HGMD Professional 2024.1 is now available, expanding the world’s largest collection of human inherited disease mutations to 510,804 entries—that’s 6,796 more than the previous release.
For over 30 years, HGMD Professional has been used worldwide by researchers, clinicians, diagnostic laboratories and genetic counselors as an essential tool for the annotation of next-generation sequencing (NGS) data in routine clinical and translational research. Founded and maintained by the Institute of Medical Genetics at Cardiff University, HGMD Professional provides users with a unique resource containing expert-curated mutations all backed by peer-reviewed publications where there is evidence of clinical impact.
HGMD Professional is powered by a team of expert curators at Cardiff University. Data are collected weekly by a combination of manual and computerized search procedures. In excess of 250 journals are scanned for articles describing germline mutations causing human genetic disease. The required data are extracted from the original articles and augmented with the necessary supporting data.
The number of disease-associated germline mutations published per year has more than doubled in the past decade (Figure 1). As rare and novel genetic mutations continue to be uncovered, having access to the latest scientific evidence is critical for timely interpretations of NGS data.
Figure 1. Mutation entries in HGMD Professional 2024.1. The number of inherited disease-associated germline mutations published per year has more than doubled since 2015.
View the complete HGMD Professional 2024.1 statistics, below.
Unlike new machine learning or artificial intelligence platforms that rapidly index millions of journal articles for mutations, HGMD Professional leverages human judgement and expertise—every catalogued mutation has been “touched” by a trained scientist to ensure accuracy, relevance, and context.
Learn more about the industry-leading database here, where you can explore features, watch videos, and request a complimentary 5-day trial.
Genetic disease is the leading cause of infant death in the United States, accounting for approximately 20% of annual infant mortality.1 Screening for genetic disease has been a long-established part of preconception and prenatal care, with a community wide screening program for Tay-Sachs disease (TSD) dating back to the 1970s; however, traditional methods of carrier screening have been offered gene-by-gene, disorder-by-disorder.
Recent developments in laboratory technologies have led to the commercial availability of expanded carrier screening (ECS) panels capable of assessing hundreds of mutations associated with genetic diseases. ECS panels have the ability to identify mutations that would otherwise not be detected. While many of the disorders on these panels are individually rare, the overall risk of having an affected offspring is 1 in 280, which is higher than the risk of having a child with a neural tube defect, for which screening is universal.2
In 2012, one of the first DNA testing and genetic counselling companies to offer ECS in the United States launched a flagship ECS panel that used next-generation sequencing (NGS) technology to assess thousands of mutations associated with more than 175 of the most relevant recessive diseases. For cancer-focused screens, the lab developed a 36 gene panel for hereditary cancer risk assessment.2
In the first three years of offering ECS, the lab screened over 400,000 individuals.3 By 2016, the lab served a network of more than 10,000 health professionals, and demand for preconception screening was soaring, owing to the increasing public awareness of the ill effects related to the transfer of genetic disease.4 Unique to the lab's ECS offering was the company’s “real-time manual curation” to support the classification of each genetic variant they encountered. Extremely thorough and highly accurate, the lab's manual literature curation enabled the company to elevate the actionable information provided to the ordering physicians and the patients they served. However, this process was labor-intensive and costly, which was ironic given the dwindling cost of DNA sequencing and the supporting technology. The question became how to scale-up without cutting corners.
Clinical decision support solutions have long been touted as the way of the future for clinical genetic testing laboratories. Combining big data analytics with advanced tools and knowledge bases, clinical decision support solutions are designed to organize, filter, and present useful information at the appropriate point in time to the person who can use it to make a decision. In 2017, the lab evaluated the use of a clinical decision support solution to help scale their genomic interpretation processes: QIAGEN Clinical Insight (QCI).*
QCI is QIAGEN’s clinical decision support solution for genetic testing laboratories. Software that reproducibly converts highly complex NGS data into clinician-ready reports, QCI is the tool through which actionable information is extracted from the sequencing results. Unlike any other clinical decision support solution on the market, QCI is largely powered by manual curation.
