Mitigating false-positive associations in rare disease gene discovery

Candidate genes and sequence variants for susceptibility to mycobacterial infection identified by whole-exome sequencing

Inborn errors of immunity are known to influence susceptibility to mycobacterial infections. The aim of this study was to characterize the genetic profile of nine patients with mycobacterial infections (eight with BCGitis and one with disseminated tuberculosis) from the Republic of Moldova using whole-exome sequencing. In total, 12 variants in eight genes known to be associated with Mendelian Susceptibility to Mycobacterial Disease (MSMD) were detected in six out of nine patients examined. In particular, a novel splice site mutation c.373–2A>C in STAT1 gene was found and functionally confirmed in a patient with disseminated tuberculosis. Trio analysis was possible for seven out of nine patients, and resulted in 23 candidate variants in 15 novel genes. Four of these genes - GBP2, HEATR3, PPP1R9B and KDM6A were further prioritized, considering their elevated expression in immune-related tissues. Compound heterozygosity was found in GBP2 in a single patient, comprising a maternally inherited missense variant c.412G>A/p.(Ala138Thr) predicted to be deleterious and a paternally inherited intronic mutation c.1149+14T>C. Functional studies demonstrated that the intronic mutation affects splicing and the level of transcript. Finally, we analyzed pathogenicity of variant combinations in gene pairs and identified five patients with putative oligogenic inheritance. In summary, our study expands the spectrum of genetic variation contributing to susceptibility to mycobacterial infections in children and provides insight into the complex/oligogenic disease-causing mode.

Seven years since the launch of the Matchmaker Exchange: The evolution of genomic matchmaking

The Matchmaker Exchange (MME) was launched in 2015 to provide a robust mechanism to discover novel disease-gene relationships. It operates as a federated network connecting databases holding relevant data using a common application programming interface, where two or more users are looking for a match for the same gene (two-sided matchmaking). Seven years from its launch, it is clear that the MME is making outstanding contributions to understanding the morbid anatomy of the genome. The number of unique genes present across the MME has steadily increased over time; there are currently >13,520 unique genes (~68% of all protein-coding genes) connected across the MME's eight genomic matchmaking nodes, GeneMatcher, DECIPHER, PhenomeCentral, MyGene2, seqr, Initiative on Rare and Undiagnosed Disease, PatientMatcher, and the RD-Connect Genome-Phenome Analysis Platform. The collective data set accessible across the MME currently includes more than 120,000 cases from over 12,000 contributors in 98 countries. The discovery of potential new disease-gene relationships is happening daily and international collaborative teams are moving these advances forward to publication, now numbering well over 500. Expansion of data sharing into routine clinical practice by clinicians, genetic counselors, and clinical laboratories has ensured access to discovery for even more individuals with undiagnosed rare genetic diseases. Tens of thousands of patients and their family members have been directly or indirectly impacted by the discoveries facilitated by two-sided genomic matchmaking. MME supports further connections to the literature (PubCaseFinder) and to human and model organism resources (Monarch Initiative) and scientists (ModelMatcher). Efforts are now underway to explore additional approaches to matchmaking at the gene or variant level 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 the years to come.

Variants in STXBP3 are Associated with Very Early Onset Inflammatory Bowel Disease, Bilateral Sensorineural Hearing Loss and Immune Dysregulation

BACKGROUND AND AIMS

Very early onset inflammatory bowel disease [VEOIBD] is characterized by intestinal inflammation affecting infants and children less than 6 years of age. To date, over 60 monogenic aetiologies of VEOIBD have been identified, many characterized by highly penetrant recessive or dominant variants in underlying immune and/or epithelial pathways. We sought to identify the genetic cause of VEOIBD in a subset of patients with a unique clinical presentation.

METHODS

Whole exome sequencing was performed on five families with ten patients who presented with a similar constellation of symptoms including medically refractory infantile-onset IBD, bilateral sensorineural hearing loss and, in the majority, recurrent infections. Genetic aetiologies of VEOIBD were assessed and Sanger sequencing was performed to confirm novel genetic findings. Western analysis on peripheral blood mononuclear cells and functional studies with epithelial cell lines were employed.

