De novo mutations (DNMs) play an important role in severe genetic disorders that reduce fitness. To better understand the role of DNMs in disease, it is important to determine the parent-of-origin and timing of the mutational events that give rise to the mutations, especially in sex-specific developmental disorders such as male infertility. However, currently available short-read sequencing approaches are not ideally suited for phasing as this requires long continuous DNA strands that span both the DNM and one or more informative SNPs. To overcome these challenges, we optimised and implemented a multiplexed long-read sequencing approach using the Oxford Nanopore technologies MinION platform. We specifically focused on improving target amplification, integrating long-read sequenced data with high-quality short-read sequence data, and developing an anchored phasing computational method. This approach was able to handle the inherent phasing challenges that arise from long-range target amplification and the normal accumulation of sequencing error associated with long-read sequencing. In total, 77 out of 109 DNMs (71%) were successfully phased and parent-of-origin identified. The majority of phased DNMs were prezygotic (90%), the accuracy of which is highlighted by the average mutant allele frequency of 49.6% and a standard error margin of 0.84%. This study demonstrates the benefits of using an integrated short-read and long-read sequencing approach for large-scale DNM phasing.

Routine exome sequencing (ES) in individuals with neurodevelopmental disorders (NDD) remains inconclusive in >50%. Research analysis of unsolved cases can identify novel candidate genes but is time consuming, subjective, and hard to compare between labs. The field therefore needs automated and standardized assessment methods to prioritize candidates for matchmaking. We developed AutoCaSc (https://autocasc.uni-leipzig.de) based on our candidate scoring scheme (CaSc). We validated our approach using synthetic trios and real in-house trio ES data. AutoCaSc consistently (94.5%) scored variants in valid novel NDD genes in the top three ranks. In 93 real trio exomes, AutoCaSc identified most (97.5%) previously manually scored variants while evaluating additional highly scoring variants missed in manual evaluation. It identified candidate variants in previously undescribed NDD candidate genes ( CNTN2, DLGAP1, SMURF1, NRXN3, PRICKLE1). AutoCaSc enables anybody to quickly screen a variant for its plausibility in NDD. After contributing >40 descriptions of NDD associated genes, we provide usage recommendations based on our extensive experience. Our implementation is capable of pipeline integration and therefore allows screening of large cohorts for candidate genes. AutoCaSc empowers even small labs to a standardized matchmaking collaboration and to contribute to the ongoing identification of novel NDD entities.

The Multiple Endocrine Neoplasia type 2 (MEN2) RET proto-oncogene database, originally published in 2008, is a comprehensive repository of all publicly available RET gene variations associated with MEN2 syndromes. The variant-specific genotype/phenotype information, age of earliest reported medullary thyroid carcinoma onset, and relevant references with a brief summary of findings are cataloged. The ACMG/AMP 2015 consensus statement on variant classification was modified specifically for MEN2 syndromes and RET variants using ClinGen sequence variant interpretation working group recommendations and ClinGen expert panel manuscripts, as well as manuscripts from the American Thyroid Association Guidelines Task Force on Medullary Thyroid Carcinoma and other MEN2 RET literature. The classifications for the 166 single unique variants in the MEN2 RET database were reanalyzed using the MEN2 RET specifically modified ACMG/AMP classification guidelines. Applying these guidelines added two new variant classifications to the database (likely benign and likely pathogenic) and resulted in clinically significant classification changes ( e.g. from pathogenic to uncertain) in 16.9% (28/166) of the original variants. Of those clinically significant changes, the highest percentage of changes, 46.4% (13/28), were changes from uncertain to benign or likely benign. The modified ACMG/AMP criteria with MEN2 RET specifications will optimize and standardize RET variant classifications.

The Matchmaker Exchange (MME) launched in 2015 to provide a robust mechanism to discover novel disease-gene relationships. This federated network connects databases holding relevant data, where two or more users are looking for a match for the same gene (two-sided matchmaking). The number of unique genes present across MME has steadily increased; there are currently >13,520 unique genes (~68% of all protein coding genes) connected across MME’s nodes, GeneMatcher, DECIPHER, PhenomeCentral, MyGene2, seqr, Initiative on Rare and Undiagnosed Disease, PatientMatcher, and the RD-Connect Genome-Phenome Analysis Platform. The dataset accessible across MME includes more than 120,000 cases from over 12,000 contributors in 98 countries. Discovery of potential new disease-gene relationships occurs daily and international collaborations are moving these connections forward to publication. Expansion of data sharing into routine clinical practice has ensured access to discovery for even more individuals with undiagnosed rare genetic disease. MME supports connections to the literature (PubCaseFinder) and to human and model organism resources (Monarch Initiative) and scientists (ModelMatcher). Efforts are underway to explore additional approaches to matchmaking where there is only one querier (one-sided matchmaking). Genomic matchmaking has proven its utility over the past 7 years and will continue to facilitate discoveries in years to come.

