Balanced Robertsonian translocation (ROB) is the most common chromosomal rearrangement in humans, with an estimated occurrence of 1 in 800 in newborn studies. Carriers are at increased risk of cancer and often diagnosed at fertility clinics after facing recurrent miscarriages, infertility, or aneuploid offspring. Genotyping carriers with DNA sequencing has been challenging because of gaps and misrepresentation of the translocation fusion site in the human reference genome. Only recently, telomere-to-telomere (T2T) human genomes successfully revealed sequences of the acrocentric short arms, including the most common ROB fusion site. A ROB results in loss of two ribosomal DNA (rDNA) arrays and its adjacent distal sequences, including the highly conserved distal junction (DJ). Here, we present a novel method to type ROB carriers directly from short sequencing reads by estimating DJ copy number. We demonstrate that our method successfully genotypes ROBs using a reference-free approach or alignments to either T2T-CHM13v2 or GRCh38. Applying the method to a cohort of healthy newborns and family members (n=4,172) as well as the UK Biobank (n=490,416), we find candidate ROBs at a frequency consistent with the previously reported 1 in 800 incidence (0.11-0.12%). In addition to ROB carriers, we report the frequency of one DJ loss (9, 2.8-3.4%) or gain (11+, 8.4-9.3%) from the two cohorts and the 1000 Genomes Project (n=3,202), and characterize the underlying structural variation in near-T2T genome assemblies from the Human Pangenome Reference Consortium. Importantly, our method provides the first sequencing-based diagnostic for Robertsonian chromosomes and can be applied to low-coverage sequencing data, enhancing its clinical applicability and enabling new studies of structural variation on the acrocentric chromosomes.

Although genomic research has predominantly relied on phenotypic ascertainment of individuals affected with heritable disease, the falling costs of sequencing allow consideration of genomic ascertainment and reverse phenotyping (the ascertainment of individuals with specific genomic variants and subsequent evaluation of physical characteristics). In this research modality, the scientific question is inverted: investigators gather individuals with a genomic variant and test the hypothesis that there is an associated phenotype via targeted phenotypic evaluations. Genomic ascertainment research is thus a model of predictive genomic medicine and genomic screening. Here, we provide our experience implementing this research method. We describe the infrastructure we developed to perform reverse phenotyping studies, including aggregating a super-cohort of sequenced individuals who consented to recontact for genomic ascertainment research. We assessed 13 studies completed at the National Institutes of Health (NIH) that piloted our reverse phenotyping approach. The studies can be broadly categorized as (1) facilitating novel genotype-disease associations, (2) expanding the phenotypic spectra, or (3) demonstrating ex vivo functional mechanisms of disease. We highlight three examples of reverse phenotyping studies in detail and describe how using a targeted reverse phenotyping approach (as opposed to phenotypic ascertainment or clinical informatics approaches) was crucial to the conclusions reached. Finally, we propose a framework and address challenges to building collaborative genomic ascertainment research programs at other institutions. Our goal is for more researchers to take advantage of this approach, which will expand our understanding of the predictive capability of genomic medicine and increase the opportunity to mitigate genomic disease.

Sex determination occurs across animal species, but most of our knowledge about its mechanisms comes from only a handful of bilaterian taxa. This limits our ability to infer the evolutionary history of sex determination within animals. In this study, we generated a linkage map of the genome of the colonial cnidarian Hydractinia symbiolongicarpus and used it to demonstrate that this species has an XX/XY sex determination system. We demonstrate that the X and Y chromosomes have pseudoautosomal and non-recombining regions. We then use the linkage map and a method based on the depth of sequencing coverage to identify genes encoded in the non-recombining region and show that many of them have male gonad-specific expression. In addition, we demonstrate that recombination rates are enhanced in the female genome and that the haploid chromosome number in Hydractinia is n = 15. These findings establish Hydractinia as a tractable non-bilaterian model system for the study of sex determination and the evolution of sex chromosomes.

The Hawaiian monk seal (HMS) is the single extant species of tropical earless seals of the genus Neomonachus. The species survived a severe bottleneck in the late 19th century and experienced subsequent population declines until becoming the subject of a NOAA-led species recovery effort beginning in 1976 when the population was fewer than 1000 animals. Like other recovering species, the Hawaiian monk seal has been reported to have reduced genetic heterogeneity due to the bottleneck and subsequent inbreeding. Here, we report a chromosomal reference assembly for a male animal produced using a variety of methods. The final assembly consisted of 16 autosomes, an X, and portions of the Y chromosomes. We compared variants in this animal to other HMS and to a frequently sequenced human sample, confirming about 12% of the variation seen in man. To confirm that the reference animal was representative of the HMS, we compared his sequence to that of 10 other individuals and noted similarly low variation in all. Variation in the major histocompatibility (MHC) genes was nearly absent compared to the orthologous human loci. Demographic analysis predicts that Hawaiian monk seals have had a long history of small populations preceding the bottleneck, and their current low levels of heterozygosity may indicate specialization to a stable environment. When we compared our reference assembly to that of other species, we observed significant conservation of chromosomal architecture with other pinnipeds, especially other phocids. This reference should be a useful tool for future evolutionary studies as well as the long-term management of this species.

