Explosive Growth of Gene Variant Numbers

Genetic Testing

Clinical geneticists have an acute need to understand pathogenicity in genomes of their patients (Figure 1). Cost per human genome has now approached $1000 each (Wetterstrand 2018). This affordable cost is allowing clinicians to start incorporating next-generation sequencing (NGS) technology into the patient diagnosis.

Variant Diversity

The American College of Medical Genetics and Genomics and the Association for Molecular Pathology recommend that variants be classified in five groups (Pathogenic, Likely Pathogenic, Uncertain Significance, Likely Benign, Benign) (Richards 2015). Further, they suggest greater efforts are needed to resolve variants of Uncertain Significance (VUS) into either Pathogenic or Benign status. The re-classification of a VUS is no small task. In a recent publication on the analysis of the clinical variant database (ClinVar), the number of known genetic variants in human disease was reported on 9/9/2016 to be 72,472 (Manolio 2017). One year later (9/21/2017), a survey of ClinVar using an online database viewer reported 359,938 known variants (Henrie 2018). This 4.9-fold increase in the number of known clinical variants over one year reflects the explosive application of whole genome sequencing in clinical diagnostics (Stavropoulos 2015, Ellingford 2016, Volk 2017).

Need for Innovation

The daunting task now is to determine the significance of each new variant. Bioinformatics can provide some insight into variant pathogenicity (Oliver 2015). Unfortunately, the number of VUS alleles has remained close to 40% year-on-year since 2015. As of the July 2018, there are 192,843 identified VUS alleles. With such high numbers, there is a pressing need to quickly correlate genotype to phenotype and determine if VUS alleles are benign or pathogenic (Cox 2015). Model systems that reconstitute mutations in a physiological context are a robust method to demonstrate variant pathogenicity (Eilbeck 2017). Traditionally, mouse models have been used to characterize defective function in VUS alleles, but the expanding universe of clinical variants is overwhelming the current capacity. Higher throughput animal modeling is needed to address the growing demand.

1. Wetterstrand KA. DNA Sequencing Costs: Data from the NHGRI Genome Sequencing Program (GSP) Accessed July 12, 2018.

2. Richards S et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015 May;17(5):405-24. doi: 10.1038/gim.2015.30. Epub 2015 Mar 5.

3. Manolio TA et al. Bedside Back to Bench: Building Bridges between Basic and Clinical Genomic Research. Cell. 2017 Mar 23;169(1):6-12. doi: 10.1016/j.cell.2017.03.005.

4. Henrie A. et al. ClinVar Miner: Demonstrating utility of a Web-based tool for viewing and filtering ClinVar data. Hum Mutat. 2018 May 23. doi: 10.1002/humu.23555.

5. Stavropoulos DJ et al. Whole Genome Sequencing Expands Diagnostic Utility and Improves Clinical Management in Pediatric Medicine. NPJ Genom Med. 2016 Jan 13;1. pii: 15012. doi: 10.1038/npjgenmed.2015.12.

6. Ellingford JM et al. Whole Genome Sequencing Increases Molecular Diagnostic Yield Compared with Current Diagnostic Testing for Inherited Retinal Disease. Ophthalmology. 2016 May;123(5):1143-50. doi: 10.1016/j.ophtha.2016.01.009.

7. Volk AE and Kubisch C. The rapid evolution of molecular genetic diagnostics in neuromuscular diseases. Curr Opin Neurol. 2017 Oct;30(5):523-528. doi: 10.1097/WCO.0000000000000478.

8. Oliver GR et al. Bioinformatics for clinical next generation sequencing. Clin Chem. 2015 Jan;61(1):124-35. doi: 10.1373/clinchem.2014.224360.

9. Cox TC. Utility and limitations of animal models for the functional validation of human sequence variants. Mol Genet Genomic Med. 2015 Sep;3(5):375-82. doi: 10.1002/mgg3.167.

10. Eilbeck K et al. Settling the score: variant prioritization and Mendelian disease. Nat Rev Genet. 2017 Oct;18(10):599-612. doi: 10.1038/nrg.2017.52.

Beginning of the Journey to the End

The purpose of this blog has many uses.  It allows me to create content exploring genome biology in more depth than what can be achieved in a mere 140 characters.  But more ambitiously, I will be embarking on a journey of self-discovery, of the molecular and structural kind.  I plan to acquire my own genome’s data at high-density read depth.  I expect to catalog over 1,000,000 variations from my genome when compared to the norm.   Of these variations, I expect about 1000 will be in the coding sequence of my genes.  About 7% of these will be obviously deleterious to gene function.  Yet, because we are diploids organisms with two copies of every gene, many of these “bad” genes will be backed up by a good copy.  As a result, I will know what is my carrier status for bad alleles. Intriguingly, I will be uncovering which important genes (or hopefully not so important genes) have had two hits that render their phenotypic consequence as defective.