The competition will run from December 1st 2021 - February 9th 2021
At Golden Helix, we love to see user examples of our software at work. It can be from a clinical or hospital laboratory or an academic, government, or commercial organization. Read on, and if you answer yes to one or more of the questions below or have great examples of your workflows, then this competition is for you.
- In what ways do you utilize NGS analysis to treat patients?
- Is there a particular disease category-focus, or are you zeroing in on a specific population?
- How do you incorporate the ACMG or AMP guidelines into your clinical workflow?
- Do you work with CNVs?
- How do you leverage our research platform for plants, animals, or humans?
The prizes for the winners:
First-Place: One-year Single-Named User (SNU) license* of either SNP & Variation Suite (SVS) or VarSeq. Additionally, the grand prize winner will receive a Dell Latitude 5000 series laptop. Best of all? The opportunity to present their research to the Golden Helix community in the form of a webcast and blog post.
Second and Third Place: One-year SNU license* for either SVS or VarSeq, as well as the opportunity to highlight their research via a webcast and blog post.
*Please Note: All licenses must be redeemed for a brand-new SNU VarSeq or SVS license and will NOT count toward an existing license renewal*
Winners will meet the following criteria*:
- The importance of the clinical or research issue and the impact it may have on the field of interest
- Disease categories, workflows, clinical outcomes and the application of VarSeq to your clinical pipeline
- The overall study design and analysis methodology and how SVS can assist in your research
*Prior publication of research is not required!
Examples from our Previous Winners:
Yearly Clinical Review of a Patient's WGS Results Leads to a New Gene Candidate for a
Complex Cardiomyopathy Phenotype
2020 First Place Winners: Robert M. Hamilton and Priyanka Kugamoorthy
The rapid evolution of genetic disease understanding and expanding genetic databases behooves clinician specialists to regularly review the status of clinical genetic test panels, reevaluating the status of identified variants. With the increasing use of WES and WGS sequencing, these results likely also need regular review. We present a four-generation family affected by sudden cardiac arrests, ventricular tachycardia and (within the last 2 generations) documented left ventricular non-compaction. An affected member underwent a 38-gene pan-cardiomyopathy clinical genetic panel with no identified pathogenic variants. Therefore, affected members from three surviving generations agreed to undergo genetic testing by whole genome sequencing in 2016. Analysis with Golden Helix SVS identified 69 shared, rare (MAF≤0.00005) coding variants in 40 genes, but none were identified to be associated with cardiac phenotypes.At clinical follow-up of the youngest family member in 2019, we elected to review our WGS results again. A recent review of the genetics and genomics of dilated cardiomyopathy now identifies the SRA1 gene as being potentially involved in this phenotype, although no human cases have yet been reported. Upon review of this families previous WGS sequencing using SVS, we identified a SRA1 c.328_329 ins GAC (p.Val110delinsGlyLeu) rare shared variant. This variant is only present in 2 of 142724 alleles in the gnomAD database and inserts an additional amino acid into a partially conserved two amino acid region within an otherwise highly conserved protein region.
Friedrichs and colleagues previously identified SRA1 as one of 3 genes within a 600kb haploblock associated with cardiomyopathy in three independent Caucasian populations. In zebrafish, sra1 morphants display severe pericardial edema. The human SRA1 region also encodes a long noncoding RNA, lnc-SRA1-2, that is modulated in heart failure, attenuates hypoxia-induced injury in experimental cardiomyocytes through PPARγ/NF-κB signalling, and promotes the activation of cardiac myofibroblasts. SRA1 knockdown in C2C12 mouse myoblast cells prevented proper muscle gene expression and cell differentiation. Cardiac genetics assessments by whole genome or whole exome sequencing also need regular review, which can be easily performed by Golden Helix SVS software. Links to new and frequently updated gene tracks and other genetic databases facilitate these reviews.
You can also view Robert and Priyanka's webcast recording here!
Enabling research translation:
Generating clinical genetic reports to improve the management of cardiovascular disease
2019 First Place Winner: Mark Trinder - MD/Ph.D. Student at the University of British Columbia
Heart disease is a leading cause of death and disability in Canada and worldwide, which largely results from the insidious process not being identified or treated until it is too late (1). This is best exemplified by patients with familial hypercholesterolemia (FH). FH is the most common autosomal dominant genetic disorder resulting from pathogenic genetic variants in the LDLR, APOB, and/or PCSK9 genes (~1 out of 225 people) (2). These genetic variants cause elevated low-density lipoprotein cholesterol, more commonly known as “bad cholesterol”, and significantly increase these patients’ risk of cardiovascular disease.
Our lab has developed a targeted next-generation sequencing assay that can accurately identify the presence of monogenic FH-causing variants or polygenic causes of hypercholesterolemia in patients with a clinical diagnosis of FH (3, 4). To realize the full value of this research, the results need to be fed back to the patients and their healthcare providers. However, this is currently recommended as best clinical practice for managing FH (5).