The knowledge base inside QCI is maintained by hundreds of Ph.D. scientists certified in clinical case curation who are committed to reading and recording all publications for a given mutation. This information is then mapped to over 2.8 million ontology classes contained within the QIAGEN Knowledge Base, providing further context by establishing relationships between variants, genes, tissue types, and pathways. When a genetic testing lab runs NGS data through QCI, the software computes the ACMG classification based on evidence curated from full-text articles, public, and private data sources. The knowledge extracted from full-text articles include observed genes, variants, function, phenotype, drug, dose, clinical cases, etc. With all this information stored in a structured knowledge base, the QIAGEN KB can quickly retrieve the relevant evidence that triggers all 28 ACMG criteria to more accurately compute an ACMG classification. Further this evidence is presented at the clinician’s fingertips for quick reference. Additionally, using natural language processing, the QIAGEN KB can auto-generate a one-sentence “finding” that is representative of the relevant evidence found in the published article.
This critical feature—automated curation of manually sourced content—saves genetic testing labs considerable time and effort when searching for variant-specific articles to satisfy the levels of evidence needed to definitively determine a classification. Especially for ECS, which is a testing practice that frequently encounters novel rare variants, the value of automation is fast becoming a necessity. To accurately and robustly appraise a novel rare variant’s pathogenicity, lab personnel must manually curate multiple lines of evidence to assess clinical significance. Therefore, if the majority of this information was autogenerated, the genomic interpretation process could be economically shortened.
The lab recognized the opportunity of integrating QCI into their curation workflow and designed a study to evaluate the concordance between the clinical evidence that QCI automatically retrieves for each observed variant classification and the clinical evidence that the lab’s curation team locates and ultimately uses in the physician reports. If the results were comparable, QCI could introduce significant time and cost savings.
The lab's manual curation workflow is outlined in Figure 1. A semi-automated process, the workflow utilizes proprietary software to initially classify variants into three categories: those with high population frequency; those that have never been reported; and those needing more information before pathogenicity can be assessed. For those remaining variants, the curation team manually searches online databases, in-house article libraries, and other available resources to find variant-specific references.
Figure 1. The lab's curation workflow
The curation workflow used to determine clinical significance of variants is summarized graphically. (a) The curation process is shown in the context of the overall laboratory workflow, in which inbound samples are eventually transformed into patient reports. (b) The curation workflow contributes lines of primary evidence that are reviewed manually, which are then combined with multiple lines of autogenerated supporting evidence to assess clinical significance.
Once evidence is collected for a variant —if any is to be found—the information is then used to assess the variant’s potential pathogenicity. As recommended by the American College of Medical Genetics (ACMG) and the Association for Molecular Pathology (AMP) published guidelines for the assessment of variants in genes associated with Mendelian diseases, the lab classifies variants following a two-step process:
First, the collected evidence is categorized into one of 28 defined criteria set forth by the ACMG-AMP guidelines and assigned a code that addresses the strength of evidence, such as population data, case-control analyses, functional data, computational predictions, allelic data, segregation studies, and de novo observations. Each code is assigned a weight (stand-alone, very strong, strong, moderate, or supporting) and direction (benign or pathogenic).
Next, the lab combines these evidence codes to arrive at one of five classifications: pathogenic (P), likely pathogenic (LP), variant of uncertain significance (VUS), likely benign (LB), or benign (B). Important in this step is the lab's ability to modify the strength of individual criteria based on expert discretion—a safeguard that goes away with computerized systems.
To determine whether QCI could provide value to the lab’s curation team, the software was tasked with pulling a bibliography for 2,324 variants that had been recently detected by the lab’s ECS and hereditary cancer risk assessment panels. For each of these variants, the curation team had been able to match at least one published article with a specific disease-gene reference. QCI’s variant bibliography was expected to present the same quantity and quality of clinical evidence.
The study found that QCI’s variant bibliography was highly concordant with lab’s manual curation efforts. Of the 2,324 unique article-variant pairs identified by the lab, QCI pulled 2,075 of the references (89.3%) and an additional 13,938 article-variant pairs not captured by the lab's curation team.
Figure 2. Overlap of bibliographic content
Figure 2 shows the overlap in content quantity between the two sources. As depicted, QCI (QIAGEN) presents significantly more data for the evaluated variants. This outcome reflects the comprehensive nature of QIAGEN’s article-centric approach, which aims to collect all publications for a given variant. While exhaustive and not always necessary, QCI’s ability to glean information from numerous sources affords the software greater accuracy in predicting variant classifications, which is seen in the second phase of the lab's evaluation.