Modeling splicing is essential for tackling the challenge of variant interpretation as each nucleotide variation can be pathogenic by affecting pre-mRNA splicing via disruption/creation of splicing motifs such as 5’/3’ splice sites, branch sites or splicing regulatory elements. Unfortunately, most in silico tools focus on a specific type of splicing motif, which is why we developed the Splicing Prediction Pipeline (SPiP) to perform, in one single bioinformatic analysis based on machine learning approach, comprehensive assessment of variant effect on different splicing motifs. We gathered a curated set of 4,616 variants scattered all along the sequence of 227 genes, with their corresponding splicing studies. Bayesian analysis provided us the number of control variants, i.e. variants without impact on splicing, to mimic the deluge of variants from high throughput sequencing data. Results show that SPiP can deal with the diversity of splicing alterations, with 83.13% sensitivity and 99% specificity to detect spliceogenic variants. Overall performance as measured by area under the receiving operator curve was 0.986, significantly better than 0.965 spliceAI for the same dataset. SPiP lends itself to a unique suite for comprehensive prediction of spliceogenicity in the genomic medicine era. SPiP is available at: [https://sourceforge.net/projects/splicing-prediction-pipeline/](https://sourceforge.net/projects/splicing-prediction-pipeline/)

With great sadness, we report that Haig Kazazian, Founding Co-Editor of Human Mutation, died on January 19, 2022 of complications related to congestive heart failure.

The vast volume of data that has been generated as a result of the next-generation sequencing revolution is overwhelming to sift through and interpret. Parsing functional vs. non-functional and benign vs. pathogenic variants continues to be a challenge. Out of three billion bases, the genomes of two given individuals will only differ by about 3 million variants (0.1%). Furthermore, only a small fraction of these are biologically-relevant and, of those that are functional, only a handful actually drive disease pathology. While whole genome and exome sequencing have transformed our collective understanding of the role that genetics plays in disease pathogenesis, there are certain conditions and populations for whom DNA-level data has failed to produce a molecular diagnosis. Patients of non-White race/non-European ancestry are disproportionately affected by “variants of unknown/uncertain significance” (VUS). This limits the scope of precision medicine for minority patients and perpetuates health disparities. VUS often include deep intronic and splicing variants which are difficult to interpret in DNA alone. RNA analysis is capable of illuminating the consequences of VUS thereby allowing for their reclassification as pathogenic vs. benign. Here we review the critical role, going forward, of transcriptome analysis for clarifying VUS in both neoplastic and non-neoplastic diseases.

The ATP-binding cassette (ABC) transporter superfamily comprises membrane proteins that efflux various substrates across extra- and intra-cellular membranes. Mutations in ABC genes cause 21 human disorders or phenotypes with Mendelian inheritance, including cystic fibrosis, adrenoleukodystrophy, retinal degeneration, cholesterol, and bile transport defects. Common polymorphisms and rare variants in ABC genes are associated with several complex phenotypes such as gout, gallstones, and cholesterol levels. Overexpression or amplification of specific drug efflux genes contributes to chemotherapy multidrug resistance. Conservation of the ATP-binding domains of ABC transporters defines the superfamily members, and phylogenetic analysis groups the 48 human ABC transporters into seven distinct subfamilies. While the conservation of ABC genes across most vertebrate species is high, there is also considerable gene duplication, deletion, and evolutionary diversification.

Neural Tube Defects (NTDs) are congenital malformations resulting from abnormal embryonic development of the brain, spine, or spinal column. The genetic etiology of human NTDs remains poorly understood despite intensive investigation. CIC, homolog of the Capicua transcription repressor, has been reported to interact with ataxin-1 (ATXN1) and participate in the pathogenesis of spinocerebellar ataxia type 1. Our previous study demonstrated that CIC loss of function (LoF) variants contributed to cerebral folate deficiency by downregulating folate receptor 1 (FOLR1) expression. Given the importance of folate transport in neural tube formation, we hypothesized that CIC variants could contribute to increased risk for NTDs by depressing embryonic folate concentrations. In this study, we examined CIC variants from whole genome sequencing (WGS) data of 140 isolated spina bifida cases and identified 8 missense variants of CIC gene. We tested the pathogenicity of the observed variants through multiple in vitro experiments. We determined that CIC variants decreased FOLR1 protein level and planar cell polarity (PCP) pathway signaling in a human cell line (HeLa). In a murine cell line (NIH3T3), CIC loss of function variants down regulated PCP signaling. Taken together, this study provides evidence supporting CIC as a risk gene for human NTD.

