A map of constrained coding regions in the human genome

The landscape of regional missense mutational intolerance quantified from 125,748 exomes

Missense variants can have a range of functional impacts depending on factors such as the specific amino acid substitution and location within the gene. To interpret their deleteriousness, studies have sought to identify regions within genes that are specifically intolerant of missense variation 1-12 . Here, we leverage the patterns of rare missense variation in 125,748 individuals in the Genome Aggregation Database (gnomAD) 13 against a null mutational model to identify transcripts that display regional differences in missense constraint. Missense-depleted regions are enriched for ClinVar 14 pathogenic variants, de novo missense variants from individuals with neurodevelopmental disorders (NDDs) 15,16 , and complex trait heritability. Following ClinGen calibration recommendations for the ACMG/AMP guidelines, we establish that regions with less than 20% of their expected missense variation achieve moderate support for pathogenicity. We create a missense deleteriousness metric (MPC) that incorporates regional constraint and outperforms other deleteriousness scores at stratifying case and control de novo missense variation, with a strong enrichment in NDDs. These results provide additional tools to aid in missense variant interpretation.

Clustering of predicted loss-of-function variants in genes linked with monogenic disease can explain incomplete penetrance

BACKGROUND

Genetic variants that severely alter protein products (e.g. nonsense, frameshift) are often associated with disease. For some genes, these predicted loss-of-function variants (pLoFs) are observed throughout the gene, whilst in others, they occur only at specific locations. We hypothesised that, for genes linked with monogenic diseases that display incomplete penetrance, pLoF variants present in apparently unaffected individuals may be limited to regions where pLoFs are tolerated. To test this, we investigated whether pLoF location could explain instances of incomplete penetrance of variants expected to be pathogenic for Mendelian conditions.

METHODS

We used exome sequence data in 454,773 individuals in the UK Biobank (UKB) to investigate the locations of pLoFs in a population cohort. We counted numbers of unique pLoF, missense, and synonymous variants in UKB in each quintile of the coding sequence (CDS) of all protein-coding genes and clustered the variants using Gaussian mixture models. We limited the analyses to genes with ≥ 5 variants of each type (16,473 genes). We compared the locations of pLoFs in UKB with all theoretically possible pLoFs in a transcript, and pathogenic pLoFs from ClinVar, and performed simulations to estimate the false-positive rate of non-uniformly distributed variants.

RESULTS

For most genes, all variant classes fell into clusters representing broadly uniform variant distributions, but genes in which haploinsufficiency causes developmental disorders were less likely to have uniform pLoF distribution than other genes (P < 2.2 × 10-6). We identified a number of genes, including ARID1B and GATA6, where pLoF variants in the first quarter of the CDS were rescued by the presence of an alternative translation start site and should not be reported as pathogenic. For other genes, such as ODC1, pLoFs were located approximately uniformly across the gene, but pathogenic pLoFs were clustered only at the end, consistent with a gain-of-function disease mechanism.

CONCLUSIONS

Our results suggest the potential benefits of localised constraint metrics and that the location of pLoF variants should be considered when interpreting variants.

Genomics of severe and treatment-resistant obsessive-compulsive disorder treated with deep brain stimulation: A preliminary investigation

Individuals with severe and treatment-resistant obsessive-compulsive disorder (trOCD) represent a small but severely disabled group of patients. Since trOCD cases eligible for deep brain stimulation (DBS) probably comprise the most severe end of the OCD spectrum, we hypothesize that they may be more likely to have a strong genetic contribution to their disorder. Therefore, while the worldwide population of DBS-treated cases may be small (~300), screening these individuals with modern genomic methods may accelerate gene discovery in OCD. As such, we have begun to collect DNA from trOCD cases who qualify for DBS, and here we report results from whole exome sequencing and microarray genotyping of our first five cases. All participants had previously received DBS in the bed nucleus of stria terminalis (BNST), with two patients responding to the surgery and one showing a partial response. Our analyses focused on gene-disruptive rare variants (GDRVs; rare, predicted-deleterious single-nucleotide variants or copy number variants overlapping protein-coding genes). Three of the five cases carried a GDRV, including a missense variant in the ion transporter domain of KCNB1, a deletion at 15q11.2, and a duplication at 15q26.1. The KCNB1 variant (hg19 chr20-47991077-C-T, NM_004975.3:c.1020G>A, p.Met340Ile) causes substitution of methionine for isoleucine in the trans-membrane region of neuronal potassium voltage-gated ion channel KV2.1. This KCNB1 substitution (Met340Ile) is located in a highly constrained region of the protein where other rare missense variants have previously been associated with neurodevelopmental disorders. The patient carrying the Met340Ile variant responded to DBS, which suggests that genetic factors could potentially be predictors of treatment response in DBS for OCD. In sum, we have established a protocol for recruiting and genomically characterizing trOCD cases. Preliminary results suggest that this will be an informative strategy for finding risk genes in OCD.

A unified analysis of evolutionary and population constraint in protein domains highlights structural features and pathogenic sites

Protein evolution is constrained by structure and function, creating patterns in residue conservation that are routinely exploited to predict structure and other features. Similar constraints should affect variation across individuals, but it is only with the growth of human population sequencing that this has been tested at scale. Now, human population constraint has established applications in pathogenicity prediction, but it has not yet been explored for structural inference. Here, we map 2.4 million population variants to 5885 protein families and quantify residue-level constraint with a new Missense Enrichment Score (MES). Analysis of 61,214 structures from the PDB spanning 3661 families shows that missense depleted sites are enriched in buried residues or those involved in small-molecule or protein binding. MES is complementary to evolutionary conservation and a combined analysis allows a new classification of residues according to a conservation plane. This approach finds functional residues that are evolutionarily diverse, which can be related to specificity, as well as family-wide conserved sites that are critical for folding or function. We also find a possible contrast between lethal and non-lethal pathogenic sites, and a surprising clinical variant hot spot at a subset of missense enriched positions.

Recurrent ATP1A1 variant Gly903Arg causes developmental delay, intellectual disability, and autism

ATP1A1 encodes a sodium-potassium ATPase that has been linked to several neurological diseases. Using exome and genome sequencing, we identified the heterozygous ATP1A1 variant NM_000701.8: c.2707G>A;p.(Gly903Arg) in two unrelated children presenting with delayed motor and speech development and autism. While absent in controls, the variant occurred de novo in one proband and co-segregated in two affected half-siblings, with mosaicism in the healthy mother. Using a specific ouabain resistance assay in mutant transfected HEK cells, we found significantly reduced cell viability. Demonstrating loss of ATPase function, we conclude that this novel variant is pathogenic, expanding the phenotype spectrum of ATP1A1.

Discovery of novel genetic syndromes in Latin America: Opportunities and challenges

Latin America (LatAm) has a rich and historically significant role in delineating both novel and well-documented genetic disorders. However, the ongoing advancements in the field of human genetics pose challenges to the relatively slow adaption of LatAm in the field. Here, we describe past and present contributions of LatAm to the discovery of novel genetic disorders, often referred as novel gene-disease associations (NGDA). We also describe the current methodologies for discovery of NGDA, taking into account the latest developments in genomics. We provide an overview of opportunities and challenges for NGDA research in LatAm considering the steps currently performed to identify and validate such associations. Given the multiple and diverse needs of populations and countries in LatAm, it is imperative to foster collaborations amongst patients, indigenous people, clinicians and scientists. Such collaborative effort is essential for sustaining and enhancing the LatAm´s contributions to the field of NGDA.