Craniosynostosis (CS) is a major birth defect in which one or more skull sutures fuse prematurely. We previously performed a genome-wide association study (GWAS) for sagittal non-syndromic CS (sNCS), identifying associations downstream from BMP2 on 20p12.3 and intronic to BBS9 on 7p14.3; analyses of imputed variants in DLG1 on 3q29 were also genome-wide significant. We followed this work with a GWAS for metopic non-syndromic NCS (mNCS), discovering a significant association intronic to BMP7 on 20q13.31. In the current study, we sequenced the associated regions on 3q29, 7p14.3, and 20p12.3, including two candidate genes (BMP2 and BMPER) near some of these regions in 83 sNCS child-parent trios, and sequenced regions on 7p14.3 and 20q13.2-q13.32 in 80 mNCS child-parent trios. These child-parent trios were selected from the original GWAS cohorts if the probands carried at least one copy of the top associated GWAS variant (rs1884302 C allele for sNCS; rs6127972 T allele for mNCS). Many of the variants sequenced in these targeted regions are strongly predicted to be within binding sites for transcription factors involved in craniofacial development or bone morphogenesis. Variants enriched in more than one trio and predicted to be damaging to gene function are prioritized for functional studies.

RationalePrevious studies identified an interaction between HLA and oral peanut exposure. HLA-DQA1*01:02 had a protective role with the induction of Ara h 2 epitope-specific IgG4 associated with peanut consumption during the LEAP clinical trial for prevention of peanut allergy, while it was a risk allele for peanut allergy in the peanut avoidance group. We have now evaluated this gene-environment interaction in two subsequent peanut oral immunotherapy (OIT) trials - IMPACT and POISED - to better understand the potential for the HLA-DQA1*01:02 allele as an indicator of higher likelihood of desensitization, sustained unresponsiveness, and peanut allergy remission.MethodsWe determined HLA-DQA1*01:02 carrier status using genome sequencing from POISED (N=118, age: 7-55yr) and IMPACT (N=126, age: 12-<48mo). We tested for association with remission, sustained unresponsiveness (SU), and desensitization in the OIT groups, as well as peanut component specific IgG4 (psIgG4) using generalized linear models and adjusting for relevant covariates and ancestry.ResultsWhile not quite statistically significant, a higher proportion of HLA-DQA1*01:02 carriers receiving OIT in IMPACT were desensitized (93%) compared to non-carriers (78%); odds ratio (OR)=5.74 (p=0.06). In this sample we also observed that a higher proportion of carriers achieved remission (35%) compared to non-carriers (22%); OR=1.26 (p=0.80). In POISED, carriers more frequently attained continued desensitization (80% versus 61% among non-carriers; OR=1.28, p=0.86) and achieved SU (52% versus 31%; OR=2.32, p=0.19). psIgG4 associations with HLA-DQA1*01:02 in the OIT arm of IMPACT which included younger study subjects recapitulated patterns noted in LEAP, but no associations of note were observed in the older POISED study subjects.ConclusionsFindings across three clinical trials show a pattern of a gene environment interaction between HLA and oral peanut exposure. Age, and prior sensitization contribute additional determinants of outcomes, consistent with a mechanism of restricted antigen recognition fundamental to driving protective immune responses to OIT.

