GA4GH phenopacket-driven characterization of genotype-phenotype correlations in Mendelian disorders
Jazyk angličtina Země Spojené státy americké Médium print-electronic
Typ dokumentu časopisecké články
PubMed
41443197
PubMed Central
PMC12824607
DOI
10.1016/j.ajhg.2025.12.001
PII: S0002-9297(25)00472-0
Knihovny.cz E-zdroje
- Klíčová slova
- Global Alliance for Genomics and Health, Human Phenotype Ontology, Mendelian disease, genotype-phenotype correlation,
- MeSH
- fenotyp MeSH
- genetické asociační studie * metody MeSH
- genetické nemoci vrozené * genetika MeSH
- genomika metody MeSH
- genotyp MeSH
- lidé MeSH
- software * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Comprehensively characterizing genotype-phenotype correlations (GPCs) in Mendelian disease would create new opportunities for improving clinical management and understanding disease biology. However, heterogeneous approaches to data sharing, reuse, and analysis have hindered progress in the field. We developed Genotype-Phenotype Statistical Evaluation of Associations (GPSEA), a software package that leverages the Global Alliance for Genomics and Health (GA4GH) Phenopacket Schema to represent case-level clinical and genetic data about individuals. GPSEA applies an independent filtering strategy to boost statistical power to detect categorical GPCs represented by Human Phenotype Ontology terms. GPSEA additionally enables visualization and analysis of continuous phenotypes, clinical severity scores, and survival data such as age of onset of disease or clinical manifestations. We applied GPSEA to 85 cohorts with 6,179 previously published individuals with variants in one of 81 genes associated with 122 Mendelian diseases and identified 253 significant GPCs, with 48 cohorts having at least one statistically significant GPC. These results highlight the power of standardized representations of clinical data for scalable discovery of GPCs in Mendelian disease.
Berlin Institute of Health at Charité Universitätsmedizin Berlin Berlin Germany
Department of Genetics University of North Carolina Chapel Hill Chapel Hill NC USA
Department of Ophthalmology University Clinic Marburg Campus Fulda Fulda Germany
Deutsches Herzzentrum der Charité Berlin Germany
Institute of Medical and Human Genetics Charité Universitätsmedizin Berlin Berlin Germany
North West Thames Regional Genetics Service Northwick Park and St Mark's Hospitals London UK
The Jackson Laboratory for Genomic Medicine 10 Discovery Drive Farmington CT 06032 USA
Zobrazit více v PubMed
Ries M., Gal A. In: Fabry Disease: Perspectives from 5 Years of FOS. Mehta A., Beck M., Sunder-Plassmann G., editors. Oxford PharmaGenesis; 2006. Genotype–phenotype correlation in Fabry disease. PubMed
Bettegowda C., Upadhayaya M., Evans D.G., Kim A., Mathios D., Hanemann C.O., REiNS International Collaboration Genotype-phenotype correlations in neurofibromatosis and their potential clinical use. Neurology. 2021;97:S91–S98. doi: 10.1212/WNL.0000000000012436. PubMed DOI PMC
MacRae C.A., Seidman C.E. Closing the Genotype-Phenotype Loop for Precision Medicine. Circulation. 2017;136:1492–1494. doi: 10.1161/CIRCULATIONAHA.117.030831. PubMed DOI PMC