An overload of missense variants in the OTOG gene may drive a higher prevalence of familial Meniere disease in the European population

Meniere disease is a complex inner ear disorder with significant familial aggregation. A differential prevalence of familial MD (FMD) has been reported, being 9-10% in Europeans compared to 6% in East Asians. A broad genetic heterogeneity in FMD has been described, OTOG being the most common mutated gene, with a compound heterozygous recessive inheritance. We hypothesize that an OTOG-related founder effect may explain the higher prevalence of FMD in the European population. Therefore, the present study aimed to compare the allele frequency (AF) and distribution of OTOG rare variants across different populations. For this purpose, the coding regions with high constraint (low density of rare variants) were retrieved in the OTOG coding sequence in Non-Finnish European (NFE).. Missense variants (AF < 0.01) were selected from a 100 FMD patient cohort, and their population AF was annotated using gnomAD v2.1. A linkage analysis was performed, and odds ratios were calculated to compare AF between NFE and other populations. Thirteen rare missense variants were observed in 13 FMD patients, with 2 variants (rs61978648 and rs61736002) shared by 5 individuals and another variant (rs117315845) shared by two individuals. The results confirm the observed enrichment of OTOG rare missense variants in FMD. Furthermore, eight variants were enriched in the NFE population, and six of them were in constrained regions. Structural modeling predicts five missense variants that could alter the otogelin stability. We conclude that several variants reported in FMD are in constraint regions, and they may have a founder effect and explain the burden of FMD in the European population.

Considerations for reporting variants in novel candidate genes identified during clinical genomic testing

Since the first novel gene discovery for a Mendelian condition was made via exome sequencing (ES), the rapid increase in the number of genes known to underlie Mendelian conditions coupled with the adoption of exome (and more recently, genome) sequencing by diagnostic testing labs has changed the landscape of genomic testing for rare disease. Specifically, many individuals suspected to have a Mendelian condition are now routinely offered clinical ES. This commonly results in a precise genetic diagnosis but frequently overlooks the identification of novel candidate genes. Such candidates are also less likely to be identified in the absence of large-scale gene discovery research programs. Accordingly, clinical laboratories have both the opportunity, and some might argue a responsibility, to contribute to novel gene discovery which should in turn increase the diagnostic yield for many conditions. However, clinical diagnostic laboratories must necessarily balance priorities for throughput, turnaround time, cost efficiency, clinician preferences, and regulatory constraints, and often do not have the infrastructure or resources to effectively participate in either clinical translational or basic genome science research efforts. For these and other reasons, many laboratories have historically refrained from broadly sharing potentially pathogenic variants in novel genes via networks like Matchmaker Exchange, much less reporting such results to ordering providers. Efforts to report such results are further complicated by a lack of guidelines for clinical reporting and interpretation of variants in novel candidate genes. Nevertheless, there are myriad benefits for many stakeholders, including patients/families, clinicians, researchers, if clinical laboratories systematically and routinely identify, share, and report novel candidate genes. To facilitate this change in practice, we developed criteria for triaging, sharing, and reporting novel candidate genes that are most likely to be promptly validated as underlying a Mendelian condition and translated to use in clinical settings.

Next-generation sequencing and bioinformatics in rare movement disorders

The ability to sequence entire exomes and genomes has revolutionized molecular testing in rare movement disorders, and genomic sequencing is becoming an integral part of routine diagnostic workflows for these heterogeneous conditions. However, interpretation of the extensive genomic variant information that is being generated presents substantial challenges. In this Perspective, we outline multidimensional strategies for genetic diagnosis in patients with rare movement disorders. We examine bioinformatics tools and computational metrics that have been developed to facilitate accurate prioritization of disease-causing variants. Additionally, we highlight community-driven data-sharing and case-matchmaking platforms, which are designed to foster the discovery of new genotype-phenotype relationships. Finally, we consider how multiomic data integration might optimize diagnostic success by combining genomic, epigenetic, transcriptomic and/or proteomic profiling to enable a more holistic evaluation of variant effects. Together, the approaches that we discuss offer pathways to the improved understanding of the genetic basis of rare movement disorders.

CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions

Machine Learning-based scoring and classification of genetic variants aids the assessment of clinical findings and is employed to prioritize variants in diverse genetic studies and analyses. Combined Annotation-Dependent Depletion (CADD) is one of the first methods for the genome-wide prioritization of variants across different molecular functions and has been continuously developed and improved since its original publication. Here, we present our most recent release, CADD v1.7. We explored and integrated new annotation features, among them state-of-the-art protein language model scores (Meta ESM-1v), regulatory variant effect predictions (from sequence-based convolutional neural networks) and sequence conservation scores (Zoonomia). We evaluated the new version on data sets derived from ClinVar, ExAC/gnomAD and 1000 Genomes variants. For coding effects, we tested CADD on 31 Deep Mutational Scanning (DMS) data sets from ProteinGym and, for regulatory effect prediction, we used saturation mutagenesis reporter assay data of promoter and enhancer sequences. The inclusion of new features further improved the overall performance of CADD. As with previous releases, all data sets, genome-wide CADD v1.7 scores, scripts for on-site scoring and an easy-to-use webserver are readily provided via https://cadd.bihealth.org/ or https://cadd.gs.washington.edu/ to the community.

A probabilistic graphical model for estimating selection coefficient of missense variants from human population sequence data

Accurately predicting the effect of missense variants is a central problem in interpretation of genomic variation. Commonly used computational methods does not capture the quantitative impact on fitness in populations. We developed MisFit to estimate missense fitness effect using biobank-scale human population genome data. MisFit jointly models the effect at molecular level ( d ) and population level (selection coefficient, s ), assuming that in the same gene, missense variants with similar d have similar s . MisFit is a probabilistic graphical model that integrates deep neural network components and population genetics models efficiently with inductive bias based on biological causality of variant effect. We trained it by maximizing probability of observed allele counts in 236,017 European individuals. We show that s is informative in predicting frequency across ancestries and consistent with the fraction of de novo mutations given s . Finally, MisFit outperforms previous methods in prioritizing missense variants in individuals with neurodevelopmental disorders.

Rare disease gene association discovery from burden analysis of the 100,000 Genomes Project data

To discover rare disease-gene associations, we developed a gene burden analytical framework and applied it to rare, protein-coding variants from whole genome sequencing of 35,008 cases with rare diseases and their family members recruited to the 100,000 Genomes Project (100KGP). Following in silico triaging of the results, 88 novel associations were identified including 38 with existing experimental evidence. We have published the confirmation of one of these associations, hereditary ataxia with UCHL1 , and independent confirmatory evidence has recently been published for four more. We highlight a further seven compelling associations: hypertrophic cardiomyopathy with DYSF and SLC4A3 where both genes show high/specific heart expression and existing associations to skeletal dystrophies or short QT syndrome respectively; monogenic diabetes with UNC13A with a known role in the regulation of β cells and a mouse model with impaired glucose tolerance; epilepsy with KCNQ1 where a mouse model shows seizures and the existing long QT syndrome association may be linked; early onset Parkinson's disease with RYR1 with existing links to tremor pathophysiology and a mouse model with neurological phenotypes; anterior segment ocular abnormalities associated with POMK showing expression in corneal cells and with a zebrafish model with developmental ocular abnormalities; and cystic kidney disease with COL4A3 showing high renal expression and prior evidence for a digenic or modifying role in renal disease. Confirmation of all 88 associations would lead to potential diagnoses in 456 molecularly undiagnosed cases within the 100KGP, as well as other rare disease patients worldwide, highlighting the clinical impact of a large-scale statistical approach to rare disease gene discovery.

Evaluating the use of paralogous protein domains to increase data availability for missense variant classification

BACKGROUND

Classification of rare missense variants remains an ongoing challenge in genomic medicine. Evidence of pathogenicity is often sparse, and decisions about how to weigh different evidence classes may be subjective. We used a Bayesian variant classification framework to investigate the performance of variant co-localisation, missense constraint, and aggregating data across paralogous protein domains ("meta-domains").

METHODS

We constructed a database of all possible coding single nucleotide variants in the human genome and used PFam predictions to annotate structurally-equivalent positions across protein domains. We counted the number of pathogenic and benign missense variants at these equivalent positions in the ClinVar database, calculated a regional constraint score for each meta-domain, and assessed this approach versus existing missense constraint metrics for classifying variant pathogenicity and benignity.

