Whole genome sequencing (WGS) is often viewed as the future of clinical diagnostics. However, there are five significant advantages over current NGS testing methods that are bringing WGS to the forefront of clinical diagnostics:
Whole Genome Sequencing in Clinical Diagnostics
A patient’s genome is composed of approximately 3 billion bases. That’s a very long string of A's, T's, C's and G's with plenty of hiding places for disease-relevant mutations. Yet common NGS testing methods sequence only a fraction of a patient’s DNA. Gene panels routinely cover <1 million bases, or about 0.02% of the genome. Exome sequencing covers much more, but still comes in at <50 million bases. That’s only about 1.5% of the genome.
In contrast, whole genome sequencing provides a comprehensive view of a patient’s genome. It covers the same bases covered by panel and exome sequencing, plus so much more. Including introns, promoters and other regulatory regions that are increasingly being shown to be important in the understanding of human disease.
At first it may seem surprising, but whole genome sequencing actually provides superior coverage of the regions targeted by exome sequencing and some panels. And the sequenced data is more accurate. The reasons are due to limitations of PCR amplification technology which is used in target enrichment and library preparation for exome sequencing.
Primers are used to selectively amplify DNA regions of interest. But different primers have different affinities for their targets. For example, primers targeting GC rich regions do not anneal well to their intended target. And they’re much more likely to incorrectly anneal at unrelated sites. The variable annealing pattern across targets leads to vastly different levels of coverage measured as read depth. Low or no read depth makes it technically impossible to detect a true variant at an affected site.
Another complicating factor is that PCR amplification introduces artifacts. These artifacts can themselves be amplified and misread as variants.
In contrast, WGS does not rely on PCR amplification. Instead, fragmentation methods are used to prepare the DNA for sequencing without an amplification step. As a result, consistent read depth is generated across the entire genome. Ensuring that variant-containing regions are not missed, and artifacts are unlikely to contaminate the sequencing results.
Furthermore, consistent read depth enables detection of structural variants, including CNVs and tandem nucleotide repeats. While panel and exome sequencing may identify small insertions and deletions confined to the target region, both will miss larger structural variants. It becomes difficult to distinguish changes in copy number when panel and exome sequencing read depth varies significantly from one target region to another. Variable, poor read coverage is impossible to distinguish from a real decrease in read depth due to copy loss.
The ability to reliably detect structural variants in whole genome sequencing data effectively enables multiple tests to be performed with one sequencing run. A test for single nucleotide variants including small insertions and deletions. Additional tests for larger structural variants, including CNVs and trinucleotide repeat expansions. With included coverage of the mitochondrial genome bringing the total to four tests performed with one sequencing run.
All together, comprehensive coverage of the genome, better exon coverage and structural variant detection means that WGS is less likely to miss variants relevant to your patient’s diagnosis.
In panel and exome sequencing, targeted regions must be specified ahead of time to enable PCR primer design. Every time the list of targeted regions changes, a new set of primers must be designed and the sample must be resequenced.
Because the DNA regions of interest are not predetermined in whole genome sequencing, one saliva or blood sample and sequencing run can support many analyses. Thanks to in silico panel technology you can independently analyze discrete slices of sequenced DNA ranging in size from a single gene to tens of thousands of genes. As a result, you can follow a conservative diagnostic path. Beginning with interrogation of one or a few candidate genes followed by iterative broadening of your search to a wider and wider candidate set, ultimately encompassing the entire exome or genome as needed - all without incurring additional sequencing costs.
Similarly, in silico panels enable survey testing of healthy, asymptomatic individuals. A focused panel can be employed to selectively screen for predisposition to a particular condition while minimizing the need to manage incidental findings. If concerns about another condition arise at a later date, it’s a simple matter to analyze the data from the original sequencing run with a new, appropriately focused in silico panel.
New information about variants and genes associated with disease becomes available every day. Similarly data analysis algorithms continue to evolve and improve. With panel and exome sequencing, we’ve established that it’s impossible to cover an additional gene, or to specifically target a newly identified regulatory region of interest, without designing a new set of primers and resequencing your sample.
Because whole genome sequencing already provides comprehensive coverage of the genome, no additional sequencing is required as our knowledge grows. Just a simple update of the analysis algorithms deployed, or of the underlying source annotation databases is sufficient to identify new causal variants.
