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Human Whole-Genome Sequencing

An Unbiased View of the Entire Human Genome

Human whole-genome sequencing (WGS) offers the most detailed view into our genetic code. WGS has the ability to evaluate every base in the genome and navigate the complexity of genomic variants that make us unique.

Previously a challenging application, human whole-genome sequencing is now one of the simplest. Advances in library preparation, sequencing, bioinformatics, and variant analysis have made it possible to go from sample to report in less than 30 hours. Whether you’re performing a comprehensive genomic evaluation or using the genome as a foundation for other studies, human whole-genome sequencing has never been more accessible.

Comprehensive Variant Detection with Human WGS

The human genome is complex, with variations from small, single nucleotide changes to large chromosomal rearrangements, and virtually everything in between. Human whole-genome sequencing is the most comprehensive application for detecting all of these variant types in a single assay.1–8

Variant types include:

  • Single nucleotide variants (SNVs)
  • Insertions and deletions (Indels)
  • Structural variants (SVs)
  • Copy number variants (CNVs)
  • Repeat expansions
  • Mitochondrial DNA (mtDNA) variants
  • Paralogs
Rapid Whole-Genome Sequencing Workflow

Our rapid workflow includes PCR-free library preparation, high-accuracy sequencing, and FPGA-accelerated analysis with a genomic evaluation platform for variant analysis and reporting.

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Featured Workflow Products

Illumina DNA PCR-Free Prep
Illumina DNA PCR-Free Prep

A high-performing, fast, and integrated workflow for sensitive applications such as human whole-genome sequencing.

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NovaSeq 6000 System
NovaSeq 6000 System

Scalable throughput and flexibility for virtually any genome, sequencing method, and scale of project.

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Bioinformatics Innovation for Human WGS

Much of our software for analyzing human whole-genome sequencing data is available on open-source platforms, allowing the bioinformatics community to collaborate, test, and ultimately improve these tools.
SpliceAI

SpliceAI is a deep neural network that accurately predicts splice junctions. Splice mutations are especially common in rare disease, autism spectrum disorders, and intellectual disability.9

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ExpansionHunter

ExpansionHunter can be used to detect large expansions of short tandem repeats, which have been shown to cause diseases like Fragile X syndrome, amyotrophic lateral sclerosis, Friedreich ataxia, Huntington’s disease, and other disorders.10,11

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PrimateAI

PrimateAI is a deep neural network using hundreds of thousands of common variants from six non-human primate species. It allows for systematic identification of pathogenic variants in humans.12

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SMNCopyNumberCaller

Spinal muscular atrophy is caused by loss of the SMN1 gene, but analysis can be challenging because SMN1 and SMN2 are nearly identical. This software accurately identifies SMN1 and SMN2 copy number from human whole-genome sequencing data.13

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How Scientists Use Human WGS

Human Whole-Genome Sequencing Best Practices

Dr. Christian Marshall of The Hospital for Sick Children explains how clinical best practices can help enable WGS for diagnosing genetic diseases.

WGS Drives Innovation in Rare Disease Research

Dr. Matt Might is Professor and Director of the Hugh Kaul Precision Medicine Institute. His son, Bertrand, was the first person to be diagnosed with NGLY1 deficiency, an ultra-rare disorder.

From Exomes to Whole Genomes

The DRAGEN platform, embedded in TruSight Software Suite, enables GeneDx to scale to whole-genome analysis and identify variants with precision.

The Genomic Insights Webinar Series

Listen as experts in the field share how their work is impacting the ability to find an answer with human whole-genome sequencing.
Calling CNVs and SVs with WGS

Andrew Gross, PhD and Shimul Chowdhury, PhD present recent advances calling CNVs and SVs from WGS.

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The Genome’s Most Difficult Puzzles

Mike Eberle, PhD discusses advances in WGS bioinformatics for calling repeat expansions and paralogs.

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The “Fully Featured” Genome

Eric Rush, MD and Tanner Hagelstrom, PhD, FACMG discuss comprehensive variant calling with WGS in a rare disease diagnostic laboratory.

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NovaSeq 6000 Flow Cell Flexibility

The NovaSeq 6000 System offers four flow cell configurations suitable for human whole-genome sequencing. When fast turnaround is required, the SP flow cells are ideal for singleton WGS and the S1 flow cells for trio WGS. S2 is a quick, powerful, and cost-effective option for two or three WGS trios. S4 offers unprecedented throughput, supporting 16 WGS samples at 40× coverage or 24 WGS samples at 30× coverage. Each NovaSeq 6000 sequencing run can accommodate one or two flow cells for flexibility and scalability.