RESULTS

Alternative pathogenic missense variants at the same amino acid position in the same protein provide strong evidence of pathogenicity (positive likelihood ratio, LR+  = 85). Additionally, clinically annotated pathogenic or benign missense variants at equivalent positions in different proteins can provide moderate evidence of pathogenicity (LR+  = 7) or benignity (LR+  = 5), respectively. Applying these approaches sequentially (through PM5) increases sensitivity for classifying pathogenic missense variants from 27 to 41%. Missense constraint can also provide strong evidence of pathogenicity for some variants, but its absence provides no evidence of benignity.

CONCLUSIONS

We propose using structurally equivalent positions across related protein domains from different genes to augment evidence for variant co-localisation when classifying novel missense variants. Additionally, we advocate adopting a numerical evidence-based approach to integrating diverse data in variant interpretation.

Transcriptome analyses of murine right and left maxilla-mandibular complex

OBJECTIVE

The objective of the study was to investigate differential gene expression between murine right and left maxilla-mandibular (MxMn) complexes.

SETTING AND SAMPLE POPULATION

Wild-type (WT) C57BL/6 embryonic (E) day 14.5 (n = 3) and 18.5 (n = 3) murine embryos.

METHODS

The E14.5 and 18.5 embryos were harvested and hemi-sectioned the MxMn complexes into right and left halves in the mid-sagittal plane. We isolated total RNA using Trizol reagent and further purified using the RNA-easy kit (QIAGEN). We confirmed equal expression of house-keeping genes in right and left halves using RT-PCR and then performed paired-end whole mRNA sequencing in LC Sciences (Houston, TX) followed by differential transcript analyses (>1 or <-1 log fold change; p < .05; q < .05; and FPKM >0.5 in 2/3 samples). The Mouse Genome Informatics and Online Mendelian Inheritance in Man databases as well as gnomAD constraint scores were used to prioritize differentially expressed transcripts.

RESULTS

There were 19 upregulated and 19 downregulated transcripts at E14.5 and 8 upregulated and 17 downregulated transcripts at E18.5 time-points. These differentially expressed transcripts were statistically significant and shown to be associated with craniofacial phenotypes in mouse models. These transcripts also have significant gnomAD constraint scores and are enriched in biological processes critical for embryogenesis.

CONCLUSIONS

We identified significant differential expression of transcripts between E14.5 and 18.5 murine right and left MxMn complexes. These findings when extrapolated to humans, they may provide a biological basis for facial asymmetry. Further experiments are required to validate these findings in murine models with craniofacial asymmetry.

Further characterization of CEP85L-associated lissencephaly type 10: Report of a three-generation family and review of the literature

Lissencephaly type 10 is a recently reported condition characterized by posterior predominant abnormalities in gyration with associated seizures, developmental delays or intellectual disability. We report a boy who presented at 5 years of age with epilepsy and developmental delays. His family history was notable for epilepsy in two prior generations associated with variable developmental and cognitive impact. Exome sequencing identified a novel missense variant in CEP85L [NM_001042475.2; c.196A>G, p.(Thr66Ala)] which segregated in four affected family members across three generations. Brain imaging of the proband demonstrated a posterior lissencephaly pattern with pachygyria, while other affected family members demonstrated a similar subcortical band heterotopia. This report expands the phenotypic spectrum of this rare disorder by describing a novel variant in CEP85L in a family with variable clinical and neuroimaging findings.

A deep catalog of protein-coding variation in 985,830 individuals

Coding variants that have significant impact on function can provide insights into the biology of a gene but are typically rare in the population. Identifying and ascertaining the frequency of such rare variants requires very large sample sizes. Here, we present the largest catalog of human protein-coding variation to date, derived from exome sequencing of 985,830 individuals of diverse ancestry to serve as a rich resource for studying rare coding variants. Individuals of African, Admixed American, East Asian, Middle Eastern, and South Asian ancestry account for 20% of this Exome dataset. Our catalog of variants includes approximately 10.5 million missense (54% novel) and 1.1 million predicted loss-of-function (pLOF) variants (65% novel, 53% observed only once). We identified individuals with rare homozygous pLOF variants in 4,874 genes, and for 1,838 of these this work is the first to document at least one pLOF homozygote. Additional insights from the RGC-ME dataset include 1) improved estimates of selection against heterozygous loss-of-function and identification of 3,459 genes intolerant to loss-of-function, 83 of which were previously assessed as tolerant to loss-of-function and 1,241 that lack disease annotations; 2) identification of regions depleted of missense variation in 457 genes that are tolerant to loss-of-function; 3) functional interpretation for 10,708 variants of unknown or conflicting significance reported in ClinVar as cryptic splice sites using splicing score thresholds based on empirical variant deleteriousness scores derived from RGC-ME; and 4) an observation that approximately 3% of sequenced individuals carry a clinically actionable genetic variant in the ACMG SF 3.1 list of genes. We make this important resource of coding variation available to the public through a variant allele frequency browser. We anticipate that this report and the RGC-ME dataset will serve as a valuable reference for understanding rare coding variation and help advance precision medicine efforts.

Macrocephaly and developmental delay caused by missense variants in RAB5C

Rab GTPases are important regulators of intracellular vesicular trafficking. RAB5C is a member of the Rab GTPase family that plays an important role in the endocytic pathway, membrane protein recycling and signaling. Here we report on 12 individuals with nine different heterozygous de novo variants in RAB5C. All but one patient with missense variants (n = 9) exhibited macrocephaly, combined with mild-to-moderate developmental delay. Patients with loss of function variants (n = 2) had an apparently more severe clinical phenotype with refractory epilepsy and intellectual disability but a normal head circumference. Four missense variants were investigated experimentally. In vitro biochemical studies revealed that all four variants were damaging, resulting in increased nucleotide exchange rate, attenuated responsivity to guanine exchange factors and heterogeneous effects on interactions with effector proteins. Studies in C. elegans confirmed that all four variants were damaging in vivo and showed defects in endocytic pathway function. The variant heterozygotes displayed phenotypes that were not observed in null heterozygotes, with two shown to be through a dominant negative mechanism. Expression of the human RAB5C variants in zebrafish embryos resulted in defective development, further underscoring the damaging effects of the RAB5C variants. Our combined bioinformatic, in vitro and in vivo experimental studies and clinical data support the association of RAB5C missense variants with a neurodevelopmental disorder characterized by macrocephaly and mild-to-moderate developmental delay through disruption of the endocytic pathway.

Genetic analysis and natural history of Charcot-Marie-Tooth disease CMTX1 due to GJB1 variants