As the cost of sequencing continues to decline, the core algorithms and tools used to analyze genomic data must scale to handle larger data volumes. Given that writing efficient distributed algorithms is difficult, most bioinformatics tools scale to multiple threads on a single cluster node if parallelizable at all. Thus it is common in workflows to scatter and gather data in an attempt to scale the workflow. However, splitting flat file formats in an ad-hoc fashion does not take advantage of data locality on a cluster and does not balance execution time on each cluster node well. ADAM provides a GenomicDataset abstraction for genomic data types (e.g. aligned reads, read fragments, features, sequences, variants, and genotypes) over Apache Spark RDDs, Datasets, and DataFrames. The GenomicDataset API provides support for piping the underlying genomic data out to a single node process through the use of an API that builds on Apache Spark's RDD.pipe API. A command line application, library, and interactive shell called Cannoli was developed on top of the ADAM GenomicDataset pipe API to parallelize commonly used bioinformatics tools. Currently, Cannoli supports aligning reads with Bowtie, Bowtie2, BWA, Minimap2, and SNAP; calling variants with FreeBayes and Samtools mpileup; normalizing variants with BCFtools norm and vt normalize; and annotating variant effects with SnpEff and Ensembl Variant Effect Predictor (VEP). Cannoli provides distribution of files across the cluster using SparkFiles mechanism (useful for e.g. BWA indexes), calling external process installed locally, or via Docker, or Singularity (mounting files to the container as necessary), and a functional API for interactive analysis in cannoli-shell or workbooks such as Jupyter, Zeppelin, or Spark Notebook. By using Cannoli, we can accelerate alignment with BWA to take approximately 10—15 minutes when running on a 1,024 core cluster. We can couple this rapid alignment pipeline with the fast preprocessing stages in ADAM and the variant calling stages in Avocado to call variants on a 60x coverage WGS dataset in approximately 45 minutes on a 1,024 core cluster.

The decreasing cost of DNA sequencing over the past decade has led to an explosion of sequencing datasets, leaving us with petabytes of data to analyze. However, current sequencing visualization tools are designed to run on single machines, which limits their scalability and interactivity on modern genomic datasets. Here, we leverage the scalability of Apache Spark to provide Mango, consisting of a Jupyter notebook and genome browser, which removes scalability and interactivity constraints by leveraging multi-node compute clusters to allow interactive analysis over terabytes of sequencing data. We demonstrate scalability of the Mango tools by performing quality control analyses on 10 terabytes of 100 high-coverage sequencing samples from the Simons Genome Diversity Project, enabling capability for interactive genomic exploration of multi-sample datasets that surpass the computational limitations of single-node visualization tools. Mango is freely available for download with full documentation at https://bdg-mango.readthedocs.io/en/latest/.

The decreasing cost of DNA sequencing over the past decade has led to an explosion of available sequencing datasets, leaving us with terabytes to petabytes of data to explore and analyze. It is critical for analysts in research and clinical settings to be able to develop new data-driven hypotheses from these datasets through bias identification, analysis of data quality, and testing different algorithms and parameter settings. However, current interactive tools for sequence analysis are designed to run on single machines that do not scale to the size of modern genomic datasets, and rely on precomputed static views, rather than allowing direct interaction with the primary dataset. Mango is a genomic sequence visualization and analysis platform that removes these constraints regarding scalability and staticity by leveraging the power of multi-node compute clusters in the cloud to allow interactive analysis over terabytes of sequencing data. Mango provides both a genome browser graphical user interface and programmable notebook form factor to allow users of varying analytical experience to explore large sequencing datasets on both private clusters and in the cloud. These tools provide a flexible environment for interactive exploration of genomic datasets, while surpassing the computational limits of single-node genomic visualization tools.

The impact of structural variants (SVs) on a variety of organisms and diseases like cancer has become increasingly evident. Methods for SV detection when studying genomic differences across cells, individuals or populations are being actively developed. Currently, just a few methods are available to compare different SVs callsets, and no specialized methods are available to annotate SVs that account for the unique characteristics of these variant types. Here, we introduce SURVIVOR_ant, a tool that compares types and breakpoints for candidate SVs from different callsets and enables fast comparison of SVs to genomic features such as genes and repetitive regions, as well as to previously established SV datasets such as from the 1000 Genomes Project. As proof of concept we compared 16 SV callsets generated by different SV calling methods on a single genome, the Genome in a Bottle sample HG002 (Ashkenazi son), and annotated the SVs with gene annotations, 1000 Genomes Project SV calls, and four different types of repetitive regions. Computation time to annotate 134,528 SVs with 33,954 of annotations was 22 seconds on a laptop.

The impact of structural variants (SVs) on a variety of organisms and diseases like cancer has become increasingly evident. Methods for SV detection when studying genomic differences across cells, individuals or populations are being actively developed. Currently, just a few methods are available to compare different SVs callsets, and no specialized methods are available to annotate SVs that account for the unique characteristics of these variant types. Here, we introduce SURVIVOR_ant, a tool that compares types and breakpoints for candidate SVs from different callsets and enables fast comparison of SVs to genomic features such as genes and repetitive regions, as well as to previously established SV datasets such as from the 1000 Genomes Project. As proof of concept we compared 16 SV callsets generated by different SV calling methods on a single genome, the Genome in a Bottle sample HG002 (Ashkenazi son), and annotated the SVs with gene annotations, 1000 Genomes Project SV calls, and four different types of repetitive regions. Computation time to annotate 134,528 SVs with 33,954 of annotations was 22 seconds on a laptop.

