*Our 2020 Abstract Competition has finished. Please come back in December 2020 to apply for our 2021 Abstract Competition.*
We would love to hear how you are using Golden Helix software in your clinical or research work. All testing labs, hospital labs, academic, government, or commercial organizations who are Golden Helix users are invited to apply.
- Do you use NGS analysis to treat patients?
- Do you have a particular disease category focus? Or are you zeroing in on a specific population?
- How do you leverage 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 stakes are high...
The first-place winner will receive a one-year Single-Named User (SNU) license* of either SNP & Variation Suite (SVS) or VarSeq and a Dell Latitude 5000 series laptop. Additionally, they will have the opportunity to present their research to the Golden Helix community in the form of a webcast and blog post.
Both the second and third place winners will receive a one-year SNU license* for either SVS or VarSeq, as well as the opportunity to highlight their research via a webcast and blog post.
*All licenses must be redeemed for a new SNU VarSeq or SVS license and cannot be used for a renewing license.*
Selections are based on:
- 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
To enter, email your abstract to firstname.lastname@example.org!
Examples from our Previous Winners:
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 Michael'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!