to interpreting LoF variants in different contexts.