Ensembl Variation - Phenotype and disease annotations

Phenotypes are imported from a number of external sources and displayed in Ensembl. On top of these, for human we display the clinical significance of variants, and map phenotype terms using ontologies.

Clinical significance

See below the list of the clinical significance terms you can find in the human Ensembl Variation database:

Icon	Value	ClinVar example	DGVa example
	affects	rs324420	nsv4684084
	association	rs6681	nsv3870545
	benign	rs677	nsv1067800
	likely benign	rs665	nsv529995
	confers sensitivity	rs6478108	-
	drug response	rs6025	nsv3875906
	pathogenic	rs5082	esv2830397
	pathogenic low penetrance	rs6025	nsv7093359
	likely pathogenic	rs381418	esv1791726
	likely pathogenic low penetrance	rs563816801	nsv6314862
	protective	rs1050501	nsv1398576
	established risk allele	rs1801581	-
	likely risk allele	rs10754555	-
	risk factor	rs5174	nsv3871638
	not provided	rs4870	nsv984836
	other	rs5275	nsv4769276
	uncertain risk allele	rs650616	-
	uncertain significance	rs1205	esv2830426

Further explanations about the clinical significance terms are available on the ClinVar website.

ClinVar rating

We use the ClinVar "four-star" rating system to indicate the quality of classification/validation of the variant:

Rating	Description	Example
	not classified by submitter	rs2139165606
	classified by single submitter	rs2136571005
	classified by multiple submitters	rs2148153587
	reviewed by expert panel	rs1566906506
	practice guideline	rs17884712

Phenotype classes

We group phenotype terms into the classes below:

These can be used to retrieve corresponding subsets of phenotype annotations for genes or in a specific region via the REST API. Phenotype pages are displayed for all classes except 'non_specified' class.

Phenotype/disease ontologies

We import ontology terms related to phenotypes, traits and diseases from a variety of sources using an automated process. Ontologies used are:

Descriptions are linked to ontology terms using:

• Mappings provided by association data sources such as Orphanet, the NHGRI-EBI GWAS catalog and ClinVar
• Annotations of OMIM terms created by HPO
• Annotations of OMIM terms created by Orphanet
• Ontology LookUp Service searches of full or truncated descriptions for exact matches to terms or synomyms
• Zooma searches of annotations curated by the European Variation Archive team

References

• Sebastian Köhler, Sandra C Doelken, Christopher J. Mungall, Sebastian Bauer, Helen V. Firth, Isabelle Bailleul-Forestier, Graeme C. M. Black, Danielle L. Brown, Michael Brudno, Jennifer Campbell, David R. FitzPatrick, Janan T. Eppig, Andrew P. Jackson, Kathleen Freson, Marta Girdea, Ingo Helbig, Jane A. Hurst, Johanna Jähn, Laird G. Jackson, Anne M. Kelly, David H. Ledbetter, Sahar Mansour, Christa L. Martin, Celia Moss, Andrew Mumford, Willem H. Ouwehand, Soo-Mi Park, Erin Rooney Riggs, Richard H. Scott, Sanjay Sisodiya, Steven Van Vooren, Ronald J. Wapner, Andrew O. M. Wilkie, Caroline F. Wright, Anneke T. Vulto-van Silfhout, Nicole de Leeuw, Bert B. A. de Vries, Nicole L. Washingthon, Cynthia L. Smith, Monte Westerfield, Paul Schofield, Barbara J. Ruef, Georgios V. Gkoutos, Melissa Haendel, Damian Smedley, Suzanna E. Lewis, and Peter N. Robinson
  The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data
  Nucl. Acids Res. (1 January 2014) 42 (D1): D966-D974
  doi:10.1093/nar/gkt1026

• Malone J, Holloway E, Adamusiak T, Kapushesky M, Zheng J, Kolesnikov N, Zhukova, A, Brazma A, Parkinson H.
  Modeling sample variables with an Experimental Factor Ontology
  Bioinformatics (2010) 26 (8): 1112-1118
  doi:10.1093/bioinformatics