More important than the number of bibliographic sources, accuracy of cited content ultimately dictates clinical significance. Counsyl measured the quality of QCI’s variant bibliography by looking at how the software would classify variants based on the information it pulled. What they found was a concordance of 98.8% of the pathogenic cases (Figure 2).
During the study period, a total of 682 variants were classified as pathogenic by lab’s genetic scientists. Of these, only eight would be downgraded to VUS utilizing only QCI bibliographies. Therefore, the false negative rate for using QCI’s bibliographies was ~1.2% and is expected to decrease to <1%. Further, for a sample of 50 VUS variants examined, none would change classification with additional unique references in QCI, primarily because QCI includes secondary reports and studies for other disease contexts that may be listed as 'reviewed but not curated' in their curations.
As a result of these positive findings, QCI bibliographies have been integrated into the lab’s manual curation workflow, eliminating the need for manual searches in the majority of cases. (Left: variant-specific page in QCI). After several months, a comparison of the time taken for reference searches before and after the adoption of QCI was performed (Figure 3).
Figure 3. Before and after adopting QCI
The goal of this evaluation was to assess whether utilization of QIAGEN’s variant-specific bibliographies could match the level of accuracy and quality of the lab’s more time-intensive manual article selection approach. Investigators concluded that there are clear benefits for adopting QCI for reference identification: an exceptionally high variant-specific article coverage, and significant time savings in a search process that can take up to ~45 minutes.
The results also serve to validate the efficacy of the lab’s previous article search and selection method, with the vast majority of variant classifications being unaltered by use of QIAGEN’s bibliographies. The lab now employs QCI bibliographies for every curated variant. Consequently, manual search methods are still employed at the lab, but can now be reserved for variants nearer VUS/pathogenic evidence thresholds.
QCI has already proven a valuable resource for increasing the efficiency of the lab’s in-house curation. Work is underway to additionally incorporate QIAGEN’s continually-updated bibliographies into the automated components of our variant classification workflows: the initial software-based auto-curation step for newly-identified variants, and the identification of those requiring re-curation in response to new publications becoming available. Accordingly, we expect QCI to further contribute to the lab’s continuing efforts to improve turnaround time by increasing curation efficiency while maintaining classification accuracy in patient reports.
*Data taken from a joint study conducted by Counsyl and QIAGEN: Cox et al. ClinGen 2017. Counsyl has since been acquired.
Learn more about QIAGEN Clinical Insight for here.
References
As of March 29, 2019, HGMD contains over 256,070 germline mutations--a major achievement in our understanding of rare and hereditary disease. For years, HGMD has been recognized as the defacto standard repository for heritable mutations. Curated by experts in the field of genetics, HGMD offers information you can trust, with an unrivaled breadth of coverage. The proof is in the numbers:
256,070 expert-curated, disease-causing germline variants
10,500+ summary reports listing all known inherited disease mutations
2,600+ peer-review journals mined by experts in the field of genetics
104,000+ peer-reviewed literature reports cited
14,500+ scientific publications cite HGMD
17,000+ new mutation entries per year
View the complete HGMD statistics
Mutations may now be viewed according to whether they have additional literature evidence (browse mutations - additional literature evidence). Categories include additional functional evidence, additional phenotypes and additional case reports.
To get the most out of your HGMD subscription, please watch the video tutorials available at our Resources webpage.
New ANNOVAR databases are now available.
Learn more about how ANNOVAR can be used with HGMD for variant annotation.
Watch a recorded webinar featuring ANNOVAR here.
Updated tracks have been released with HGMD 2019.1 content for all HGMD-related tracks. Additional major updates include TRANSFAC® release 2019.1, and PROETOME™ release 2019.1.
QCI Interpret for Rare and Hereditary Disease is clinical decision support software that provides current scientific and clinical evidence to classify variants according to ACMG and ACOG interpretation guidelines.
