Review

. 2022 Apr;604(7906):437-446.

doi: 10.1038/s41586-022-04601-8. Epub 2022 Apr 20.

The Human Pangenome Project: a global resource to map genomic diversity

Ting Wang^{1

2

3}, Lucinda Antonacci-Fulton⁴, Kerstin Howe⁵, Heather A Lawson⁶, Julian K Lucas⁷, Adam M Phillippy⁸, Alice B Popejoy⁹, Mobin Asri⁷, Caryn Carson^{6

10

4}, Mark J P Chaisson¹¹, Xian Chang⁷, Robert Cook-Deegan¹², Adam L Felsenfeld¹³, Robert S Fulton⁴, Erik P Garrison¹⁴, Nanibaa' A Garrison^{15

16

17}, Tina A Graves-Lindsay⁴, Hanlee Ji¹⁸, Eimear E Kenny^{19

20

21}, Barbara A Koenig²², Daofeng Li^{6

10

4}, Tobias Marschall²³, Joshua F McMichael⁴, Adam M Novak⁷, Deepak Purushotham^{6

10

4}, Valerie A Schneider²⁴, Baergen I Schultz¹³, Michael W Smith¹³, Heidi J Sofia¹³, Tsachy Weissman²⁵, Paul Flicek²⁶, Heng Li^{27

28}, Karen H Miga²⁹, Benedict Paten³⁰, Erich D Jarvis^{31

32}, Ira M Hall³³, Evan E Eichler^{34

35}, David Haussler^{36

37}; Human Pangenome Reference Consortium

Affiliations

¹ Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA. twang@wustl.edu.
² Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA. twang@wustl.edu.
³ McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA. twang@wustl.edu.
⁴ McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.
⁵ Wellcome Sanger Institute, Cambridge, UK.
⁶ Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA.
⁷ UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA.
⁸ Genome Informatics Section, National Human Genome Research Institute, Bethesda, MD, USA.
⁹ Epidemiology Division, Department of Public Health Sciences, University of California, Davis, CA, USA.
¹⁰ Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA.
¹¹ Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
¹² Arizona State University, Barrett & O'Connor Washington Center, Washington DC, USA.
¹³ National Institutes of Health (NIH)-National Human Genome Research Institute, Bethesda, MD, USA.
¹⁴ Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA.
¹⁵ Institute for Society & Genetics, College of Letters and Science, University of California, Los Angeles, Los Angeles, CA, USA.
¹⁶ Institute for Precision Health, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
¹⁷ Division of General Internal Medicine & Health Services Research, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
¹⁸ Department of Medicine, Stanford University, School of Medicine, Stanford, CA, USA.
¹⁹ Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
²⁰ Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
²¹ Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
²² Program in Bioethics and Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA.
²³ Heinrich Heine University, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Düsseldorf, Germany.
²⁴ National Center for Biotechnology Information (NCBI), National Library of Medicine, Bethesda, MD, USA.
²⁵ Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
²⁶ European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK. flicek@ebi.ac.uk.
²⁷ Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. hli@jimmy.harvard.edu.
²⁸ Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA. hli@jimmy.harvard.edu.
²⁹ UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA. khmiga@ucsc.edu.
³⁰ UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA. bpaten@ucsc.edu.
³¹ Vertebrate Genome Lab and and Laboratory of Neurogenetics of Language, The Rockefeller University, New York, NY, USA. ejarvis@rockefeller.edu.
³² Howard Hughes Medical Institute, Chevy Chase, MD, USA. ejarvis@rockefeller.edu.
³³ Yale School of Medicine, New Haven, CT, USA. ira.hall@yale.edu.
³⁴ Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA. eee@gs.washington.edu.
³⁵ Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA. eee@gs.washington.edu.
³⁶ UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA. haussler@ucsc.edu.
³⁷ Howard Hughes Medical Institute, University of California, Santa Cruz, CA, USA. haussler@ucsc.edu.

PMID: 35444317
PMCID: PMC9402379
DOI: 10.1038/s41586-022-04601-8

Review

The Human Pangenome Project: a global resource to map genomic diversity

Ting Wang et al. Nature. 2022 Apr.