Robinson P.N., Köhler S., Bauer S., Seelow D., Horn D., Mundlos S. The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease. Am. J. Hum. Genet. 2008;83:610–615. doi: 10.1016/j.ajhg.2008.09.017. PubMed DOI PMC
Köhler S., Vasilevsky N.A., Engelstad M., Foster E., McMurry J., Aymé S., Baynam G., Bello S.M., Boerkoel C.F., Boycott K.M., et al. The Human Phenotype Ontology in 2017. Nucleic Acids Res. 2017;45:D865–D876. doi: 10.1093/nar/gkw1039. PubMed DOI PMC
Köhler S., Doelken S.C., Mungall C.J., Bauer S., Firth H.V., Bailleul-Forestier I., Black G.C.M., Brown D.L., Brudno M., Campbell J., et al. The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res. 2014;42:D966–D974. doi: 10.1093/nar/gkt1026. PubMed DOI PMC
Pehlivan D., Bengtsson J.D., Bajikar S.S., Grochowski C.M., Lun M.Y., Gandhi M., Jolly A., Trostle A.J., Harris H.K., Suter B., et al. Structural variant allelic heterogeneity in MECP2 duplication syndrome provides insight into clinical severity and variability of disease expression. Genome Med. 2024;16:146. doi: 10.1186/s13073-024-01411-7. PubMed DOI PMC
Alecu J.E., Tam A., Richter S., Quiroz V., Schierbaum L., Saffari A., Ebrahimi-Fakhari D. Quantitative natural history modeling of HPDL-related disease based on cross-sectional data reveals genotype-phenotype correlations. Genet. Med. 2025;27 doi: 10.1016/j.gim.2024.101349. PubMed DOI PMC
Dardas Z., Fatih J.M., Jolly A., Dawood M., Du H., Grochowski C.M., Jones E.G., Jhangiani S.N., Wehrens X.H.T., Liu P., et al. NODAL variants are associated with a continuum of laterality defects from simple D-transposition of the great arteries to heterotaxy. Genome Med. 2024;16:53. doi: 10.1186/s13073-024-01312-9. PubMed DOI PMC
Bosch E., Popp B., Güse E., Skinner C., van der Sluijs P.J., Maystadt I., Pinto A.M., Renieri A., Bruno L.P., Granata S., et al. Elucidating the clinical and molecular spectrum of SMARCC2-associated NDD in a cohort of 65 affected individuals. Genet. Med. 2023;25 doi: 10.1016/j.gim.2023.100950. PubMed DOI
Calame D.G., Guo T., Wang C., Garrett L., Jolly A., Dawood M., Kurolap A., Henig N.Z., Fatih J.M., Herman I., et al. Monoallelic variation in DHX9, the gene encoding the DExH-box helicase DHX9, underlies neurodevelopment disorders and Charcot-Marie-Tooth disease. Am. J. Hum. Genet. 2023;110:1394–1413. doi: 10.1016/j.ajhg.2023.06.013. PubMed DOI PMC
Guatibonza Moreno P., Pardo L.M., Pereira C., Schroeder S., Vagiri D., Almeida L.S., Juaristi C., Hosny H., Loh C.C.Y., Leubauer A., et al. At a glance: the largest Niemann-Pick type C1 cohort with 602 patients diagnosed over 15 years. Eur. J. Hum. Genet. 2023;31:1108–1116. doi: 10.1038/s41431-023-01408-7. PubMed DOI PMC
Dingemans A.J.M., Truijen K.M.G., van de Ven S., Bernier R., Bongers E.M.H.F., Bouman A., de Graaff-Herder L., Eichler E.E., Gerkes E.H., De Geus C.M., et al. The phenotypic spectrum and genotype-phenotype correlations in 106 patients with variants in major autism gene CHD8. Transl. Psychiatry. 2022;12:421. doi: 10.1038/s41398-022-02189-1. PubMed DOI PMC