QCI Interpret connects you to HGMD, plus 25 additional public and propriety sources. The software provides you with an expansive variant bibliography with full transparency to the underlying evidence, enabling you report confidently and scale efficiently. Learn more
Structural variants affect large regions of the human genome and also play a significant role in gene expression (1, 2). They are typically detected with short Illumina or long PacBio reads, or a combination of both approaches. The new Advanced Structural Variant Detection (ASVD) plugin focuses on the short read approach, and is able to detect structural variants using short Illumina reads from whole genome sequencing (WGS). It supports the detection of the most frequently occurring structural variant types in the human genome such as deletions, duplications, and insertions (1).
The ASVD plugin checks read mappings for evidence of breakpoints using “unaligned end” signatures. “Unaligned end” refers to the end of a read that does not map to the reference sequence. At biological breakpoints, it is expected that multiple reads display unaligned ends, giving rise to a signature. A statistical model evaluates the likelihood of each breakpoint based on the probabilities of supporting reads. Breakpoint signatures and coverage information are next processed together in a series of steps. These include specialized alignment algorithms, copy number variation (CNV) detection, and local de novo assembly. If multiple structural variant calls are based on the set of breakpoints, the optimal calls given the breakpoint evidence are reported as the final set of detected structural variants.
Detected breakpoints and structural variants can be viewed together with the read mappings and the reference sequence. Track tables can then be used to filter and select individual breakpoints and structural variants as shown in the example in Figure 1.
Figure 1. Genome track view of the reference sequence, the read mapping of the sample and a track with the structural variant calls. The table view of the structural variant calls track allows interactive filtering and viewing of the results. An example of “unaligned ends”, i.e. ends of reads that do not match the reference genome, are seen as transparent ends of lines representing reads in the mapping.
To evaluate the performance of the ASVD plugin, we compared it to Illumina's Manta. Recent benchmarks against Delly and Lumpy showed that Manta had superior performance (3, 4).
We made use of two recent data sets from Huddleston et al. (5) and Shi et al. (6) to evaluate the ASVD plugin and Manta. Both of these studies used PacBio reads for contig assembly and structural variant detection concerning the GRCh38 reference.
While Shi et al. utilized a diploid genome from an anonymous Chinese individual HX1, Huddleston et al. sequenced two effectively haploid human genomes from hydatidiform moles CHM1 and CHM13 that hence lack allelic variations. Haploid genomes facilitate contig assembly and structural variant detection compared with diploid genomes, and we considered the CHM1 and CHM13 sets the most reliable truth sets available to our knowledge at the time of testing.
We combined the CHM1 and CHM13 sets to produce a diploid truth set, which contained 66.5% more calls than HX1. We believe this difference is mainly caused by the difficulty in detecting structural variants in a diploid genome, where Huddleston et al. showed that they were unable to recover the majority of their heterozygous calls when using an effectively diploid version of CHM1 and CHM13.
CHM1 and CHM13 Illumina reads were sampled to create three different sets of 20x, 40x, and 80x coverages, while the reads available for HX1 provided coverage of 75x. We note that our benchmarking method does not evaluate alternate but equivalent variant representations and that the truth set calls may not always be precise. We, therefore, used an error margin of 50 base pairs when comparing ASVD and Manta calls with structural variants in each truth set (see also special notes for further details regarding benchmarks and data preparation).
Table 1: Benchmark of ASVD and Manta on artificial diploid WGS reads at varying coverage, obtained by sampling form a mix of CHM1 and CHM13 reads in addition to a HX1 comparison. A SV was considered a true positive, if the call was within 50 bp of the truth.
| Dataset | Model | Correct | Wrong | Precision | Sensitivity |
| 20x | ASVD | 3561 | 355 | 0.909 | 0.109 |
| Manta | 2992 | 327 | 0.901 | 0.092 | |
| 40x | ASVD | 4896 | 520 | 0.904 | 0.150 |
| Manta | 4835 | 754 | 0.865 | 0.148 | |
| 80x | ASVD | 4924 | 582 | 0.894 | 0.151 |
| Manta | 6566 | 1242 | 0.840 | 0.201 | |
| HX1 (75x) | ASVD | 3398 | 2007 | 0.629 | 0.174 |
| Manta | 4230 | 2326 | 0.645 | 0.216 |
The ASVD plugin and Manta perform comparably across the different sets. Both the ASVD plugin and Manta showed significantly more “false positives” in the HX1 set compared with the Huddleston et al. 20x - 80x sets. We believe this is an artifact of real SVs that are present, but not included in the HX1 truth set.