While it’s true that at current market rates WGS is more expensive than panel and exome sequencing, the cost of sequencing is decreasing rapidly and WGS provides a better value. It provides the ability to perform multiple analyses, including structural variant analysis and mitochondrial genome analysis, with a single sequencing run. As well as the ability to rerun analyses when new disease-relevant information becomes available, also without additional sequencing runs. Combined with its superior coverage of a patient’s DNA and greater likelihood of detecting the relevant variants, WGS is clearly the standard to use in clinical diagnostics.
But aren’t these various types of analyses of WGS data difficult to perform? Quite simply, yes. Without powerful and effective bioinformatic pipelines and tools it can be impossible to identify the important information found within an individual’s genome sequence. This is why the Genomic Intelligence® platform is an essential tool in the analysis of WGS data - whether through the Variantyx Unity™ rare disease testing services performed by our board-certified medical geneticists or through use of the underlying pipeline and integrated diagnostic console by labs and hospitals.
Publications & Downloadable Brochures
Comparison of breast cancer metastasis models reveals a possible mechanism of tumor aggressiveness.
Pillar N, Polsky AL, Weissglas-Volkov D, Shomron N.
Cell Death Dis. 2018 Oct 10;9(10):1040.
Loss of protocadherin-12 leads to Diencephalic-Mesencephalic Junction Dysplasia syndrome.
Loss of protocadherin-12 leads to Diencephalic-Mesencephalic Junction Dysplasia syndrome.
Guemez-Gamboa A, Çaglayan AO, Stanley V, Gregor A, Zaki M, Saleem SN, Musaev D, McEvoy-Venneri J, Belandres D, Akizu N, Silhavy JL, Schroth J, Rosti RO, Copeland B, Lewis SM, Fang R, Issa MY, Per H, Gumus H, Bayram AK, Kumandas S, Akgumus GT, Erson-Omay EZ, Yasuno K, Bilguvar K, Gali H, Pillar N, Shomron N, Weissglas-Volkov D, Porat Y, Einhorn Y, Gabriel S, Ben-Zeev B, Gunel M, Gleeson JG.
Ann Neurol. 2018 Sep 3. Epub ahead of print.
SMYD1 is the underlying gene for the AnWj negative blood group phenotype.
Yahalom V, Pillar N, Zhao Y, Modan S, Fang M, Yosephi L, Asher O, Shinar E, Celniker G, Resnik-Wolf H, Brantz Y, Hauschner H, Rosenberg N, Cheng L, Shomron N, Pras E.
Eur J Haematol. 2018 Jun 29. Epub ahead of print.
Analysis of microRNAs in familial Mediterranean fever.
Amarilyo G, Pillar N, Ben-Zvi I, Weissglas-Volkov D, Zalcman J, Harel L, Livneh A, Shomron N.
PLoS One. 2018 May 22;13(5):e0197829.
Filaggrin 2 deficiency results in abnormal cell-cell adhesion in the cornified cell layers and causes peeling skin syndrome type A.
Mohamad J, Sarig O, Godsel LM, Peled A, Malchin N, Bochner R, Vodo D, Rabinowitz T, Pavlovsky M, Taiber S, Fried M, Eskin-Schwartz M, Assi S, Shomron N, Uitto J, Koetsier JL, Bergman R, Green KJ, Sprecher E.
J Invest Dermatol. 2018 Jun 26. pii: S0022-202X(18)31969-9.
Punctate palmoplantar keratoderma: an unusual mutation causing an unusual phenotype.
Vodo D, Sarig O, Jeddah D, Malchin N, Eskin-Schwarz M, Mohamad J, Rabinowitz T, Goldberg I, Shomron N, Khamaysi Z, Bergman R, Sprecher E.
Br J Dermatol. 2018 Jun;178(6):1455-1457.
Prenatal course of metaphyseal anadysplasia associated with homozygous mutation in MMP9 identified by exome sequencing.
Sharony R, Borochowitz Z, Cohen L, Storch A, Rosenfeld R, Modai S, Reinstein E. Hum
Genet. 2017 Jul;136(7):835-845.