Crawford K., Xian J., Helbig K.L., Galer P.D., Parthasarathy S., Lewis-Smith D., Kaufman M.C., Fitch E., Ganesan S., O’Brien M., et al. Computational analysis of 10,860 phenotypic annotations in individuals with SCN2A-related disorders. Genet. Med. 2021;23:1263–1272. doi: 10.1038/s41436-021-01120-1. PubMed DOI PMC
van der Spek J., den Hoed J., Snijders Blok L., Dingemans A.J.M., Schijven D., Nellaker C., Venselaar H., Astuti G.D.N., Barakat T.S., Bebin E.M., et al. Inherited variants in CHD3 show variable expressivity in Snijders Blok-Campeau syndrome. Genet. Med. 2022;24:1283–1296. doi: 10.1016/j.gim.2022.02.014. PubMed DOI
Zhang C., Jolly A., Shayota B.J., Mazzeu J.F., Du H., Dawood M., Soper P.C., Ramalho de Lima A., Ferreira B.M., Coban-Akdemir Z., et al. Novel pathogenic variants and quantitative phenotypic analyses of Robinow syndrome: WNT signaling perturbation and phenotypic variability. HGG Adv. 2022;3 doi: 10.1016/j.xhgg.2021.100074. PubMed DOI PMC
Hebebrand M., Hüffmeier U., Trollmann R., Hehr U., Uebe S., Ekici A.B., Kraus C., Krumbiegel M., Reis A., Thiel C.T., Popp B. The mutational and phenotypic spectrum of TUBA1A-associated tubulinopathy. Orphanet J. Rare Dis. 2019;14:38. doi: 10.1186/s13023-019-1020-x. PubMed DOI PMC
Casanova E.L., Gerstner Z., Sharp J.L., Casanova M.F., Feltus F.A. Widespread genotype-phenotype correlations in intellectual disability. Front. Psychiatry. 2018;9:535. doi: 10.3389/fpsyt.2018.00535. PubMed DOI PMC
van der Sluijs P.J., Jansen S., Vergano S.A., Adachi-Fukuda M., Alanay Y., AlKindy A., Baban A., Bayat A., Beck-Wödl S., Berry K., et al. The ARID1B spectrum in 143 patients: from nonsyndromic intellectual disability to Coffin-Siris syndrome. Genet. Med. 2019;21:1295–1307. doi: 10.1038/s41436-018-0330-z. PubMed DOI PMC
Chiorean A., Farncombe K.M., Delong S., Andric V., Ansar S., Chan C., Clark K., Danos A.M., Gao Y., Giles R.H., et al. Large scale genotype- and phenotype-driven machine learning in Von Hippel-Lindau disease. Hum. Mutat. 2022;43:1268–1285. doi: 10.1002/humu.24392. PubMed DOI PMC
Chiu T.L.-H., Leung D., Chan K.-W., Yeung H.M., Wong C.-Y., Mao H., He J., Vignesh P., Liang W., Liew W.K., et al. Phenomic analysis of chronic granulomatous disease reveals more severe integumentary infections in X-linked compared with autosomal recessive chronic granulomatous disease. Front. Immunol. 2021;12 doi: 10.3389/fimmu.2021.803763. PubMed DOI PMC
Jacobsen J.O.B., Baudis M., Baynam G.S., Beckmann J.S., Beltran S., Buske O.J., Callahan T.J., Chute C.G., Courtot M., Danis D., et al. The GA4GH Phenopacket schema defines a computable representation of clinical data. Nat. Biotechnol. 2022;40:817–820. doi: 10.1038/s41587-022-01357-4. PubMed DOI PMC
Danis D., Jacobsen J.O.B., Wagner A.H., Groza T., Beckwith M.A., Rekerle L., Carmody L.C., Reese J., Hegde H., Ladewig M.S., et al. Phenopacket-tools: Building and validating GA4GH phenopackets. PLoS One. 2023;18 doi: 10.1371/journal.pone.0285433. PubMed DOI PMC
Ladewig M.S., Jacobsen J.O.B., Wagner A.H., Danis D., El Kassaby B., Gargano M., Groza T., Baudis M., Steinhaus R., Seelow D., et al. GA4GH phenopackets: A practical introduction. Adv. Genet. 2023;4 doi: 10.1002/ggn2.202200016. PubMed DOI PMC