A pathogenic variant in LDLR, APOB, or PCSK9 can be identified in 30–80% of patients with clinically-diagnosed familial hypercholesterolemia (FH). Alternatively, ~20% of clinical FH is thought to have a polygenic cause. The cardiovascular disease (CVD) risk associated with polygenic versus monogenic FH is unclear. The objective of this study was to investigate the impact of genotype, including monogenic and polygenic causes of FH, on CVD risk among patients with clinically diagnosed FH. We hypothesized that FH patients with monogenic FH variants and elevated low-density lipoprotein cholesterol polygenic risk scores would have greater risk of CVD than patients in whom no causative variant is identified.
We will describe how we are using VarSeq® software to screen next-generation sequencing DNA results for for both monogenic and polygenic causes of FH. In addition, we will use FH as an example to demonstrate how the American College of Medical Genetics and Genomics/Association for Molecular Pathology joint guidelines for variant interpretation and classification can be easily applied to DNA sequencing data to generate meaningful clinical reports.
You can also view Mark's webcast recording here!
2018 First Place Winner: Michael Iacocca - Research Trainee, Dr. Robert Hegel's Laboratory at Robarts Research Institute
Familial hypercholesterolemia (FH) is a heritable condition of severely elevated LDL cholesterol, characterized by premature atherosclerotic cardiovascular disease. FH affects an estimated 1 in 250 individuals worldwide, and is considered to be the most frequent monogenic disorder encountered in clinical practice. Although FH has multiple genetic etiologies, the large majority (>90%) of defined cases result from autosomal codominant mutations in the LDL receptor gene (LDLR).
In providing a molecular diagnosis for FH, the current procedure often includes targeted next-generation sequencing (NGS) panels for the detection of small-scale DNA variants, followed by multiplex ligation-dependent probe amplification (MLPA) in LDLR for the detection of whole-exon copy number variants (CNVs). The latter is essential as ~10% of FH cases are attributed to CNVs in LDLR; accounting for them decreases false-negative findings. Here, we have determined the potential of replacing MLPA with bioinformatic analysis (VarSeq) applied to NGS data, which uses depth of coverage analysis as its principal method to identify whole-exon CNV events. In analysis of 388 FH patient samples, there was 100% concordance in LDLR CNV detection between these two methods: 38 reported CNVs identified by MLPA were also successfully detected by NGS + VarSeq, while 350 samples negative for CNVs by MLPA were also negative by NGS + VarSeq. This result suggests that MLPA is dispensable, significantly reducing costs, resources, and analysis time associated with the routine diagnostic screening for FH, while promoting more widespread assessment of this important class of mutations across diagnostic laboratories.
You can also view Michael's webcast recording here!
2017 Dual-First Place Winner: Dr. Reza Sailani - Michael Snyder Laboratory, Department of Genetics at Stanford University
Dr. Reza Sailani is a Research Fellow in the Genetics department at Stanford University. To provide an overview of his research, Sailani explains the following two recent studies he has conducted:
- Association of AHSG with alopecia and mental retardation (APMR) syndrome: Alopecia with mental retardation syndrome (APMR) is a very rare autosomal recessive condition that is associated with total or partial absence of hair from the scalp and other parts of the body as well as variable intellectual disability. Here we present whole-exome sequencing results of a large consanguineous family segregating APMR syndrome with seven affected family members. Our study revealed a novel predicted pathogenic, homozygous missense mutation in the AHSG gene.
- WISP3 mutation associated with Pseudorheumatoid Dysplasia: Progressive pseudorheumatoid dysplasia (PPD) is a skeletal dysplasia characterized by predominant involvement of articular cartilage with progressive joint stiffness. Here we report genetic characterization of a consanguineous family segregating an uncharacterized form of skeletal dysplasia. Whole exome sequencing in four affected siblings and parents resulted in identification of a loss of function homozygous mutation in the WISP3 gene leading to diagnosis of PPD in the affected individuals. The identified variant is rare and predicted to cause premature termination of the WISP3 protein.
You can also view Dr. Sailani's webcast recording here!
2017 Dual-First Place Winner: Dr. Jingga Inlora - Post-Doc Fellow, Michael Snyder Laboratory,
Department of Genetics at Stanford University
Recent advances in next-generation sequencing (NGS) technologies have brought a paradigm shift in how researchers investigate common and rare diseases. While whole genome sequencing remains costly, whole exome sequencing (WES) is less expensive and has recently been introduced into clinical practices such as disease treatment, screening and prenatal diagnosis. Recent success of WES has uncovered numerous disease-causing mutations and disease-predisposing variants throughout the genome.
Here we report four cases of Mendelian disorders observed in affected families. Using WES and bioinformatics techniques, we identified variants in each disease case, which co-segregates with the disease and are compatible with the phenotype.
You can also view Dr. Inlora's webcast recording here!