Charcot-Marie-Tooth disease (CMT) due to GJB1 variants (CMTX1) is the second most common form of CMT. It is an X-linked disorder characterized by progressive sensory and motor neuropathy with males affected more severely than females. Many reported GJB1 variants remain classified as variants of uncertain significance (VUS). In this large, international, multicentre study we prospectively collected demographic, clinical and genetic data on patients with CMT associated with GJB1 variants. Pathogenicity for each variant was defined using adapted American College of Medical Genetics criteria. Baseline and longitudinal analyses were conducted to study genotype-phenotype correlations, to calculate longitudinal change using the CMT Examination Score (CMTES), to compare males versus females, and pathogenic/likely pathogenic (P/LP) variants versus VUS. We present 387 patients from 295 families harbouring 154 variants in GJB1. Of these, 319 patients (82.4%) were deemed to have P/LP variants, 65 had VUS (16.8%) and three benign variants (0.8%; excluded from analysis); an increased proportion of patients with P/LP variants compared with using ClinVar's classification (74.6%). Male patients (166/319, 52.0%, P/LP only) were more severely affected at baseline. Baseline measures in patients with P/LP variants and VUS showed no significant differences, and regression analysis suggested the disease groups were near identical at baseline. Genotype-phenotype analysis suggested c.-17G>A produces the most severe phenotype of the five most common variants, and missense variants in the intracellular domain are less severe than other domains. Progression of disease was seen with increasing CMTES over time up to 8 years follow-up. Standard response mean (SRM), a measure of outcome responsiveness, peaked at 3 years with moderate responsiveness [change in CMTES (ΔCMTES) = 1.3 ± 2.6, P = 0.00016, SRM = 0.50]. Males and females progressed similarly up to 8 years, but baseline regression analysis suggested that over a longer period, females progress more slowly. Progression was most pronounced for mild phenotypes (CMTES = 0-7; 3-year ΔCMTES = 2.3 ± 2.5, P = 0.001, SRM = 0.90). Enhanced variant interpretation has yielded an increased proportion of GJB1 variants classified as P/LP and will aid future variant interpretation in this gene. Baseline and longitudinal analysis of this large cohort of CMTX1 patients describes the natural history of the disease including the rate of progression; CMTES showed moderate responsiveness for the whole group at 3 years and higher responsiveness for the mild group at 3, 4 and 5 years. These results have implications for patient selection for upcoming clinical trials.

Rare variants found in clinical gene panels illuminate the genetic and allelic architecture of orofacial clefting

PURPOSE

Orofacial clefts (OFCs) are common birth defects including cleft lip, cleft lip and palate, and cleft palate. OFCs have heterogeneous etiologies, complicating clinical diagnostics because it is not always apparent if the cause is Mendelian, environmental, or multifactorial. Sequencing is not currently performed for isolated or sporadic OFCs; therefore, we estimated the diagnostic yield for 418 genes in 841 cases and 294 controls.

METHODS

We evaluated 418 genes using genome sequencing and curated variants to assess their pathogenicity using American College of Medical Genetics criteria.

RESULTS

9.04% of cases and 1.02% of controls had "likely pathogenic" variants (P < .0001), which was almost exclusively driven by heterozygous variants in autosomal genes. Cleft palate (17.6%) and cleft lip and palate (9.09%) cases had the highest yield, whereas cleft lip cases had a 2.80% yield. Out of 39 genes with likely pathogenic variants, 9 genes, including CTNND1 and IRF6, accounted for more than half of the yield (4.64% of cases). Most variants (61.8%) were "variants of uncertain significance", occurring more frequently in cases (P = .004), but no individual gene showed a significant excess of variants of uncertain significance.

CONCLUSION

These results underscore the etiological heterogeneity of OFCs and suggest sequencing could reduce the diagnostic gap in OFCs.

Genic constraint against nonsynonymous variation across the mouse genome

BACKGROUND

Selective constraint, the depletion of variation due to negative selection, provides insights into the functional impact of variants and disease mechanisms. However, its characterization in mice, the most commonly used mammalian model, remains limited. This study aims to quantify mouse gene constraint using a new metric called the nonsynonymous observed expected ratio (NOER) and investigate its relationship with gene function.

RESULTS

NOER was calculated using whole-genome sequencing data from wild mouse populations (Mus musculus sp and Mus spretus). Positive correlations were observed between mouse gene constraint and the number of associated knockout phenotypes, indicating stronger constraint on pleiotropic genes. Furthermore, mouse gene constraint showed a positive correlation with the number of pathogenic variant sites in their human orthologues, supporting the relevance of mouse models in studying human disease variants.

CONCLUSIONS

NOER provides a resource for assessing the fitness consequences of genetic variants in mouse genes and understanding the relationship between gene constraint and function. The study's findings highlight the importance of pleiotropy in selective constraint and support the utility of mouse models in investigating human disease variants. Further research with larger sample sizes can refine constraint estimates in mice and enable more comprehensive comparisons of constraint between mouse and human orthologues.

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.

ClinPrior: an algorithm for diagnosis and novel gene discovery by network-based prioritization

BACKGROUND

Whole-exome sequencing (WES) and whole-genome sequencing (WGS) have become indispensable tools to solve rare Mendelian genetic conditions. Nevertheless, there is still an urgent need for sensitive, fast algorithms to maximise WES/WGS diagnostic yield in rare disease patients. Most tools devoted to this aim take advantage of patient phenotype information for prioritization of genomic data, although are often limited by incomplete gene-phenotype knowledge stored in biomedical databases and a lack of proper benchmarking on real-world patient cohorts.

METHODS

We developed ClinPrior, a novel method for the analysis of WES/WGS data that ranks candidate causal variants based on the patient's standardized phenotypic features (in Human Phenotype Ontology (HPO) terms). The algorithm propagates the data through an interactome network-based prioritization approach. This algorithm was thoroughly benchmarked using a synthetic patient cohort and was subsequently tested on a heterogeneous prospective, real-world series of 135 families affected by hereditary spastic paraplegia (HSP) and/or cerebellar ataxia (CA).

RESULTS

ClinPrior successfully identified causative variants achieving a final positive diagnostic yield of 70% in our real-world cohort. This includes 10 novel candidate genes not previously associated with disease, 7 of which were functionally validated within this project. We used the knowledge generated by ClinPrior to create a specific interactome for HSP/CA disorders thus enabling future diagnoses as well as the discovery of novel disease genes.

CONCLUSIONS

ClinPrior is an algorithm that uses standardized phenotype information and interactome data to improve clinical genomic diagnosis. It helps in identifying atypical cases and efficiently predicts novel disease-causing genes. This leads to increasing diagnostic yield, shortening of the diagnostic Odysseys and advancing our understanding of human illnesses.

In silico Analysis of Two Novel Variants in the Pyruvate Carboxylase (PC) Gene Associated with the Severe Form of PC Deficiency

Background

Inborne errors of metabolism are a common cause of neonatal death. This study evaluated the acute early-onset metabolic derangement and death in two unrelated neonates.

Methods

Whole-exome sequencing (WES), Sanger sequencing, homology modeling, and in silico bioinformatics analysis were employed to assess the effects of variants on protein structure and function.

Results

WES revealed a novel homozygous variant, p.G303Afs*40 and p.R156P, in the pyruvate carboxylase (PC) gene of each neonate, which both were confirmed by Sanger sequencing. Based on the American College of Medical Genetics and Genomics guidelines, the p.G303Afs*40 was likely pathogenic, and the p.R156P was a variant of uncertain significance (VUS). Nevertheless, a known variant at position 156, the p.R156Q, was also a VUS. Protein secondary structure prediction showed changes in p.R156P and p.R156Q variants compared to the wild-type protein. However, p.G303Afs*40 depicted significant changes at C-terminal. Furthermore, comparing the interaction of wild-type and variant proteins with the ATP ligand during simulations, revealed a decreased affinity to the ATP in all the variants. Moreover, analysis of Single nucleotide polymorphism impacts on PC protein using Polyphen-2, SNAP2, FATHMM, and SNPs&GO servers predicted both R156P and R156Q as damaging variants. Likewise, free energy calculations demonstrated the destabilizing effect of both variants on PC.

Conclusion

This study confirmed the pathogenicity of both variants and suggested them as a cause of type B Pyruvate carboxylase deficiency. The results of this study would provide the family with prenatal diagnosis and expand the variant spectrum in the PC gene,which is beneficial for geneticists and endocrinologists.