RESULTS

In each of the ten patients, we identified damaging heterozygous or biallelic variants in the Syntaxin-Binding Protein 3 gene [STXBP3], a protein known to regulate intracellular vesicular trafficking in the syntaxin-binding protein family of molecules, but not associated to date with either VEOIBD or sensorineural hearing loss. These mutations interfere with either intron splicing or protein stability and lead to reduced STXBP3 protein expression. Knock-down of STXBP3 in CaCo2 cells resulted in defects in cell polarity.

CONCLUSION

Overall, we describe a novel genetic syndrome and identify a critical role for STXBP3 in VEOIBD, sensorineural hearing loss and immune dysregulation.

A Survey of Compound Heterozygous Variants in Pediatric Cancers and Structural Birth Defects

Compound heterozygous (CH) variants occur when two recessive alleles are inherited and the variants are located at different loci within the same gene in a given individual. CH variants are important contributors to many different types of recessively inherited diseases. However, many studies overlook CH variants because identification of this type of variant requires knowing the parent of origin for each nucleotide. Using computational methods, haplotypes can be inferred using a process called "phasing," which estimates the chromosomal origin of most nucleotides. In this paper, we used germline, phased, whole-genome sequencing (WGS) data to identify CH variants across seven pediatric diseases (adolescent idiopathic scoliosis: n = 16, congenital heart defects: n = 709, disorders of sex development: n = 79, ewing sarcoma: n = 287, neuroblastoma: n = 259, orofacial cleft: n = 107, and syndromic cranial dysinnervation: n = 172), available as parent-child trios in the Gabriella Miller Kids First Data Resource Center. Relatively little is understood about the genetic underpinnings of these diseases. We classified CH variants as "potentially damaging" based on minor allele frequencies (MAF), Combined Annotation Dependent Depletion scores, variant impact on transcription or translation, and gene-level frequencies in the disease group compared to a healthy population. For comparison, we also identified homozygous alternate (HA) variants, which affect both gene copies at a single locus; HA variants represent an alternative mechanism of recessive disease development and do not require phasing. Across all diseases, 2.6% of the samples had a potentially damaging CH variant and 16.2% had a potentially damaging HA variant. Of these samples with potentially damaging variants, the average number of genes per sample was 1 with a CH variant and 1.25 with a HA variant. Across all samples, 5.1 genes per disease had a CH variant, while 35.6 genes per disease had a HA variant; on average, only 4.3% of these variants affected common genes. Therefore, when seeking to identify potentially damaging variants of a putatively recessive disease, CH variants should be considered as potential contributors to disease development. If CH variants are excluded from analysis, important candidate genes may be overlooked.

Longin CF, Ganal M, Himmelbach A, Reif JC, Schulthess AW. Exome association analysis sheds light onto leaf rust (Puccinia triticina) resistance genes currently used in wheat breeding (Triticum aestivum L.)

Resistance breeding is crucial for a sustainable control of leaf rust (Puccinia triticina) in wheat (Triticum aestivum L.) while directly targeting functional variants is the Holy Grail for efficient marker-assisted selection and map-based cloning. We assessed the limits and prospects of exome association analysis for severity of leaf rust in a large hybrid wheat population of 1574 single-crosses plus their 133 parents. After imputation and quality control, exome sequencing revealed 202 875 single-nucleotide polymorphisms (SNPs) covering 19.7% of the high-confidence annotated gene space. We performed intensive data mining and found significant associations for 2171 SNPs corresponding to 50 different loci. Some of these associations mapped in the proximity of the already known resistance genes Lr21, Lr34-B, Lr1 and Lr10, while other associated genomic regions, such as those on chromosomes 1A and 3D, harboured several annotated genes putatively involved in resistance. Validation with an independent population helped to narrow down the list of putative resistance genes that should be targeted by fine-mapping. We expect that the proposed strategy of intensive data mining coupled with validation will significantly influence research in plant genetics and breeding.

Phenogenon: Gene to phenotype associations for rare genetic diseases

As high-throughput sequencing is increasingly applied to the molecular diagnosis of rare Mendelian disorders, a large number of patients with diverse phenotypes have their genetic and phenotypic data pooled together to uncover new gene-phenotype relations. We introduce Phenogenon, a statistical tool that combines, Human Phenotype Ontology (HPO) annotated patient phenotypes, gnomAD allele population frequency, and Combined Annotation Dependent Depletion (CADD) score for variant pathogenicity, in order to jointly predict the mode of inheritance and gene-phenotype associations. We ran Phenogenon on our cohort of 3,290 patients who had undergone whole exome sequencing. Among the top associations, we recapitulated previously known, such as "SRD5A3—Abnormal full-field electroretinogram—recessive" and "GRHL2 –Nail dystrophy—recessive", and discovered one potentially novel, “RRAGA–Abnormality of the skin—dominant”. We also developed an interactive web interface available at https://phenogenon.phenopolis.org to visualise and explore the results.

