the implicitome: a resource for inferring gene-disease associations

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[email protected] The implicitome: a resource for inferring gene-disease associations Kristina Hettne Human Genetics Department, LUMC NVHG FALL SYMPOSIUM, PAPENDAL, ARNHEM

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Page 1: The implicitome: a resource for inferring gene-disease associations

[email protected]

The implicitome: a resource for inferring gene-disease associations

Kristina Hettne

Human Genetics Department, LUMC

NVHG FALL SYMPOSIUM, PAPENDAL, ARNHEM

Page 2: The implicitome: a resource for inferring gene-disease associations

Problem: biological complexity and data overload

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Goh, K.-I. et al. The human disease network. Proceedings of the National Academy of Sciences 104, 8685-8690 (2008)

Page 3: The implicitome: a resource for inferring gene-disease associations

Solution: Literature-wide association study (LWAS)

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Slide adapted from Erik Schultes

Page 4: The implicitome: a resource for inferring gene-disease associations

Concept profile matching enables implicit links

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(Co-occurrence)

(No co-occurrence)

Page 5: The implicitome: a resource for inferring gene-disease associations

The gene-disease implicitome

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= Max number of associations

Page 6: The implicitome: a resource for inferring gene-disease associations

Can LWAS predict GWAS?

Retrospective study:

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https://www.ebi.ac.uk/gwas/ LWAS (literature)

January 1980 - July 2012 January 2013 - August 2014

Page 7: The implicitome: a resource for inferring gene-disease associations

LWAS meets GWAS

• 238 gene-disease pairs from GWAS, representing 35 diseases

• 194 had concept profiles and could be exposed from LWAS

• The best 5% LWAS: 45 gene-disease pairs overlap with GWAS

• 23%, 4.6-fold enrichment, 5% would be random

• The best 1% LWAS: 12 gene-disease pairs overlap with GWAS

• 6%, 6-fold enrichment, 1% would be random

• Take home message: we can predict now proven associations!

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Slide adapted from Erik Schultes

Page 8: The implicitome: a resource for inferring gene-disease associations

Example: ERAP1 and Behçet’s disease

LWAS connecting Y concepts in 2012:

• HLA-B

• Ankylosing spondylitis

Confirmed in 2013:

Nature Genetics 2013 Feb;45(2):202-7: HLA-B interacts with ERAP1. ERAP1 is

one of the three risk loci shared with Ankylosing spondylitis and psoriasis, which

are thought to involve pathogenic pathways similar to Behçet’s disease.

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Image source: http://www.arthritisresearchuk.org/arthritis-information/conditions/behcets-syndrome/symptoms.aspx Slide adapted from Erik Schultes

ERAP1 Behçet’s disease

Page 9: The implicitome: a resource for inferring gene-disease associations

LWAS association type classification: top 105

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For example the gene-disease pair TTBK1-SCA11, where TTBK1 and TTBK2 are isoforms of the TTBK gene, and a mutation in the TTBK2 form is causing the disease.

Page 10: The implicitome: a resource for inferring gene-disease associations

Example novel (Type IV) association

CYP2R1 had a strong association with Smith-Lemli-Opitz syndrome (SLOS)

Connecting Y concepts:

• DHCR7 (defects cause SLOS)

• cholesta-5,7-dien-3beta-ol (=7-dihydrocholesterol)

Reasoning:

• Pathological hallmark of SLOS is increased levels of

7-dihydrocholesterol

• 7-dihydrocholesterol is a precursor of vitamin D3

• Defects in CYP2R1 are known to affect vitamin D3

levels

• Thus, LWAS implicates CYP2R1 in SLOS since defects may potentially lead to

7-dihydrocholesterol accumulation

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Image source: http://www.nature.com/ejhg/journal/v16/n5/fig_tab/ejhg200810f3.html Slide adapted from Erik Schultes

Page 11: The implicitome: a resource for inferring gene-disease associations

Explore the implicitome with http://knowledge.bio

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Page 12: The implicitome: a resource for inferring gene-disease associations

Contact us!

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Code is open source, and data are available for download

http://biosemantics.humgen.nl/

[email protected]

Page 13: The implicitome: a resource for inferring gene-disease associations

Acknowledgements

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LUMC:

Erik Schultes

Mark Thompson

Herman H.H.B.M. van Haagen

Eelke van der Horst

Rajaram Kaliyaperumal

Eleni Mina

Zuotian Tatum

Jeroen F.J. Laros

Emmelien Aten

Johan den Dunnen

Gert-Jan J.B. van Ommen

Marco Roos

Peter A.C. 't Hoen

Barend Mons

EMC:

Erik M. van Mulligen

Jan A. Kors

Martijn Schuemie

SCRIPPS:

Li S. Tong

Richard Bruskiewich

Benjamin M. Good

Andrew Su

Funding: Dutch Center of Medical Systems

Biology, ODEX4all, Melton Foundation, John

Templeton Foundation, Open PHACTS, National

Institutes of Health, SURF Foundation, RD-

Connect

NVHG Fall Symposium 2015