As part of our ongoing commitment to empowering the genetics community, Golden Helix is hosting its sixth annual competition for abstracts! All testing labs, hospital labs, academic, government or commercial organizations who are Golden Helix users are invited to apply. We would love to hear how you are using Golden Helix software in your clinical or research work. 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 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 their choice of a one-year single-name user (SNU) license of SNP & Variation Suite (SVS) or a one-year SNU license of VarSeq, as well as a new 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 their choice of a one-year SNU license to either SVS or VarSeq as well as the opportunity to highlight their research via a webcast and blog post.

To enter, email your abstract to!

Details for entering:

  • To enter, email your abstract to with your name, organization and phone number.
  • Applications will be accepted between Monday, December 3, 2018, and Wednesday, January 30, 2019
  • Abstracts must be in .doc or .pdf format only
  • Winners will be notified on Wednesday, February 6, 2019, and announced on Tuesday, February 12, 2019, via a blog post on Our 2 SNPs...

Selections will be made 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

Using NGS to detect CNVs in familial hypercholesterolemia

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!

An Overview of Two Studies Focused on Whole Exome Sequencing at Stanford University

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!

Identifying genetic variants associated with rare Mendelian Diseases

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!

To enter, email your abstract to!