We assessed the performance separately for short and long structural variants for the typical scenario of 40x coverage Illumina whole genome sequencing. A detailed comparison of results for the Huddleston et al. 40x coverage set in table 2, where we only considered variants of minimum 50 base pairs and applied a cut-off between short and long structural variants at 100 base pairs.
Table 2: Benchmark of ASVD and Manta for short and long structural variants for the 40x coverage Illumina data set. A SV was considered a true positive, if the call was within 50 bp of the truth.
| Length | Model | Correct | Wrong | Precision | Sensitivity | |
| Deletions | 50 – 100 | ASVD | 934 | 87 | 0.915 | 0.179 |
| Manta | 1084 | 203 | 0.842 | 0.208 | ||
| 100 – 10000 | ASVD | 2170 | 219 | 0.908 | 0.322 | |
| Manta | 2113 | 264 | 0.889 | 0.314 | ||
| Insertions | 50 – 100 | ASVD | 734 | 94 | 0.886 | 0.099 |
| Manta | 836 | 134 | 0.862 | 0.112 | ||
| 100 – 10000 | ASVD | 1058 | 120 | 0.898 | 0.081 | |
| Manta | 802 | 153 | 0.840 | 0.061 |
We observed instances where the ASVD plugin and Manta made equivalent calls that appeared correct, but were not present or were represented differently in a truth set. This resulted in lower precision and sensitivity values overall for both tools that is likely to be the case.
These benchmarks suggest that ASVD and Manta have very comparable performances for short SVs and that ASVD performs slightly better than Manta for longer CVs.
(1) Sudmant, P.H., et al. (2015) An integrated map of structural variation in 2,504 human genomes. Nature, 526
(2) Chiang, C., et al. (2017) The impact of structural variation on human gene expression. Nat. Genet. 49(5):692-699.
(3) Chen, X,., et al. (2016) Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications, Bioinformatics. 32(8):1220-2.
(4) Sedlazeck F J., et al. (2018) Accurate detection of complex structural variations using single-molecule sequencing. Nat. Methods. 15(6):461-468.
(5) Huddleston, J., et al. (2017) Discovery and genotyping of structural variation from long-read haploid genome sequence data. Genome Res. 27(5):677-685.
(6) Shi et al. (2016) Long-read sequencing and de novo assembly of a Chinese genome. Nat. Comm. 30(7):12065.
Check out these recent articles citing Biomedical Genomics Workbench, a comprehensive, highly accurate NGS data analysis platform, providing researchers with a user-friendly, customizable human hereditary disease and cancer analysis solution for biomarker discovery and validation. Below are a few examples of how researchers from Pennsylvania to Japan are using Biomedical Genomics Workbench to accelerate their research.
Relaxin Reverses Inflammatory and Immune Signals in Aged Hearts
First author: Brian Martin
A team based out of the University of Pennsylvania studied the cardiovascular benefits of relaxin—a pregnancy hormone—on both young and old rats to determine its effects on the heart’s aging process. They extracted RNA and analyzed genomic changes, importing raw transcript data into Biomedical Genomics Workbench and mapping reads to the rat reference genome. The study, which ran in PLOS ONE, concluded that relaxin both alters gene transcription and suppresses inflammatory pathways and genes associated with heart failure and aging. This has therapeutic potential for cardiovascular and inflammation-related diseases, such as heart failure, diabetes and atrial fibrillation.
Comparison of Genetic Profiling of Primary Central Nervous System (CNS) Lymphoma Before and After Extra-CNS Relapse
First author: Kosuke Toyoda
In 2017, a team of Japanese scientists studied the mechanism of chemotherapy resistance in lymphomas of the CNS (central nervous system), which were previously identified as promising targets for immune checkpoint blockade therapy. They performed comprehensive genomic analysis in the hope of better understanding tumor oncogenic evolution and overcoming the immune privilege. The team compared the impact of extra-CNS relapse, using Biomedical Genomics Workbench to call variants. Their report, which ran in Blood Journal, suggested that the evolution of mutations enabled systemic disease progression with a breakthrough of immune privilege, characterized by immunological overpowering and the dysregulation of B-cell proliferation signaling.