. 2022 Apr;604(7906):437-446.

doi: 10.1038/s41586-022-04601-8. Epub 2022 Apr 20.

Authors

Affiliations

¹ Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA. twang@wustl.edu.
² Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA. twang@wustl.edu.
³ McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA. twang@wustl.edu.
⁴ McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.
⁵ Wellcome Sanger Institute, Cambridge, UK.
⁶ Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA.
⁷ UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA.
⁸ Genome Informatics Section, National Human Genome Research Institute, Bethesda, MD, USA.
⁹ Epidemiology Division, Department of Public Health Sciences, University of California, Davis, CA, USA.
¹⁰ Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA.
¹¹ Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
¹² Arizona State University, Barrett & O'Connor Washington Center, Washington DC, USA.
¹³ National Institutes of Health (NIH)-National Human Genome Research Institute, Bethesda, MD, USA.
¹⁴ Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA.
¹⁵ Institute for Society & Genetics, College of Letters and Science, University of California, Los Angeles, Los Angeles, CA, USA.
¹⁶ Institute for Precision Health, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
¹⁷ Division of General Internal Medicine & Health Services Research, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
¹⁸ Department of Medicine, Stanford University, School of Medicine, Stanford, CA, USA.
¹⁹ Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
²⁰ Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
²¹ Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
²² Program in Bioethics and Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA.
²³ Heinrich Heine University, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Düsseldorf, Germany.
²⁴ National Center for Biotechnology Information (NCBI), National Library of Medicine, Bethesda, MD, USA.
²⁵ Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
²⁶ European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK. flicek@ebi.ac.uk.
²⁷ Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. hli@jimmy.harvard.edu.
²⁸ Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA. hli@jimmy.harvard.edu.
²⁹ UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA. khmiga@ucsc.edu.
³⁰ UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA. bpaten@ucsc.edu.
³¹ Vertebrate Genome Lab and and Laboratory of Neurogenetics of Language, The Rockefeller University, New York, NY, USA. ejarvis@rockefeller.edu.
³² Howard Hughes Medical Institute, Chevy Chase, MD, USA. ejarvis@rockefeller.edu.
³³ Yale School of Medicine, New Haven, CT, USA. ira.hall@yale.edu.
³⁴ Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA. eee@gs.washington.edu.
³⁵ Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA. eee@gs.washington.edu.
³⁶ UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA. haussler@ucsc.edu.
³⁷ Howard Hughes Medical Institute, University of California, Santa Cruz, CA, USA. haussler@ucsc.edu.

PMID: 35444317
PMCID: PMC9402379
DOI: 10.1038/s41586-022-04601-8

Abstract

The human reference genome is the most widely used resource in human genetics and is due for a major update. Its current structure is a linear composite of merged haplotypes from more than 20 people, with a single individual comprising most of the sequence. It contains biases and errors within a framework that does not represent global human genomic variation. A high-quality reference with global representation of common variants, including single-nucleotide variants, structural variants and functional elements, is needed. The Human Pangenome Reference Consortium aims to create a more sophisticated and complete human reference genome with a graph-based, telomere-to-telomere representation of global genomic diversity. Here we leverage innovations in technology, study design and global partnerships with the goal of constructing the highest-possible quality human pangenome reference. Our goal is to improve data representation and streamline analyses to enable routine assembly of complete diploid genomes. With attention to ethical frameworks, the human pangenome reference will contain a more accurate and diverse representation of global genomic variation, improve gene-disease association studies across populations, expand the scope of genomics research to the most repetitive and polymorphic regions of the genome, and serve as the ultimate genetic resource for future biomedical research and precision medicine.

PubMed Disclaimer

Conflict of interest statement

Conflict of interest

None declared.