X-linked elliptocytosis with impaired growth is related to mutated AMMECR1
Basel-Vanagaite L, Pillar N, Isakov O, Smirin-Yosef P, Lagovsky I, Orenstein N, Salmon-Divon M, Tamary H, Zaft T, Bazak L, Meyerovitch J, Pelli T, Botchan S, Farberov L, Weissglas-Volkov D2, Shomron N.
Gene. 2017 Mar 30;606:47-52.
A rare variant in the FHL1 gene associated with X-linked recessive hypoparathyroidism
Pillar N, Pleniceanu O, Fang M, Ziv L, Lahav E, Botchan S, Cheng L, Dekel B, Shomron N.
Hum Genet. 2017 Jul;136(7):835-845.
Epidermolytic Ichthyosis Sine Epidermolysis
Eskin-Schwartz M, Drozhdina M, Sarig O, Gat A, Jackman T, Isakov O, Shomron N, Samuelov L, Malchin N, Peled A, Vodo D, Hovnanian A, Ruzicka T, Koshkin S, Harmon RM, Koetsier JL, Green KJ, Paller AS, Sprecher E.
Am J Dermatopathol. 2017 Jun;39(6):440-444
Differential analysis of mutations in the Jewish population and their implications for diseases
Einhorn Y, Weissglas-Volkov D, Carmi S, Ostrer H, Friedman E, Shomron N.
Genet Res (Camb). 2017 May 15;99:e3
Calpain 12 Function Revealed through the Study of an Atypical Case of Autosomal Recessive Congenital Ichthyosis
Bochner R, Samuelov L, Sarig O, Li Q, Adase CA, Isakov O, Malchin N, Vodo D, Shayevitch R, Peled A, Yu BD, Fainberg G, Warshauer E, Adir N, Erez N, Gat A, Gottlieb Y, Rogers T, Pavlovsky M, Goldberg I, Shomron N, Sandilands A, Campbell LE, MacCallum S, McLean WH, Ast G, Gallo RL, Uitto J, Sprecher E.
J Invest Dermatol. 2017 Feb;137(2):385-393
Mutations in TSPEAR, Encoding a Regulator of Notch Signaling, Affect Tooth and Hair Follicle Morphogenesis
Peled A, Sarig O, Samuelov L, Bertolini M, Ziv L., Weissglas-Volkov D, Eskin-Schwartz M, Adase C, Malchin N, Bochner R, Fainberg G, Goldberg I, Sugawara K, Baniel A, Tsuruta D, Luxenburg C, Adir N, Duverger O, Morasso M, Shalev S, Gallo R, Shomron N, Paus R, and Sprecher E.
PLoS Genet. 2016 Oct; 12(10): e1006369
Whole-exome sequencing in individuals with multiple cardiovascular risk factors and normal coronary arteries.
Abramowitz Y, Roth A, Keren G, Isakov O, Shomron N, Laitman Y, Weissglas-Volkov D, Arbel Y, Banai S, Finkelstein A, Friedman E.
Coron Artery Dis. 2016 Jun;27(4):257-66.
Somatic Mosaicism for a "Lethal" GJB2 Mutation Results in a Patterned Form of Spiny Hyperkeratosis without Eccrine Involvement
Eskin-Schwartz M, Metzger Y, Peled A, Weissglas-Volkov D, Malchin N, Gat A, Vodo D, Mevorah B, Shomron N, Sprecher E, Sarig O.
Pediatr Dermatol. 2016 May;33(3):322-6
Actionable clinical decisions based on comprehensive genomic evaluation in asymptomatic adults
Pillar N, Isakov O, Weissglas-Volkov D, Botchan S, Friedman E, Arber N, Shomron N.
Mol Genet Genomic Med. 2015 Sep;3(5):433-9
Rare genetic variants in Tunisian Jewish patients suffering from age-related macular degeneration
Pras E, Kristal D, Shoshany N, Volodarsky D, Vulih I, Celniker G, Isakov O, Shomron N, Pras E.
J Med Genet. 2015 Jul;52(7):484-92
Non-syndromic retinitis pigmentosa due to mutations in the mucopolysaccharidosis type IIIC gene, heparan-alpha-glucosaminide N-acetyltransferase (HGSNAT)
Haer-Wigman L, Newman H, Leibu R, Bax NM, Baris HN, Rizel L, Banin E, Massarweh A, Roosing S, Lefeber DJ, Zonneveld-Vrieling MN, Isakov O, Shomron N, Sharon D, Den Hollander AI, Hoyng CB, Cremers FP, Ben-Yosef T.