Danis D., Bamshad M.J., Bridges Y., Caballero-Oteyza A., Cacheiro P., Carmody L.C., Chimirri L., Chong J.X., Coleman B., Dalgleish R., et al. A corpus of GA4GH phenopackets: Case-level phenotyping for genomic diagnostics and discovery. HGG Adv. 2025;6 doi: 10.1016/j.xhgg.2024.100371. PubMed DOI PMC
Bourgon R., Gentleman R., Huber W. Independent filtering increases detection power for high-throughput experiments. Proc. Natl. Acad. Sci. USA. 2010;107:9546–9551. doi: 10.1073/pnas.0914005107. PubMed DOI PMC
Jordan V.K., Fregeau B., Ge X., Giordano J., Wapner R.J., Balci T.B., Carter M.T., Bernat J.A., Moccia A.N., Srivastava A., et al. Genotype-phenotype correlations in individuals with pathogenic RERE variants. Hum. Mutat. 2018;39:666–675. doi: 10.1002/humu.23400. PubMed DOI PMC
de Vries B.B., White S.M., Knight S.J., Regan R., Homfray T., Young I.D., Super M., McKeown C., Splitt M., Quarrell O.W., et al. Clinical studies on submicroscopic subtelomeric rearrangements: a checklist. J. Med. Genet. 2001;38:145–150. doi: 10.1136/jmg.38.3.145. PubMed DOI PMC
Bland J.M., Altman D.G. The logrank test. BMJ. 2004;328:1073. doi: 10.1136/bmj.328.7447.1073. PubMed DOI PMC
UniProt Consortium UniProt: The universal protein knowledgebase in 2025. Nucleic Acids Res. 2025;53:D609–D617. doi: 10.1093/nar/gkae1010. PubMed DOI PMC
Amberger J.S., Bocchini C.A., Scott A.F., Hamosh A. OMIM.org: leveraging knowledge across phenotype-gene relationships. Nucleic Acids Res. 2019;47:D1038–D1043. doi: 10.1093/nar/gky1151. PubMed DOI PMC
Gracia-Diaz C., Zhou Y., Yang Q., Maroofian R., Espana-Bonilla P., Lee C.-H., Zhang S., Padilla N., Fueyo R., Waxman E.A., et al. Gain and loss of function variants in EZH1 disrupt neurogenesis and cause dominant and recessive neurodevelopmental disorders. Nat. Commun. 2023;14:4109. doi: 10.1038/s41467-023-39645-5. PubMed DOI PMC
Alkan C., Coe B.P., Eichler E.E. Genome structural variation discovery and genotyping. Nat. Rev. Genet. 2011;12:363–376. doi: 10.1038/nrg2958. PubMed DOI PMC
Benjamini Y. Discovering the false discovery rate: False Discovery Rate. J. R. Stat. Soc. Series B Stat. Methodol. 2010;72:405–416. doi: 10.1111/j.1467-9868.2010.00746.x. DOI
Xu C., Jia W., Cheng X., Ying H., Chen J., Xu J., Guan Q., Zhou X., Zheng D., Li G., Zhao J. Genotype-phenotype correlation study and mutational and hormonal analysis in a Chinese cohort with 21-hydroxylase deficiency. Mol. Genet. Genomic Med. 2019;7 doi: 10.1002/mgg3.671. PubMed DOI PMC
Chang E.H., Zabner J. Precision genomic medicine in cystic fibrosis. Clin. Transl. Sci. 2015;8:606–610. doi: 10.1111/cts.12292. PubMed DOI PMC
Vestito L., Jacobsen J.O.B., Walker S., Cipriani V., Harris N.L., Haendel M.A., Mungall C.J., Robinson P., Smedley D. Efficient reinterpretation of rare disease cases using Exomiser. NPJ Genom. Med. 2024;9:65. doi: 10.1038/s41525-024-00456-2. PubMed DOI PMC