A Role for Genetic Modifiers in Tubulointerstitial Kidney Diseases

With the increased availability of genomic sequencing technologies, the molecular bases for kidney diseases such as nephronophthisis and mitochondrially inherited and autosomal-dominant tubulointerstitial kidney diseases (ADTKD) has become increasingly apparent. These tubulointerstitial kidney diseases (TKD) are monogenic diseases of the tubulointerstitium and result in interstitial fibrosis and tubular atrophy (IF/TA). However, monogenic inheritance alone does not adequately explain the highly variable onset of kidney failure and extra-renal manifestations. Phenotypes vary considerably between individuals harbouring the same pathogenic variant in the same putative monogenic gene, even within families sharing common environmental factors. While the extreme end of the disease spectrum may have dramatic syndromic manifestations typically diagnosed in childhood, many patients present a more subtle phenotype with little to differentiate them from many other common forms of non-proteinuric chronic kidney disease (CKD). This review summarises the expanding repertoire of genes underpinning TKD and their known phenotypic manifestations. Furthermore, we collate the growing evidence for a role of modifier genes and discuss the extent to which these data bridge the historical gap between apparently rare monogenic TKD and polygenic non-proteinuric CKD (excluding polycystic kidney disease).

Chromatin regulators in the TBX1 network confer risk for conotruncal heart defects in 22q11.2DS

Congenital heart disease (CHD) affecting the conotruncal region of the heart, occurs in 40-50% of patients with 22q11.2 deletion syndrome (22q11.2DS). This syndrome is a rare disorder with relative genetic homogeneity that can facilitate identification of genetic modifiers. Haploinsufficiency of TBX1, encoding a T-box transcription factor, is one of the main genes responsible for the etiology of the syndrome. We suggest that genetic modifiers of conotruncal defects in patients with 22q11.2DS may be in the TBX1 gene network. To identify genetic modifiers, we analyzed rare, predicted damaging variants in whole genome sequence of 456 cases with conotruncal defects and 537 controls, with 22q11.2DS. We then performed gene set approaches and identified chromatin regulatory genes as modifiers. Chromatin genes with recurrent damaging variants include EP400, KAT6A, KMT2C, KMT2D, NSD1, CHD7 and PHF21A. In total, we identified 37 chromatin regulatory genes, that may increase risk for conotruncal heart defects in 8.5% of 22q11.2DS cases. Many of these genes were identified as risk factors for sporadic CHD in the general population. These genes are co-expressed in cardiac progenitor cells with TBX1, suggesting that they may be in the same genetic network. The genes KAT6A, KMT2C, CHD7 and EZH2, have been previously shown to genetically interact with TBX1 in mouse models. Our findings indicate that disturbance of chromatin regulatory genes impact the TBX1 gene network serving as genetic modifiers of 22q11.2DS and sporadic CHD, suggesting that there are some shared mechanisms involving the TBX1 gene network in the etiology of CHD.

Deep structured learning for variant prioritization in Mendelian diseases

Effective computer-aided or automated variant evaluations for monogenic diseases will expedite clinical diagnostic and research efforts of known and novel disease-causing genes. Here we introduce MAVERICK: a Mendelian Approach to Variant Effect pRedICtion built in Keras. MAVERICK is an ensemble of transformer-based neural networks that can classify a wide range of protein-altering single nucleotide variants (SNVs) and indels and assesses whether a variant would be pathogenic in the context of dominant or recessive inheritance. We demonstrate that MAVERICK outperforms all other major programs that assess pathogenicity in a Mendelian context. In a cohort of 644 previously solved patients with Mendelian diseases, MAVERICK ranks the causative pathogenic variant within the top five variants in over 95% of cases. Seventy-six percent of cases were solved by the top-ranked variant. MAVERICK ranks the causative pathogenic variant in hitherto novel disease genes within the first five candidate variants in 70% of cases. MAVERICK has already facilitated the identification of a novel disease gene causing a degenerative motor neuron disease. These results represent a significant step towards automated identification of causal variants in patients with Mendelian diseases.

Enhanced mitochondrial genome analysis: bioinformatic and long-read sequencing advances and their diagnostic implications

INTRODUCTION

Primary mitochondrial diseases (PMDs) comprise a large and heterogeneous group of genetic diseases that result from pathogenic variants in either nuclear DNA (nDNA) or mitochondrial DNA (mtDNA). Widespread adoption of next-generation sequencing (NGS) has improved the efficiency and accuracy of mtDNA diagnoses; however, several challenges remain.

AREAS COVERED

In this review, we briefly summarize the current state of the art in molecular diagnostics for mtDNA and consider the implications of improved whole genome sequencing (WGS), bioinformatic techniques, and the adoption of long-read sequencing, for PMD diagnostics.

EXPERT OPINION

We anticipate that the application of PCR-free WGS from blood DNA will increase in diagnostic laboratories, while for adults with myopathic presentations, WGS from muscle DNA may become more widespread. Improved bioinformatic strategies will enhance WGS data interrogation, with more accurate delineation of mtDNA and NUMTs (nuclear mitochondrial DNA segments) in WGS data, superior coverage uniformity, indirect measurement of mtDNA copy number, and more accurate interpretation of heteroplasmic large-scale rearrangements (LSRs). Separately, the adoption of diagnostic long-read sequencing could offer greater resolution of complex LSRs and the opportunity to phase heteroplasmic variants.

Estimating clinical risk in gene regions from population sequencing cohort data

While pathogenic variants can significantly increase disease risk, it is still challenging to estimate the clinical impact of rare missense variants more generally. Even in genes such as BRCA2 or PALB2, large cohort studies find no significant association between breast cancer and rare missense variants collectively. Here, we introduce REGatta, a method to estimate clinical risk from variants in smaller segments of individual genes. We first define these regions by using the density of pathogenic diagnostic reports and then calculate the relative risk in each region by using over 200,000 exome sequences in the UK Biobank. We apply this method in 13 genes with established roles across several monogenic disorders. In genes with no significant difference at the gene level, this approach significantly separates disease risk for individuals with rare missense variants at higher or lower risk (BRCA2 regional model OR = 1.46 [1.12, 1.79], p = 0.0036 vs. BRCA2 gene model OR = 0.96 [0.85, 1.07] p = 0.4171). We find high concordance between these regional risk estimates and high-throughput functional assays of variant impact. We compare our method with existing methods and the use of protein domains (Pfam) as regions and find REGatta better identifies individuals at elevated or reduced risk. These regions provide useful priors and are potentially useful for improving risk assessment for genes associated with monogenic diseases.

Identifying and predicting the pathogenic effects of a novel variant inducing severe early onset MMA: a bioinformatics approach

BACKGROUND

Methylmalonic acidemia (MMA) is a rare metabolic disorder resulting from functional defects in methylmalonyl-CoA mutase. Mutations in the MMAB gene are responsible for the cblB type of vitamin B12-responsive MMA.

RESULTS

This study used Whole-exome sequencing (WES), Sanger sequencing, linkage analysis, and in-silico evaluation of the variants' effect on protein structure and function to confirm their pathogenicity in a 2-day-old neonate presenting an early-onset metabolic crisis and death. WES revealed a homozygous missense variant on chromosome 12, the NM_052845.4 (MMAB):c.557G > A, p.Arg186Gln, in exon 7, a highly conserved and hot spot region for pathogenic variants. After being confirmed by Sanger sequencing, the wild-type and mutant proteins' structure and function were modeled and examined using in-silico bioinformatics tools and compared to the variant NM_052845.4 (MMAB):c.556C > T, p.Arg186Trp, a known pathogenic variant at the same position. Comprehensive bioinformatics analysis showed a significant reduction in the stability of variants and changes in protein-protein and ligand-protein interactions. Interestingly, the variant c.557G > A, p.Arg186Gln depicted more variations in the secondary structure and less binding to the ATP and B12 ligands compared to the c.556C > T, p.Arg186Trp, the known pathogenic variant.

CONCLUSION

This study succeeded in expanding the variant spectra of the MMAB, forasmuch as the variant c.557G > A, p.Arg186Gln is suggested as a pathogenic variant and the cause of severe MMA and neonatal death. These results benefit the prenatal diagnosis of MMA in the subsequent pregnancies and carrier screening of the family members. Furthermore, as an auxiliary technique, homology modeling and protein structure and function evaluations could provide geneticists with a more accurate interpretation of variants' pathogenicity.