Assessing the GeneRead SNP for Analysis of Low-Template and PCR-Inhibitory Samples
First author: Maja Sidstedt
When forensic DNA laboratories use massive parallel sequencing for human identification purposes, chances are good that the DNA samples are heterogeneous and of varying quality. SNP assays must therefore be able to handle impurities and low amounts of DNA. Using Biomedical Genomics Workbench to analyze sequencing data, a Swedish team evaluated the GeneRead Individual Identity SNP panel, which handled multiple extraction methods and withstood inhibitor solutions and was concluded to be satisfactory for casework-like samples. Read about the study, which ran in PLOS ONE in January this year.
To request your no-obligation trial of Biomedical Genomics Workbench, just click here.
Human Gene Mutation Database (HGMD®) is the gold standard industry-leading resource for comprehensive coverage of published human inherited disease mutations. Unlike other mutation databases, HGMD mutations are backed by peer-reviewed publications where there is evidence of clinical impact.
You can also prioritize variants by disease concepts via the drop-down menu.
You now have the ability to browse HGMD phenotypes mapped to the UMLS (grouped into disease concepts, e.g., blood disorders) during the phenotype search. You can also filter results using these disease concepts in batch search mode (see above).
To get the most out of your HGMD subscription, please watch the video tutorials available at our Resources webpage.
Learn more about how ANNOVAR can be used with HGMD for variant annotation.
Watch a recorded webinar featuring ANNOVAR here.
View the complete Genome Trax™ statistics
Updated tracks have been released concurrent with the HGMD release for all HGMD-related tracks. Additional major tracks updated include TRANSFAC® release 2018.3, PROTEOME™ release 2018.3.
This year ESHG is celebrating it's 50th anniversary in beautiful Copenhagen! Come and meet us at booths 540 and 542 and find out how our solutions are helping to unlock the complexities of liquid biopsy, clinical oncology, hereditary disease, microbial genomics and more.
We're also hosting a lunchtime Corporate Satellite Meeting on Sunday, May 28 where lunch boxes are provided. Here's the schedule:
Using seamlessly integrated preanalytical, next-generation sequencing and bioinformatics solutions, and leveraging expertise in translational and clinical research to refine our understanding of human genetics and diseases
Time: 11.15 a.m. – 12.50 p.m.
Room: Berlin
Chair: Anja Wild and Phoebe Loh, QIAGEN, Hilden
11.15 a.m. – 11.20 a.m.
Welcome
11.20 a.m. – 11.50 a.m.
Molecular analysis of thyroid nodules – detection of gene mutations and fusion genes by DNA/RNA sequencing
Dr. Egbert Schulze, Molecular Genetics Laboratory, Heidelberg, Germany
11.50 a.m. – 12.15 p.m.
Circulating Cell Free DNA Pre-Analytics: Importance of ccfDNA Stabilization and Extraction for Liquid Biopsy Applications
Dr. Dominic O’Neil, QIAGEN, Hilden, Germany
12.15 p.m. – 12.45 p.m.
Leveraging Unique Molecular Indices to improve low-frequency variant estimation and calling in QIASeq v3 panels
Bjarni Vilhjalmsson, QIAGEN, Aarhus, Denmark
12.45 p.m. – 12.50 p.m.
Closing
As usual, we've prepared some poster presentations covering the latest enhancements to various application areas:
Electronic-poster number: E-P16.13
Presenter: Stuart Tugendreich, Principal Scientist, QIAGEN Bioinformatics
Title: Integrative approach to biomarker discovery by performing comparative analysis of two cancers Hepatocellular carcinoma and Endometrioid endometrial carcinoma using genetics and transcriptomics from RNA sequencing data.
Poster number: P14.049A
When: 10.15 a.m. - 11.15 a.m., Sunday, May 28
Presenter: Rupert Yip, Director of Global Product Management, Genetic Disease, QIAGEN Bioinformatics
Title: Prioritizing causal variants for rare, inherited syndromes, using patient phenotypes
We look forward to seeing as many of you there as possible.