Robinson P.N., Ravanmehr V., Jacobsen J.O.B., Danis D., Zhang X.A., Carmody L.C., Gargano M.A., Thaxton C.L., UNC Biocuration Core. Karlebach G., et al. Interpretable Clinical Genomics with a Likelihood Ratio Paradigm. Am. J. Hum. Genet. 2020;107:403–417. doi: 10.1016/j.ajhg.2020.06.021. PubMed DOI PMC
Kim H.Y., Ko J.M. Clinical management and emerging therapies of FGFR3-related skeletal dysplasia in childhood. Ann. Pediatr. Endocrinol. Metab. 2022;27:90–97. doi: 10.6065/apem.2244114.057. PubMed DOI PMC
Rojnueangnit K., Xie J., Gomes A., Sharp A., Callens T., Chen Y., Liu Y., Cochran M., Abbott M.-A., Atkin J., et al. High incidence of Noonan syndrome features including short stature and pulmonic stenosis in patients carrying NF1 missense mutations affecting p.Arg1809: Genotype-phenotype correlation: Human mutation. Hum. Mutat. 2015;36:1052–1063. doi: 10.1002/humu.22832. PubMed DOI PMC
Arnaud P., Milleron O., Hanna N., Ropers J., Ould Ouali N., Affoune A., Langeois M., Eliahou L., Arnoult F., Renard P., et al. Clinical relevance of genotype-phenotype correlations beyond vascular events in a cohort study of 1500 Marfan syndrome patients with FBN1 pathogenic variants. Genet. Med. 2021;23:1296–1304. doi: 10.1038/s41436-021-01132-x. PubMed DOI PMC
Castellani C., De Boeck K., De Wachter E., Sermet-Gaudelus I., Simmonds N.J., Southern K.W., ECFS Diagnostic Network Working Group ECFS standards of care on CFTR-related disorders: Updated diagnostic criteria. J. Cyst. Fibros. 2022;21:908–921. doi: 10.1016/j.jcf.2022.09.011. PubMed DOI
Gonzalo S., Kreienkamp R., Askjaer P. Hutchinson-Gilford Progeria Syndrome: A premature aging disease caused by LMNA gene mutations. Ageing Res. Rev. 2017;33:18–29. doi: 10.1016/j.arr.2016.06.007. PubMed DOI PMC
Grossmann S., Bauer S., Robinson P.N., Vingron M. Improved detection of overrepresentation of Gene-Ontology annotations with parent child analysis. Bioinformatics. 2007;23:3024–3031. doi: 10.1093/bioinformatics/btm440. PubMed DOI
Nannenberg E.A., van Rijsingen I.A.W., van der Zwaag P.A., van den Berg M.P., van Tintelen J.P., Tanck M.W.T., Ackerman M.J., Wilde A.A.M., Christiaans I. Effect of ascertainment bias on estimates of patient mortality in inherited cardiac diseases. Circ. Genom. Precis. Med. 2018;11 doi: 10.1161/CIRCGEN.117.001797. PubMed DOI
Corvol H., Blackman S.M., Boëlle P.-Y., Gallins P.J., Pace R.G., Stonebraker J.R., Accurso F.J., Clement A., Collaco J.M., Dang H., et al. Genome-wide association meta-analysis identifies five modifier loci of lung disease severity in cystic fibrosis. Nat. Commun. 2015;6:8382. doi: 10.1038/ncomms9382. PubMed DOI PMC
Dareng E.O., Tyrer J.P., Barnes D.R., Jones M.R., Yang X., Aben K.K.H., Adank M.A., Agata S., Andrulis I.L., Anton-Culver H., et al. Polygenic risk modeling for prediction of epithelial ovarian cancer risk. Eur. J. Hum. Genet. 2022;30:349–362. doi: 10.1038/s41431-021-00987-7. PubMed DOI PMC
Graefe A.S.L., Hübner M.R., Rehburg F., Sander S., Klopfenstein S.A.I., Alkarkoukly S., Grönke A., Weyersberg A., Danis D., Zschüntzsch J., et al. An ontology-based rare disease common data model harmonising international registries, FHIR, and Phenopackets. Sci. Data. 2025;12:234. doi: 10.1038/s41597-025-04558-z. PubMed DOI PMC