Using phenotypic similarity to improve rare disease identification in PhenomeCentral

Transcription

1 Using phenotypic similarity to improve rare disease identification in PhenomeCentral Orion Buske, Marta Girdea, Sergiu Dumitriu, Tal Friedman, Jonathan Zung, Cornelius Boerkoel, Kym Boycott, Michael Brudno Genome Informatics, 21 Sept 2014

2 Rare diseases are common prevalence rare: < 1 / 2,000 (in EU) disease

3 ~6% of people are affected with one of over 7,000 rare diseases (Orphanet)

4 rare genetic disease

5 might not recognize known disease insufficient sample size for novel gene

6

7 PhenomeCentral

8 Patient matching enables: 1. diagnosis of both known and novel diseases 2. improved power to identify candidate genes

9 to match patients effectively, we need a" standardized, semantically-linked" phenotype vocabulary

10 Next-generation phenotyping Human Phenotype Ontology (HPO): 10,400+ terms 57,000+ links to 5,000+ OMIM Disorders abnormality eye disease neurologic skeletal... abnormal eye morphology coloboma globe abnormality

11 Finding similar patients

12 Finding similar patients p term from OMIM corpus IC(term) = log(1/p term ) LS(term) IC(term) - max parents IC(parent)

13 Experimental data synthetic patients 1. from OMIM real patients from 2. PhenomeCentral

14 Experimental data synthetic patients 1. from OMIM real patients from 2. PhenomeCentral similar to (Köhler et al. 2009) and (Zemojtel et al. 2014) generated pairs of patients from random diseases - 50% autosomal dominant, 50% autosomal recessive imprecision: terms replaced by random ancestor noise added: 20% random terms

15 Experimental data synthetic patients 1. from OMIM real patients from 2. PhenomeCentral 491 rare disease patients part of cohort "correct" if any patient in same cohort ranked in top N

16 Phenotypic matching performance Synthetic (n=1040) Real (n=171/491) Fraction of patients with valid match in top N JC (avg) JC (avg- best) Lin (avg) Lin (avg- best) Jaccard (avg) Jaccard (avg- best) UI simgic simgic- LS pair-wise bag-wise JC (avg) JC (avg- best) Lin (avg) Lin (avg- best) Jaccard (avg) Jaccard (avg- best) UI simgic simgic- LS Top 1 Top 5 Top 1 Top 5

17 Identifying candidate genes

18 Identifying candidate genes

19 Identifying candidate genes

20 Identifying candidate genes (Robinson et al., 2014)

21 Identifying candidate genes

22 Scoring genes for a pair of patients A. average of Exomiser scores B. PhenomeCentral score: pheno * geno * similarity(o) G(o, gene) geno where: pheno= min(p(p1, gene), P(p2, gene)) geno = min(g(p1, gene), G(p2, gene))

23 Scoring genes for a pair of patients

24 Experimental exome data synthetic patients from 1. HGMD GP real patients from 2. PhenomeCentral

25 Experimental exome data synthetic patients from 1. HGMD GP real patients from 2. PhenomeCentral similar to (Robinson et al. 2014) HGMD variant spike-in - random variant(s) associated with disease 1000 Genomes Project exome background

26 Experimental exome data synthetic patients from 1. HGMD GP real patients from 2. PhenomeCentral 491 rare disease patients - 78 with exomes, and solved or lead genes "correct" if any listed gene ranked in top N

27 Gene prioritization performance Synthetic (n=1040) Real (n=78/491) Fraction of patients with correct gene in top N Exomiser Average (1p) Average (5p) PC (1p) PC (5p) Exomiser Average (1p) Average (5p) PC (1p) PC (5p) Top 1 Top 5 Top 25 Top 1 Top 5 Top 25

28 Results so far Patient with atypical presentation of MFDM correctly matched to other MFDM patients Blind rediscovery of STIM1 association... these are intra-consortium matches

29 PhenomeCentral is a Matchmaker Find out about other similar patients Easily connect with other clinicians Each Patient Record can be: Public Visible to all registered users Private Only visible to specified users/consortia Matchable Private visibility, but existence can be "discovered" by users who submit similar patients 29/28

30 Step 1: submit your patient Predictive search Select positive and negative terms Add a VCF file and/or gene list Set each record to Private, Public or Matchable

31 Step 2: see patients similar to yours

32 Step 3: contact the other submitter

33 PhenomeCentral (phenomecentral.org) 500+ patients with rare genetic disorders - all deeply phenotyped - most with exome data - most undiagnosed 250+ rare disease scientists/clinicians

34 Acknowledgements UofT and SickKids Michael Brudno, Marta Gîrdea, Sergiu Dumitriu, Jonathan Zung, Marc Fiume, Andriy Misyura, Anton Kats, Heather Trang, Bailey Gallinger CAREforRARE Kym Boycott, Sarah Sawyer, Taila Hartley, Chandree Beauleiu NIH-UDP Neal Boerkoel, Amanda Links, David Adams, William Bone HPO, Monarch, Exomiser Peter Robinson, Melissa Haendel, Damian Smedley, Jules Jacobsen Funding: Genome Canada (CAREforRARE), CIHR, NSERC, SickKids Restracomp

35 ? (see Michael Brudno for post-doc opportunities)