Vi ses!
Nearly 250 million people around the world are affected by rare diseases, which are typically genetic in nature. Their rarity means that these diseases are not well understood, and funding to research and cure them is often limited. Genome sequencing has contributed to a far better characterization of rare disease by allowing scientists to home in on causal variants. For researchers who work on rare diseases, time is often the enemy. Solutions that provide fast, easy, and profound insights can significantly improve patient care. Clinical genome and exome sequencing can be integrated more broadly into the routine practice of medicine for the betterment of public health.
We are therefore thrilled to share details here about our collaboration with the Rare Genomics Institute (RG). We’ve provided RG with access to our Hereditary Disease Solution for interpreting whole exome and genome data, so that their scientists can use the tool to better understand rare diseases by identifying potential causal mutations missed by other platforms and methods. This collaboration expands their access to our genomic data interpretation tool. According to RG analyst William Chiu, “Ingenuity has a very intuitive user interface, one can easily zoom in to a short-list of potential mutations of interests in a few clicks.”
Ingenuity Variant Analysis features robust algorithms and the deeply curated QIAGEN Knowledge Base, enabling quick identification of known or novel causal variants in disease genes and discovery of novel variants or genes by leveraging pathway and network analysis.
ASHG 2016 was an exciting event for us. We loved the beautiful city of Vancouver, BC, and our calendars were packed with speakerships, poster presentations and meetings with peers and colleagues. We also announced our new Sample-to-Insight solutions for liquid biopsies and hereditary diseases — which included our bioinformatics solutions, and our booth was buzzing with people who wanted to learn more. Our public-facing ASHG activities were a germane reflection of the event’s overarching theme: “Sharing Discoveries. Shaping our Future.” Over the course of five days during ASHG, QIAGEN Bioinformatics staff delivered six separate in-booth presentations, five poster presentations and an educational workshop focused on liquid biopsy, RNA-seq, and hereditary diseases.
If you missed them, or would like to see them again, you can see Jean-Noel Billaud's presentation on an Integrative approach to biomarker discovery: Comparative analysis of two cancers using genomics and transcriptomics from RNA sequencing data here, and Helge Martens' on Rapid identification and prioritization of pathogenic variants associated with anomalies of the kidneys and urinary tract here.
We were not the only ones who were busy during ASHG. The Broad Institute’s new beta of its Genome Aggregation Database, or “gnomAD” was announced, which boasts information from 126,216 human exomes and 15,136 whole human genomes and doubles the number of exomes available from the ExAC population database. This news resonated strongly with us because we’re championing similar efforts with the Allele Frequency Community — our opt-in community resource which encourages the sharing of anonymized, pooled frequency statistics among laboratories. The industry’s continued drumbeat toward precision medicine was another recurring theme, going hand-in-hand with the strong focus on Canada’s efforts to adopt its own version of 2008’s U.S. Genetic Information Nondiscrimination Act. We also saw continued buzz around CRISPR technology, with several ASHG sessions dedicated to both the implications and obligations inherent in genome editing technology.
We hope you enjoyed your time at ASHG and we hope to see you soon. If you have questions about liquid biopsy or related solutions, do not hesitate to contact us.
Our next big event will be AMP 2016 in Charlotte, NC from Nov. 10-12 and the NGS Congress in London from Nov. 10-11. Keep an eye on this site for updates about what we’ll be doing there. We hope you enjoyed your time at ASHG and we hope to see you soon. If you have questions about liquid biopsy or related solutions, do not hesitate to contact us.
The AGBT Precision Health meeting will take place in Scottsdale, Arizona on September 22-24, 2016. We will be there and we hope you'll come visit us at booth #13.
We offer the most comprehensive sample to insight NGS workflow and we're looking forward to tell you much more about it at the AGBT Precision Health meeting. Stop by our booth #13 to learn more about:
Leveraging biological pathways and network analytics to identify disease-causing mutations from clinical genome and exome sequence (CGES) data
Poster #510
Presenter: Sohela Shah, PhD, Genome Scientist, QIAGEN Bioinformatics
Date and time: September 23, 1:30 p.m. - 3:00 p.m.
Learn more about our hereditary disease solution
Get more details about AGBT Precision Health