Aberrantly Expressed Hsa_circ_0060762 and CSE1L as Potential Peripheral Blood Biomarkers for ALS

Amyotrophic lateral sclerosis (ALS) is a rapidly progressive adult-onset neurodegenerative disease that is often diagnosed with a delay due to initial non-specific symptoms. Therefore, reliable and easy-to-obtain biomarkers are an absolute necessity for earlier and more accurate diagnostics. Circular RNAs (circRNAs) have already been proposed as potential biomarkers for several neurodegenerative diseases. In this study, we further investigated the usefulness of circRNAs as potential biomarkers for ALS. We first performed a microarray analysis of circRNAs on peripheral blood mononuclear cells of a subset of ALS patients and controls. Among the differently expressed circRNA by microarray analysis, we selected only the ones with a host gene that harbors the highest level of conservation and genetic constraints. This selection was based on the hypothesis that genes under selective pressure and genetic constraints could have a major role in determining a trait or disease. Then we performed a linear regression between ALS cases and controls using each circRNA as a predictor variable. With a False Discovery Rate (FDR) threshold of 0.1, only six circRNAs passed the filtering and only one of them remained statistically significant after Bonferroni correction: hsa_circ_0060762 and its host gene CSE1L. Finally, we observed a significant difference in expression levels between larger sets of patients and healthy controls for both hsa_circ_0060762 and CSE1L. CSE1L is a member of the importin β family and mediates inhibition of TDP-43 aggregation; the central pathogenicity in ALS and hsa_circ_0060762 has binding sites for several miRNAs that have been already proposed as biomarkers for ALS. In addition, receiver operating characteristics curve analysis showed diagnostic potential for CSE1L and hsa_circ_0060762. Hsa_circ_0060762 and CSE1L thus represent novel potential peripheral blood biomarkers and therapeutic targets for ALS.

Genomics of severe and treatment-resistant obsessive-compulsive disorder treated with deep brain stimulation: a preliminary investigation

Individuals with severe and treatment-resistant obsessive-compulsive disorder (trOCD) represent a small but severely disabled group of patients. Since trOCD cases eligible for deep brain stimulation (DBS) probably comprise the most severe end of the OCD spectrum, we hypothesize that they may be more likely to have a strong genetic contribution to their disorder. Therefore, while the worldwide population of DBS-treated cases may be small (~300), screening these individuals with modern genomic methods may accelerate gene discovery in OCD. As such, we have begun to collect DNA from trOCD cases who qualify for DBS, and here we report results from whole exome sequencing and microarray genotyping of our first five cases. All participants had previously received DBS in the bed nucleus of stria terminalis (BNST), with two patients responding to the surgery and one showing a partial response. Our analyses focused on gene-disruptive rare variants (GDRVs; rare, predicted-deleterious single-nucleotide variants or copy number variants overlapping protein-coding genes). Three of the five cases carried a GDRV, including a missense variant in the ion transporter domain of KCNB1, a deletion at 15q11.2, and a duplication at 15q26.1. The KCNB1 variant (hg19 chr20-47991077-C-T, NM_004975.3:c.1020G>A, p.Met340Ile) causes substitution of methionine for isoleucine in the trans-membrane region of neuronal potassium voltage-gated ion channel KV2.1. This KCNB1 substitution (Met340Ile) is located in a highly constrained region of the protein where other rare missense variants have previously been associated with neurodevelopmental disorders. The patient carrying the Met340Ile variant responded to DBS, which suggests that genetic factors could potentially be predictors of treatment response in DBS for OCD. In sum, we have established a protocol for recruiting and genomically characterizing trOCD cases. Preliminary results suggest that this will be an informative strategy for finding risk genes in OCD.

PTPA variants and impaired PP2A activity in early-onset parkinsonism with intellectual disability

The protein phosphatase 2A complex (PP2A), the major Ser/Thr phosphatase in the brain, is involved in a number of signalling pathways and functions, including the regulation of crucial proteins for neurodegeneration, such as alpha-synuclein, tau and LRRK2. Here, we report the identification of variants in the PTPA/PPP2R4 gene, encoding a major PP2A activator, in two families with early-onset parkinsonism and intellectual disability. We carried out clinical studies and genetic analyses, including genome-wide linkage analysis, whole-exome sequencing, and Sanger sequencing of candidate variants. We next performed functional studies on the disease-associated variants in cultured cells and knock-down of ptpa in Drosophila melanogaster. We first identified a homozygous PTPA variant, c.893T>G (p.Met298Arg), in patients from a South African family with early-onset parkinsonism and intellectual disability. Screening of a large series of additional families yielded a second homozygous variant, c.512C>A (p.Ala171Asp), in a Libyan family with a similar phenotype. Both variants co-segregate with disease in the respective families. The affected subjects display juvenile-onset parkinsonism and intellectual disability. The motor symptoms were responsive to treatment with levodopa and deep brain stimulation of the subthalamic nucleus. In overexpression studies, both the PTPA p.Ala171Asp and p.Met298Arg variants were associated with decreased PTPA RNA stability and decreased PTPA protein levels; the p.Ala171Asp variant additionally displayed decreased PTPA protein stability. Crucially, expression of both variants was associated with decreased PP2A complex levels and impaired PP2A phosphatase activation. PTPA orthologue knock-down in Drosophila neurons induced a significant impairment of locomotion in the climbing test. This defect was age-dependent and fully reversed by L-DOPA treatment. We conclude that bi-allelic missense PTPA variants associated with impaired activation of the PP2A phosphatase cause autosomal recessive early-onset parkinsonism with intellectual disability. Our findings might also provide new insights for understanding the role of the PP2A complex in the pathogenesis of more common forms of neurodegeneration.

mvPPT: A Highly Efficient and Sensitive Pathogenicity Prediction Tool for Missense Variants

Next-generation sequencing technologies both boost the discovery of variants in the human genome and exacerbate the challenges of pathogenic variant identification. In this study, we developed Pathogenicity Prediction Tool for missense variants (mvPPT), a highly sensitive and accurate missense variant classifier based on gradient boosting. mvPPT adopts high-confidence training sets with a wide spectrum of variant profiles, and extracts three categories of features, including scores from existing prediction tools, frequencies (allele frequencies, amino acid frequencies, and genotype frequencies), and genomic context. Compared with established predictors, mvPPT achieves superior performance in all test sets, regardless of data source. In addition, our study also provides guidance for training set and feature selection strategies, as well as reveals highly relevant features, which may further provide biological insights into variant pathogenicity. mvPPT is freely available at http://www.mvppt.club/.

A novel de novo FEM1C variant is linked to neurodevelopmental disorder with absent speech, pyramidal signs and limb ataxia

The principal component of the protein homeostasis network is the ubiquitin-proteasome system. Ubiquitination is mediated by an enzymatic cascade involving, i.e. E3 ubiquitin ligases, many of which belong to the cullin-RING ligases family. Genetic defects in the ubiquitin-proteasome system components, including cullin-RING ligases, are known causes of neurodevelopmental disorders. Using exome sequencing to diagnose a pediatric patient with developmental delay, pyramidal signs and limb ataxia, we identified a de novo missense variant c.376G>C; p.(Asp126His) in the FEM1C gene encoding a cullin-RING ligase substrate receptor. This variant alters a conserved amino acid located within a highly constrained coding region and is predicted as pathogenic by most in silico tools. In addition, a de novo FEM1C mutation of the same residue p.(Asp126Val) was associated with an undiagnosed developmental disorder, and the relevant variant (FEM1CAsp126Ala) was found to be functionally compromised in vitro. Our computational analysis showed that FEM1CAsp126His hampers protein substrate binding. To further assess its pathogenicity, we used the nematode Caenorhabditis elegans. We found that the FEM-1Asp133His animals (expressing variant homologous to the FEM1C p.(Asp126Val)) had normal muscle architecture yet impaired mobility. Mutant worms were sensitive to the acetylcholinesterase inhibitor aldicarb but not levamisole (acetylcholine receptor agonist), showing that their disabled locomotion is caused by synaptic abnormalities and not muscle dysfunction. In conclusion, we provide the first evidence from an animal model suggesting that a mutation in the evolutionarily conserved FEM1C Asp126 position causes a neurodevelopmental disorder in humans.

dbCNV: deleteriousness-based model to predict pathogenicity of copy number variations

BACKGROUND

Copy number variation (CNV) is a type of structural variation, which is a gain or loss event with abnormal changes in copy number. Methods to predict the pathogenicity of CNVs are required to realize the relationship between these variants and clinical phenotypes. ClassifyCNV, X-CNV, StrVCTVRE, etc. have been trained to predict the pathogenicity of CNVs, but few studies have been reported based on the deleterious significance of features.

