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Endocrine Abstracts (2017) 50 P181 | DOI: 10.1530/endoabs.50.P181

SFEBES2017 Poster Presentations Clinical Biochemistry (13 abstracts)

Personalized medicine and endocrine disorders: the challenges of interpreting genetic variants

Alessia David , SIrawit Ittisoponpisan & Michael JE Sternberg

Centre for Bioinformatics & System Biology, Imperial College London, London, UK.

Introduction: Genetic projects, such as 100KGenomes, are identifying a vast amount of genetic variants that require interpretation. Several variants lack sufficient evidence to be classified as neutral or damaging. Such variants are annotated as ‘unclassified’ and interpretation of their biological effect is of paramount importance, but remains a major challenge. Variant predictors are widely used to prioritize variants for further studies. However, they report a damaging effect for a large proportion of neutral variants, thus, limiting their utility in assessing unclassified variants.

We performed a systematic analysis of unclassified missense variants in genes causing endocrine disorders and assessed the contribution of protein structure analysis to their interpretation.

Methods: We examined 383,655 missense variants annotated in ClinVar and Uniprot. Genes causing endocrine neoplasias and disorders were identified by automatically mapping OMIM entries to the ICD-10 catalog. Variant effect predictions were obtained from PolyPhen2, SIFT and MutationAssessor. Experimental protein structures were obtained from ProteinDataBank and analysed for disruption of physico-chemical features.

Results: We identified 641 genes and 11,734 variants (damaging=6,171(52.6%), neutral=1,473(12.5%), unclassified=4,090(34.9%)). 1,494(36.5%) unclassified variants distributed in 118 genes could be mapped onto protein 3Dstructures.

1,498 variants were predicted damaging: 564(37.6%) were structurally analysed and 141(25%) confirmed damaging. 1,109 variants were predicted neutral: 319(28.7%) were structurally analysed and 297(93%) confirmed neutral. 711 variants were predicted damaging by two-out-of-three predictors: 258(36.2%) were structurally analysed and 40(15%) confirmed damaging. 694 variants were predicted neutral by two-out-of-three predictors: 216(31.1%) were structurally analysed and 190(88.0%) confirmed neutral. In 78 variants, results were available from only two predictors and were contradictory: 26(33.3%) were structurally analysed and a deleterious effect confirmed in 9.

Conclusion: Evidence of damage in protein structural stability provide strong evidence of a variant deleterious effect and can greatly help to prioritize genetic variations for further in vitro studies in patients with endocrine disorders.

Volume 50

Society for Endocrinology BES 2017

Harrogate, UK
06 Nov 2017 - 08 Nov 2017

Society for Endocrinology 

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