Clinical and research laboratories extensively use exome sequencing due to its high diagnostic rates, cost savings, impact on clinical management, and efficacy for disease gene discovery. While the rates of disease gene discovery have steadily increased, only ~16% of genes in the genome have confirmed disease associations. Here we describe our diagnostic laboratory’s disease gene discovery and ongoing data-sharing efforts with GeneMatcher. In total, we submitted 246 candidates from 243 unique genes to GeneMatcher, of which 45.93% are now clinically characterized. Submissions with at least one case meeting our candidate genes reporting criteria were significantly more likely to be characterized as of October 2021 compared to genes with no candidates meeting our reporting criteria (p=0.025). We reported relevant findings related to these gene-disease associations for 480 probands. In 219 (45.63%) instances, these results were reclassifications after an initial candidate gene (uncertain) or negative report. Since 2013, we have co-authored 105 publications focused on delineating gene-disease associations. Diagnostic laboratories are pivotal for disease gene discovery efforts and can screen phenotypes based on genotype matches, contact clinicians of relevant cases, and issue proactive reclassification reports. GeneMatcher is a critical resource in these efforts.

A major challenge in validating genetic causes for patients with rare diseases (RDs) is the difficulty in identifying other RD patients with overlapping phenotypes and variants in the same candidate gene. This process, known as matchmaking, requires robust data sharing solutions in order to be effective. In 2014 we launched PhenomeCentral, a RD data repository capable of collecting computer-readable genotypic and phenotypic data for the purposes of RD matchmaking. Over the past 7 years PhenomeCentral’s features have been expanded and its dataset has consistently grown. There are currently 1,615 users registered on PhenomeCentral, which have contributed over 12,000 patient cases. Most of these cases contain detailed phenotypic terms, with a significant portion also providing genomic sequence data or other forms of clinical information. Matchmaking within PhenomeCentral, and with connections to other data repositories in the Matchmaker Exchange, have collectively resulted in over 60,000 matches, which have facilitated multiple gene discoveries. The collection of deep phenotypic and genotypic data has also positioned PhenomeCentral well to support next generation of matchmaking initiatives that utilize genome sequencing data, ensuring that PhenomeCentral will remain a useful tool in solving undiagnosed RD cases in the years to come.

DECIPHER (https://www.deciphergenomics.org) is a free web platform for sharing anonymised phenotype-linked variant data from rare disease patients. Its dynamic interpretation interfaces contextualise genomic and phenotypic data to enable more informed variant interpretation, incorporating international standards for variant classification. DECIPHER supports almost all types of germline and mosaic variation in the nuclear and mitochondrial genome: sequence variants, short tandem repeats, copy-number variants and large structural variants. Patient phenotypes are deposited using Human Phenotype Ontology (HPO) terms, supplemented by quantitative data, which is aggregated to derive gene-specific phenotypic summaries. It hosts data from >250 projects from ~40 countries, openly sharing ~40,000 patient records containing >51,000 variants and >172,000 phenotype terms. The rich phenotype-linked variant data in DECIPHER drives rare disease research and diagnosis by enabling patient matching within DECIPHER and with other resources, and has been cited in >2,600 publications. In this paper, we describe the types of data deposited to DECIPHER, the variant interpretation tools, and patient matching interfaces which make DECIPHER an invaluable rare disease resource.

The use of whole-genome sequencing (WGS) has accelerated the pace of gene discovery and highlighted the need for open and collaborative data sharing in the search for novel disease genes and variants. GeneMatcher (GM) is designed to facilitate connections between researchers, clinicians, health-care providers and others to help in the identification of additional patients with variants in the same candidate disease genes. The Illumina Clinical Services Laboratory offers a WGS test for patients with suspected rare and undiagnosed genetic disease and regularly submits potential candidate genes to GM to strengthen gene-disease relationships. We describe our experience with GM, including criteria for evaluation of candidate genes, and our workflow for the submission and review process. We have made 69 submissions, 36 of which are currently active. Ten per cent of submissions have resulted in publications, with an additional 14 submissions part of ongoing collaborations and expected to result in a publication.