Biomedical data, in particular omics datasets are being generated at an unprecedented rate. This is due to the falling costs of generating experimental data, improved accuracy and better accessibility to different omics platforms such as genomics, proteomics and metabolomics1,2. As a result, the number of deposited datasets in public repositories originating from various omics approaches has increased dramatically in recent years. With strong support from scientific journals and funders, public data sharing is increasingly considered to be a good scientific practice, facilitating the confirmation of original results, increasing the reproducibility of the analyses, enabling the exploration of new or related hypotheses, and fostering the identification of potential errors, discouraging fraud3. This increase in public data deposition of omics results is a good starting point, but opens up a series of new challenges. For example the research community must now find more efficient ways for storing, organizing and providing access to biomedical data across platforms. These challenges range from achieving a common representation framework for the datasets and the associated metadata from different omics fields, to the availability of efficient methods, protocols and file formats for data exchange between multiple repositories. Therefore, there is a great need for development of new platforms and applications to make possible to search datasets across different omics fields, making such information accessible to the end-user. The FAIR paradigm describes a set of guiding principles to address many of these issues, and aims to make data Findable, Accessible, Interoperable and Re-usable(https://www.force11.org/group/fairgroup/fairprinciples).

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.

We have designed and developed a data integration and visualization platform that provides evidence about the association of known and potential drug targets with diseases. The platform is designed to support identification and prioritization of biological targets for follow-up. Each drug target is linked to a disease using integrated genome-wide data from a broad range of data sources. The platform provides either a target-centric workflow to identify diseases that may be associated with a specific target, or a disease-centric workflow to identify targets that may be associated with a specific disease. Users can easily transition between these target- and disease-centric workflows. The Open Targets Validation Platform is accessible at https://www.targetvalidation.org.

Current genomics visualization tools are intended for a single node environment and lack the scalability required to visualize multiple whole genome samples. Data from the 1000 Genomes Project provides 1.6 terabytes of variant data and over 14 terabytes of alignment data. However, typical genomic visualizations materialize less than 10 kbp, only 3.3e-7% of the genome. Mango is a visualization browser that selectively materializes and organizes genomic data to provide fast in-memory queries. Mango materializes data from persistent storage as the user requests different regions of the genome. This data is efficiently partitioned and organized in memory using interval trees. This interval based organizational structure supports ad hoc queries, filters, and joins across multiple samples at a time, enabling exploratory interaction with genomic data.

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.

Paul Flicek and colleagues provide an update on the European Genome-phenome Archive (EGA), a service of the European Bioinformatics Institute (EMBL-EBI) and the Center for Genome Regulation (CRG). The authors describe the EGA policies and infrastructure, how access decisions are made, methods for data submission and future plans for expansion of this database. The European Genome-phenome Archive (EGA) is a permanent archive that promotes the distribution and sharing of genetic and phenotypic data consented for specific approved uses but not fully open, public distribution. The EGA follows strict protocols for information management, data storage, security and dissemination. Authorized access to the data is managed in partnership with the data-providing organizations. The EGA includes major reference data collections for human genetics research.

collaboration and working with RD-Connect Cathy Turner1, Alexis Brice2, Marta Girdea3, Holm Grassner4, Nicolas Levy5, Hanns Lochmuller1, Olafur Magnusson6, Justin Paschall7, Rachel Thompson1, Birte Zurek4, Brunhilde Wirth8, Gert-Jan van Ommen9, Olaf Riess4, Volker Straub1 1Institute for Genetic Medicine, Newcastle University, UK; 2INSERM, Groupe Hospitalier Pitie Salpetriere, France; 3PhenoTips, Canada; 4Institute of Medical Genetics and Applied Genomics, University of Tubingen, Germany; 5Department of Medical Genetics, Universite d’Aix Marseille, France; 6deCODE, Iceland; 7European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), UK ; 8Institute of Human Genetics, University Hospital Cologne, Germany; 9Department of Human Genetics, Leiden University Medical Center, The Netherlands

Research into rare diseases is typically fragmented by data type and disease. Individual efforts often have poor interoperability and do not systematically connect data across clinical phenotype, genomic data, biomaterial availability, and research/trial data sets. Such data must be linked at both an individual-patient and whole-cohort level to enable researchers to gain a complete view of their disease and patient population of interest. Data access and authorization procedures are required to allow researchers in multiple institutions to securely compare results and gain new insights. Funded by the European Union's Seventh Framework Programme under the International Rare Diseases Research Consortium (IRDiRC), RD-Connect is a global infrastructure project initiated in November 2012 that links genomic data with registries, biobanks, and clinical bioinformatics tools to produce a central research resource for rare diseases.