Hum Mol Genet. 2015 Jul 1;24(13):3742-51
Crowdfunding effort identifies the causative mutation in a patient with nystagmus, microcephaly, dystonia and hypomyelination
Isakov O, Lev D, Blumkin L, Celniker G, Leshinsky-Silver E, Shomron N.
J Genet Genomics. 2015 Feb 20;42(2):79-81
An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge
Brownstein CA, et al
Genome Biol. 2014 Mar 25;15(3):R53
Cole Disease Results from Mutations in ENPP1
Eytan O, Morice-Picard F, Sarig O, Ezzedine K, Isakov O, Li Q, Ishida-Yamamoto A, Shomron N, Goldsmith T, Fuchs-Telem D, Adir N, Uitto J, Orlow SJ, Taieb A, Sprecher E.
Am J Hum Genet. 2013 Oct 3;93(4):752-7
Desmoglein 1 deficiency results in severe dermatitis, multiple allergies and metabolic wasting
Samuelov L, Sarig O, Harmon RM, Rapaport D, Ishida-Yamamoto A, Isakov O, Koetsier JL, Gat A, Goldberg I, Bergman R, Spiegel R, Eytan O, Geller S, Peleg S, Shomron N, Goh CS, Wilson NJ, Smith FJ, Pohler E, Simpson MA, McLean WH, Irvine AD, Horowitz M, McGrath JA, Green KJ, Sprecher E.
Nat Genet. 2013 Oct;45(10):1244-8
Missense mutation in the MEN1 gene discovered through whole exome sequencing co-segregates with familial hyperparathyroidism
Isakov O, Rinella ES, Olchovsky D, Shimon I, Ostrer H, Shomron N, Friedman
Genet Res (Camb). 2013 Aug;95(4):114-20
Exome sequencing analysis: a guide to disease variant detection
Isakov O, Perrone M, Shomron
Methods Mol Biol. 2013;1038:137-58
Assembly algorithms for deep sequencing data: basics and pitfalls
Kol N, Shomron N.
Methods Mol Biol. 2013;1038:81-91
Analysis of insertion-deletion from deep-sequencing data: software evaluation for optimal detection
Neuman JA, Isakov O, Shomron N.
Brief Bioinform. 2013 Jan;14(1):46-55
Familial pityriasis rubra pilaris is caused by mutations in CARD14
Fuchs-Telem D, Sarig O, van Steensel MA, Isakov O, Israeli S, Nousbeck J, Richard K, Winnepenninckx V, Vernooij M, Shomron N, Uitto J, Fleckman P, Richard G, Sprecher E.
Am J Hum Genet. 2012 Jul 13;91(1):163-70
GenomeGems: evaluation of genetic variability from deep sequencing data
Ben-Zvi S, Givati A, Shomron N.
BMC Res Notes. 2012 Jul 2;5:338
Targeted genomic capture and massively parallel sequencing to identify genes for hereditary hearing loss in Middle Eastern families
Brownstein Z, Friedman LM, Shahin H, Oron-Karni V, Kol N, Abu Rayyan A, Parzefall T, Lev D, Shalev S, Frydman M, Davidov B, Shohat M, Rahile M, Lieberman S, Levy-Lahad E, Lee MK, Shomron N, King MC, Walsh T, Kanaan M, Avraham KB.
Genome Biol. 2011 Sep 14;12(9):R89
Molecular Risk Factors for Schizophrenia
Modai S, Shomron N.
Trends Mol Med. 2016 Mar;22(3):242-53
Variantyx's Genomic Intelligence® is an automated end-to-end platform that simplifies NGS data analysis, interpretation and clinical reporting. All aspects of the NGS testing process are seamlessly integrated to deliver the highest level of operational efficiency coupled with superior diagnostic results.
Once the variant(s) to be reported are selected and desired comments and follow-up recommendations are entered by your team, a single button click automatically generates a white-labeled clinical report customized with your institution’s branding and medicolegal language.