RESULTS

From single nucleotide polymorphism (SNP), gene and region dimensions, we collected 79 informative features that quantitatively describe the characteristics of CNV, such as CNV length, the number of protein genes, the number of three prime untranslated region. Then, according to the deleterious significance, we formulated quantitative methods for features, which fall into two categories: the first is variable type, including maximum, minimum and mean; the second is attribute type, which is measured by numerical sum. We used Gradient Boosted Trees (GBT) algorithm to construct dbCNV, which can be used to predict pathogenicity for five-tier classification and binary classification of CNVs. We demonstrated that the distribution of most feature values was consistent with the deleterious significance. The five-tier classification model accuracy for 0.85 and 0.79 in loss and gain CNVs, which proved that it has high discrimination power in predicting the pathogenicity of five-tier classification CNVs. The binary model achieved area under curve (AUC) values of 0.96 and 0.81 in the validation set, respectively, in gain and loss CNVs.

CONCLUSION

The performance of the dbCNV suggest that functional deleteriousness-based model of CNV is a promising approach to support the classification prediction and to further understand the pathogenic mechanism.

Insights into gene tissue specificity and protein-protein interactions in the context of purifying selection in humans

BACKGROUND

How much are natural selection and gene characteristics, such as the number of protein-protein interactions (PPIs), tissue specificity (𝞽), and expression level, connected?

METHODS

In order to investigate these relationships, we combined different metrics linked to genetic constraints and analyzed their distribution concerning PPIs, 𝞽 and expression levels.

RESULTS

We discovered a positive correlation between genetic constraints, PPIs, and expression levels in all tissues. On the other hand, we obtained a negative correlation between genetic constraints and 𝞽. Furthermore, the fraction of variance in PPI and 𝞽 explained by the constraints metrics is around 6% and 10%, respectively.

CONCLUSIONS

We observed that the variance of expression of tissue-specific genes seems not related to their level of selection constraints, which is the opposite of what is found on non-tissue-specific genes. Overall these observations would help to elucidate the relationship between natural selection and gene features.

Mendelian inheritance revisited: dominance and recessiveness in medical genetics

Understanding the consequences of genotype for phenotype (which ranges from molecule-level effects to whole-organism traits) is at the core of genetic diagnostics in medicine. Many measures of the deleteriousness of individual alleles exist, but these have limitations for predicting the clinical consequences. Various mechanisms can protect the organism from the adverse effects of functional variants, especially when the variant is paired with a wild type allele. Understanding why some alleles are harmful in the heterozygous state - representing dominant inheritance - but others only with the biallelic presence of pathogenic variants - representing recessive inheritance - is particularly important when faced with the deluge of rare genetic alterations identified by high throughput DNA sequencing. Both awareness of the specific quantitative and/or qualitative effects of individual variants and the elucidation of allelic and non-allelic interactions are essential to optimize genetic diagnosis and counselling.

Genomic structural variation: A complex but important driver of human evolution

Structural variants (SVs)-including duplications, deletions, and inversions of DNA-can have significant genomic and functional impacts but are technically difficult to identify and assay compared with single-nucleotide variants. With the aid of new genomic technologies, it has become clear that SVs account for significant differences across and within species. This phenomenon is particularly well-documented for humans and other primates due to the wealth of sequence data available. In great apes, SVs affect a larger number of nucleotides than single-nucleotide variants, with many identified SVs exhibiting population and species specificity. In this review, we highlight the importance of SVs in human evolution by (1) how they have shaped great ape genomes resulting in sensitized regions associated with traits and diseases, (2) their impact on gene functions and regulation, which subsequently has played a role in natural selection, and (3) the role of gene duplications in human brain evolution. We further discuss how to incorporate SVs in research, including the strengths and limitations of various genomic approaches. Finally, we propose future considerations in integrating existing data and biospecimens with the ever-expanding SV compendium propelled by biotechnology advancements.

The phenotypic spectrum of pathogenic ATP1A1 variants expands: the novel p.P600R substitution causes demyelinating Charcot-Marie-Tooth disease

BACKGROUND

Charcot-Marie-Tooth disease (CMT) is a genetically and clinically heterogeneous group of inherited neuropathies. Monoallelic pathogenic variants in ATP1A1 were associated with axonal and intermediate CMT. ATP1A1 encodes for the catalytic α1 subunit of the Na⁺/ K⁺ ATPase. Besides neuropathy, other associated phenotypes are spastic paraplegia, intellectual disability, and renal hypomagnesemia. We hereby report the first demyelinating CMT case due to a novel ATP1A1 variant.

METHODS

Whole-exome sequencing on the patient's genomic DNA and Sanger sequencing to validate and confirm the segregation of the identified p.P600R ATP1A1 variation were performed. To evaluate functional effects, blood-derived mRNA and protein levels of ATP1A1 and the auxiliary β1 subunit encoded by ATP1B1 were investigated. The ouabain-survival assay was performed in transfected HEK cells to assess cell viability, and two-electrode voltage clamp studies were performed in Xenopus oocytes.

RESULTS

The variant was absent in the local and global control datasets, falls within a highly conserved protein position, and is in a missense-constrained region. The expression levels of ATP1A1 and ATP1B1 were significantly reduced in the patient compared to healthy controls. Electrophysiology indicated that ATP1A1^p.P600R injected Xenopus oocytes have reduced Na⁺/ K⁺ ATPase function. Moreover, HEK cells transfected with a construct encoding ATP1A1^p.P600R harbouring variants that confers ouabain insensitivity displayed a significant decrease in cell viability after ouabain treatment compared to the wild type, further supporting the pathogenicity of this variant.

CONCLUSION

Our results further confirm the causative role of ATP1A1 in peripheral neuropathy and broaden the mutational and phenotypic spectrum of ATP1A1-associated CMT.

Mapping the Constrained Coding Regions in the Human Genome to Their Corresponding Proteins

Constrained Coding Regions (CCRs) in the human genome have been derived from DNA sequencing data of large cohorts of healthy control populations, available in the Genome Aggregation Database (gnomAD) [1]. They identify regions depleted of protein-changing variants and thus identify segments of the genome that have been constrained during human evolution. By mapping these DNA-defined regions from genomic coordinates onto the corresponding protein positions and combining this information with protein annotations, we have explored the distribution of CCRs and compared their co-occurrence with different protein functional features, previously annotated at the amino acid level in public databases. As expected, our results reveal that functional amino acids involved in interactions with DNA/RNA, protein-protein contacts and catalytic sites are the protein features most likely to be highly constrained for variation in the control population. More surprisingly, we also found that linear motifs, linear interacting peptides (LIPs), disorder-order transitions upon binding with other protein partners and liquid-liquid phase separating (LLPS) regions are also strongly associated with high constraint for variability. We also compared intra-species constraints in the human CCRs with inter-species conservation and functional residues to explore how such CCRs may contribute to the analysis of protein variants. As has been previously observed, CCRs are only weakly correlated with conservation, suggesting that intraspecies constraints complement interspecies conservation and can provide more information to interpret variant effects.