Enabling Global Clinical Collaborations on Identifiable Patient Data: The Minerva Initiative

The clinical utility of computational phenotyping for both genetic and rare diseases is increasingly appreciated; however, its true potential is yet to be fully realized. Alongside the growing clinical and research availability of sequencing technologies, precise deep and scalable phenotyping is required to serve unmet need in genetic and rare diseases. To improve the lives of individuals affected with rare diseases through deep phenotyping, global big data interrogation is necessary to aid our understanding of disease biology, assist diagnosis, and develop targeted treatment strategies. This includes the application of cutting-edge machine learning methods to image data. As with most digital tools employed in health care, there are ethical and data governance challenges associated with using identifiable personal image data. There are also risks with failing to deliver on the patient benefits of these new technologies, the biggest of which is posed by data siloing. The Minerva Initiative has been designed to enable the public good of deep phenotyping while mitigating these ethical risks. Its open structure, enabling collaboration and data sharing between individuals, clinicians, researchers and private enterprise, is key for delivering precision public health.

Calculating the statistical significance of rare variants causal for Mendelian and complex disorders

BACKGROUND

With the expanding use of next-gen sequencing (NGS) to diagnose the thousands of rare Mendelian genetic diseases, it is critical to be able to interpret individual DNA variation. To calculate the significance of finding a rare protein-altering variant in a given gene, one must know the frequency of seeing a variant in the general population that is at least as damaging as the variant in question.

METHODS

We developed a general method to better interpret the likelihood that a rare variant is disease causing if observed in a given gene or genic region mapping to a described protein domain, using genome-wide information from a large control sample. Based on data from 2504 individuals in the 1000 Genomes Project dataset, we calculated the number of individuals who have a rare variant in a given gene for numerous filtering threshold scenarios, which may be used for calculating the significance of an observed rare variant being causal for disease. Additionally, we calculated mutational burden data on the number of individuals with rare variants in genic regions mapping to protein domains.

RESULTS

We describe methods to use the mutational burden data for calculating the significance of observing rare variants in a given proportion of sequenced individuals. We present SORVA, an implementation of these methods as a web tool, and we demonstrate application to 20 relevant but diverse next-gen sequencing studies. Specifically, we calculate the statistical significance of findings involving multi-family studies with rare Mendelian disease and a large-scale study of a complex disorder, autism spectrum disorder. If we use the frequency counts to rank genes based on intolerance for variation, the ranking correlates well with pLI scores derived from the Exome Aggregation Consortium (ExAC) dataset (ρ = 0.515), with the benefit that the scores are directly interpretable.

CONCLUSIONS

We have presented a strategy that is useful for vetting candidate genes from NGS studies and allows researchers to calculate the significance of seeing a variant in a given gene or protein domain. This approach is an important step towards developing a quantitative, statistics-based approach for presenting clinical findings.

MECP2 variation in Rett syndrome-An overview of current coverage of genetic and phenotype data within existing databases

Rett syndrome (RTT) is a monogenic rare disorder that causes severe neurological problems. In most cases, it results from a loss-of-function mutation in the gene encoding methyl-CPG-binding protein 2 (MECP2). Currently, about 900 unique MECP2 variations (benign and pathogenic) have been identified and it is suspected that the different mutations contribute to different levels of disease severity. For researchers and clinicians, it is important that genotype-phenotype information is available to identify disease-causing mutations for diagnosis, to aid in clinical management of the disorder, and to provide counseling for parents. In this study, 13 genotype-phenotype databases were surveyed for their general functionality and availability of RTT-specific MECP2 variation data. For each database, we investigated findability and interoperability alongside practical user functionality, and type and amount of genetic and phenotype data. The main conclusions are that, as well as being challenging to find these databases and specific MECP2 variants held within, interoperability is as yet poorly developed and requires effort to search across databases. Nevertheless, we found several thousand online database entries for MECP2 variations and their associated phenotypes, diagnosis, or predicted variant effects, which is a good starting point for researchers and clinicians who want to provide, annotate, and use the data.