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Human Whole-Genome Sequencing Publications

The Medical Genome Initiative

The consortium was formed to provide practical guidance and support the development of standards for the use of clinical whole-genome sequencing.

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WGS Captures Diverse Spectrum of Disease-Causing Genetic Variation

This paper compares whole-genome sequencing to chromosomal microarray analysis for identifying different types of genetic variants.

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Power of WGS for Rare Diseases in Underserved Areas

The iHope Program demonstrated the benefit of WGS in a resource-limited dysmorphology clinic in northern Mexico.

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Related Solutions

Rare Disease Genomics

Rare diseases affect about 1 in 2,000 people. There are more than 7,000 known rare diseases and more discovered every year.

Human WGS for Rare Disease

Whole-genome sequencing has the potential to end diagnostic odysseys for patients with rare disease.

Population Genomics

Population genomics programs integrate large-scale genomic and clinical data into a learning health system, driving health care innovation.

Cancer Whole-Genome Sequencing

Cancer whole-genome sequencing informs analysis of oncogenes, tumor suppressors, and other risk factors.

Noninvasive Prenatal Testing

NIPT analyzes cell-free DNA from a maternal blood sample to screen for certain chromosomal conditions as early as the first trimester.

References
  1. Lionel AC, Costain G, Monfared N, et al. Improved diagnostic yield compared with targeted gene sequencing panels suggests a role for whole-genome sequencing as a first-tier genetic test. Genet Med. 2018; 20(4):435-443.
  2. Sanghvi RV, Buhay CJ, Powell, V et al. Characterizing reduced coverage regions through comparison of exome and genome sequencing data across 10 centers. Genet Med. 2018; 20(8)855-866.
  3. Dolzhenko E, van Vugt JJ, Shaw RJ, Bekritsky, et al. Detection of long repeat expansions from PCR-free whole-genome sequence data. Genome Res. 2017;27(11):1895-1903.
  4. Gross A, Ajay SS, Rajan V, et al. Copy number variants in clinical WGS: deployment and interpretation for rare and undiagnosed disease. Genetic Med. 2019;21(5):1121-1130.
  5. Alfares A, Aloraini T, Subaie LA, et al. Whole-genome sequencing offers additional but limited clinical utility compared with reanalysis of whole-exome sequencing. Genet Med. 2018;20(11):1328-1333.
  6. Lindstrand A, Eisfeldt J, Pettersson M, et al. From cytogenetics to cytogenomics: whole genomes sequencing as a first-line test comprehensively captures the diverse spectrum of disease-causing genetic variation underlying intellectual disability. Genome Med. 2019;11(1):68.
  7. Chen X, Sanchis-Juan A, French CE, et al. Spinal muscular atrophy diagnosis and carrier screening from genome sequencing data. Genet Med. 2020;22(5):945-953.
  8. Chen X, Schulz-Trieglaff O, Shaw R, et al. Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics. 2016;32(8):1220–1222.
  9. Jaganathan K, Kyriazopoulou Panagiotopoulou S, McRae JF, et al. Predicting Splicing from Primary Sequence with Deep Learning. Cell. 2019;176(3):535-548.e24. doi:10.1016/j.cell.2018.12.015.
  10. Dolzhenko E, van Vugt JJFA, Shaw RJ, et al. Detection of long repeat expansions from PCR-free whole-genome sequence data. Genome Res. 2017;27(11):1895-1903. doi:10.1101/gr.225672.117.
  11. Dolzhenko E, Deshpande V, Schlesinger F, et al. ExpansionHunter: a sequence-graph-based tool to analyze variation in short tandem repeat regions. Bioinformatics. 2019;35(22):4754-4756. doi:10.1093/bioinformatics/btz431.
  12. Sundaram L, Gao H, Padigepati SR, et al. Predicting the clinical impact of human mutation with deep neural networks [published correction appears in Nat Genet. 2019 Feb;51(2):364]. Nat Genet. 2018;50(8):1161-1170. doi:10.1038/s41588-018-0167-z.
  13. Chen X, Sanchis-Juan A, French CE, et al. Spinal muscular atrophy diagnosis and carrier screening from genome sequencing data. Genet Med. 2020;22(5):945-953. doi:10.1038/s41436-020-0754-0.