Combining globally search for a regular expression and print matching lines with bibliographic monitoring of genomic database improves diagnosis

Introduction: Exome sequencing has a diagnostic yield ranging from 25% to 70% in rare diseases and regularly implicates genes in novel disorders. Retrospective data reanalysis has demonstrated strong efficacy in improving diagnosis, but poses organizational difficulties for clinical laboratories. Patients and methods: We applied a reanalysis strategy based on intensive prospective bibliographic monitoring along with direct application of the GREP command-line tool (to "globally search for a regular expression and print matching lines") in a large ES database. For 18 months, we submitted the same five keywords of interest [(intellectual disability, (neuro)developmental delay, and (neuro)developmental disorder)] to PubMed on a daily basis to identify recently published novel disease-gene associations or new phenotypes in genes already implicated in human pathology. We used the Linux GREP tool and an in-house script to collect all variants of these genes from our 5,459 exome database. Results: After GREP queries and variant filtration, we identified 128 genes of interest and collected 56 candidate variants from 53 individuals. We confirmed causal diagnosis for 19/128 genes (15%) in 21 individuals and identified variants of unknown significance for 19/128 genes (15%) in 23 individuals. Altogether, GREP queries for only 128 genes over a period of 18 months permitted a causal diagnosis to be established in 21/2875 undiagnosed affected probands (0.7%). Conclusion: The GREP query strategy is efficient and less tedious than complete periodic reanalysis. It is an interesting reanalysis strategy to improve diagnosis.

Systematic assays and resources for the functional annotation of non-coding variants

Identification of genetic variation in individual genomes is now a routine procedure in human genetic research and diagnostics. For many variants, however, insufficient evidence is available to establish a pathogenic effect, particularly for variants in non-coding regions. Furthermore, the sheer number of candidate variants renders testing in individual assays virtually impossible. While scalable approaches are being developed, the selection of methods and resources and the application of a given framework to a particular disease or trait remain major challenges. This limits the translation of results from both genome-wide association studies and genome sequencing. Here, we discuss computational and experimental approaches available for functional annotation of non-coding variation.

Biobanking as a Tool for Genomic Research: From Allele Frequencies to Cross-Ancestry Association Studies

In recent years, great advances have been made in the field of collection, storage, and analysis of biological samples. Large collections of samples, biobanks, have been established in many countries. Biobanks typically collect large amounts of biological samples and associated clinical information; the largest collections include over a million samples. In this review, we summarize the main directions in which biobanks aid medical genetics and genomic research, from providing reference allele frequency information to allowing large-scale cross-ancestry meta-analyses. The largest biobanks greatly vary in the size of the collection, and the amount of available phenotype and genotype data. Nevertheless, all of them are extensively used in genomics, providing a rich resource for genome-wide association analysis, genetic epidemiology, and statistical research into the structure, function, and evolution of the human genome. Recently, multiple research efforts were based on trans-biobank data integration, which increases sample size and allows for the identification of robust genetic associations. We provide prominent examples of such data integration and discuss important caveats which have to be taken into account in trans-biobank research.

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.

Predicting functional effect of missense variants using graph attention neural networks

Accurate prediction of damaging missense variants is critically important for interpreting a genome sequence. Although many methods have been developed, their performance has been limited. Recent advances in machine learning and the availability of large-scale population genomic sequencing data provide new opportunities to considerably improve computational predictions. Here we describe the graphical missense variant pathogenicity predictor (gMVP), a new method based on graph attention neural networks. Its main component is a graph with nodes that capture predictive features of amino acids and edges weighted by co-evolution strength, enabling effective pooling of information from the local protein context and functionally correlated distal positions. Evaluation of deep mutational scan data shows that gMVP outperforms other published methods in identifying damaging variants in TP53, PTEN, BRCA1 and MSH2. Furthermore, it achieves the best separation of de novo missense variants in neuro developmental disorder cases from those in controls. Finally, the model supports transfer learning to optimize gain- and loss-of-function predictions in sodium and calcium channels. In summary, we demonstrate that gMVP can improve interpretation of missense variants in clinical testing and genetic studies.

Echtvar: compressed variant representation for rapid annotation and filtering of SNPs and indels

Germline and somatic variants within an individual or cohort are interpreted with information from large cohorts. Annotation with this information becomes a computational bottleneck as population sets grow to terabytes of data. Here, we introduce echtvar, which efficiently encodes population variants and annotation fields into a compressed archive that can be used for rapid variant annotation and filtering. Most variants, represented by chromosome, position and alleles are encoded into 32-bits-half the size of previous encoding schemes and at least 4 times smaller than a naive encoding. The annotations, stored separately within the same archive, are also encoded and compressed. We show that echtvar is faster and uses less space than existing tools and that it can effectively reduce the number of candidate variants. We give examples on germ-line and somatic variants to document how echtvar can facilitate exploratory data analysis on genetic variants. Echtvar is available at https://github.com/brentp/echtvar under an MIT license.

Heterozygous UCHL1 loss-of-function variants cause a neurodegenerative disorder with spasticity, ataxia, neuropathy, and optic atrophy

PURPOSE

Biallelic variants in UCHL1 have been associated with a progressive early-onset neurodegenerative disorder, autosomal recessive spastic paraplegia type 79. In this study, we investigated heterozygous UCHL1 variants on the basis of results from cohort-based burden analyses.

METHODS

Gene-burden analyses were performed on exome and genome data of independent cohorts of patients with hereditary ataxia and spastic paraplegia from Germany and the United Kingdom in a total of 3169 patients and 33,141 controls. Clinical data of affected individuals and additional independent families were collected and evaluated. Patients' fibroblasts were used to perform mass spectrometry-based proteomics.

RESULTS

UCHL1 was prioritized in both independent cohorts as a candidate gene for an autosomal dominant disorder. We identified a total of 34 cases from 18 unrelated families, carrying 13 heterozygous loss-of-function variants (15 families) and an inframe insertion (3 families). Affected individuals mainly presented with spasticity (24/31), ataxia (28/31), neuropathy (11/21), and optic atrophy (9/17). The mass spectrometry-based proteomics showed approximately 50% reduction of UCHL1 expression in patients' fibroblasts.

CONCLUSION

Our bioinformatic analysis, in-depth clinical and genetic workup, and functional studies established haploinsufficiency of UCHL1 as a novel disease mechanism in spastic ataxia.

Testing for association with rare variants in the coding and non-coding genome: RAVA-FIRST, a new approach based on CADD deleteriousness score

Rare variant association tests (RVAT) have been developed to study the contribution of rare variants widely accessible through high-throughput sequencing technologies. RVAT require to aggregate rare variants in testing units and to filter variants to retain only the most likely causal ones. In the exome, genes are natural testing units and variants are usually filtered based on their functional consequences. However, when dealing with whole-genome sequence (WGS) data, both steps are challenging. No natural biological unit is available for aggregating rare variants. Sliding windows procedures have been proposed to circumvent this difficulty, however they are blind to biological information and result in a large number of tests. We propose a new strategy to perform RVAT on WGS data: “RAVA-FIRST” (RAre Variant Association using Functionally-InfoRmed STeps) comprising three steps. (1) New testing units are defined genome-wide based on functionally-adjusted Combined Annotation Dependent Depletion (CADD) scores of variants observed in the gnomAD populations, which are referred to as “CADD regions”. (2) A region-dependent filtering of rare variants is applied in each CADD region. (3) A functionally-informed burden test is performed with sub-scores computed for each genomic category within each CADD region. Both on simulations and real data, RAVA-FIRST was found to outperform other WGS-based RVAT. Applied to a WGS dataset of venous thromboembolism patients, we identified an intergenic region on chromosome 18 enriched for rare variants in early-onset patients. This region that was missed by standard sliding windows procedures is included in a TAD region that contains a strong candidate gene. RAVA-FIRST enables new investigations of rare non-coding variants in complex diseases, facilitated by its implementation in the R package Ravages.