The amount of data available from genomic medicine has revolutionized the approach to identify the determinants underlying many rare diseases. The task of confirming a genotype-phenotype causality for a patient affected with a rare genetic disease is often challenging. In this context, the establishment of the MatchMaker Exchange (MME) network has assumed a pivotal role in bridging heterogeneous patient information stored on different medical and research servers. MME has made it possible to solve rare disease cases by “matching” the genotypic and phenotypic characteristics of a patient of interest with patient data available at other clinical facilities participating in the network. Here, we present PatientMatcher (https://github.com/Clinical-Genomics/patientMatcher), an open-source Python and MongoDB-based software solution developed by Clinical Genomics facility at the Science for Life Laboratory in Stockholm. PatientMatcher is designed as a standalone MME server, but can easily communicate via REST API with external applications managing genetic analyses and patient data. The MME node is being implemented in clinical production in collaboration with the Genomic Medicine Center Karolinska at the Karolinska University Hospital. PatientMatcher is written to implement the MME API and provides several customizable settings, including a custom-fit similarity score algorithm and adjustable matching results notifications.

Over 7,000 rare genetic diseases have been identified, and millions of newborns are affected by severe rare genetic diseases each year. A variety of Human Phenotype Ontology (HPO)-based clinical decision support systems (CDSS) and patient repositories have been developed to support clinicians in diagnosing patients with suspected rare genetic diseases. In September 2017, we released PubCaseFinder (https://pubcasefinder.dbcls.jp), a web-based CDSS that provides ranked lists of genetic and rare diseases using HPO-based phenotypic similarities, where top-listed diseases represent the most likely differential diagnosis. We also developed a Matchmaker Exchange (MME) application programming interface (API) to query PubCaseFinder, which has been adopted by several patient repositories. In this paper, we describe notable updates regarding PubCaseFinder, the GeneYenta matching algorithm implemented in PubCaseFinder, and the PubCaseFinder API. The updated GeneYenta matching algorithm improves the performance of the CDSS automated differential diagnosis function. Moreover, the updated PubCaseFinder and new API empower patient repositories participating in MME and medical professionals to actively use HPO-based resources.

Microdeletions and gross deletions are important causes (~20%) of human inherited disease. Their genomic locations are strongly influenced by the local DNA sequence environment. Yet no systematic study has examined the generative mechanisms. Here, we obtained 42,098 pathogenic microdeletions and gross deletions from the Human Gene Mutation Database (HGMD) that together form a continuum of germline deletions ranging in size from 1 bp to 28,394,429 bp. We analyzed the sequence within 1-kb of the breakpoint junctions and found the frequencies of non-B DNA-forming repeats, GC content, and the presence of seven of 78 specific sequence motifs in the vicinity of pathogenic deletions correlated with deletion length for deletions of length ≤30 bp. Furthermore, we found the repeats of DR, GQ, and STR appear to be important for the formation of longer deletions (>30 bp) but not for the formation of shorter deletions (≤30 bp) and significantly (Chi-square test P-value < 2E-16) more microhomologies were identified in flanking short deletions than long deletions (length >30 bp). We provide evidence to support a functional distinction between microdeletions and gross deletions. A deletion length cut-off of 25-30 bp may serve as an objective means to functionally distinguish microdeletions from gross deletions.

Runs of long homozygous stretches (ROH) are considered to be the result of consanguinity and usually contain recessive deleterious disease causing mutations (Szpiech et al., 2013). Several algorithms have been developed to detect ROHs. Here, we developed a simple, alternative strategy by examining X chromosome non-pseudoautosomal region to detect the ROHs from next generation sequencing data utilizing the genotype probabilities and the Hidden Markov Model algorithm as a tool, namely ROHMM. It is implemented purely in java and contains both command-line and a graphical user interface. We tested ROHMM on simulated data as well as real population data from 1000G Project and a clinical sample. Our results have shown that ROHMM can perform robustly producing highly accurate homozygosity estimations under all conditions thereby meeting and even exceeding the performance of its natural competitors.

Genome sequencing(GS) has been applied in the diagnosis of global developmental delay(GDD)/intellectual disability(ID). However, the performance in those with inconclusive results from chromosomal microarray analysis(CMA) and exome sequencing(ES) is unknown. We recruited 100 pediatric GDD/ID patients from multiple sites in China from February 2018 to August 2020 for GS. Patients have received at least one genomic diagnostic test prior to enrollment. Reanalysis of CMA/ES data was performed. The yield of GS was calculated and explanations for missed diagnoses by CMA/ES were investigated. Clinical utility was assessed by interviewing the parents by phone. The overall diagnostic yield of GS was 23%. Seven families could have been solved with reanalysis of ES data. 13 families were missed by previous CMA/ES due to improper method. Three remained unsolved after ES reanalysis due to allele dropout, complex variants missed by ES, and a CNV in untranslated regions. Follow-up of the diagnosed families revealed that nine families experienced changes in clinical management, including identification of targeted treatments, cessation of unnecessary treatment, and considerations for family planning. GS demonstrated high diagnostic yield and clinical utility in this cohort of undiagnosed GDD/ID patients, detecting a wide range of variant types of different sizes in a single workflow.