Figures

**Figure 1:. The Human Pangenome Reference Consortium.**
This diagram provides an overview of the HPRC’s several components. **Collect**: I,000 Genomes samples jump-start the project and will be followed by additional samples collected through community engagement and recruitment. Sample selection efforts will ensure the graph-based reference captures global human genomic diversity. **Sequence**: Long-read and long-range technologies are used to generate genome graphs and bridge gaps in difficult-to-assemble genomic regions. **Assemble**: Telomere-to-telomere finished diploid genomes will foster variant discovery, especially in complex, difficult to assemble genomic regions. **Construct**: Scalable bioinformatics approaches assemble, QC, call variants, and benchmark graph assembly accuracy. The graph is annotated with gene descriptions and transcriptome data, making it more accessible and interpretable. **Utilize**: Collaboration across scientific and stakeholder communities will create a new ecosystem of analysis tools. Clinical applications and research use will involve analysis, validation, interpretation, and publication of results. **Outreach**: Members of the HPRC Outreach community engage and educate the user community and broadly share all genomic products and informatics platforms. **ELSI**: ELSI scholars will develop selection processes and policy frameworks that meet investigator needs while respecting research partner autonomy and cultural norms.

**Figure 2:. Standards were developed through a pilot benchmark study of one individual.**
Multiple long-read and long-range technologies and computational methods were evaluated to develop the combination of platforms and an automated pipeline that provides the most complete and accurate genome graph.

**Figure 3:. The Human Pangenome Reference.**
Graph-aware mappers can be used to genotype samples by directly mapping against the graph. This simplified example shows how to create a pangenome graph for four people and calculate the allele frequency of three variants. Iterating through each individual produces the graph structure, which improves as new genomes are added. Genomic data is arranged into a sequence variation map based on edges. Alternative haplotypes are depicted as alternate pathways across the graph, with the edges being the primary data-bearing elements. The pangenome reference catalogs genomic variation and allows for population- scale analysis thanks to its graph structure. Tracing a path through the network and connecting sequences at access edges yields haplotypes for individuals. For clinical interpretation, allele frequencies are reported.

See this image and copyright information in PMC

References

1. Lander ES et al. Initial sequencing and analysis of the human genome. Nature 409, 860–921, doi: 10.1038/35057062 (2001). - DOI - PubMed
1. Venter JC et al. The sequence of the human genome. Science 291, 1304–1351. (2001). - PubMed
1. Gibbs RA The Human Genome Project changed everything. Nat Rev Genet, doi: 10.1038/s41576-020-0275-3 (2020). - DOI - PMC - PubMed
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1. Green RE et al. A draft sequence of the Neandertal genome. Science 328, 710–722, doi: 10.1126/science.1188021 (2010). - DOI - PMC - PubMed

Key References

1. • ⁶¹ Cheng H, Concepcion GT, Feng X, Zhang H & Li H Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. Nat Methods 18, 170–175, doi: 10.1038/s41592-020-01056-5 (2021).
  o Hifiasm is a haplotype-resolved assembler specifically designed for PacBio HiFi reads, that aims to represent haplotype information in a phased assembly graph.
1. • ²² Nurk S et al. The complete sequence of a human genome. bioRxiv, 2021.2005.2026.445798, doi: 10.1101/2021.05.26.445798 (2021).
  o The first complete genome assembly issued from the Telomere-to-telomere (T2T) Consortium, which closed all remaining gaps in the GRCh38 including all acrocentric short arms, segmental duplications, and human centromeric regions.
1. • ²⁰ Miga KH et al. Telomere-to-telomere assembly of a complete human X chromosome. Nature 585, 79–84, doi: 10.1038/s41586-020-2547-7 (2020).
  o The sequence of the first complete human chromosome.
1. • ⁶⁴ Li H, Feng X & Chu C The design and construction of reference pangenome graphs with minigraph. Genome Biol 21, 265, doi: 10.1186/s13059-020-02168-z (2020).
  o Minigraph toolkit used to efficiently construct a pangenome graph, useful for mapping and for constructing graphs encoding structural variation.
1. • ⁷⁰ Paten B et al. Cactus: Algorithms for genome multiple sequence alignment. Genome Res 21, 1512–1528, doi: 10.1101/gr.123356.111 (2011).
  o Cactus is a highly accurate, reference-free multiple genome alignment program useful to study general rearrangement and copy number variation.

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The Human Pangenome Project: a global resource to map genomic diversity

Affiliations

The Human Pangenome Project: a global resource to map genomic diversity

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

Key References

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources

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