When "N of 2" is not enough: integrating statistical and functional data in gene discovery

The expanding use of genomic sequencing promises to improve clinical diagnostics and to drive the discovery of new disease genes. Candidate genes are increasingly being identified through recurrent cases (e.g., two or more independent cases [“N of 2”] in which variants are present in the same gene). These second case hits provide statistical evidence of an association, which may then be combined with functional validation or familial segregation studies to bolster the evidence that a gene is truly causal. Here, we discuss how to integrate different forms of functional evidence with human genetics case and segregation data to improve the significance of new disease–gene associations.

International Cooperation to Enable the Diagnosis of All Rare Genetic Diseases

Provision of a molecularly confirmed diagnosis in a timely manner for children and adults with rare genetic diseases shortens their "diagnostic odyssey," improves disease management, and fosters genetic counseling with respect to recurrence risks while assuring reproductive choices. In a general clinical genetics setting, the current diagnostic rate is approximately 50%, but for those who do not receive a molecular diagnosis after the initial genetics evaluation, that rate is much lower. Diagnostic success for these more challenging affected individuals depends to a large extent on progress in the discovery of genes associated with, and mechanisms underlying, rare diseases. Thus, continued research is required for moving toward a more complete catalog of disease-related genes and variants. The International Rare Diseases Research Consortium (IRDiRC) was established in 2011 to bring together researchers and organizations invested in rare disease research to develop a means of achieving molecular diagnosis for all rare diseases. Here, we review the current and future bottlenecks to gene discovery and suggest strategies for enabling progress in this regard. Each successful discovery will define potential diagnostic, preventive, and therapeutic opportunities for the corresponding rare disease, enabling precision medicine for this patient population.

Quantification of Phenotype Information Aids the Identification of Novel Disease Genes

Next-generation sequencing led to the identification of many potential novel disease genes. The presence of mutations in the same gene in multiple unrelated patients is, however, a priori insufficient to establish that these genes are truly involved in the respective disease. Here, we show how phenotype information can be incorporated within statistical approaches to provide additional evidence for the causality of mutations. We developed a broadly applicable statistical model that integrates gene-specific mutation rates, cohort size, mutation type, and phenotype frequency information to assess the chance of identifying de novo mutations affecting the same gene in multiple patients with shared phenotype features. We demonstrate our approach based on the frequency of phenotype features present in a unique cohort of 6,149 patients with intellectual disability. We show that our combined approach can decrease the number of patients required to identify novel disease genes, especially for patients with combinations of rare phenotypes. In conclusion, we show how integrating genotype-phenotype information can aid significantly in the interpretation of de novo mutations in potential novel disease genes.

Gene discovery for Mendelian conditions via social networking: de novo variants in KDM1A cause developmental delay and distinctive facial features

PURPOSE

The pace of Mendelian gene discovery is slowed by the "n-of-1 problem"-the difficulty of establishing the causality of a putatively pathogenic variant in a single person or family. Identification of an unrelated person with an overlapping phenotype and suspected pathogenic variant in the same gene can overcome this barrier, but it is often impeded by lack of a convenient or widely available way to share data on candidate variants/genes among families, clinicians, and researchers.

METHODS

Social networking among families, clinicians, and researchers was used to identify three children with variants of unknown significance in KDM1A and similar phenotypes.

RESULTS

De novo variants in KDM1A underlie a new syndrome characterized by developmental delay and distinctive facial features.

CONCLUSION

Social networking is a potentially powerful strategy to discover genes for rare Mendelian conditions, particularly those with nonspecific phenotypic features. To facilitate the efforts of families to share phenotypic and genomic information with each other, clinicians, and researchers, we developed the Repository for Mendelian Genomics Family Portal (RMD-FP; http://uwcmg.org/#/family). Design and development of MyGene2 (http://www.mygene2.org), a Web-based tool that enables families, clinicians, and researchers to search for gene matches based on analysis of phenotype and exome data deposited into the RMD-FP, is under way.Genet Med 18 8, 788-795.

The Matchmaker Exchange: a platform for rare disease gene discovery

There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for "the needle in a haystack" to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease-specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can "match" these cases to build evidence for causality. However, serendipity has never proven to be a reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. Three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow.