Phenotype-driven Approaches to Enhance Variant Prioritization and Diagnosis of Rare Disease

Overview

Journal Hum Mutat

Specialty Genetics

Date 2022 Apr 7

PMID 35391505

Authors

Julius O B Jacobsen

Catherine Kelly

Valentina Cipriani

Genomics England Research Consortium

Christopher J Mungall

Justin Reese

Daniel Danis

Peter N Robinson

Damian Smedley

Affiliations

Soon will be listed here.

Abstract

Rare disease diagnostics and disease gene discovery have been revolutionized by whole-exome and genome sequencing but identifying the causative variant(s) from the millions in each individual remains challenging. The use of deep phenotyping of patients and reference genotype-phenotype knowledge, alongside variant data such as allele frequency, segregation, and predicted pathogenicity, has proved an effective strategy to tackle this issue. Here we review the numerous tools that have been developed to automate this approach and demonstrate the power of such an approach on several thousand diagnosed cases from the 100,000 Genomes Project. Finally, we discuss the challenges that need to be overcome if we are going to improve detection rates and help the majority of patients that still remain without a molecular diagnosis after state-of-the-art genomic interpretation.

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References

Robinson P, Ravanmehr V, Jacobsen J, Danis D, Zhang X, Carmody L . Interpretable Clinical Genomics with a Likelihood Ratio Paradigm. Am J Hum Genet. 2020; 107(3):403-417. PMC: 7477017. DOI: 10.1016/j.ajhg.2020.06.021. View

Anderson D, Baynam G, Blackwell J, Lassmann T . Personalised analytics for rare disease diagnostics. Nat Commun. 2019; 10(1):5274. PMC: 6872807. DOI: 10.1038/s41467-019-13345-5. View

Ferreira C . The burden of rare diseases. Am J Med Genet A. 2019; 179(6):885-892. DOI: 10.1002/ajmg.a.61124. View

Yang H, Wang K . Genomic variant annotation and prioritization with ANNOVAR and wANNOVAR. Nat Protoc. 2015; 10(10):1556-66. PMC: 4718734. DOI: 10.1038/nprot.2015.105. View

Yang Y, Muzny D, Xia F, Niu Z, Person R, Ding Y . Molecular findings among patients referred for clinical whole-exome sequencing. JAMA. 2014; 312(18):1870-9. PMC: 4326249. DOI: 10.1001/jama.2014.14601. View

Jacobsen J, Kelly C, Cipriani V, Consortium G, Mungall C, Reese J . Phenotype-driven approaches to enhance variant prioritization and diagnosis of rare disease. Hum Mutat. 2022; 43(8):1071-1081. PMC: 9288531. DOI: 10.1002/humu.24380. View

Jaganathan K, Kyriazopoulou Panagiotopoulou S, McRae J, Fazel Darbandi S, Knowles D, Li Y . Predicting Splicing from Primary Sequence with Deep Learning. Cell. 2019; 176(3):535-548.e24. DOI: 10.1016/j.cell.2018.12.015. View

Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J . 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; 17(5):405-24. PMC: 4544753. DOI: 10.1038/gim.2015.30. View

Zhao M, Havrilla J, Fang L, Chen Y, Peng J, Liu C . Phen2Gene: rapid phenotype-driven gene prioritization for rare diseases. NAR Genom Bioinform. 2020; 2(2):lqaa032. PMC: 7252576. DOI: 10.1093/nargab/lqaa032. View

10.

Danis D, Jacobsen J, Balachandran P, Zhu Q, Yilmaz F, Reese J . SvAnna: efficient and accurate pathogenicity prediction of coding and regulatory structural variants in long-read genome sequencing. Genome Med. 2022; 14(1):44. PMC: 9047340. DOI: 10.1186/s13073-022-01046-6. View

11.

Hombach D, Schuelke M, Knierim E, Ehmke N, Schwarz J, Fischer-Zirnsak B . MutationDistiller: user-driven identification of pathogenic DNA variants. Nucleic Acids Res. 2019; 47(W1):W114-W120. PMC: 6602447. DOI: 10.1093/nar/gkz330. View

12.

Smedley D, Schubach M, Jacobsen J, Kohler S, Zemojtel T, Spielmann M . A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease. Am J Hum Genet. 2016; 99(3):595-606. PMC: 5011059. DOI: 10.1016/j.ajhg.2016.07.005. View

13.

Smedley D, Smith K, Martin A, Thomas E, McDonagh E, Cipriani V . 100,000 Genomes Pilot on Rare-Disease Diagnosis in Health Care - Preliminary Report. N Engl J Med. 2021; 385(20):1868-1880. PMC: 7613219. DOI: 10.1056/NEJMoa2035790. View

14.

Rooney Riggs E, Andersen E, Cherry A, Kantarci S, Kearney H, Patel A . Technical standards for the interpretation and reporting of constitutional copy-number variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen). Genet Med. 2019; 22(2):245-257. PMC: 7313390. DOI: 10.1038/s41436-019-0686-8. View

15.

Matalonga L, Hernandez-Ferrer C, Piscia D, Schule R, Synofzik M, Topf A . Solving patients with rare diseases through programmatic reanalysis of genome-phenome data. Eur J Hum Genet. 2021; 29(9):1337-1347. PMC: 8440686. DOI: 10.1038/s41431-021-00852-7. View

16.

Zemojtel T, Kohler S, Mackenroth L, Jager M, Hecht J, Krawitz P . Effective diagnosis of genetic disease by computational phenotype analysis of the disease-associated genome. Sci Transl Med. 2014; 6(252):252ra123. PMC: 4512639. DOI: 10.1126/scitranslmed.3009262. View

17.

Thompson R, Papakonstantinou Ntalis A, Beltran S, Topf A, de Paula Estephan E, Polavarapu K . Increasing phenotypic annotation improves the diagnostic rate of exome sequencing in a rare neuromuscular disorder. Hum Mutat. 2019; 40(10):1797-1812. DOI: 10.1002/humu.23792. View

18.

Karczewski K, Francioli L, Tiao G, Cummings B, Alfoldi J, Wang Q . The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020; 581(7809):434-443. PMC: 7334197. DOI: 10.1038/s41586-020-2308-7. View

19.

Posey J, ODonnell-Luria A, Chong J, Harel T, Jhangiani S, Coban Akdemir Z . Insights into genetics, human biology and disease gleaned from family based genomic studies. Genet Med. 2019; 21(4):798-812. PMC: 6691975. DOI: 10.1038/s41436-018-0408-7. View

20.

Zhu X, Petrovski S, Xie P, Ruzzo E, Lu Y, McSweeney K . Whole-exome sequencing in undiagnosed genetic diseases: interpreting 119 trios. Genet Med. 2015; 17(10):774-81. PMC: 4791490. DOI: 10.1038/gim.2014.191. View